WO2022030629A1 - Method for predicting symptom, therapy adequateness, and/or treatment result for viral infection - Google Patents

Method for predicting symptom, therapy adequateness, and/or treatment result for viral infection Download PDF

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WO2022030629A1
WO2022030629A1 PCT/JP2021/029375 JP2021029375W WO2022030629A1 WO 2022030629 A1 WO2022030629 A1 WO 2022030629A1 JP 2021029375 W JP2021029375 W JP 2021029375W WO 2022030629 A1 WO2022030629 A1 WO 2022030629A1
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mmp
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和幸 吉崎
賀津子 宇野
仁 藤宮
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和幸 吉崎
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
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    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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Definitions

  • the present disclosure relates to methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. More specifically, the present disclosure relates to a method of predicting the degree of symptom of a subject infected with a virus and a method of predicting therapeutic suitability and treatment result thereof. More specifically, the present disclosure relates to a technique for predicting the symptoms of a disease caused by a coronavirus or the like in advance, and appropriately providing a preventive method or a therapeutic agent thereof to a patient.
  • Viral infectious diseases are still influential enough to pose a threat to humankind even in modern times, and viral diseases such as AIDS (HIV) and Ebola hemorrhagic fever continue to take time to control. .. Furthermore, in recent years, infectious diseases such as SARS (SARS-CoV), MERS (MERS coronavirus), and COVID-19 (SARS-CoV-2) have an influence that changes daily life.
  • SARS SARS-CoV
  • MERS MERS
  • COVID-19 SARS-CoV-2
  • the present disclosure provides: (Item 1) A method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment results. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. Including, how. (Item 2) The virus infection symptom, treatment suitability, and / or treatment result is a virus infection symptom.
  • the method according to item 1, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
  • (Item 3) The method according to item 1 or 2, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • (Item 4) The method according to any one of items 1 to 3, wherein the biological parameter contains at least one marker that is not an inflammatory cytokine.
  • (Item 5) The method according to any one of items 1 to 4, wherein the biological parameter is a gene product (protein).
  • (Item 6) The method according to any one of items 1 to 5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item 7) Any one of items 1 to 6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item 8) The method according to any one of items 1 to 7, wherein the virus infection is SARS-CoV-2 infection.
  • (Item 9) The method according to any one of items 1 to 8, wherein the prediction of the symptom includes the prediction of the degree of symptom.
  • (Item 10) The method according to any one of items 1 to 9, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL.
  • the method according to any one of items 1 to 10. The one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method of any one of items 1-12, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Items 1-11, including parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin,
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , The method of any one of items 1-11, comprising a parameter selected from the group consisting of Leptin, OncostatinM, and VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • MCP-1 MCP-1
  • M-CSF MIG
  • b-NGF b-NGF
  • PDGF-bb VEGF
  • APRIL BAFF
  • sCD30 IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item 17C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item 17E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item 19) The method according to any one of items 1 to 18, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item A1) A method for preventing or treating a virus infection in a subject. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. With the step of preventing or treating the subject based on the prediction of viral infection symptoms, treatment suitability, and / or treatment outcome. Including, how.
  • the prediction step is an index for predicting the virus infection symptom, the treatment suitability, and / or the treatment result by analyzing the biological parameter by comparing the biological parameter with a reference value.
  • the step of preventing or treating includes a step of administering a therapeutic agent to the subject when the subject is predicted to be a seriously ill patient.
  • the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item A6) The method according to any one of items A1 to A5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item A7) Any one of items A1 to A6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item A8) The method according to any one of items A1 to A7, wherein the virus infection is SARS-CoV-2 infection.
  • the therapeutic agents include remdesivir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, tocilizumab, sarilumab, tofacitinib, baricitinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, rubrismab, -001, dexamethasone, casiribimab / imdebimab, baricitinib / etecebimab, sotrovimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakinra
  • the method according to any one of items A1 to A8, which
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP- 1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, M
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • IL-12 IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP- 1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13,
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items A1 to A11, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Items A1 to A10 comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items A10, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. (Item A16C)
  • the one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item A16C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item A16E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • the reference value is a value of the biological parameter in a healthy person.
  • (Item A18) The method according to any one of items A1 to A17, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item B1) A method in which one or more biological parameters in a subject are used as an index for predicting the viral infection symptom, treatment suitability, and / or treatment result of the subject.
  • (Item B2) The method according to item B1, which comprises the step of obtaining one or more biological parameters in the subject.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.).
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • the biological parameter comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item B9) The method according to any one of items B1 to B8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item B10) Any one of items B1 to B9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item B11) The method according to any one of items B1 to B10, wherein the virus infection is SARS-CoV-2 infection.
  • (Item B12) The method according to any one of items B1 to B11, wherein the symptom prediction includes a symptom degree prediction.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items B1 to B14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item B1-B13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items B13, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL. -10, IL-12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF -Bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL -11, IL-20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item B19C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item B21) The method according to any one of items B1 to B20, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item C1) An in vitro method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. Including, how. (Item C2)
  • the virus infection symptom, the treatment suitability, and / or the prediction of the treatment result are the virus infection symptom.
  • the prediction of the symptom is the prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb , RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, M
  • the virus infection symptom, the treatment suitability, and / or the prediction of the treatment result is the virus infection symptom.
  • the prediction of the symptom is the prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL.
  • the method according to item C1. The step according to item C1 or C2, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
  • Method. The method according to any one of items C1 to C3, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item C7) The method according to any one of items C1 to C6, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item C8) Any one of items C1 to C7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item C9) The method according to any one of items C1 to C8, wherein the virus infection is SARS-CoV-2 infection.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF- ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT,
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item C1-C9, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items C9, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item C15C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item C15E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item C17) The method according to any one of items C1 to C16, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item D1) An in vitro method in which one or more biological parameters in a subject are used as indicators for predicting the subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • (Item D2) The method according to item D1, which comprises the step of obtaining one or more biological parameters in the subject.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item D1 or D2.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the method according to any one of items D1 to D3, which comprises the parameters to be added.
  • Method. (Item D4A)
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • Method. (Item D5) The method according to any one of items D1 to D4, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the biological parameter comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • Item D8 The method according to any one of items D1 to D7, further comprising a step of analyzing the biological parameter by comparing the biological parameter with a reference value.
  • (Item D9) The method according to any one of items D1 to D8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item D10) Any one of items D1 to D9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item D11) The method according to any one of items D1 to D10, wherein the virus infection is SARS-CoV-2 infection.
  • (Item D12) The method according to any one of items D1 to D11, wherein the symptom prediction includes a symptom degree prediction.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items D1 to D14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item D1 to D13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items D13, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item D19C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item E1.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the biomarker according to item E1 or E2. (Item E3A)
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • (Item E8) Any one of items E1 to E7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The biomarker described in the section.
  • (Item E9) The biological marker according to any one of items E1 to E8, wherein the virus infection is SARS-CoV-2 infection.
  • (Item E10) The biological marker according to any one of items E1 to E9, wherein the prediction of the symptom includes a prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the biomarker according to any one of items E1 to E11 comprising a parameter selected from the group.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35.
  • the biomarker according to any one of items E1 to E12 comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item E1-E11, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT.
  • the biomarker according to any one of the above.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and VCAM-1, the biomarker according to any one of items E1 to E11 selected from the group.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. (Item E17C)
  • the one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item E17C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • Item F1 A reagent, a kit, or a device for use in the method according to any one of the above items, or a combination thereof.
  • (Item F2) Reagents, kits, or devices, or combinations thereof, for use in predicting viral infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
  • Reagents, kits, or devices, or combinations thereof are examples of the virus infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • the reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
  • the step according to item 1 includes the step of applying the biological parameter to a predetermined prediction model and outputting the probability regarding the prediction of the viral infection symptom, the treatment suitability, and / or the treatment result. the method of.
  • the bioparameters are based on biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (specified by SNP, etc.), etc.).
  • the method according to item G1 Contains information about at least one selected from the group of The method according to item G1, wherein the predictive model is created based on at least one selected from the group consisting of biomolecular parameters, clinical data, historical information and treatment information.
  • the biological parameter comprises information on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotypes.
  • the predictive model was created based on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment effect information, safety information, and status history information.
  • the method according to item G1 or G2 which is a thing.
  • (Item G4) The method according to any one of items G1 to G3, wherein the prediction model is generated based on reinforcement learning or a neural network.
  • (Item G5) A computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method is: A step of causing the computer to obtain one or more biological parameters in the subject. A step of causing the computer to apply the biological parameters to a predetermined predictive model and output probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results. Including the program.
  • (Item G6) A recording medium that stores a computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • Recording medium including.
  • (Item G7) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of subjects.
  • a means for obtaining one or more biological parameters in the subject As a means of applying the biological parameters to a predetermined predictive model and outputting probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results.
  • Including the system. (Item G8) A computer program that causes a computer to execute a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment result. The step of inputting one or more biological parameters in the subject into the computer, A step of causing the computer to perform calculations to predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters. Including the program.
  • Recording medium including.
  • (Item G10) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of a subject.
  • a means for inputting one or more biological parameters in the subject As a means of performing calculations that predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
  • the methods, programs, storage media, and systems of the present disclosure as described above may include any of the features described elsewhere herein.
  • the optimal treatment method and / or the treatment result when the subject is infected with the virus so that the optimal treatment according to the degree of the symptoms of each individual patient can be performed. Can be applied.
  • Such a method is not an ad hoc treatment, but can maximize the therapeutic effect for each individual, and it is also possible to formulate the most effective treatment policy for each patient. Even if a pandemic is caused by a virus, it can greatly contribute to avoiding the collapse of medical care.
  • FIG. 1 is a schematic diagram showing the relationship between the treatment route model for constructing a predictive model for predicting the symptom degree and the treatment suitability assuming the patient's condition and the prediction of the symptom degree and the treatment suitability. ..
  • FIG. 2 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a healthy subject and statistically processing the expression level for each measured biological parameter.
  • FIG. 3 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a COVID-19 severely ill patient and statistically processing the expression level for each measured biological parameter.
  • FIG. 1 is a schematic diagram showing the relationship between the treatment route model for constructing a predictive model for predicting the symptom degree and the treatment suitability assuming the patient's condition and the prediction of the symptom degree and the treatment suitability. ..
  • FIG. 2 shows the results of performing a cytokine / chemokine /
  • FIG. 4 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a rheumatism patient and statistically processing the expression level for each measured biological parameter.
  • FIG. 5 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a severely ill COVID-19 patient and statistically processing the expression level for each measured biological parameter.
  • "COVID-19-0" is a patient before Actemra treatment
  • COVID-19-1w is a patient about 1 to 5 days after Actemra treatment
  • COVID-19-1w is a patient about 7 days after Actemra treatment.
  • FIG. 6 is a diagram showing changes in IL-6 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 7 is a diagram showing changes in IL-1Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 8 is a diagram showing changes in IP-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 9 is a diagram showing changes in BAFF before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 10 is a diagram showing changes in APRIL before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 11 is a diagram showing changes in VCAM-1 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 12 is a diagram showing changes in IFN-28A before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 10 is a diagram showing changes in APRIL before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 11 is a diagram showing changes in VCAM-1 before and after treatment with tocilizumab
  • FIG. 13 is a diagram showing changes in IL-29 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 14 is a diagram showing changes in IFN-a2 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 15 is a diagram showing changes in IFN-b before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 16 is a diagram showing changes in IFN-g before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 17 is a diagram showing changes in TNF-a before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 18 is a diagram showing changes in sgp130 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 19 is a diagram showing changes in IL12 (p40) before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 20 is a diagram showing changes in IL-6Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 21 is a diagram showing changes in IL-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 22 is a diagram showing changes in TWEAK before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • coronaviridae is the largest coronaviridae belonging to Noroviruses, and includes coronaviridae, Arteriviridae, Mesoniviridae, Roniviridae and the like. Coronavirus is further divided into four genera: ⁇ , ⁇ , ⁇ , coronavirus, and ⁇ , ⁇ coronavirus causes infection in 10% to 30% of the human airway / intestinal tract.
  • Viruses of the genus Coronavirus exist as RNA viruses wrapped in an envelope. Its diameter is about 80-120 nm and the genetic material is the largest of all RNA viruses. There are three glycoproteins on the surface of the virus particle, spine glycoprotein (S, Spike Protein, receptor binding site, cell lysis and antigen); membrane glycoprotein (E, Envelope Protein, Envelope Protein, small, binding to cell membrane). Protein); Membrane Protein (M, Membrane Protein, membrane transporter for nutrients, aggregates the emergence of new viruses and the formation of viral envelopes). Hemagglutinin Glycoprotein (HE protein, Hemagglutinin- There is esterase).
  • the virus determines the tissue or host of the virus, primarily through the binding of the Spike protein (S protein) to the host cell receptor through viral entry.
  • S protein Spike protein
  • Both the N-terminal domain (S1-NTD) and the C-terminal domain (S1-CTD) of the coronavirus S protein S1 subunit can be a receptor binding domain (RBD). It is believed that S1-NTD binds to glycosyl receptors and S1-CTD binds to protein receptors.
  • Coronaviruses known to cause cold-like syndromes that infect humans include: HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2. Be done.
  • biological parameter refers to any parameter related to the living body, such as a factor related to the living body (for example, a cytokine or a receptor (ACE-R1 or the like), a signaling molecule, etc.), a protein, a polynucleotide or the like.
  • Biological parameters absolute, relative, variable, etc.
  • clinical data of the organism two-dimensional or three-dimensional images of the organism (eg, X-ray CT image), parameters related to the pathogen in the organism (eg, infection). It includes, but is not limited to, factors (eg, amounts or levels of bacteria, viruses, etc.), treatment history, treatment efficacy information, safety information, state history information, and the like.
  • Clinical data is used interchangeably with clinical information and includes any clinically available data or information, including laboratory data, clinical symptoms, findings, and the like.
  • the biomolecular parameter includes a biomolecular parameter
  • the biomolecule parameter includes a biological parameter such as a protein or a polynucleotide expressed in a sample collected from a subject.
  • Bioparameters can be nucleic acids, nucleic acid fragments, polynucleotides, oligonucleotides, polypeptides, peptide fragments or proteins that can be detected or quantified, or their values or variations in their values.
  • Bioparameters include IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL.
  • sample may be arbitrary, and includes, for example, serum, plasma, peripheral blood, blood, urine, saliva, and the like, but is not limited thereto.
  • serum is used for biomolecular parameters and the like. Therefore, in certain embodiments of the present disclosure, the methods of the present disclosure are carried out using parameters obtained from measurements in serum collected from a subject. In terms of viral load, nasal mucus and saliva can be used instead of serum.
  • symptom refers to the deviation of normal function or sensation in a patient, reflecting the presence of an abnormal condition or disease. Includes subjective or objective ones.
  • symptoms can be expressed as symptomatic (or severe), for example, severe, moderate (may be divided into levels, eg, moderate (I), moderate (II), etc.). It may be classified into mild, asymptomatic (invisible), etc.).
  • a virus infection symptom refers to a symptom at the time of virus infection. The following classifications may be typically adopted for the viral infection symptoms of COVID-19.
  • the term "predictive model” refers to a model for predicting one event (eg, a medical event such as viral infection symptoms, treatment suitability, and / or treatment result) from another factor. It can be expressed using a prediction formula, a coefficient, or the like. For example, based on the present disclosure, early asymptomatic, mild, based on post-infection symptom severity (which is interchangeably used herein as “symptomatic” or “severity”, they have the same meaning). It is classified into moderate (I), moderate (II), severe, etc., and the initial information such as blood virus amount, biomolecular weight, antibody presence and absence, clinical findings, treatment history, etc. of the patient according to the degree of symptom and over time.
  • a stochastic transition model for example, in the case of n ⁇ 200
  • deep neural network AI analysis
  • the present disclosure provides a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • Such a method comprises obtaining one or more biological parameters in the subject and predicting the viral infection symptom, therapeutic suitability, and / or treatment outcome based on the biological parameters.
  • the present disclosure provides a method for predicting a subject's viral infection symptoms.
  • the methods of the present disclosure can predict the degree of symptom of a viral infection in a subject, or whether it will be aggravated.
  • the method of the present disclosure is to obtain biological parameters from a sample taken from a subject and to predict viral infection symptoms by comparing the obtained parameters with parameters of the same type in a healthy subject. Indicators can be provided.
  • the bioparameters used can be, but are not limited to, biomolecular parameters. Even when the biomolecule parameter is used, other biomolecule parameters (clinical data, image data, etc.) may be used in combination in addition to the biomolecule parameter.
  • virus infection symptoms can be predicted based on biological parameters obtained from a sample collected from a subject. For example, for severe, moderate, and mild virus-infected patients, the above-mentioned biological parameters are measured in advance, and regression analysis is performed to calculate the degree of symptoms (objective variable) and the measured values (explanatory variables) of the above parameters. Then, a method of applying the measured values of the parameters of the subject to be predicted to the regression equation can be mentioned.
  • the probability of severe illness is determined in advance or based on experience as to whether or not it is applied to each therapeutic drug, and when the actually calculated probability of severe illness is equal to or higher than a predetermined standard, the specific treatment is performed. It can also be determined to administer the drug. For example, if the predicted probability of severe illness is 50% or higher, 60% or higher, 70% or higher, 80% or higher, 85% or higher, 90% or higher, or 95% or higher, it is determined to administer the specific therapeutic agent. can do. Specific values of such criteria can be appropriately set based on experience, etc., in addition to the specific numerical values described in the present specification, and specific values described in the present specification. It can be adopted even if it is not.
  • predictions are made using bioparameters in a sample taken from a subject, so the method of the present disclosure is the amount of bioparameters in the sample (eg, in serum or peripheral blood) or It may include the step of measuring the concentration.
  • the sample can be obtained from a subject by collecting blood from a subject by a means well known in the art such as a blood collection tube.
  • the serum may be separated and then dispensed and stored frozen.
  • the amount of virus in a sample collected from a subject may be quantified by means such as virus-specific PCR.
  • the method of the present disclosure predicts whether the symptom becomes severe when the subject is infected with the virus, or whether the symptom is cured by mild or moderate illness.
  • the subject to be predicted is not particularly limited, and may be a person who does not have the virus or a patient who has already suffered from the virus. Further, in the method of the present disclosure, the subject to be predicted is not limited to medication history, medical history, gender, age, and the like.
  • the causative virus of the symptom predicted by the method of the present disclosure is not particularly limited, but includes, for example, a virus that infects an animal (for example, a human) (including a DNA virus or an RNA virus).
  • Adenovirus family virus for example, herpes virus family virus (for example, simple herpes virus, varicella / herpes zoster virus, cytomegalovirus, EB virus), hepatitis, which are viruses that affect humans and show cold-like syndrome.
  • Viruses hepatitis C virus, hepatitis B virus
  • immunodeficiency viruses such as HIV
  • coronaviruses such as HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2 are included.
  • coronaviruses there are four types of coronaviruses that infect humans: human coronavirus 229E, OC43, NL63, and HKU-1 as the causative viruses of colds, and severe acute respiratory syndrome (SARS) that occurred in 2002, which causes severe pneumonia.
  • coronavirus coronavirus
  • MERS Middle East Respiratory Syndrome
  • new coronavirus 2019-nCoV, SARS-CoV-2
  • the new coronavirus is classified into the same beta coronavirus genus as the SARS coronavirus, and the gene of the new coronavirus is highly homologous to the gene of the SARS coronavirus (about 80%), and further, the acceptance similar to the SARS coronavirus.
  • ACE1 or ACE2 the body
  • ACE1 or ACE2 the body
  • differences in ACE1 or ACE2 subtypes manifest in differences in infectivity.
  • the clinical symptoms of the new coronavirus are said to progress from influenza-like symptoms such as headache, high fever, malaise, and cough to pneumonia, which is the main complaint of dyspnea in severe cases.
  • Symptomatic treatments such as fever reduction and respiratory support are taken, and diversion or new development of vaccines, antiviral drugs (favipiravir, remdesivir, etc.) and immunotherapeutic agents is underway.
  • methods for measuring viral genes such as the PCR method and other rapid diagnostic methods, are being developed. Since the gene sequence of the new coronavirus is close to that of the SARS coronavirus and is also homologous to the SARS-like coronavirus derived from the bat, it is likely that the bat carries the virus from which the new coronavirus originated. Conceivable.
  • the virus has typical characteristics of the coronavirus family and belongs to the beta coronavirus 2B strain. Comparing the entire genome sequence of COVID-19 virus with the other available genome sequences of ⁇ coronavirus, the closest relationship is the coronavirus strain BatCov RaTG13, such as bat SARS, with identity 96. It was shown to be%. Virus isolation is from human airway epithelial cells, Vero It has been carried out on various cell lines such as E6 and Huh-7, and these samples can also be used. Most people infected with the COVID-19 virus recover with mild illness.
  • the degree of symptom predicted by the method of the present disclosure is not particularly limited and can be appropriately set depending on the type of virus.
  • Severe (dyspnea, septic shock, and / or tachypnea / disorder), and other mild to moderate.
  • the methods of the present disclosure include IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, as parameters used to predict viral infection symptoms.
  • the Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL- 6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF), Inframation1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22,
  • the methods of the present disclosure include bioparameters such as IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3.
  • the methods of the present disclosure include bioparameters such as IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30. , CD163, Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-22.
  • bioparameters such as IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30.
  • CD163, Chitinase-3 gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-22.
  • the biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 ( p70), IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL-28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, LIGHT.
  • the methods of the present disclosure include bioparameters such as IL-1Ra, IL-13, IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, IL-28A, IL. Parameters selected from the group consisting of -29, Pentraxin-3, and sTNF-R1 are preferred.
  • the biological parameters are selected from the group consisting of IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, Pentraxin-3, sTNF-R1. Parameters are preferred.
  • the biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10. , IL-12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP Parameters
  • the biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF- ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, Parameters selected from the group consisting of IL-35, LIGHT, MMP-1, and Pentraxin-3 are preferred.
  • a group of parameters whose numerical values change in any of mild, moderate I, moderate II, and severe can be selected in Table 3.
  • CTACK GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL.
  • IL-17A, IL-18, IP-10 MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL-20, IL-22, From the group consisting of IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, and TWEAK. It can be the parameter of choice. Since the above biological parameters change gradually or gradually from mild to moderate I, moderate II, and severe, they can be used as parameters to indicate the severity of symptoms, and the severity
  • the biological parameters are a group of parameters whose numerical values are changed in any of mild, moderate I, moderate II, and severe in Table 3, and are changed in influenza. Can be selected without, for example, CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16.
  • IL-17A MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL- Parameters selected from the group consisting of 6Ra, IL-20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. Can be. Since the above biological parameters change gradually or gradually in mild, moderate I, moderate II, and severe cases, they can be used as parameters indicating the severity of symptoms, and are further distinguished from influenza virus. It is possible to predict the severity of the disease.
  • a group of parameters whose numerical values are changing in any of moderate I or II and severe can be selected, for example, HGF, IFN. -A2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL-6Ra, IL-10, IL-11, IL It can be a parameter selected from the group consisting of -28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. Since the numerical values of the above biological parameters change stepwise or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and it is possible
  • a parameter group in which the numerical values are changed in any of moderate I or II and severe, and which is not changed in influenza is selected.
  • It can be a parameter selected from the group consisting of -NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Since the above biological parameters change gradually or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and can be further distinguished from influenza virus, and the severity is predicted. Is also possible in part.
  • a parameter group whose numerical value changes only in severe cases can be selected in Table 3, for example, MCP-3, IFN-g, IL-12. It can be a parameter selected from the group consisting of (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the above biological parameters are markers peculiar to severely ill patients and can be used as parameters for identifying severely ill patients.
  • a parameter group whose numerical value changes only in severe cases and whose numerical value does not change in influenza can be selected, for example, IFN. It can be a parameter selected from the group consisting of ⁇ g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the above biological parameters are markers peculiar to severely ill patients, can be used as parameters for identifying severely ill patients, and can be further distinguished from influenza virus.
  • a group of parameters whose expression is altered (increased or decreased) by tosirizumab treatment and / or dexmedetomidin treatment can be selected, for example, Eotaxin.
  • G-CSF IFN-g, IL-2, IL-4, IL-1ra, IL-6, IL-8, IL-17A, IP-10, MCP-1 (MCAF), MIP-1b, TNF-b , APRIL, BAFF, sCD30, sCD163, gp130, IFN-a2, IFN-b, IL-6Ra, IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, IL-35, It can be a parameter selected from the group consisting of MMP-1 and TWEAK.
  • the above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
  • a group of parameters whose expression is changed by tocilizumab treatment but not by dexmedetomidine treatment can be selected in Table 3, for example, IL-1ra, TNF. It can be a parameter selected from the group consisting of ⁇ b, IL-12 (p40), IL-20, IL-28A, IL-29, and IL-35.
  • the above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
  • a parameter group whose numerical value changes only in severe cases in Table 3 and which is also judged as “+” in Table 2 is selected.
  • the above biological parameters are markers peculiar to severely ill patients, and are markers commonly found in various cohorts, and can be more preferably used as parameters for identifying severely ill patients.
  • the parameters individually evaluated in FIGS. 6 to 22 can be selected as the biological parameters, for example, IL-6, IL-1Ra, IP-10, BAFF, APRIL, and the like. From VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8 It can be a parameter selected from the group of. As a result of various analyzes, the above biological parameters are examples that can be advantageously used as severity levels and various diagnostic markers.
  • one of the above-mentioned parameters may be used alone as a symptom prediction parameter, but from the viewpoint of predicting a virus infection symptom with higher accuracy, among these. It is preferable to use two or more kinds.
  • the inflammatory marker is at least not an inflammatory cytokine.
  • It can contain one type of marker. That is, for example, by using chemokines and markers other than inflammatory markers such as markers involved in angiogenesis and alveolar formation, not only the inflammatory state due to virus infection but also lung symptoms associated with virus infection and blood vessels can be used. At the same time, the involvement of thrombosis due to the activity of the endothelium can be investigated, and viral infection can be predicted with high probability.
  • cytokines, chemokines and soluble receptors can be measured by known methods, known measurement kits.
  • Reagents used in a measurement system utilizing such an antigen-antibody reaction can be provided individually or as a set as a diagnostic agent for determining each biologic.
  • the biological parameters as described above can be used as biomarkers because they can predict the degree of viral infection symptoms in the subject. Therefore, in one aspect of the present disclosure, one or more molecular markers for predicting viral infection symptoms of a subject can be provided, and as the molecular marker, the above-mentioned biological parameters can be adopted. ..
  • the disclosure is a method of treating a virus-infected patient, The step of obtaining one or more biological parameters in the patient and A step of analyzing the biological parameter by comparing the biological parameter with a reference value to predict the viral infection symptom of the patient and a step of administering a therapeutic agent to the patient when the patient is predicted to be a seriously ill patient.
  • the therapeutic agent is not particularly limited, and prevention of a protein subunit, a live attenuated virus, a vaccine using an inactivated virus, a DNA vaccine, an RNA vaccine, a vaccine using a non-replicating virus vector, and the like.
  • remdecibir In addition to vaccines, remdecibir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, corticosteroids, tosirizumab, salilmab, tofacitinib, varicitinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, rubrismab, erythran 001, dexamethasone, cashiribimab / imdebimab, bamuranibimab / etesebimab, sotrobimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakin Stage patient plasma
  • Oxygen inhalation Extracorporeal membrane oxygenation Intravenous drip: Steroid agent IL-6 agent such as Actemra (tocilizumab) Oral: Antiviral agent (favipiravir (Abigan), remdesivir, etc.) Antithrombotic drugs (eg, heparin, warfarin, aspirin).
  • ECMO Oxygen inhalation Extracorporeal membrane oxygenation
  • prophylactic or therapeutic agents such as inhibitors of the biomolecule can be developed, such inhibitors.
  • Such prophylactic or therapeutic agents are also within the scope of this disclosure.
  • any available vaccine such as mRNA type vaccine, DNA type vaccine, inactivated protein vaccine, inactivated vaccine, attenuated vaccine, live vaccine and the like can be used, and for example, Tojnameran (Pfizer-BioNTech) can be used.
  • S Janssen COVID-19 Vaccine
  • NVX-CoV2373 Novavax COVID-19 Vaccine
  • the present disclosure can further provide a diagnostic agent or test agent for carrying out the predictive method of the present disclosure, and specifically, the diagnostic agent or test agent of the present disclosure is for predicting a viral infection symptom. It is a diagnostic agent or a test agent and is characterized by containing a reagent capable of detecting at least one parameter selected from the above-mentioned biological parameters.
  • the diagnostic agent or test agent of the present disclosure is a diagnostic agent or test agent used in a method for predicting viral infection symptoms by measuring the amount or concentration of biological parameters in a sample taken from a subject.
  • the diagnostic agent comprises a reagent for detecting the biological parameter. After predicting the symptoms of infection before the actual infection, by setting an appropriate threshold for the degree or probability of the infection, administration of a prophylactic drug such as a vaccine to prevent viral infection, or the actual virus. It can be used to design treatment plans in the event of infection.
  • reagents for the required bioparameters can be selected and used based on the information described herein. When there are a plurality of such reagents, they may be provided separately, may be provided together as a set, or may be provided as a kit together with other necessary reagents (for example, a color former).
  • Such biological parameters can be measured by a measurement system using an antigen-antibody reaction such as ELISA, and as reagents capable of detecting biological parameters, specifically, an antibody that can specifically bind to the biological parameter, and The fragment is mentioned. Further, an antibody that can specifically bind to a biological parameter may be bound on an appropriate support and provided as an antibody array.
  • an antigen-antibody reaction such as ELISA
  • the method of the present disclosure is to compare biological parameters obtained from a sample taken from a subject with, if necessary, parameters of the same type in a healthy subject to indicate viral infection symptoms, therapeutic suitability, and the like. And / or treatment outcomes can be predicted.
  • the values of the biological parameters for each subject obtained as described above can be logarithmically converted. This makes it possible to get closer to the normal distribution.
  • each of the biological parameters obtained from a severely ill person with the value of a healthy person, it is possible to find a biological parameter whose expression changes specifically for the severely ill person, and the biological parameter thereof.
  • Subject viral infection symptoms, treatment suitability, and / or treatment outcomes can be predicted on the basis of increased or decreased expression of.
  • the present disclosure is, in another aspect, a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • the step of obtaining one or more biological parameters in the subject, and A method may be provided that comprises applying the biological parameters to a predetermined predictive model and outputting probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • prediction models generated based on reinforcement learning or neural networks may provide methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes of a subject.
  • the biomolecular parameter includes a biomolecular parameter (eg, a factor indicating an in vivo response such as the amount of cytokine or its fluctuation, a factor determining the compatibility of a virus on the biological side such as ACR-R1 isotype).
  • a biomolecular parameter eg, a factor indicating an in vivo response such as the amount of cytokine or its fluctuation, a factor determining the compatibility of a virus on the biological side such as ACR-R1 isotype.
  • Clinical data including clinical findings, X-ray CT images), and amount and / or type of virus in the body (including blood viral load, type of virus variant (identifiable by SNP, etc.), etc.) It can contain information about at least one.
  • the biological parameter comprises information about at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotype. Can be done.
  • Predictive models include biomolecular parameters (such as the amount of cytokines or their fluctuations), clinical data (clinical findings, X-ray CT images, etc.), historical information (treatment history, state history information, etc.) and treatment method information (therapeutic effect information, safety). Factors selected from (sex information, etc.) may be used.
  • the predictive model is at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment efficacy information, safety information, and status history information. Can be generated based on.
  • the clinical endpoints used to generate the predictive model are not particularly limited and include various treatments treated according to the patient's symptoms and their effects, as well as clinical data over time in the patient. Any clinical information can be used.
  • the clinical data is not particularly limited and can be appropriately selected depending on the virus for which the symptom is predicted.
  • clinical data that is known to fluctuate particularly in a target virus-infected patient can be used, specifically, leukocyte count (per ⁇ l), erythrocyte count (per ⁇ l), and absolute neutrophil count.
  • clinical data includes clinical symptoms including fever, nasal juice, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral cavity.
  • Test values including mucosal virus test, blood virus test, leukocyte (WBC), neutrophil (Neu), lymphocyte (Lym), platelet (Plt), blood pigment (Hb), CRPvFib, albumin (Alb), AST , ALT, lactate dehydrogenase (LDH), AL-ph, creatinine, procalcitonin, prothrombin time, FDP, blood test values such as DD dimer, and antibody test values including IgG antibody and IgM antibody. It is preferable to use clinical evaluation variables such as one or more clinical symptoms, findings, or laboratory values.
  • clinical data include fever, chest CT, FDP, ferritin, absolute lymphocyte count, CRP, and / or antibody test values, and biomolecular parameters IL-6, IL-8, FGF, IFN ⁇ , It is preferred to use IFN ⁇ , IFN ⁇ , IP-10, BAFF, IL-11, and / or Ferraxin.
  • the treatment history for generating a predictive model includes a history of past treatments (eg, administration history of antiviral drugs, etc.), and a history of arbitrary data of the subject.
  • treatment history includes presence / absence of oxygen inhalation, inhalation volume, duration and / or effect, presence / absence of ECMO, duration and / or effect, infusion of steroids and Actemra.
  • Presence / absence, dosage, duration, and / or effect, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or time-dependent information on the effect.
  • antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin
  • the therapeutic effect information or safety information for generating a predictive model includes the presence or absence of oxygen inhalation, the amount of inhalation, the period and / or the effect, the presence or absence of ECMO, the period and / or the effect.
  • Presence / absence, dosage, duration, and / or effect of infusion of steroids and Actemra, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or Efficacy and / or safety information can be used.
  • FIG. 1 The treatment route model on the left side of FIG. 1 summarizes the patient's condition so far. Since hospitals are not always hospitalized from the mild stage, the routes are divided according to the stage at the time of admission. In the case of continuation from mild illness, it is a model that predicts therapeutic demand while improving the system of symptom degree prediction model in which each step is connected.
  • the right side of FIG. 1 is the established state transition model, and each step is a combination model based on the conventional machine learning model.
  • Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data.
  • the degree of symptom is divided into ranks such as "asymptomatic”, “mild”, “moderate (I)”, “moderate (II)", and "severe”, and the result is obtained as a probability value corresponding to each rank. Can be done.
  • the predictive model is that the target patients are asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients). It is possible to acquire and generate the symptoms and biological parameters of the name). For less than 200 people, a statistically established transition model can be used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network can be used.
  • patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. can be used as evaluation candidate items, and each of them mainly consists of respiratory condition.
  • the stage symptom classification can be the objective variable.
  • the virus in blood can be quantified using the PCR method, and 90 items of biomolecular measurement can be quantified using Bio-Plex.
  • Evaluation candidate items can be specified for each symptom degree stage, and the concordance rate of restage prediction and the establishment of symptom degree prediction can be calculated.
  • treatment for each stage is assumed, optimal treatment prediction is performed, and optimal treatment is applied according to the predicted symptom degree.
  • the degree of symptom is predicted for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe, the suitability of the treatment method is predicted, and the optimum treatment can be performed.
  • multivariate analysis can be used in generating predictive models, and multivariate analysis can use LASSO (Least Absolute Shrinkage and Selection Operator) (Trevor et. Al .: Statistical). Learning with Sparsity: The Lasso and Generalizations, CRC Press, 2015).
  • LASSO Least Absolute Shrinkage and Selection Operator
  • This is a method of performing parameter estimation and variable selection at the same time (a technology that combines regression estimation and optimization theory of system engineering), and it is possible to calculate even if the explanatory variables are larger than the number of samples.
  • the background is the assumption that few variables are really valid. For example, in gene information analysis, the number of explanatory variables is 20,000, and the number of samples can be several hundred. You can get the regression coefficient while selecting the explanatory variables. Since the representative explanatory variable is automatically selected, the problem of multicollinearity does not occur. Therefore, the explanatory variables can be determined while numerically evaluating the degree of overfitting as much as possible in the limited data.
  • the performance of the model is the prediction obtained for the remaining 20% of the data obtained by constructing the regression model by the LASSO using 80% of the data of the data set divided into 8 to 2 in advance. It can be evaluated using Cohen's d from the distribution of values (J. Cohen: Statistical Power Analysis for the Behavioral Sciences, second edition, Statistics Press. ). Cohen's d is a value that does not depend on the sample size. Cohen's d is the difference between the mean values of the two groups divided by the combined (pooled) standard deviation of the two groups. The performance of the predictive model can be evaluated using Cohen's d between the verification group and the learning group, and in the medical field, a model with Cohen's d of 1.2 or more is preferable. ..
  • the techniques provided in the present disclosure allow the prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes by appropriately selecting biological parameters and applying them to predictive models.
  • the present disclosure obtains biological parameter data from a subject, determines the current viral infection status and symptoms, predicts how severe it will be in the near future, and how the degree of symptoms will be. It can be predicted whether it will change. Furthermore, since it is possible to determine the current virus infection status and symptoms, predict how severe the disease will be in the near future, and predict how the degree of symptoms will change, what kind of treatment will be performed at this time. It is possible to determine whether it is appropriate or optimal, or which of the available treatments is optimal, or to what extent the symptoms are in the course of treatment. It is possible to predict how much the symptoms will worsen and the degree of symptoms can be suppressed.
  • the prognosis is better for the subject tolerable to moderate (II).
  • the present disclosure can improve its accuracy as it is used. It can be expressed using a certain prediction formula, coefficient, and so on. For example, based on the present disclosure, it is classified into early asymptomatic, mild, moderate (I), moderate (II), severe, etc. based on the degree of post-infection symptom, and the amount of virus in the blood of the patient according to the degree of symptom. , Biomolecular weight, presence / absence of antibody, clinical findings, initial information such as treatment history and time-dependent information are used as evaluation items, and the degree of symptom at each stage is used as the objective variable. Alternatively, deep neural networks (AI analysis) can be used to further improve the predictive model and use it sequentially.
  • AI analysis can be used to further improve the predictive model and use it sequentially.
  • the determination method or analysis method can be executed by a program. That is, it is a computer program that causes a computer to execute a process of a method for predicting a virus infection symptom of a subject, wherein the method is a step of causing the computer to obtain one or a plurality of biological parameters in the subject.
  • a program may be provided to the computer comprising a step of analyzing the bioparameters by comparing them with reference values and using them as indicators for predicting viral infection symptoms.
  • a computer program that causes a computer to perform processing of a method in which one or more biological parameters in a subject are used as an index for predicting a viral infection symptom of the subject, wherein the method causes the computer to perform the subject.
  • a computer program that causes a computer to process a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method causes the computer to perform one or more of the subjects. It comprises the steps of obtaining a plurality of bioparameters and causing the computer to apply the bioparameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • the program may be offered.
  • a recording medium containing such a program may also be provided.
  • the recording medium can be a non-temporary recording medium.
  • the system may include a program for causing a computer to perform the methods described herein, eg, may include a recording medium containing such a program. Further, a computer (for example, a computer) for executing the instruction instructed by the program may be provided.
  • the arithmetic unit may be physically integrated or may consist of a plurality of physically separated components. Inside these arithmetic units, functions corresponding to a learning unit, a calculation unit, an acquisition unit, and the like can be provided as needed.
  • the system of the present disclosure can be realized as an ultra-multifunctional LSI manufactured by integrating a plurality of components on one chip, and specifically, a microprocessor, a ROM (Read Only Memory), and a RAM. It may be a computer system configured to include (Random Access Memory) and the like. A computer program is stored in the ROM. When the microprocessor operates according to a computer program, the system LSI achieves its function.
  • a system LSI it may be referred to as an IC, an LSI, a super LSI, or an ultra LSI due to the difference in the degree of integration.
  • the method of making an integrated circuit is not limited to the LSI, and may be realized by a dedicated circuit or a general-purpose processor.
  • An FPGA Field Programmable Gate Array
  • a reconfigurable processor that can reconfigure the connection and settings of the circuit cells inside the LSI may be used.
  • an integrated circuit technology that replaces an LSI appears due to advances in semiconductor technology or another technology derived from it, it is naturally possible to integrate functional blocks using that technology. The application of biotechnology may be possible.
  • one aspect of the present disclosure is for such viral infection symptoms, treatment suitability, and / or prediction of treatment outcomes or prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • the system for identifying indicators but also the prediction of virus infection symptoms or the prediction of virus infection symptoms, or the prediction of virus infection symptoms, which steps through the characteristic components included in the system for identifying indicators for predicting virus infection symptoms. It may be an index for predicting a virus infection symptom, a value generation, a discrimination / classification method.
  • the present disclosure may be a computer program that causes a computer to execute each characteristic step included in feature amount reduction, feature amount extraction, pain discrimination / estimation model generation, and pain discrimination / estimation.
  • one embodiment of the present disclosure identifies indicators for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes or for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. It may be a computer program that causes the computer to perform each of the characteristic steps included in the method. Also, one aspect of the present disclosure may be a computer-readable, non-temporary recording medium on which such a computer program is recorded.
  • the present disclosure may be realized as an embodiment using the cloud, IoT and AI, and the prediction technology of the present disclosure may be provided as an all-inclusive system or device.
  • the biological parameters are mainly measured and the results are displayed as a prediction device, and the calculation and the calculation of the discrimination model are performed by a server or the cloud.
  • Some or all of these can be performed using IoT (Internet of Things) and / or artificial intelligence (AI).
  • the apparatus of the present disclosure also stores a prediction model and makes a discrimination on the spot, but a semi-standalone type, which is a form in which the main calculation such as the calculation of the discrimination model is performed on a server or the cloud, can be assumed. .. Since it is not always possible to send and receive at some places such as hospitals, a model that can be used even when shielded is assumed.
  • the present disclosure describes a subject's viral infection symptoms, therapeutic suitability, And / or a program that causes a computer to execute a method of predicting a treatment result, the method of which is a) a step of obtaining a biological parameter from a model subject, and b) a step of generating a predictive model based on the biological parameter.
  • a program, and recording media, systems and equipment containing it, including steps to apply biological parameters from the subject to a predictive model to predict viral infection symptoms, treatment suitability, and / or treatment outcomes. offer.
  • the generation of the prediction model is realized on the server located away from the clinical site, and only the acquisition of biometric parameters is input at the clinical site or the clinical test site near it. It may be in the form of.
  • a simple biological parameter acquisition device may be provided, whereby the calculation result may be instantly converted into a clinical setting so that an instant judgment can be made.
  • SaaS Software as a service
  • a function for improving the prediction model may be provided.
  • selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can.
  • This function may be in the part that generates the predictive model, or it may be provided as a separate module.
  • This predictive model improvement function is, for example, option 1 (period 1 year, once or twice a year), option 2 (period 1 year, once every 1 or 2 months), option 3 (period extension, once or twice a year). ), Option 4 (extension of period +1, once every two months) and other options may be provided.
  • Data can be saved as needed.
  • Data storage is usually provided on the server side, but it may be on the terminal side as well as the cloud type as well as the all-equipped type.
  • data storage is standard (for example, up to 10 GB in the cloud), option 1 (for example, 1 TB increase in the cloud), option 2 (setting parameters in the cloud and split storage), option 3. It may offer the option of (save in the cloud by discriminant model). Save data, download data from all sold devices to create big data, update discriminant models over time, build new models, for example, new ones other than COVID-19 It is possible to provide new discrimination model software such as pandemics. It may have a data analysis option. Here, it is possible to provide patient pattern classification (searching for patient clusters based on discrimination accuracy and pattern change of feature amount).
  • each component may be configured by dedicated hardware or may be realized by executing a software program suitable for each component.
  • Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.
  • program is used in the usual meaning used in the relevant field, and describes the processes to be performed by a computer in order, and is treated as a "thing" by law. All computers operate according to the program. In modern computers, programs are represented as data and stored in recording media or storage devices.
  • the "recording medium” is a recording medium in which the program for executing the present disclosure is stored, and the recording medium may be any as long as the program can be recorded.
  • the recording medium may be an external storage device such as a ROM, an HDD, a magnetic disk, or a flash memory such as a USB memory that can be stored internally, but the present invention is not limited thereto.
  • system refers to a configuration for executing the method or program of the present invention, and originally means a system or organization for accomplishing an object, and a plurality of elements are systematically configured. In the field of computers, it refers to the entire configuration of hardware, software, OS, network, etc.
  • Example 1-1 Cytokine / chemokine / soluble receptor assay (1)
  • Cytokines and bimarkers were quantified using the multiplex cytokine array system Bio-Plex 200 (Bio-Rad Laboratories, CA, USA) according to the manufacturer's instructions. Serum was collected from 13 severely ill patients with COVID-19 and 42 healthy subjects before treatment and centrifuged at 1600 xg for 10 minutes. Serum samples were frozen at ⁇ 80 ° C. until analysis. Cytokines / chemokines / soluble receptors in the patient were simultaneously quantified.
  • the Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL- 8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF), Information1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27,
  • Example 1-2 Cytokine / chemokine / soluble receptor assay (2)
  • each cytokine / chemokine / soluble receptor shown was quantified using the Bio-Plex Pro Human Cytokine Screening 48-Plex Panel and the Bio-Plex Pro Human Inflammation 1,37-Plex Panel. These multiplex cytokine arrays were measured according to the manufacturer's instructions. Data acquisition and analysis were performed using Bio-Plex Manager software version 5.0.
  • Example 2 statistical analysis
  • the expression levels of biological parameters for each subject obtained using Bio-Plex 200 were compared.
  • cytokines / chemokines / soluble receptors were simultaneously quantified using sera obtained from rheumatoid arthritis patients in addition to healthy subjects and COVID-19 severely ill patients. All parameters were logarithmically converted and used because the distribution is more normalized. After that, the values were classified into values (green) included in the range of 10 to 90% of the values appearing in healthy subjects, higher values (red), and lower values (light green). The results are shown in FIGS. 2 to 5. In FIGS.
  • COVID-19-0 is a patient before Actemra treatment
  • COVID-19-1w is a patient about 1 to 5 days after Actemra treatment
  • COVID-19-1w is Actemra treatment.
  • Patients about 7 days after, and “COVID-19-2w” represent patients about 10 to 14 days after Actemra treatment, respectively.
  • COVID-19 infection symptoms can be predicted.
  • Pentraxin-3 it is considered that severe COVID-19 infection can be predicted with extremely high sensitivity by using Pentraxin-3.
  • Example 3 Prediction of asymptomatic, mild, and moderately ill persons
  • Informed consent will be obtained for those who are positive for nasal mucosal juice COVID-19 and whose stage is confirmed by the degree of signification of respiratory symptoms that have been tested for antibodies.
  • Blood is collected from the specified blood collection tube, and after serum separation, it is dispensed and stored frozen.
  • the amount of virus in the blood is quantified by COVID-19-specific PCR.
  • 90 kinds of biomolecules are quantitatively measured using BioPlex, Millipore, and R & D cytokine measurement kits of BioRad.
  • the viral load, biomolecular weight, presence / absence of antibody, clinical findings, and past treatment methods are used as evaluation items, and the evaluation items for predicting the degree of symptoms are specified. Based on the above, it is possible to acquire biological parameters for predicting asymptomatic, mild, and moderately ill patients in addition to severely ill patients, and it is considered that COVID-19 infection symptoms can be predicted using these parameters. Be done.
  • Example 4 Generation of prediction model
  • each symptom level is re-determined, and a machine learning probabilistic transition model or deep neural network statistical analysis method using AI is used. Select the most suitable therapeutic agent and treatment method.
  • the analysis model is shown in Fig. 1.
  • the patient's symptoms and clinical data over time are learned.
  • the model is to predict the therapeutic request while improving the system of the symptom degree prediction model in which each step is connected.
  • the right side of FIG. 1 is an established state transition model, and each step is a combination model based on a conventional machine learning model.
  • Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data.
  • the degree of symptom is divided into ranks such as "asymptomatic”, “mild”, “moderate (I)”, “moderate (II)", and "severe”, and the result is obtained as a probability value corresponding to each rank. Can be done.
  • Target patients include asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients) symptoms and biological parameters. get.
  • a statistically established transition model is used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network is used.
  • Patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. are used as evaluation candidate items.
  • the objective variable is the symptom classification of each stage, mainly the respiratory condition.
  • the virus in blood is quantified using the PCR method, and 90 items of biomolecule measurement are quantified using Bio-Plex as in Example 1.
  • the evaluation candidate items are specified for each symptom degree stage, and the concordance rate of the restage prediction and the establishment of the symptom degree prediction are calculated.
  • the predicted symptom degree treatment for each stage is assumed, optimal treatment is predicted, and optimal treatment is applied. This makes it possible to predict the suitability of the treatment method and perform optimal treatment.
  • Clinical data includes clinical symptoms including fever, nasal discharge, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral mucosa virus test, blood.
  • Test values including medium virus test, WBC, Neu, Lym, Plt, Hb, CRP, Flb, Alb, AST, ALT, LDH, AL-ph, Creatinin, Procalcitonin, Prothrombin time, FDP, D-D dimer, etc.
  • Use clinical endpoints such as blood test values and one or more clinical symptoms, findings, or test values selected from antibody test values including IgG and IgM antibodies.
  • the treatment history and status history information for generating a predictive model include past treatment history (for example, administration history of antiviral drugs and the like), and history of arbitrary data of the target subject (for example, clinical data).
  • the treatment history includes the presence / absence of oxygen inhalation, inhalation amount, duration and / or effect, presence / absence of ECMO, duration and / or effect, presence / absence of infusion of steroids and Actemra, dosage, etc.
  • Example 5 Use of prediction model: virus infectivity / symptom degree prediction
  • virus infectivity / symptom degree prediction is performed using the prediction model generated in Example 4.
  • the selected biological parameters obtained from the subject are applied to the obtained predictive model, and the obtained probabilities are used for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe.
  • the degree of symptom can be classified into the degree of symptom, or the degree of symptom can be displayed as a numerical value (for example, displayed on a 100% or 100-point scale).
  • an action plan that should be taken in the future. For example, even if the virus infection is positive (for example, PCR, LAMP method, or antigen method), if it is judged to be asymptomatic or mild by the method of the present disclosure, it is not in the hospital but in a simple accommodation facility or at home. It is possible to make a decision to perform both isolation, and for subjects who are at risk of becoming severe or have a high probability of becoming severe, they can be placed in facilities for the severely ill (hospitals and facilities including ICU). Alternatively, those who are determined to remain in moderate illness can be assigned to a hospital that can accommodate moderate illness.
  • the virus infection for example, PCR, LAMP method, or antigen method
  • the attending physician can select and administer appropriate treatment that is known or available at that time to the subject.
  • Example 6 Use of prediction model: treatment suitability and treatment result prediction
  • treatment suitability and treatment result prediction are performed in this example.
  • the selected bioparameters obtained from the subject can be applied to the obtained prediction model to calculate items related to treatment suitability and / or treatment result prediction.
  • the compatibility of a certain therapeutic agent eg, remdesivir, actemra, steroid agent, etc.
  • therapeutic method ECMO, oxygen inhalation therapy, etc.
  • Such therapeutic suitability can be expressed numerically or verbal.
  • the treatment result can be calculated for a therapeutic agent (for example, remdesivir, actemra, steroid agent, etc.) or a therapeutic method (ECMO, oxygen inhalation therapy, etc.).
  • the treatment result is, for example, YES-NO, that is, whether it responds or not, or the degree of improvement (for example, how the degree of symptom changes from before treatment to after treatment, or how it takes) is calculated. can do.
  • Predictive models can be applied to these on hand or available treatments or combinations thereof to compare improvements and select the optimal method from among them.
  • Example 7 Self-study model
  • the prediction model once generated in Example 4 can be improved while actually using it.
  • These models may be performed in a closed environment in a specific hospital, or may be learned by taking in data outside the facility as open and taking in as big data. In that case, the data to be captured can be standardized and machine-learned.
  • selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can.
  • Example 8 Changes in biological parameters in Actemra and Avigan treatment
  • serum obtained from severely infected COVIDEO-19 infected patients was used before and after treatment of the biological parameters shown in the table below. I investigated the fluctuation.
  • Treatment with Actemra and Avigan reduced the expression levels of IL-6, IP-10, IL-19, Pentraxin-3, BAFF, APRIL, IL-1 ⁇ , Osteopontin, and MCP-1, and APRIL, TNF- ⁇ , MMP-1, IL-1Ra, IL-2, IL-17, IL-10, IL-12, IL-13, IL-15, FGF, IFN- ⁇ , PDGF, IFN- ⁇ 2, VEGF, CD30, IFN- ⁇ , IL-11, IL-20, IL-22, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, TNF- ⁇ R1, TSLP, IL- 7,27p IFN- ⁇ , 27p IL-2, 27p IL-8, 27p IL-10, IL-26, MIP-1 ⁇ , Osteocalcin, eotaxin, MMP-2, MMP-3, IL-4, IL-5, The
  • Example 9 Changes in biological parameters in treatment with tocilizumab or dexmedetomidine
  • TZ tocilizumab
  • DX dexmedetomidine
  • various serums obtained from severely infected COVID-19 infected patients were used.
  • the changes in the biological parameters of Tocilizumab before and after treatment were investigated.
  • the results are shown in FIGS. 6 to 22.
  • Bioparameters include IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130.
  • the expression levels of IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-a2, etc. decreased after treatment, and the expression levels of sgp130, IL-6Ra, TWEAK, etc. decreased after treatment. The amount has risen.
  • This disclosure provides a technique for individually predicting the degree of symptom of a virus-infected patient and applying an optimal treatment method according to the degree of symptom to eliminate severely ill patients, and is available in the pharmaceutical industry.

Abstract

The present disclosure provides a method for predicting a symptom of a viral infection.　The present disclosure provides a method for predicting a symptom of a viral infection in a subject, the method comprising a step for acquiring one biological parameter or a plurality of biological parameters in the subject and a step for comparing the biological parameter or each of the biological parameters with a reference value to analyze the biological parameter or the biological parameters, and employing the biological parameter or each of the biological parameters as a measure for the prediction of the symptom of the viral infection. When a subject is infected with a virus, the subject can know a symptom of the disease in advance. Therefore, a most suitable treatment can be applied depending on the level of the symptom in each individual patient.

Description

ウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法Methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes
　本開示は、ウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法に関する。より具体的には、本開示は、ウイルスに感染した被験者の症状度を予測する方法およびそれに対する治療法適合性および治療結果を予測する方法に関する。より詳細には、本開示は、コロナウイルスなどに起因する疾患の症状を事前に予測し、その予防法や治療薬を患者に適切に提供するための技術に関する。 The present disclosure relates to methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. More specifically, the present disclosure relates to a method of predicting the degree of symptom of a subject infected with a virus and a method of predicting therapeutic suitability and treatment result thereof. More specifically, the present disclosure relates to a technique for predicting the symptoms of a disease caused by a coronavirus or the like in advance, and appropriately providing a preventive method or a therapeutic agent thereof to a patient.
　ウイルス性の感染症は現代においてもなお人類に対して脅威を及ぼすほどの影響力をもっており、例えばＡＩＤＳ（ＨＩＶ）やエボラ出血熱などのウイルス性の疾患はその抑制までに時間がかかる状況が続いている。さらに近年においては、ＳＡＲＳ（ＳＡＲＳ－ＣｏＶ）やＭＥＲＳ（ＭＥＲＳコロナウイルス）、あるいはＣＯＶＩＤ－１９（ＳＡＲＳ－ＣｏＶ－２）などの感染症が、日常生活を一変させるほどの影響力を持っている。 Viral infectious diseases are still influential enough to pose a threat to humankind even in modern times, and viral diseases such as AIDS (HIV) and Ebola hemorrhagic fever continue to take time to control. .. Furthermore, in recent years, infectious diseases such as SARS (SARS-CoV), MERS (MERS coronavirus), and COVID-19 (SARS-CoV-2) have an influence that changes daily life.
　ＣＯＶＩＤ－１９などの新たな感染症の対策において求められるのは、たとえ感染したとしても、個別にその症状度を予測し、その症状度に応じた適切な治療法を適用することにより、重症患者をなくすことである。そこで本発明者らは、ウイルス感染患者をその症状度に応じて重症者、中等症者、軽症者などに分類し、そのそれぞれの患者における生体パラメータを分析することにより、ウイルス感染症状、治療法適合性、治療結果等を予測するための方法を見出した。 Countermeasures for new infectious diseases such as COVID-19 are required for severely ill patients, even if they are infected, by individually predicting the degree of symptom and applying appropriate treatment methods according to the degree of symptom. Is to get rid of. Therefore, the present inventors classify virus-infected patients into severely ill, moderately ill, mildly ill, etc. according to the degree of symptom, and analyze the biological parameters of each patient to analyze the symptom of virus infection and the treatment method. We have found a method for predicting compatibility, treatment results, etc.
　したがって、本開示は、以下を提供する。
（項目１）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法であって、
　前記被験者における１または複数の生体パラメータを得る工程と、
　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程と
を含む、方法。
（項目２）　前記ウイルス感染症状、治療法適合性、および／または治療結果が、ウイルス感染症状であり、
　前記予測する工程は、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、ウイルス感染症状を予測するための指標とする工程を含む、項目１に記載の方法。
（項目３）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目１または２に記載の方法。
（項目４）　前記生体パラメータは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目１～３のいずれか一項に記載の方法。
（項目５）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目１～４のいずれか一項に記載の方法。
（項目６）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目１～５のいずれか一項に記載の方法。
（項目７）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目１～６のいずれか一項に記載の方法。
（項目８）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目１～７のいずれか一項に記載の方法。
（項目９）　前記症状の予測が症状度の予測を含む、項目１～８のいずれか一項に記載の方法。
（項目１０）　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、項目１～９のいずれか一項に記載の方法。
（項目１１）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、項目１～１０のいずれか一項に記載の方法。
（項目１１Ａ）
　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目１～１０のいずれか一項に記載の方法。
（項目１２）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目１～１１のいずれか一項に記載の方法。
（項目１３）　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目１～１２のいずれか一項に記載の方法。
（項目１４）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目１～１１のいずれか一項に記載の方法。
（項目１５）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目１～１１のいずれか一項に記載の方法。
（項目１６）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目１～１１のいずれか一項に記載の方法。
（項目１７）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目１～１１のいずれか一項に記載の方法。
（項目１７Ａ）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１７Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目１８）　前記基準値が健常者における前記生体パラメータの値である、項目２～１７のいずれか一項に記載の方法。
（項目１９）　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、項目１～１８のいずれか一項に記載の方法。
（項目Ａ１）　被験者においてウイルス感染を予防または治療する方法であって、
　前記被験者における１または複数の生体パラメータを得る工程と、
　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程と
　前記ウイルス感染症状、治療法適合性、および／または治療結果の予測に基づいて、前記被験者を予防または治療する工程と
を含む、方法。
（項目Ａ２）　前記予測する工程は、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測するための指標とする工程を含み、
　前記予防または治療する工程は、前記被験者が重症患者と予測された場合に、前記被験者に治療薬を投与する工程を含む、項目Ａ１に記載の方法。
（項目Ａ３）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目Ａ１またはＡ２に記載の方法。
（項目Ａ４）　前記炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目Ａ１～Ａ３のいずれか一項に記載の方法。
（項目Ａ５）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目Ａ１～Ａ４のいずれか一項に記載の方法。
（項目Ａ６）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目Ａ１～Ａ５のいずれか一項に記載の方法。
（項目Ａ７）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目Ａ１～Ａ６のいずれか一項に記載の方法。
（項目Ａ８）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目Ａ１～Ａ７のいずれか一項に記載の方法。
（項目Ａ９）　前記治療薬が、レムデシビル、ファビピラビル、シクレソニド、ナファモスタット、カモスタット、イベルメクチン、ステロイド剤、トシリズマブ、サリルマブ、トファシチニブ、バリシチニブ、ルキソリチニブ、アカラブルチニブ、ラブリズマブ、エリトラン、イブジラスト、ＬＹ３１２７８０４、オチリマブ、ＨＬＣＭ０５１、ＡＤＲ－００１、デキサメタゾン、カシリビマブ／イムデビマブ、バムラニビマブ／エテセビマブ、ソトロビマブ、ＶＩＲ－７８３２、ＡＺＤ７４４２、モルヌピラビル、ＡＴ－５２７、ＢＩ７６７５５１、ＰＦ－０７３０４８１４、ＰＦ－０７３２１３３２、ＶＩＲ－２７０３、アナキンラ、ヒドロキシクロロキン、ロピナビル・リトナビル、回復期患者血漿、デクスメデトミジン、およびフルボキサミンからなる群から選択される１または複数の薬剤である、項目Ａ１～Ａ８のいずれか一項に記載の方法。
（項目Ａ１０）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃ　ＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、項目Ａ１～Ａ９のいずれか一項に記載の方法。
（項目Ａ１０Ａ）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃ　ＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ａ１～Ａ９のいずれか一項に記載の方法。
（項目Ａ１１）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目Ａ１～Ａ１０のいずれか一項に記載の方法。
（項目Ａ１２）　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目Ａ１～Ａ１１のいずれか一項に記載の方法。
（項目Ａ１３）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目Ａ１～Ａ１０のいずれか一項に記載の方法。
（項目Ａ１４）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ａ１～Ａ１０のいずれか一項に記載の方法。
（項目Ａ１５）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ａ１～Ａ１０のいずれか一項に記載の方法。
（項目Ａ１６）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ａ１～Ａ１０のいずれか一項に記載の方法。
（項目Ａ１６Ａ）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１６Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ａ１７）　前記基準値が健常者における前記生体パラメータの値である、項目Ａ２～Ａ１６のいずれか一項に記載の方法。
（項目Ａ１８）　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、項目Ａ１～Ａ１７のいずれか一項に記載の方法。
（項目Ｂ１）　被験者における１または複数の生体パラメータを、前記被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための指標とする方法。
（項目Ｂ２）　前記被験者における１または複数の生体パラメータを得る工程を含む、項目Ｂ１に記載の方法。
（項目Ｂ３）　前記１または複数の生体パラメータが、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）を含む、項目Ｂ１またはＢ２に記載の方法。
（項目Ｂ４）前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ３のいずれか一項に記載の方法。
（項目Ｂ４Ａ）前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ３のいずれか一項に記載の方法。（項目Ｂ５）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目Ｂ１～Ｂ４のいずれか一項に記載の方法。
（項目Ｂ６）　前記生体パラメータは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目Ｂ１～Ｂ５のいずれか一項に記載の方法。
（項目Ｂ７）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目Ｂ１～Ｂ６のいずれか一項に記載の方法。
（項目Ｂ８）　さらに、前記生体パラメータを基準値と比較することで前記生体パラメータを分析する工程を含む、項目Ｂ１～Ｂ７のいずれか一項に記載の方法。
（項目Ｂ９）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目Ｂ１～Ｂ８のいずれか一項に記載の方法。
（項目Ｂ１０）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目Ｂ１～Ｂ９のいずれか一項に記載の方法。
（項目Ｂ１１）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目Ｂ１～Ｂ１０のいずれか一項に記載の方法。
（項目Ｂ１２）　前記症状の予測が症状度の予測を含む、項目Ｂ１～Ｂ１１のいずれか一項に記載の方法。
（項目Ｂ１３）　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、項目Ｂ１～Ｂ１２のいずれか一項に記載の方法。
（項目Ｂ１４）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１３のいずれか一項に記載の方法。
（項目Ｂ１５）　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１４のいずれか一項に記載の方法。
（項目Ｂ１６）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１３のいずれか一項に記載の方法。
（項目Ｂ１７）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１３のいずれか一項に記載の方法。
（項目Ｂ１８）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１３のいずれか一項に記載の方法。
（項目Ｂ１９）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｂ１～Ｂ１３のいずれか一項に記載の方法。
（項目Ｂ１９Ａ）　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ１９Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｂ２０）　前記基準値が健常者における前記生体パラメータの値である、項目Ｂ８～Ｂ１９のいずれか一項に記載の方法。
（項目Ｂ２１）　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、項目Ｂ１～Ｂ２０のいずれか一項に記載の方法。
（項目Ｃ１）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するためのインビトロの方法であって、
　前記被験者における１または複数の生体パラメータを得る工程と、
　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程と
を含む、方法。
（項目Ｃ２）　前記ウイルス感染症状、治療法適合性、および／または治療結果の予測が、ウイルス感染症状であり、
　前記症状の予測が症状度の予測であり、
　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、項目Ｃ１に記載の方法。
（項目Ｃ２Ａ）　前記ウイルス感染症状、治療法適合性、および／または治療結果の予測が、ウイルス感染症状であり、
　前記症状の予測が症状度の予測であり、
　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｃ１に記載の方法。
（項目Ｃ３）　前記予測する工程は、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、ウイルス感染症状を予測するための指標とする工程を含む、項目Ｃ１またはＣ２に記載の方法。
（項目Ｃ４）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目Ｃ１～Ｃ３のいずれか一項に記載の方法。
（項目Ｃ５）　前記炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目Ｃ１～Ｃ４のいずれか一項に記載の方法。
（項目Ｃ６）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目Ｃ１～Ｃ５のいずれか一項に記載の方法。
（項目Ｃ７）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目Ｃ１～Ｃ６のいずれか一項に記載の方法。
（項目Ｃ８）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目Ｃ１～Ｃ７のいずれか一項に記載の方法。
（項目Ｃ９）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目Ｃ１～Ｃ８のいずれか一項に記載の方法。
（項目Ｃ１０）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ９のいずれか一項に記載の方法。
（項目Ｃ１１）
　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ１０のいずれか一項に記載の方法。
（項目Ｃ１２）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ９のいずれか一項に記載の方法。
（項目Ｃ１３）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ９のいずれか一項に記載の方法。
（項目Ｃ１４）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ９のいずれか一項に記載の方法。
（項目Ｃ１５）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｃ１～Ｃ９のいずれか一項に記載の方法。
（項目Ｃ１５Ａ）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１５Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｃ１６）　前記基準値が健常者における前記生体パラメータの値である、項目Ｃ３～Ｃ１５のいずれか一項に記載の方法。
（項目Ｃ１７）　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、項目Ｃ１～Ｃ１６のいずれか一項に記載の方法。
（項目Ｄ１）　被験者における１または複数の生体パラメータを、前記被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための指標とするインビトロの方法。
（項目Ｄ２）　前記被験者における１または複数の生体パラメータを得る工程を含む、項目Ｄ１に記載の方法。
（項目Ｄ３）　前記１または複数の生体パラメータが、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）を含む、項目Ｄ１またはＤ２に記載の方法。（項目Ｄ４）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ３のいずれか一項に記載の方法。方法。
（項目Ｄ４Ａ）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ３のいずれか一項に記載の方法。方法。
（項目Ｄ５）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目Ｄ１～Ｄ４のいずれか一項に記載の方法。
（項目Ｄ６）　前記生体パラメータは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目Ｄ１～Ｄ５のいずれか一項に記載の方法。
（項目Ｄ７）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目Ｄ１～Ｄ６のいずれか一項に記載の方法。
（項目Ｄ８）　さらに、前記生体パラメータを基準値と比較することで前記生体パラメータを分析する工程を含む、項目Ｄ１～Ｄ７のいずれか一項に記載の方法。
（項目Ｄ９）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目Ｄ１～Ｄ８のいずれか一項に記載の方法。
（項目Ｄ１０）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目Ｄ１～Ｄ９のいずれか一項に記載の方法。
（項目Ｄ１１）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目Ｄ１～Ｄ１０のいずれか一項に記載の方法。
（項目Ｄ１２）　前記症状の予測が症状度の予測を含む、項目Ｄ１～Ｄ１１のいずれか一項に記載の方法。
（項目Ｄ１３）　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、項目Ｄ１～Ｄ１２のいずれか一項に記載の方法。
（項目Ｄ１４）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１３のいずれか一項に記載の方法。
（項目Ｄ１５）　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１４のいずれか一項に記載の方法。
（項目Ｄ１６）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１３のいずれか一項に記載の方法。
（項目Ｄ１７）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１３のいずれか一項に記載の方法。
（項目Ｄ１８）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１３のいずれか一項に記載の方法。
（項目Ｄ１９）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、項目Ｄ１～Ｄ１３のいずれか一項に記載の方法。
（項目Ｄ１９Ａ）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ１９Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｄ２０）　前記基準値が健常者における前記生体パラメータの値である、項目Ｄ８～Ｄ１９のいずれか一項に記載の方法。
（項目Ｄ２１）　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、項目Ｄ１～Ｄ２０のいずれか一項に記載の方法。
（項目Ｅ１）　１または複数の生体パラメータを含む、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための生体マーカー。
（項目Ｅ２）　前記１または複数の生体パラメータが、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）を含む、項目Ｅ１に記載の生体マーカー。
（項目Ｅ３）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択される、項目Ｅ１またはＥ２に記載の生体マーカー。
（項目Ｅ３Ａ）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、項目Ｅ１またはＥ２に記載の生体マーカー。
（項目Ｅ４）　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、項目Ｅ１～Ｅ３のいずれか一項に記載の生体マーカー。（項目Ｅ５）　前記炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含む、項目Ｅ１～Ｅ４のいずれか一項に記載の生体マーカー。
（項目Ｅ６）　前記生体パラメータは、遺伝子産物（タンパク質）である、項目Ｅ１～Ｅ５のいずれか一項に記載の生体マーカー。
（項目Ｅ７）　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、項目Ｅ１～Ｅ６のいずれか一項に記載の生体マーカー。
（項目Ｅ８）　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、項目Ｅ１～Ｅ７のいずれか一項に記載の生体マーカー。
（項目Ｅ９）　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、項目Ｅ１～Ｅ８のいずれか一項に記載の生体マーカー。
（項目Ｅ１０）　前記症状の予測が症状度の予測を含む、項目Ｅ１～Ｅ９のいずれか一項に記載の生体マーカー。
（項目Ｅ１１）　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、項目Ｅ１～Ｅ１０のいずれか一項に記載の生体マーカー。
（項目Ｅ１２）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、項目Ｅ１～Ｅ１１のいずれか一項に記載の生体マーカー。
（項目Ｅ１３）　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、項目Ｅ１～Ｅ１２のいずれか一項に記載の生体マーカー。（項目Ｅ１４）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、項目Ｅ１～Ｅ１１のいずれか一項に記載の生体マーカー。
（項目Ｅ１５）　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、項目Ｅ１～Ｅ１１のいずれか一項に記載の生体マーカー。
（項目Ｅ１６）　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、項目Ｅ１～Ｅ１１のいずれか一項に記載の生体マーカー。
（項目Ｅ１７）　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、項目Ｅ１～Ｅ１１のいずれか一項に記載の生体マーカー。
（項目Ｅ１７Ａ）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ａ２）
　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｂ）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｂ２）
　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｃ）
　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｃ２）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｄ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｅ）
　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｅ１７Ｆ）
　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、上記項目のいずれか一項に記載の方法。
（項目Ｆ１）　上記項目のいずれか一項に記載の方法に使用するための試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｆ２）　被験者の１または複数の生体パラメータからのウイルス感染症状、治療法適合性、および／または治療結果の予測に使用するための試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｆ３）　前記生体パラメータを測定するための手段、薬剤またはデバイスを含む、項目Ｆ１またはＦ２に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｆ４）　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を行う計算ユニットをさらに備える、項目Ｆ１～Ｆ３のいずれか一項に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｆ５）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択される、項目Ｆ１～Ｆ４のいずれか一項に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｆ５Ａ）　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、項目Ｆ１～Ｆ４のいずれか一項に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。
（項目Ｇ１）　前記予測する工程は、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力する工程を含む、項目１に記載の方法。
（項目Ｇ２）　前記生体パラメータは、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）からなる群より選択される少なくとも１つに関する情報を含み、
　前記予測モデルは、生体分子パラメータ、臨床データ、履歴情報および治療法情報からなる群より選択される少なくとも１つに基づいて作出されたものである、項目Ｇ１に記載の方法。
（項目Ｇ３）　前記生体パラメータは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、血中ウイルス量、およびＡＣＲ－Ｒ１アイソタイプからなる群より選択される少なくとも１つに関する情報を含み、
　前記予測モデルは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、治療履歴、治療効果情報、安全性情報、および状態履歴情報からなる群より選択される少なくとも１つに基づいて作出されたものである、項目Ｇ１またはＧ２に記載の方法。
（項目Ｇ４）　前記予測モデルは、強化学習またはニューラルネットワークに基づいて生成されたものである、項目Ｇ１～Ｇ３のいずれか一項に記載の方法。
（項目Ｇ５）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムであって、前記方法は、
　前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程と、
　前記コンピュータに、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果に関する確率を出力させる工程と
　を含む、プログラム。
（項目Ｇ６）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムを格納する記録媒体であって、前記方法は、
　前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程と、
　前記コンピュータに、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力させる工程と
　を含む、記録媒体。
（項目Ｇ７）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するためのシステムであって、
　前記被験者における１または複数の生体パラメータを得る手段と、
　前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果に関する確率を出力する手段と
　を含む、システム。
（項目Ｇ８）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法をコンピュータに実行させるコンピュータプログラムであって、前記方法は、
　前記コンピュータに、前記被験者における１または複数の生体パラメータを入力する工程と、
　前記コンピュータに、前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行させる工程と
を含む、プログラム。
（項目Ｇ９）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法をコンピュータに実行させるコンピュータプログラムを格納する記録媒体であって、前記方法は、
　前記コンピュータに、前記被験者における１または複数の生体パラメータを入力する工程と、
　前記コンピュータに、前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行させる工程と
を含む、記録媒体。
（項目Ｇ１０）　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するためのシステムであって、
　前記被験者における１または複数の生体パラメータを入力する手段と、
　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行する手段と
を含む、システム。
　上述のような本開示の方法、プログラム、記憶媒体、およびシステムは、本明細書の他の箇所に記載される任意の特徴を含むことができる。 Accordingly, the present disclosure provides:
(Item 1) A method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment results.
The step of obtaining one or more biological parameters in the subject, and
A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters.
Including, how.
(Item 2) The virus infection symptom, treatment suitability, and / or treatment result is a virus infection symptom.
The method according to item 1, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
(Item 3) The method according to item 1 or 2, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
(Item 4) The method according to any one of items 1 to 3, wherein the biological parameter contains at least one marker that is not an inflammatory cytokine.
(Item 5) The method according to any one of items 1 to 4, wherein the biological parameter is a gene product (protein).
(Item 6) The method according to any one of items 1 to 5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item 7) Any one of items 1 to 6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
(Item 8) The method according to any one of items 1 to 7, wherein the virus infection is SARS-CoV-2 infection.
(Item 9) The method according to any one of items 1 to 8, wherein the prediction of the symptom includes the prediction of the degree of symptom.
(Item 10) The method according to any one of items 1 to 9, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
(Item 11) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα , IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The method according to any one of items 1 to 10, which comprises the parameters to be used.
(Item 11A)
The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The method according to any one of items 1 to 10.
(Item 12) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of items 1 to 11, which comprises a parameter selected from the group.
(Item 13) The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method of any one of items 1-12, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
(Item 14) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Items 1-11, including parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
(Item 15) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the method according to any one of items 1 to 11, comprising a parameter selected from the group consisting of VCAM-1.
(Item 16) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1, the method according to any one of items 1 to 11.
(Item 17) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , The method of any one of items 1-11, comprising a parameter selected from the group consisting of Leptin, OncostatinM, and VCAM-1.
(Item 17A)
The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
(Item 17A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item 17B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item 17B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item 17C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item 17C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item 17D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item 17E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item 17F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item 18) The method according to any one of items 2 to 17, wherein the reference value is a value of the biological parameter in a healthy person.
(Item 19) The method according to any one of items 1 to 18, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
(Item A1) A method for preventing or treating a virus infection in a subject.
The step of obtaining one or more biological parameters in the subject, and
A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters.
With the step of preventing or treating the subject based on the prediction of viral infection symptoms, treatment suitability, and / or treatment outcome.
Including, how.
(Item A2) The prediction step is an index for predicting the virus infection symptom, the treatment suitability, and / or the treatment result by analyzing the biological parameter by comparing the biological parameter with a reference value. Including the process of
The method according to item A1, wherein the step of preventing or treating includes a step of administering a therapeutic agent to the subject when the subject is predicted to be a seriously ill patient.
(Item A3) The method according to item A1 or A2, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
(Item A4) The method according to any one of items A1 to A3, wherein the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
(Item A5) The method according to any one of items A1 to A4, wherein the biological parameter is a gene product (protein).
(Item A6) The method according to any one of items A1 to A5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item A7) Any one of items A1 to A6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
(Item A8) The method according to any one of items A1 to A7, wherein the virus infection is SARS-CoV-2 infection.
(Item A9) The therapeutic agents include remdesivir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, tocilizumab, sarilumab, tofacitinib, baricitinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, rubrismab, -001, dexamethasone, casiribimab / imdebimab, baricitinib / etecebimab, sotrovimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakinra The method according to any one of items A1 to A8, which is one or more drugs selected from the group consisting of convalescent patient plasma, dexmedetomidin, and fluboxamine.
(Item A10) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP- 1β, PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL- From the group consisting of 2Rα, IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The method according to any one of items A1 to A9, which comprises the selected parameter.
(Item A10A) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP- 1β, PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 Selected from the group consisting of γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The method according to any one of items A1 to A9, which comprises the above-mentioned parameters.
(Item A11) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of items A1 to A10, which comprises a parameter selected from the group.
(Item A12) The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items A1 to A11, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
(Item A13) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Items A1 to A10 comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
(Item A14) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the method according to any one of items A1 to A10, comprising a parameter selected from the group consisting of VCAM-1.
(Item A15) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1, the method according to any one of items A1 to A10, which comprises a parameter selected from the group.
(Item A16) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items A10, comprising a parameter selected from the group consisting of VCAM-1.
(Item A16A)
The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
(Item A16A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item A16B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item A16B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item A16C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item A16C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item A16D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item A16E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item A16F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item A17) The method according to any one of items A2 to A16, wherein the reference value is a value of the biological parameter in a healthy person.
(Item A18) The method according to any one of items A1 to A17, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
(Item B1) A method in which one or more biological parameters in a subject are used as an index for predicting the viral infection symptom, treatment suitability, and / or treatment result of the subject.
(Item B2) The method according to item B1, which comprises the step of obtaining one or more biological parameters in the subject.
(Item B3) The one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). The method according to item B1 or B2, comprising).
(Item B4) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα , IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The method according to any one of items B1 to B3, which comprises the parameters to be added.
(Item B4A) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, and VCAM-1. The method according to any one of items B1 to B3, which comprises parameters. (Item B5) The method according to any one of items B1 to B4, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
(Item B6) The method according to any one of items B1 to B5, wherein the biological parameter comprises at least one marker that is not an inflammatory cytokine.
(Item B7) The method according to any one of items B1 to B6, wherein the biological parameter is a gene product (protein).
(Item B8) The method according to any one of items B1 to B7, further comprising a step of analyzing the biological parameter by comparing the biological parameter with a reference value.
(Item B9) The method according to any one of items B1 to B8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item B10) Any one of items B1 to B9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
(Item B11) The method according to any one of items B1 to B10, wherein the virus infection is SARS-CoV-2 infection.
(Item B12) The method according to any one of items B1 to B11, wherein the symptom prediction includes a symptom degree prediction.
(Item B13) The method according to any one of items B1 to B12, wherein the prediction of the symptom includes a prediction of whether or not the viral infection symptom of the subject becomes severe.
(Item B14) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of items B1 to B13, comprising a parameter selected from the group.
(Item B15) The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items B1 to B14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
(Item B16) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Item B1-B13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
(Item B17) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the method according to any one of items B1 to B13, comprising a parameter selected from the group consisting of VCAM-1.
(Item B18) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and the method according to any one of items B13, comprising a parameter selected from the group consisting of VCAM-1.
(Item B19) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items B13, comprising a parameter selected from the group consisting of VCAM-1.
(Item B19A) The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL. -10, IL-12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF -Bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL -11, IL-20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -The method according to any one of the above items, comprising parameters selected from the group consisting of R2, TSLP, and TWEAK.
(Item B19A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item B19B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item B19B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item B19C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item B19C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item B19D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item B19E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item B19F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item B20) The method according to any one of items B8 to B19, wherein the reference value is a value of the biological parameter in a healthy person.
(Item B21) The method according to any one of items B1 to B20, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
(Item C1) An in vitro method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
The step of obtaining one or more biological parameters in the subject, and
A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters.
Including, how.
(Item C2) The virus infection symptom, the treatment suitability, and / or the prediction of the treatment result are the virus infection symptom.
The prediction of the symptom is the prediction of the degree of symptom.
The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα, IL-3. , IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. Included, the method of item C1.
(Item C2A) The virus infection symptom, the treatment suitability, and / or the prediction of the treatment result is the virus infection symptom.
The prediction of the symptom is the prediction of the degree of symptom.
The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The method according to item C1.
(Item C3) The step according to item C1 or C2, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom. Method.
(Item C4) The method according to any one of items C1 to C3, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
(Item C5) The method according to any one of items C1 to C4, wherein the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
(Item C6) The method according to any one of items C1 to C5, wherein the biological parameter is a gene product (protein).
(Item C7) The method according to any one of items C1 to C6, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item C8) Any one of items C1 to C7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
(Item C9) The method according to any one of items C1 to C8, wherein the virus infection is SARS-CoV-2 infection.
(Item C10) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of items C1 to C9, which comprises a parameter selected from the group.
(Item C11)
The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, The method according to any one of items C1 to C10, comprising a parameter selected from the group consisting of MMP-1 and Pentraxin-3.
(Item C12) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Item C1-C9, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
(Item C13) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the method according to any one of items C1 to C9, comprising a parameter selected from the group consisting of VCAM-1.
(Item C14) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and the method according to any one of items C9, comprising parameters selected from the group consisting of VCAM-1.
(Item C15) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items C9, comprising a parameter selected from the group consisting of VCAM-1.
(Item C15A)
The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
(Item C15A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item C15B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item C15B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item C15C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item C15C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item C15D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item C15E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item C15F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item C16) The method according to any one of items C3 to C15, wherein the reference value is a value of the biological parameter in a healthy person.
(Item C17) The method according to any one of items C1 to C16, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
(Item D1) An in vitro method in which one or more biological parameters in a subject are used as indicators for predicting the subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
(Item D2) The method according to item D1, which comprises the step of obtaining one or more biological parameters in the subject.
(Item D3) The one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item D1 or D2. (Item D4) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα , IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The method according to any one of items D1 to D3, which comprises the parameters to be added. Method.
(Item D4A) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, and VCAM-1. The method according to any one of items D1 to D3, which comprises parameters. Method.
(Item D5) The method according to any one of items D1 to D4, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
(Item D6) The method according to any one of items D1 to D5, wherein the biological parameter comprises at least one marker that is not an inflammatory cytokine.
(Item D7) The method according to any one of items D1 to D6, wherein the biological parameter is a gene product (protein).
(Item D8) The method according to any one of items D1 to D7, further comprising a step of analyzing the biological parameter by comparing the biological parameter with a reference value.
(Item D9) The method according to any one of items D1 to D8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item D10) Any one of items D1 to D9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
(Item D11) The method according to any one of items D1 to D10, wherein the virus infection is SARS-CoV-2 infection.
(Item D12) The method according to any one of items D1 to D11, wherein the symptom prediction includes a symptom degree prediction.
(Item D13) The method according to any one of items D1 to D12, wherein the prediction of the symptom includes a prediction of whether or not the viral infection symptom of the subject becomes severe.
(Item D14) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of items D1 to D13, comprising parameters selected from the group.
(Item D15) The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items D1 to D14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
(Item D16) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Item D1 to D13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
(Item D17) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the method according to any one of items D1 to D13, comprising a parameter selected from the group consisting of VCAM-1.
(Item D18) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1, the method according to any one of items D1 to D13, comprising a parameter selected from the group.
(Item D19) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items D13, comprising a parameter selected from the group consisting of VCAM-1.
(Item D19A)
The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
(Item D19A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item D19B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item D19B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item D19C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item D19C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item D19D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item D19E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item D19F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item D20) The method according to any one of items D8 to D19, wherein the reference value is a value of the biological parameter in a healthy person.
(Item D21) The method according to any one of items D1 to D20, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
(Item E1) A biological marker for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, including one or more biological parameters.
(Item E2) The one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item E1.
(Item E3) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα , IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The biomarker according to item E1 or E2.
(Item E3A) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, and VCAM-1. , Item E1 or E2.
(Item E4) The biological marker according to any one of items E1 to E3, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker. (Item E5) The biological marker according to any one of items E1 to E4, wherein the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
(Item E6) The biological marker according to any one of items E1 to E5, wherein the biological parameter is a gene product (protein).
(Item E7) The biological marker according to any one of items E1 to E6, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
(Item E8) Any one of items E1 to E7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The biomarker described in the section.
(Item E9) The biological marker according to any one of items E1 to E8, wherein the virus infection is SARS-CoV-2 infection.
(Item E10) The biological marker according to any one of items E1 to E9, wherein the prediction of the symptom includes a prediction of the degree of symptom.
(Item E11) The biological marker according to any one of items E1 to E10, wherein the prediction of the symptom includes a prediction of whether or not the viral infection symptom of the subject becomes severe.
(Item E12) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF , APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The biomarker according to any one of items E1 to E11, comprising a parameter selected from the group.
(Item E13) The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35. The biomarker according to any one of items E1 to E12, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3. (Item E14) The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL Item E1-E11, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The biomarker according to any one of the above.
(Item E15) The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130. , IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A , IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK , ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Lite , And the biomarker according to any one of items E1 to E11, selected from the group consisting of VCAM-1.
(Item E16) The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44 , ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1, the biological marker according to any one of items E1 to E11.
(Item E17) The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and VCAM-1, the biomarker according to any one of items E1 to E11 selected from the group.
(Item E17A)
The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
(Item E17A2)
The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method described in any one of the above items.
(Item E17B)
The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL In any one of the above items, including parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described.
(Item E17B2)
The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Method.
(Item E17C)
The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, comprising parameters selected from the group consisting of.
(Item E17C2)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item E17D)
The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
(Item E17E)
From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method according to any one of the above items, including the parameters selected.
(Item E17F)
The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method according to any one of the above items, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
(Item F1) A reagent, a kit, or a device for use in the method according to any one of the above items, or a combination thereof.
(Item F2) Reagents, kits, or devices, or combinations thereof, for use in predicting viral infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
(Item F3) The reagent, kit, or device, or a combination thereof, according to item F1 or F2, which comprises means, agents or devices for measuring the biological parameters.
(Item F4) The item according to any one of items F1 to F3, further comprising a calculation unit that performs calculations for predicting the virus infection symptom, treatment suitability, and / or treatment result based on the biological parameters. Reagents, kits, or devices, or combinations thereof.
(Item F5) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα , IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
(Item F5A) The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β , PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1 , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, and VCAM-1. , The reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
(Item G1) The step according to item 1 includes the step of applying the biological parameter to a predetermined prediction model and outputting the probability regarding the prediction of the viral infection symptom, the treatment suitability, and / or the treatment result. the method of.
(Item G2) The bioparameters are based on biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (specified by SNP, etc.), etc.). Contains information about at least one selected from the group of
The method according to item G1, wherein the predictive model is created based on at least one selected from the group consisting of biomolecular parameters, clinical data, historical information and treatment information.
(Item G3) The biological parameter comprises information on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotypes.
The predictive model was created based on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment effect information, safety information, and status history information. The method according to item G1 or G2, which is a thing.
(Item G4) The method according to any one of items G1 to G3, wherein the prediction model is generated based on reinforcement learning or a neural network.
(Item G5) A computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method is:
A step of causing the computer to obtain one or more biological parameters in the subject.
A step of causing the computer to apply the biological parameters to a predetermined predictive model and output probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results.
Including the program.
(Item G6) A recording medium that stores a computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
A step of causing the computer to obtain one or more biological parameters in the subject.
A step of causing the computer to apply the biological parameters to a predetermined prediction model and output probabilities regarding the prediction of viral infection symptoms, treatment suitability, and / or treatment results.
Recording medium, including.
(Item G7) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of subjects.
A means for obtaining one or more biological parameters in the subject,
As a means of applying the biological parameters to a predetermined predictive model and outputting probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results.
Including the system.
(Item G8) A computer program that causes a computer to execute a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment result.
The step of inputting one or more biological parameters in the subject into the computer,
A step of causing the computer to perform calculations to predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
Including the program.
(Item G9) A recording medium containing a computer program that causes a computer to execute a method for predicting a subject's viral infection symptom, treatment suitability, and / or treatment result, wherein the method is:
The step of inputting one or more biological parameters in the subject into the computer,
A step of causing the computer to perform calculations to predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
Recording medium, including.
(Item G10) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of a subject.
A means for inputting one or more biological parameters in the subject,
As a means of performing calculations that predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
Including the system.
The methods, programs, storage media, and systems of the present disclosure as described above may include any of the features described elsewhere herein.
　本開示において、上記１または複数の特徴は、明示された組み合わせに加え、さらに組み合わせて提供されうることが意図される。本開示のなおさらなる実施形態および利点は、必要に応じて以下の詳細な説明を読んで理解すれば、当業者に認識される。 In the present disclosure, it is intended that the above one or more features may be provided in a further combination in addition to the specified combinations. Further embodiments and advantages of the present disclosure will be appreciated by those of skill in the art upon reading and understanding the following detailed description as appropriate.
　本開示によれば、被験者がウイルスに罹患した場合の疾患の症状、最適な治療法および／または治療結果を事前に知ることができるため、個々の患者の症状の程度に応じた最適な治療を施すことができる。このような手法は場当たり的な治療ではなく、個人毎に治療効果を最大限に引き出すことができ、患者毎に最も有効な治療方針を立てることも可能になる。仮にウイルスに起因してパンデミックが生じたとしても、医療崩壊という事態を回避させることに大きく貢献できる。 According to the present disclosure, it is possible to know in advance the symptoms of the disease, the optimal treatment method and / or the treatment result when the subject is infected with the virus, so that the optimal treatment according to the degree of the symptoms of each individual patient can be performed. Can be applied. Such a method is not an ad hoc treatment, but can maximize the therapeutic effect for each individual, and it is also possible to formulate the most effective treatment policy for each patient. Even if a pandemic is caused by a virus, it can greatly contribute to avoiding the collapse of medical care.
図１は患者の状態を想定し、症状度や治療適合性を予測するための予測モデル構築のための治療経路モデルと、症状度や治療適合性の予測との関連性を示す模式図である。FIG. 1 is a schematic diagram showing the relationship between the treatment route model for constructing a predictive model for predicting the symptom degree and the treatment suitability assuming the patient's condition and the prediction of the symptom degree and the treatment suitability. .. 図２は、健常者から得た試料を用いて、サイトカイン／ケモカイン／可溶性レセプターアッセイをおこない、測定した生体パラメータ毎に発現量を統計処理した結果である。FIG. 2 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a healthy subject and statistically processing the expression level for each measured biological parameter. 図３は、ＣＯＶＩＤ－１９重症患者から得た試料を用いて、サイトカイン／ケモカイン／可溶性レセプターアッセイをおこない、測定した生体パラメータ毎に発現量を統計処理した結果である。FIG. 3 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a COVID-19 severely ill patient and statistically processing the expression level for each measured biological parameter. 図４は、リウマチ患者から得た試料を用いて、サイトカイン／ケモカイン／可溶性レセプターアッセイをおこない、測定した生体パラメータ毎に発現量を統計処理した結果である。FIG. 4 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a rheumatism patient and statistically processing the expression level for each measured biological parameter. 図５は、ＣＯＶＩＤ－１９重症患者から得た試料を用いて、サイトカイン／ケモカイン／可溶性レセプターアッセイをおこない、測定した生体パラメータ毎に発現量を統計処理した結果である。「ＣＯＶＩＤ－１９－０」はアクテムラ治療前の患者、「ＣＯＶＩＤ－１９－１ｗ未満」はアクテムラ治療から１～５日前後の患者、「ＣＯＶＩＤ－１９－１ｗ」はアクテムラ治療から７日前後の患者、「ＣＯＶＩＤ－１９－２ｗ」はアクテムラ治療から１０～１４日前後の患者をそれぞれ表す。FIG. 5 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a severely ill COVID-19 patient and statistically processing the expression level for each measured biological parameter. "COVID-19-0" is a patient before Actemra treatment, "COVID-19-1w" is a patient about 1 to 5 days after Actemra treatment, and "COVID-19-1w" is a patient about 7 days after Actemra treatment. , "COVID-19-2w" represent patients about 10 to 14 days after Actemra treatment, respectively. 図６は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ－６の変動を示す図である。FIG. 6 is a diagram showing changes in IL-6 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図７は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ－１Ｒａの変動を示す図である。FIG. 7 is a diagram showing changes in IL-1Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図８は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＰ－１０の変動を示す図である。FIG. 8 is a diagram showing changes in IP-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図９は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＢＡＦＦの変動を示す図である。FIG. 9 is a diagram showing changes in BAFF before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１０は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＡＰＲＩＬの変動を示す図である。FIG. 10 is a diagram showing changes in APRIL before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１１は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＶＣＡＭ－１の変動を示す図である。FIG. 11 is a diagram showing changes in VCAM-1 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１２は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＦＮ－２８Ａの変動を示す図である。FIG. 12 is a diagram showing changes in IFN-28A before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１３は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ－２９の変動を示す図である。FIG. 13 is a diagram showing changes in IL-29 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１４は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＦＮ－ａ２の変動を示す図である。FIG. 14 is a diagram showing changes in IFN-a2 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１５は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＦＮ－ｂの変動を示す図である。FIG. 15 is a diagram showing changes in IFN-b before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１６は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＦＮ－ｇの変動を示す図である。FIG. 16 is a diagram showing changes in IFN-g before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１７は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＴＮＦ－ａの変動を示す図である。FIG. 17 is a diagram showing changes in TNF-a before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１８は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるｓｇｐ１３０の変動を示す図である。FIG. 18 is a diagram showing changes in sgp130 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図１９は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ１２（ｐ４０）の変動を示す図である。FIG. 19 is a diagram showing changes in IL12 (p40) before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図２０は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ－６Ｒａの変動を示す図である。FIG. 20 is a diagram showing changes in IL-6Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図２１は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＩＬ－１０の変動を示す図である。FIG. 21 is a diagram showing changes in IL-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients. 図２２は、ＣＯＶＩＤ－１９感染患者から得た試料を用いて、トシリズマブまたはデクスメデトミジン治療の前後におけるＴＷＥＡＫの変動を示す図である。FIG. 22 is a diagram showing changes in TWEAK before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
　以下、本開示を最良の形態を示しながら説明する。本明細書の全体にわたり、単数形の表現は、特に言及しない限り、その複数形の概念をも含むことが理解されるべきである。したがって、単数形の冠詞（例えば、英語の場合は「ａ」、「ａｎ」、「ｔｈｅ」など）で言及される用語は、特に言及しない限りその複数形の概念をも含むことが理解されるべきである。本明細書において使用される用語はまた、特に言及しない限り当該分野で通常用いられる意味で用いられることが理解されるべきである。従って、通常用いられる意味と異なって定義されない限り、本明細書中で使用される全ての専門用語および科学技術用語は、本開示の属する分野の当業者によって一般的に理解されるのと同じ意味を有する。本明細書の説明と当該分野の説明との相違がある場合、本明細書（定義を含めて）が優先する。 Hereinafter, this disclosure will be described while showing the best form. Throughout the specification, it should be understood that the singular representation also includes its plural concept, unless otherwise noted. Therefore, it is understood that terms referred to in singular articles (eg, "a", "an", "the" in English) also include the plural concept unless otherwise noted. Should be. It should be understood that the terms used herein are also used in the meaning commonly used in the art unless otherwise noted. Accordingly, unless defined differently from what is commonly used, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Have. In the event of any discrepancy between the description herein and the description in the art, this specification (including definitions) will prevail.
　（定義）
　最初に本開示において使用される用語および一般的な技術を説明する。 (Definition)
First, the terms and general techniques used in this disclosure will be described.
　本明細書において、「コロナウイルス科」とは、Ｎｏｒｏｖｉｒｕｓｅｓに属する最大のウイルス科であり、その中にはコロナウイルス科、Ａｒｔｅｒｉｖｉｒｉｄａｅ、Ｍｅｓｏｎｉｖｉｒｉｄａｅ、Ｒｏｎｉｖｉｒｉｄａｅなどが含まれる。コロナウイルスはさらに４つの属、すなわちα，β，γ，コロナウイルスに分かれ、α，βコロナウイルスはヒトの気道・腸管の１０％～３０％に感染を引き起こす。 In the present specification, the "coronaviridae" is the largest coronaviridae belonging to Noroviruses, and includes coronaviridae, Arteriviridae, Mesoniviridae, Roniviridae and the like. Coronavirus is further divided into four genera: α, β, γ, coronavirus, and α, β coronavirus causes infection in 10% to 30% of the human airway / intestinal tract.
　コロナウイルス属のウイルスはエンベロープ（ｅｎｖｅｌｏｐｅ）に包まれたＲＮＡウイルスとして存在する。その直径は約８０～１２０ｎｍで，遺伝物質は全ＲＮＡウイルスの中で最大である。ウイルス粒子の表面には３つの糖タンパク質があり、棘突起糖タンパク質（Ｓ、Ｓｐｉｋｅ　Ｐｒｏｔｅｉｎ、受容体結合部位、細胞溶解と抗原）；膜糖タンパク質（Ｅ、Ｅｎｖｅｌｏｐｅ　Ｐｒｏｔｅｉｎ　Ｅｎｖｅｌｏｐｅ　Ｐｒｏｔｅｉｎ、小さい、細胞膜と結合するタンパク質）；膜糖タンパク質（Ｍ、Ｍｅｍｂｒａｎｅ　Ｐｒｏｔｅｉｎ　Ｍｅｍｂｒａｎｅ　Ｐｒｏｔｅｉｎ、栄養を膜輸送体、新型ウイルスの出芽とウイルスエンベロープの形成を集合する）である。少数の種類としてヘモグロビン糖タンパク（ＨＥタンパク質，Ｈａｅｍａｇｌｕｔｉｎｉｎ　Ｈｅｍａｇｇｌｕｔｉｎｉｎ-
ｅｓｔｅｒａｓｅ）がある。ウイルスは主にＳｐｉｋｅタンパク（Ｓタンパク質）と宿主細胞受容体の結合を通じてウイルスの侵入を介し、ウイルスの組織または宿主を決定する。コロナウイルスＳタンパクＳ１サブユニットのＮ末端ドメイン（Ｓ１-ＮＴＤ）とＣ末
端ドメイン（Ｓ１-ＣＴＤ）はいずれも受容体結合ドメイン（ＲＢＤ）とすることができ
る。Ｓ１-ＮＴＤは糖受容体に，Ｓ１-ＣＴＤはタンパク質受容体に結合すると考えられている。 Viruses of the genus Coronavirus exist as RNA viruses wrapped in an envelope. Its diameter is about 80-120 nm and the genetic material is the largest of all RNA viruses. There are three glycoproteins on the surface of the virus particle, spine glycoprotein (S, Spike Protein, receptor binding site, cell lysis and antigen); membrane glycoprotein (E, Envelope Protein, Envelope Protein, small, binding to cell membrane). Protein); Membrane Protein (M, Membrane Protein, membrane transporter for nutrients, aggregates the emergence of new viruses and the formation of viral envelopes). Hemagglutinin Glycoprotein (HE protein, Hemagglutinin-
There is esterase). The virus determines the tissue or host of the virus, primarily through the binding of the Spike protein (S protein) to the host cell receptor through viral entry. Both the N-terminal domain (S1-NTD) and the C-terminal domain (S1-CTD) of the coronavirus S protein S1 subunit can be a receptor binding domain (RBD). It is believed that S1-NTD binds to glycosyl receptors and S1-CTD binds to protein receptors.
　ヒトに対して感染する風邪様症候群の原因ウイルスとして知られているコロナウイルスには以下が挙げられる：ＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶおよびＳＡＲＳ－ＣｏＶ－２等が挙げられる。 Coronaviruses known to cause cold-like syndromes that infect humans include: HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2. Be done.
　本明細書において「生体パラメータ」とは、生体に関する任意のパラメータをいい、その生体に関連する因子（例えば、サイトカインや受容体（ＡＣＥ－Ｒ１等）、シグナル伝達分子等）タンパク質やポリヌクレオチド等の生物学的パラメータ（絶対量、相対量、変動量等）、その生体の臨床データ、その生体の二次元または三次元画像（例えば、Ｘ線ＣＴ画像）、その生体における病原体に関するパラメータ（例えば、感染因子（例えば、細菌、ウイルスなど）の量またはレベルなど）、治療履歴、治療効果情報、安全性情報、および状態履歴情報などを含むがこれらに限定されない。臨床データは臨床情報と交換可能に使用され、臨床で入手可能な任意のデータまたは情報を含み、臨床検査データ、臨床症状、所見などを含む。 As used herein, the term "biological parameter" refers to any parameter related to the living body, such as a factor related to the living body (for example, a cytokine or a receptor (ACE-R1 or the like), a signaling molecule, etc.), a protein, a polynucleotide or the like. Biological parameters (absolute, relative, variable, etc.), clinical data of the organism, two-dimensional or three-dimensional images of the organism (eg, X-ray CT image), parameters related to the pathogen in the organism (eg, infection). It includes, but is not limited to, factors (eg, amounts or levels of bacteria, viruses, etc.), treatment history, treatment efficacy information, safety information, state history information, and the like. Clinical data is used interchangeably with clinical information and includes any clinically available data or information, including laboratory data, clinical symptoms, findings, and the like.
　１つの具体的な例では、生体パラメータは、生体分子パラメータを含み、生体分子パラメータとしては、被験者から採取された試料において発現するタンパク質やポリヌクレオチド等の生物学的パラメータが含まれる。生体パラメータは、検出または定量化され得る核酸、核酸断片、ポリヌクレオチド、オリゴヌクレオチド、ポリペプチド、ペプチド断片またはタンパク質、またはその値もしくはその値の変動値などであり得る。生体パラメータとしては、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１などの生体分子パラメータの他、症状（発熱、鼻汁、咳嗽、倦怠、悪心、嘔吐、下痢、腹部症状）、検査項目（肺Ｘ線写真、肺ＣＴ所見、ｐＯ２）、ウイルス検査データ（鼻粘膜、口腔粘膜、血中等）、血液検査データ（例えば、白血球（ＷＢＣ）、好中球（Ｎｅｕ）、リンパ球（Ｌｙｍ）、血小板（Ｐｌｔ）、血色素（Ｈｂ）、ＣＲＰｖＦｉｂ、アルブミン（Ａｌｂ）、ＡＳＴ、ＡＬＴ、乳酸脱水素酵素（ＬＤＨ）、ＡＬ－ｐｈ、クレアチニン、プロカルシトニン、プロトロンビン時間、ＦＤＰ、Ｄ－Ｄ　ｄｉｍｅｒ等）、抗体（ＩｇＧ抗体、ＩｇＭ抗体）を含むが、これらに限定されない。 In one specific example, the biomolecular parameter includes a biomolecular parameter, and the biomolecule parameter includes a biological parameter such as a protein or a polynucleotide expressed in a sample collected from a subject. Bioparameters can be nucleic acids, nucleic acid fragments, polynucleotides, oligonucleotides, polypeptides, peptide fragments or proteins that can be detected or quantified, or their values or variations in their values. Bioparameters include IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL. -13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb, RANTES, TNF -Α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL -19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP -3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN-γTR1 , VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, and other biomolecular parameters, as well as symptoms (fever, nasal juice, cough, Malaise, nausea, vomiting, diarrhea, abdominal symptoms), test items (lung X-ray photograph, lung CT findings, pO 2 ), virus test data (nasal mucosa, oral mucosa, blood, etc.), blood test data (eg, white blood cells (eg, white blood cells (eg, leukocytes)) WBC), neutrophils (Neu), lymphocytes (Lym), platelets (Plt), blood pigment (Hb), CRPvFib, albumin (Alb), AST, ALT, lactate dehydrogenase (LDH), AL-ph, creatinine. , Procalcitonin, prothrombin time, FDP, DD dimer, etc.), antibodies (IgG antibody, IgM antibody), but not limited to these.
　本明細書において「試料」は任意のものであってもよく、例えば、血清、血漿、末梢血、血液、尿、唾液等を含むがこれらに限定されない。１つの実施形態では、生体分子パラメータなどでは、血清が使用される。したがって、本開示のある実施形態では、被験者から採取された血清における測定から得られたパラメータを用いて、本開示の方法が実施される。ウイルス量などでは、血清ではなく鼻腔粘液や唾液なども用いることができる。 In the present specification, the "sample" may be arbitrary, and includes, for example, serum, plasma, peripheral blood, blood, urine, saliva, and the like, but is not limited thereto. In one embodiment, serum is used for biomolecular parameters and the like. Therefore, in certain embodiments of the present disclosure, the methods of the present disclosure are carried out using parameters obtained from measurements in serum collected from a subject. In terms of viral load, nasal mucus and saliva can be used instead of serum.
　本明細書において「症状」とは、異常な状態や疾患の存在を反映して、患者における正常な機能または感覚が逸脱することをいう。主観的または客観的なものを含む。本開示では、症状は、症状度（または重篤度）で表現することができ、例えば、重症、中等症（レベルに分けてもよく、例えば、中等症（Ｉ）、中等症（ＩＩ）、軽症、無症状（不顕性）等）などに分類して表現してもよい。例えば、ウイルス感染症状は、ウイルス感染の際の症状をいう。ＣＯＶＩＤ－１９のウイルス感染症状については、代表的に、以下のような分類を採用してもよい。 As used herein, the term "symptom" refers to the deviation of normal function or sensation in a patient, reflecting the presence of an abnormal condition or disease. Includes subjective or objective ones. In the present disclosure, symptoms can be expressed as symptomatic (or severe), for example, severe, moderate (may be divided into levels, eg, moderate (I), moderate (II), etc.). It may be classified into mild, asymptomatic (invisible), etc.). For example, a virus infection symptom refers to a symptom at the time of virus infection. The following classifications may be typically adopted for the viral infection symptoms of COVID-19.
Figure JPOXMLDOC01-appb-T000001
Figure JPOXMLDOC01-appb-T000001
　本明細書において「予測モデル」とは、ある事象（例えば、ウイルス感染症状、治療法適合性、および／または治療結果などの医学的事象）を別の因子から予測するためのモデルをいい、ある予測式、係数などを用いて表現され得る。例えば、本開示に基づいて、感染後の症状度合い（本明細書では「症状度」または「重症度」と交換可能に使用され、これらは同じ意味である）に基づいて早期無症状、軽症、中等症（Ｉ）、中等症（ＩＩ）、重症等に分類し、症状度に応じた患者の血中ウイルス量、バイオ分子量、抗体有無、及び臨床所見、さらに治療歴等の初期情報及び経時的情報を評価項目とし、それぞれの段階の症状度を目的変数として、強化学習による確率遷移モデル（例えば、ｎ＜２００の場合）、またはディープニューラルネットワーク（ＡＩ解析）を構築し、それを用いて症状度を知り、各々の治療法による治療効果予測モデルを確立し、有効性を予測することが例示される。これらの予測モデルを用い、患者個々に予測することによって最適治療を適応することが可能であり、個人毎に治療効果を最大限に引き出すことができる。 As used herein, the term "predictive model" refers to a model for predicting one event (eg, a medical event such as viral infection symptoms, treatment suitability, and / or treatment result) from another factor. It can be expressed using a prediction formula, a coefficient, or the like. For example, based on the present disclosure, early asymptomatic, mild, based on post-infection symptom severity (which is interchangeably used herein as "symptomatic" or "severity", they have the same meaning). It is classified into moderate (I), moderate (II), severe, etc., and the initial information such as blood virus amount, biomolecular weight, antibody presence and absence, clinical findings, treatment history, etc. of the patient according to the degree of symptom and over time. Using information as an evaluation item and the symptom degree of each stage as the objective variable, construct a stochastic transition model (for example, in the case of n <200) or deep neural network (AI analysis) by augmentation learning, and use it to symptom. It is exemplified by knowing the degree, establishing a therapeutic effect prediction model for each treatment method, and predicting the effectiveness. Using these predictive models, it is possible to adapt the optimal treatment by predicting each patient individually, and it is possible to maximize the therapeutic effect for each individual.
（好ましい実施形態）
　以下に本開示の好ましい実施形態を説明する。以下に提供される実施形態は、本開示のよりよい理解のために提供されるものであり、本開示の範囲は以下の記載に限定されるべきでないことが理解される。したがって、当業者は、本明細書中の記載を参酌して、本開示の範囲内で適宜改変を行うことができることは明らかである。また、本開示の以下の実施形態は単独でも使用されあるいはそれらを組み合わせて使用することができることが理解される。 (Preferable embodiment)
Hereinafter, preferred embodiments of the present disclosure will be described. It is understood that the embodiments provided below are provided for a better understanding of the present disclosure and the scope of the present disclosure should not be limited to the following description. Therefore, it is clear that a person skilled in the art can make appropriate modifications within the scope of the present disclosure in consideration of the description in the present specification. It is also understood that the following embodiments of the present disclosure may be used alone or in combination thereof.
　本開示は、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法を提供する。このような方法は被験者における１または複数の生体パラメータを得る工程と、生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程とを含む。 The present disclosure provides a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome. Such a method comprises obtaining one or more biological parameters in the subject and predicting the viral infection symptom, therapeutic suitability, and / or treatment outcome based on the biological parameters.
　（ウイルス感染症状の予測）
　１つの局面において、本開示は、被験者のウイルス感染症状を予測するための方法を提供する。１つの実施形態において、本開示の方法は、被験者におけるウイルス感染による症状の症状度を予測し、または重症化するかどうかを予測することができる。１つの実施形態において、本開示の方法は、被験者から採取した試料から生体パラメータを取得し、その取得したパラメータと、健常者における同一種類のパラメータとを比較することでウイルス感染症状を予測するための指標を提供することができる。 (Prediction of virus infection symptoms)
In one aspect, the present disclosure provides a method for predicting a subject's viral infection symptoms. In one embodiment, the methods of the present disclosure can predict the degree of symptom of a viral infection in a subject, or whether it will be aggravated. In one embodiment, the method of the present disclosure is to obtain biological parameters from a sample taken from a subject and to predict viral infection symptoms by comparing the obtained parameters with parameters of the same type in a healthy subject. Indicators can be provided.
　１つの実施形態では、使用される生体パラメータは生体分子パラメータであり得るが、それに限定されない。生体分子パラメータを利用する場合においても、生体分子パラメータ以外に他の生体パラメータ（臨床データや画像データ等）を併用してもよい。 In one embodiment, the bioparameters used can be, but are not limited to, biomolecular parameters. Even when the biomolecule parameter is used, other biomolecule parameters (clinical data, image data, etc.) may be used in combination in addition to the biomolecule parameter.
　本開示の一実施形態において、被験者から採取した試料から得た生体パラメータに基づいて、ウイルス感染症状を予測することができる。例えば、重症、中等症、軽症のそれぞれのウイルス感染患者について、予め上記の生体パラメータの測定を行い、回帰分析によって症状の程度（目的変数）と上記のパラメータの測定値（説明変数）の回帰式を求めておき、当該回帰式に、予測対象となる被験者のパラメータの測定値を当てはめる方法が挙げられる。 In one embodiment of the present disclosure, virus infection symptoms can be predicted based on biological parameters obtained from a sample collected from a subject. For example, for severe, moderate, and mild virus-infected patients, the above-mentioned biological parameters are measured in advance, and regression analysis is performed to calculate the degree of symptoms (objective variable) and the measured values (explanatory variables) of the above parameters. Then, a method of applying the measured values of the parameters of the subject to be predicted to the regression equation can be mentioned.
　あるいは、重症の確率について、予めあるいは経験に基づいて個々の治療薬についてそれぞれ適用するかどうかの基準を定めておき、実際に算出された重症の確率が所定の基準以上の場合にその特定の治療薬を投与すると判定することもできる。例えば、予測した重症の確率が、５０％以上、６０％以上、７０％以上、８０％以上、８５％以上、９０％以上、または９５％以上の場合に、その特定の治療薬を投与すると判定することができる。このような基準の具体的な値は、本明細書に記載される具体的な数値のほか、経験等に基づいて適宜設定することが可能であり、本明細書に記載される具体的な値以外であっても採用することができる。 Alternatively, the probability of severe illness is determined in advance or based on experience as to whether or not it is applied to each therapeutic drug, and when the actually calculated probability of severe illness is equal to or higher than a predetermined standard, the specific treatment is performed. It can also be determined to administer the drug. For example, if the predicted probability of severe illness is 50% or higher, 60% or higher, 70% or higher, 80% or higher, 85% or higher, 90% or higher, or 95% or higher, it is determined to administer the specific therapeutic agent. can do. Specific values of such criteria can be appropriately set based on experience, etc., in addition to the specific numerical values described in the present specification, and specific values described in the present specification. It can be adopted even if it is not.
　本開示の一実施形態では、被験者から採取した試料中の生体パラメータを用いて予測が行われることから、本開示の方法は、試料中（例えば、血清または末梢血中）の生体パラメータの量または濃度を測定する工程を包含し得る。試料は被験者から採血管などの本分野において周知の手段で採血するなどして得ることができ、例えば血液の場合には、血清分離後分注し凍結保存してもよい。一実施形態において、本開示の方法では、被験者から採取した試料中のウイルス量をウイルス特異的ＰＣＲなどの手段によって定量してもよい。 In one embodiment of the present disclosure, predictions are made using bioparameters in a sample taken from a subject, so the method of the present disclosure is the amount of bioparameters in the sample (eg, in serum or peripheral blood) or It may include the step of measuring the concentration. The sample can be obtained from a subject by collecting blood from a subject by a means well known in the art such as a blood collection tube. For example, in the case of blood, the serum may be separated and then dispensed and stored frozen. In one embodiment, in the method of the present disclosure, the amount of virus in a sample collected from a subject may be quantified by means such as virus-specific PCR.
　一実施形態において、本開示の方法では、被験者がウイルスに罹患した場合にその症状が重症となるのか、それとも軽症や中等症で治癒していくのかなどを予測する。本開示の方法において、予測の対象となる被験者は、特に制限されるものではなく、ウイルスに罹患していない者またはすでにウイルスの罹患した患者であってもよい。また本開示の方法において、予測の対象となる被験者は、投薬履歴、既往歴、性別、年齢なども問わない。 In one embodiment, the method of the present disclosure predicts whether the symptom becomes severe when the subject is infected with the virus, or whether the symptom is cured by mild or moderate illness. In the method of the present disclosure, the subject to be predicted is not particularly limited, and may be a person who does not have the virus or a patient who has already suffered from the virus. Further, in the method of the present disclosure, the subject to be predicted is not limited to medication history, medical history, gender, age, and the like.
　一実施形態において、本開示の方法で予測する症状の原因ウイルスとしては、特に限られるものではないが、例えば動物（例えば、ヒト）に対して感染するウイルス（ＤＮＡウイルスまたはＲＮＡウイルスを含む。）が挙げられ、ヒトに罹患して風邪様症候群を示すウイルスであるアデノウイルス科のウイルス、ヘルペスウイルス科のウイルス（例えば、単純ヘルペスウイルス、水痘・帯状疱疹ウイルス、サイトメガロウイルス、EBウイルス）、肝炎ウイルス（Ｃ型肝炎ウイルス、Ｂ型肝炎ウイルス）、ＨＩＶ等の免疫不全ウイルス、ＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶおよびＳＡＲＳ－ＣｏＶ－２等のコロナウイルスが含まれる。 In one embodiment, the causative virus of the symptom predicted by the method of the present disclosure is not particularly limited, but includes, for example, a virus that infects an animal (for example, a human) (including a DNA virus or an RNA virus). Adenovirus family virus, herpes virus family virus (for example, simple herpes virus, varicella / herpes zoster virus, cytomegalovirus, EB virus), hepatitis, which are viruses that affect humans and show cold-like syndrome. Viruses (hepatitis C virus, hepatitis B virus), immunodeficiency viruses such as HIV, and coronaviruses such as HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2 are included.
　ヒトに感染するコロナウイルスとしては、風邪の原因ウイルスとしてヒトコロナウイルス２２９Ｅ、ＯＣ４３、ＮＬ６３、ＨＫＵ－１の４種類、そして、重篤な肺炎を引き起こす２００２年に発生した重症急性呼吸器症候群（ＳＡＲＳ）コロナウイルスと２０１２年に発生した中東呼吸器症候群（ＭＥＲＳ）コロナウイルス、および２０１９年に発生したいわゆる新型コロナウイルス（２０１９－ｎＣｏＶ、ＳＡＲＳ－ＣｏＶ－２）の３種類が知られている。新型コロナウイルスは、ＳＡＲＳコロナウイルスと同じベータコロナウイルス属に分類され、新型コロナウイルスの遺伝子はＳＡＲＳコロナウイルスの遺伝子と相同性が高く（約８０％程度）、さらに、ＳＡＲＳコロナウイルスと類似する受容体（ＡＣＥ１またはＡＣＥ２）を使ってヒトの細胞に吸着・侵入することが最近の研究で報告されている。ＡＣＥ１またはＡＣＥ２のサブタイプの違いによって感染性の違いに顕れることも報告されている。新型コロナウイルスは、臨床症状は、頭痛、高熱、倦怠感、咳などのインフルエンザ様症状から、重症例では呼吸困難を主訴とする肺炎に進行するとされている。解熱や呼吸補助などの対症療法がとられ、ワクチン、抗ウイルス薬（ファビピラビル、レムデシビル等）、免疫療法剤に関する転用または新規開発が進められている。検査は、ウイルス遺伝子を測定する手法、例えば、ＰＣＲ法やその他の迅速診断の開発が進められている。新型コロナウイルスの遺伝子配列は、ＳＡＲＳコロナウイルスに近く、さらにコウモリ由来のＳＡＲＳ様コロナウイルスにも相同性があることから、おそらくコウモリがこの新型コロナウイルスの起源となったウイルスを保持していると考えられる。例えば、新型コロナウイルス感染症（ＣＯＶＩＤ－１９）に関するＷＨＯ－中国合同ミッション報告書によれば、このウイルスにはコロナウイルスファミリーに典型的な特徴があり、ベータコロナウイルス２Ｂ系統に属していることが示され、ＣＯＶＩＤ－１９ウイルスの全ゲノム配列とβコロナウイルスの他の利用可能なゲノム配列を比較すると、最も近い関係にあるのがコウモリＳＡＲＳのようなコロナウイルス株ＢａｔＣｏｖ　ＲａＴＧ１３であり、同一性９６％であることを示した。ウイルスの単離は、ヒト気道上皮細胞、Ｖｅｒｏ
　Ｅ６、Ｈｕｈ－７などのさまざまな細胞株で実施されており、これらの試料も利用することができる。ＣＯＶＩＤ－１９ウイルスに感染したほとんどの人は、軽症で回復する。肺炎以外と肺炎の症例を含む、検査確定例の多くは、軽症から中等症であるとされており、１０～２０％が重症（呼吸困難、頻呼吸＞＝３０／分、血中酸素飽和度＝＜９３％、ＰａＯ２／ＦｉＯ２比＜３００、および／または２４～４８時間以内に肺野の５０％を超える浸潤）、１０％弱が重篤（呼吸不全、敗血症性ショック、および／または多臓器不全／障害）とされている。無症候性感染の報告もあるが、感染が発覚／報告された日に無症候である比較的まれな症例の多くはその後発症しているとされている。実際の無症候性感染の割合は不明であるが、比較的まれであるとされている。 There are four types of coronaviruses that infect humans: human coronavirus 229E, OC43, NL63, and HKU-1 as the causative viruses of colds, and severe acute respiratory syndrome (SARS) that occurred in 2002, which causes severe pneumonia. ) Coronavirus, the Middle East Respiratory Syndrome (MERS) coronavirus that occurred in 2012, and the so-called new coronavirus (2019-nCoV, SARS-CoV-2) that occurred in 2019 are known. The new coronavirus is classified into the same beta coronavirus genus as the SARS coronavirus, and the gene of the new coronavirus is highly homologous to the gene of the SARS coronavirus (about 80%), and further, the acceptance similar to the SARS coronavirus. Recent studies have reported that the body (ACE1 or ACE2) is used to adsorb and invade human cells. It has also been reported that differences in ACE1 or ACE2 subtypes manifest in differences in infectivity. The clinical symptoms of the new coronavirus are said to progress from influenza-like symptoms such as headache, high fever, malaise, and cough to pneumonia, which is the main complaint of dyspnea in severe cases. Symptomatic treatments such as fever reduction and respiratory support are taken, and diversion or new development of vaccines, antiviral drugs (favipiravir, remdesivir, etc.) and immunotherapeutic agents is underway. For testing, methods for measuring viral genes, such as the PCR method and other rapid diagnostic methods, are being developed. Since the gene sequence of the new coronavirus is close to that of the SARS coronavirus and is also homologous to the SARS-like coronavirus derived from the bat, it is likely that the bat carries the virus from which the new coronavirus originated. Conceivable. For example, according to the WHO-China Joint Mission Report on New Coronavirus Infection (COVID-19), the virus has typical characteristics of the coronavirus family and belongs to the beta coronavirus 2B strain. Comparing the entire genome sequence of COVID-19 virus with the other available genome sequences of β coronavirus, the closest relationship is the coronavirus strain BatCov RaTG13, such as bat SARS, with identity 96. It was shown to be%. Virus isolation is from human airway epithelial cells, Vero
It has been carried out on various cell lines such as E6 and Huh-7, and these samples can also be used. Most people infected with the COVID-19 virus recover with mild illness. Most confirmed cases, including cases other than pneumonia and cases of pneumonia, are considered to be mild to moderate, and 10 to 20% are severe (dyspnea, tachypnea> = 30 / min, blood oxygen saturation. = <93%, PaO 2 / FiO 2 ratio <300, and / or more than 50% infiltration of the lung field within 24-48 hours), less than 10% severe (dyspnea, septic shock, and / or Multi-organ failure / disorder). Although there are reports of asymptomatic infections, many of the relatively rare cases that are asymptomatic on the day the infection is discovered / reported are said to have subsequently developed. The actual rate of asymptomatic infections is unknown, but is considered to be relatively rare.
　予防ワクチンとしては、タンパク質サブユニット、弱毒化生ウイルス、不活化ウイルスを用いた開発の他、ＤＮＡワクチン、ＲＮＡワクチン、非複製性ウイルスベクターを用いての開発がなされている。 As preventive vaccines, in addition to development using protein subunits, live attenuated viruses, and inactivated viruses, developments have been made using DNA vaccines, RNA vaccines, and non-replicating virus vectors.
　一実施形態において、本開示の方法で予測する症状の程度としては、特に限られるものではなく、ウイルスの種類によって適宜設定することができ、例えば上記に挙げたようなコロナウイルスの場合には、重症（呼吸困難、頻呼吸＞＝３０／分、血中酸素飽和度＝＜９３％、ＰａＯ２／ＦｉＯ２比＜３００、および／または２４～４８時間以内に肺野の５０％を超える浸潤）、重篤（呼吸不全、敗血症性ショック、および／または多臓器不全／障害）、およびその他の軽症から中等症に分類することができる。 In one embodiment, the degree of symptom predicted by the method of the present disclosure is not particularly limited and can be appropriately set depending on the type of virus. For example, in the case of corona virus as described above, Severe (dyspnea, tachypnea> = 30 / min, blood oxygen saturation = <93%, PaO 2 / FiO 2 ratio <300, and / or infiltration of more than 50% of the lung field within 24-48 hours) , Severe (dyspnea, septic shock, and / or tachypnea / disorder), and other mild to moderate.
　他の実施形態において、本開示の方法で予測する症状の程度としては、例えばコロナウイルスの場合、症状のない者を無症候者、軽度症状(咳のみ)：ＰａＯ２＞＝９６％の者を軽症者；軽度呼吸器症状（息切れ、一部酸素マスク）：９３％＜ＳＯ２＜９６％および＝＜９３％の者を中等症（Ｉ）者；酸素吸入、人工呼吸器管理：肺炎所見（Ｌ型）ＰＳＯ２＝＜９３％の者を中等症（ＩＩ）者；ＥＣＭＯ：重症肺炎（Ｈ型）、肺水腫の者を重症者に分類することもできる。 In other embodiments, the degree of symptom predicted by the method of the present disclosure is, for example, in the case of coronavirus, a person without symptom is a symptom-free person, and a person with mild symptom (cough only): PaO 2 > = 96%. Mildly ill; mild respiratory symptoms (shortness of breath, partial oxygen mask): 93% <SO 2 <96% and = <93% of moderately ill (I); oxygen inhalation, ventilator management: pneumonia findings ( Those with L-type) PSO 2 = <93% can be classified as moderate (II); ECMO: severe pneumonia (H-type), and those with pulmonary edema can be classified as severe.
　一実施形態において、本開示の方法では、ウイルス感染症状を予測するために使用されるパラメータとしては、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択される１または複数のパラメータ（本明細書において「生体パラメータ」ともいう）を使用することができる。 In one embodiment, the methods of the present disclosure include IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, as parameters used to predict viral infection symptoms. IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP- 10, MCP-1, MIP-1α, MIP-1β, PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL- 6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL- 32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Land, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα, IL-3, IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, One or more parameters (also referred to herein as "biological parameters") selected from the group consisting of M-CSF and TNF-β can be used.
　一実施形態において、本開示の方法では、生体パラメータとして、ＴｈｅＢｉｏ－ＰｌｅｘＨｕｍａｎ　Ｃｙｔｏｋｉｎｅ　２７－Ｐｌｅｘ　Ｐａｎｅｌ（２７項目）（ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ）、Ｉｎｆｌａｍｍａｔｉｏｎ１　ｋｉｔ（３７項目）（ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ：Ｂｉｏ－ＲａｄＬａｂｏｒａｔｏｒｉｅｓ、ＣＡ、ＵＳＡ）、Ｂｉｏ－Ｐｌｅｘ　Ｐｒｏ　ヒトサイトカインスクリーニング４８－Ｐｌｅｘパネル（４８項目）（ＦＧＦ　ｂａｓｉｃ、Ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＬ－１β、ＩＬ－１ｒａ、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＧＲＯ－α、ＨＧＦ、ＩＦＮ－α２、ＬＩＦ、ＭＣＰ－３、ＩＬ－１０、ＩＬ－１２（ｐ７０）　、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ＢＢ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＣＴＡＣＫ、ＭＩＦ、ＴＲＡＩＬ、ＩＬ－１８、Ｍ－ＣＳＦ、ＴＮＦ－β）、および／またはＲ＆Ｄ　ＬｕｍｉｎｉｘＳｃｒｅｅｎｉｎｇ　Ａｓｓａｙ　２１ｐｌｅｘ（２１項目）（ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１：Ｒ＆Ｄ　Ｓｙｓｔｅｍｓ、Ｉｎｃ．Ｍｉｎｎｅａｐｏｌｉｓ、ＭＮ　５５４１３　Ｔｏｌｌ　Ｆｒｅｅ　ＵＳＡ、Ｃａｎａｄａ）を用いて測定可能なパラメータを使用することができる。 In one embodiment, in the method of the present disclosure, the Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL- 6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb, RANTES, TNF-α, VEGF), Inframation1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK: Bio-RadLaboratories, CA, USA), Bio-Plex Pro Human Cytokine Screening 48-Plex Panel (48 items) G-CSF, GM-CSF, IFN-γ, IL-1β, IL-1ra, IL-1α, IL-2Rα, IL-3, IL-12 (p40), IL-16, IL-2, IL-4 , IL-5, IL-6, IL-7, IL-8, IL-9, GRO-α, HGF, IFN-α2, LIF, MCP-3, IL-10, IL-12 (p70), IL- 13, IL-15, IL-17, IP-10, MCP-1 (MCAF), MIG, β-NGF, SCF, SCGF-β, SDF-1α, MIP-1α, MIP-1β, PDGF-BB, RANTES , TNF-α, VEGF, CTACK, MIF, TRAIL, IL-18, M-CSF, TNF-β), and / or R & D Luminix Planning Assay 21plex (21 items) (ADAMTS13, Angiocytoin-2, BMP-2, CD40Ligan) CX3CL1, HG F, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, VCAM-1: R & D Systems , Inc. Parameters measurable using Minneapolis, MN 55413 Doll Free USA, Canada) can be used.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータが好ましい。 In one embodiment, the methods of the present disclosure include bioparameters such as IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3. , Gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL -28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM , OncostatinM, and VCAM-1 are preferred parameters selected from the group.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータが好ましい。 In one embodiment, the methods of the present disclosure include bioparameters such as IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30. , CD163, Chitinase-3, gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-22. , IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1 , STNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Land, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / CDL1 -Preferably parameters selected from the group consisting of Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、ＬＩＧＨＴからなる群から選択されるパラメータが好ましい。 In one embodiment, in the methods of the present disclosure, the biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 ( p70), IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ , IL-28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, LIGHT.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－１Ｒａ、ＩＬ－１３、ＩＰ－１０、ＭＣＰ－１、ＰＤＧＦ－ｂｂ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－２９、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１からなる群から選択されるパラメータが好ましい。 In one embodiment, the methods of the present disclosure include bioparameters such as IL-1Ra, IL-13, IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, IL-28A, IL. Parameters selected from the group consisting of -29, Pentraxin-3, and sTNF-R1 are preferred.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＰ－１０、ＭＣＰ－１、ＰＤＧＦ－ｂｂ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＩＬ－１１、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１からなる群から選択されるパラメータが好ましい。 In one embodiment, in the methods of the present disclosure, the biological parameters are selected from the group consisting of IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, Pentraxin-3, sTNF-R1. Parameters are preferred.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータが好ましい。 In one embodiment, in the methods of the present disclosure, the biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10. , IL-12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α , VEGF, APRIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP Parameters selected from the group consisting of are preferred.
　一実施形態において、本開示の方法では、生体パラメータとして、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータが好ましい。 In one embodiment, in the methods of the present disclosure, the biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, Parameters selected from the group consisting of IL-35, LIGHT, MMP-1, and Pentraxin-3 are preferred.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、軽症、中等症I、中等症ＩＩ、および重症のいずれかで数値が変化しているパラメータ群を選択することができ、例えば、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。　以上の生体パラメータは、軽症、中等症I、中等症ＩＩ、および重症にかけて数値が段階的にあるいは漸次変化するので、症状の重篤度合いを示すパラメータとして利用することができ、重症化予測も一部可能であり得る。 In one embodiment, in the method of the present disclosure, as biological parameters, a group of parameters whose numerical values change in any of mild, moderate I, moderate II, and severe can be selected in Table 3. For example, CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL. -16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL-20, IL-22, From the group consisting of IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, and TWEAK. It can be the parameter of choice. Since the above biological parameters change gradually or gradually from mild to moderate I, moderate II, and severe, they can be used as parameters to indicate the severity of symptoms, and the severity can be predicted. It can be partly possible.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、軽症、中等症I、中等症ＩＩ、および重症のいずれかで数値が変化しているパラメータ群であって、インフルエンザにおいて変化のないものを選択することができ、例えば、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。以上の生体パラメータは、軽症、中等症I、中等症ＩＩ、および重症にかけて数値が段階的にあるいは漸次変化するので、症状の重篤度合いを示すパラメータとして利用することができ、さらにインフルエンザウイルスと区別することができ、重症化予測も一部可能であり得る。 In one embodiment, in the method of the present disclosure, the biological parameters are a group of parameters whose numerical values are changed in any of mild, moderate I, moderate II, and severe in Table 3, and are changed in influenza. Can be selected without, for example, CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16. , IL-17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL- Parameters selected from the group consisting of 6Ra, IL-20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. Can be. Since the above biological parameters change gradually or gradually in mild, moderate I, moderate II, and severe cases, they can be used as parameters indicating the severity of symptoms, and are further distinguished from influenza virus. It is possible to predict the severity of the disease.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、中等症IまたはＩＩ、および重症のいずれでも数値が変化しているパラメータ群を選択することができ、例えば、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータとすることができる。以上の生体パラメータは、中等症～重症にかけて数値が段階的にあるいは漸次変化するので、症状の重篤度合いを示すパラメータとして利用することができ、重症化予測も一部可能であり得る。 In one embodiment, in the method of the present disclosure, as biological parameters, in Table 3, a group of parameters whose numerical values are changing in any of moderate I or II and severe can be selected, for example, HGF, IFN. -A2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL-6Ra, IL-10, IL-11, IL It can be a parameter selected from the group consisting of -28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. Since the numerical values of the above biological parameters change stepwise or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and it is possible to partially predict the severity of the symptoms.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、中等症IまたはＩＩ、および重症のいずれでも数値が変化しているパラメータ群であって、インフルエンザにおいて変化のないものを選択することができ、例えば、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータとすることができる。以上の生体パラメータは、中等症～重症にかけて数値が段階的にあるいは漸次変化するので、症状の重篤度合いを示すパラメータとして利用することができ、さらにインフルエンザウイルスと区別することができ、重症化予測も一部可能であり得る。 In one embodiment, in the method of the present disclosure, as the biological parameters, in Table 3, a parameter group in which the numerical values are changed in any of moderate I or II and severe, and which is not changed in influenza is selected. For example, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG, b. It can be a parameter selected from the group consisting of -NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Since the above biological parameters change gradually or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and can be further distinguished from influenza virus, and the severity is predicted. Is also possible in part.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、重症のみで数値が変化しているパラメータ群を選択することができ、例えば、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。以上の生体パラメータは、重症者特有のマーカーであり、重症患者を特定するパラメータとして利用することができる。 In one embodiment, in the method of the present disclosure, as biological parameters, a parameter group whose numerical value changes only in severe cases can be selected in Table 3, for example, MCP-3, IFN-g, IL-12. It can be a parameter selected from the group consisting of (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The above biological parameters are markers peculiar to severely ill patients and can be used as parameters for identifying severely ill patients.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、重症のみで数値が変化しているパラメータ群であって、インフルエンザにおいて変化のないものを選択することができ、例えば、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。以上の生体パラメータは、重症者特有のマーカーであり、重症患者を特定するパラメータとして利用することができ、さらにインフルエンザウイルスと区別することができる。 In one embodiment, in the method of the present disclosure, as biological parameters, in Table 3, a parameter group whose numerical value changes only in severe cases and whose numerical value does not change in influenza can be selected, for example, IFN. It can be a parameter selected from the group consisting of −g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The above biological parameters are markers peculiar to severely ill patients, can be used as parameters for identifying severely ill patients, and can be further distinguished from influenza virus.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、トシリズマブ治療および／またはデクスメデトミジン治療によって発現が変化（上昇または低下）するパラメータ群を選択することができ、例えば、Ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－４、ＩＬ－１ｒａ、ＩＬ－６、ＩＬ－８、ＩＬ－１７Ａ、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＰ－１ｂ、ＴＮＦ－ｂ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、ｇｐ１３０、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＬ－６Ｒａ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３５、ＭＭＰ－１、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。以上の生体パラメータは、重症度合いの他、治療により緩和または治癒する度合いの指標としても用いることができるパラメータとして利用することができ、その意味で、重症化予測も一部可能であり得る。 In one embodiment, in the methods of the present disclosure, as biological parameters, in Table 3, a group of parameters whose expression is altered (increased or decreased) by tosirizumab treatment and / or dexmedetomidin treatment can be selected, for example, Eotaxin. G-CSF, IFN-g, IL-2, IL-4, IL-1ra, IL-6, IL-8, IL-17A, IP-10, MCP-1 (MCAF), MIP-1b, TNF-b , APRIL, BAFF, sCD30, sCD163, gp130, IFN-a2, IFN-b, IL-6Ra, IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, IL-35, It can be a parameter selected from the group consisting of MMP-1 and TWEAK. The above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、トシリズマブ治療によって発現が変化するが、デクスメデトミジン治療では変化しないパラメータ群を選択することができ、例えば、ＩＬ－１ｒａ、ＴＮＦ－ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータとすることができる。以上の生体パラメータは、重症度合いの他、治療により緩和または治癒する度合いの指標としても用いることができるパラメータとして利用することができ、その意味で、重症化予測も一部可能であり得る。 In one embodiment, in the method of the present disclosure, as biological parameters, a group of parameters whose expression is changed by tocilizumab treatment but not by dexmedetomidine treatment can be selected in Table 3, for example, IL-1ra, TNF. It can be a parameter selected from the group consisting of −b, IL-12 (p40), IL-20, IL-28A, IL-29, and IL-35. The above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
　一実施形態において、本開示の方法では、生体パラメータとして、表３において、重症のみで数値が変化しているパラメータ群であって、表２においても「+」判定がされているものを選択することができ、例えば、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータとすることができる。以上の生体パラメータは、重症者特有のマーカーであり、さらに種々のコホートで共通してみられているマーカーであり、重症患者を特定するパラメータとしてさらに好ましく利用することができる。 In one embodiment, in the method of the present disclosure, as biological parameters, a parameter group whose numerical value changes only in severe cases in Table 3 and which is also judged as “+” in Table 2 is selected. Can be selected from the group consisting of, for example, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. It can be a parameter. The above biological parameters are markers peculiar to severely ill patients, and are markers commonly found in various cohorts, and can be more preferably used as parameters for identifying severely ill patients.
　一実施形態において、本開示の方法では、生体パラメータとして、図６～２２において個別に評価したパラメータを選択することができ、例えば、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータとすることができる。以上の生体パラメータは、種々の解析の結果、重篤度レベルや各種診断マーカとして有利に使用することができる例である。 In one embodiment, in the method of the present disclosure, the parameters individually evaluated in FIGS. 6 to 22 can be selected as the biological parameters, for example, IL-6, IL-1Ra, IP-10, BAFF, APRIL, and the like. From VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8 It can be a parameter selected from the group of. As a result of various analyzes, the above biological parameters are examples that can be advantageously used as severity levels and various diagnostic markers.
　一実施形態において、本開示の方法では、上述のパラメータの中の１種を単独で用いて症状予測パラメータとしてもよいが、より高い精度でウイルス感染症状を予測するという観点から、これらの中の２種以上を用いることが好ましい。 In one embodiment, in the method of the present disclosure, one of the above-mentioned parameters may be used alone as a symptom prediction parameter, but from the viewpoint of predicting a virus infection symptom with higher accuracy, among these. It is preferable to use two or more kinds.
　一実施形態において、本開示の方法では、上述の生体パラメータとして、少なくとも１種のサイトカイン類と、少なくとも１種の炎症性マーカーとを用いることが好ましく、炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含むことができる。すなわち、例えば、ケモカインや、血管形成、肺胞形成等に関与するマーカーなどの炎症性マーカー以外のものを利用することにより、ウイルス感染による炎症状態だけではなく、ウイルス感染に伴う肺症状や、血管内皮の活性による血栓形成の関与なども同時に調べることができ、ウイルス感染を高い確率で予測することができる。 In one embodiment, in the methods of the present disclosure, it is preferable to use at least one cytokine and at least one inflammatory marker as the above-mentioned biological parameters, and the inflammatory marker is at least not an inflammatory cytokine. It can contain one type of marker. That is, for example, by using chemokines and markers other than inflammatory markers such as markers involved in angiogenesis and alveolar formation, not only the inflammatory state due to virus infection but also lung symptoms associated with virus infection and blood vessels can be used. At the same time, the involvement of thrombosis due to the activity of the endothelium can be investigated, and viral infection can be predicted with high probability.
　パラメータとして使用され得るサイトカイン、ケモカインおよび可溶性受容体の生体試料中の各濃度は、ＥＬＩＳＡのような抗原抗体反応を利用した測定系によって測定できることは公知であり、これらの測定キットも市販されている。したがって、本開示の方法において、サイトカイン、ケモカインおよび可溶性受容体は、公知の方法、公知の測定キットにより測定することができる。このような抗原抗体反応を利用した測定系に使用する試薬は、各生物学的製剤の判定用の診断剤として個別にまたはセットで提供することができる。 It is known that the concentrations of cytokines, chemokines and soluble receptors that can be used as parameters in biological samples can be measured by a measurement system utilizing an antigen-antibody reaction such as ELISA, and these measurement kits are also commercially available. .. Therefore, in the methods of the present disclosure, cytokines, chemokines and soluble receptors can be measured by known methods, known measurement kits. Reagents used in a measurement system utilizing such an antigen-antibody reaction can be provided individually or as a set as a diagnostic agent for determining each biologic.
　上記に説明したような生体パラメータは、被験者におけるウイルス感染症状の程度を予測し得るものであるため、バイオマーカーとして用いることができる。したがって、本開示の一つの局面において、被験者のウイルス感染症状を予測するための１または複数の分子マーカーを提供することができ、分子マーカーとしては、上記のような生体パラメータを採用することができる。 The biological parameters as described above can be used as biomarkers because they can predict the degree of viral infection symptoms in the subject. Therefore, in one aspect of the present disclosure, one or more molecular markers for predicting viral infection symptoms of a subject can be provided, and as the molecular marker, the above-mentioned biological parameters can be adopted. ..
　（予防・治療）
　一つの局面では、本開示は、ウイルス感染患者を治療する方法であって、
　前記患者における１または複数の生体パラメータを得る工程と、
　前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、前記患者のウイルス感染症状を予測する工程と
　前記患者が重症患者と予測された場合に、前記患者に治療薬を投与する工程とを含む、方法を提供する。 (Prevention / Treatment)
In one aspect, the disclosure is a method of treating a virus-infected patient,
The step of obtaining one or more biological parameters in the patient and
A step of analyzing the biological parameter by comparing the biological parameter with a reference value to predict the viral infection symptom of the patient and a step of administering a therapeutic agent to the patient when the patient is predicted to be a seriously ill patient. Provides methods, including.
　一実施形態において、治療薬は特に限られるものではなく、タンパク質サブユニット、弱毒化生ウイルス、不活化ウイルスを用いたワクチン、ＤＮＡワクチン、ＲＮＡワクチン、非複製性ウイルスベクターを用いたワクチンなどの予防ワクチンの他、レムデシビル、ファビピラビル、シクレソニド、ナファモスタット、カモスタット、イベルメクチン、ステロイド剤、コルチコステロイド、トシリズマブ、サリルマブ、トファシチニブ、バリシチニブ、ルキソリチニブ、アカラブルチニブ、ラブリズマブ、エリトラン、イブジラスト、ＬＹ３１２７８０４、オチリマブ、ＨＬＣＭ０５１、ＡＤＲ－００１、デキサメタゾン、カシリビマブ／イムデビマブ、バムラニビマブ／エテセビマブ、ソトロビマブ、ＶＩＲ－７８３２、ＡＺＤ７４４２、モルヌピラビル、ＡＴ－５２７、ＢＩ７６７５５１、ＰＦ－０７３０４８１４、ＰＦ－０７３２１３３２、ＶＩＲ－２７０３、アナキンラ、ヒドロキシクロロキン、ロピナビル・リトナビル、回復期患者血漿、デクスメデトミジン、およびフルボキサミンなどを挙げることができる。 In one embodiment, the therapeutic agent is not particularly limited, and prevention of a protein subunit, a live attenuated virus, a vaccine using an inactivated virus, a DNA vaccine, an RNA vaccine, a vaccine using a non-replicating virus vector, and the like. In addition to vaccines, remdecibir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, corticosteroids, tosirizumab, salilmab, tofacitinib, varicitinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, rubrismab, erythran 001, dexamethasone, cashiribimab / imdebimab, bamuranibimab / etesebimab, sotrobimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakin Stage patient plasma, dexmedetomidin, and fluboxamine can be mentioned.
　１つの実施形態では、上記に加えまたは上記と独立して、以下の治療法を含めることができる。
　酸素吸入　
　体外式膜型人工肺（Ｅｘｔｒａｃｏｒｐｏｒｅａｌ　ｍｅｍｂｒａｎｅ　ｏｘｙｇｅｎａｔｉｏｎ；ＥＣＭＯ）
　点滴：ステロイド剤
　　　アクテムラ（トシリズマブ）等のＩＬ－６剤
内服：抗ウイルス薬（ファビピラビル（アビガン）、レムデシビルなど）
　　抗血栓薬（例えば、ヘパリン、ワーファリン、アスピリン）。 In one embodiment, the following treatments may be included in addition to or independently of the above.
Oxygen inhalation
Extracorporeal membrane oxygenation (ECMO)
Intravenous drip: Steroid agent IL-6 agent such as Actemra (tocilizumab) Oral: Antiviral agent (favipiravir (Abigan), remdesivir, etc.)
Antithrombotic drugs (eg, heparin, warfarin, aspirin).
　本開示において、有用であることが判明した生体パラメータ（特に生体分子パラメータ）が特定されたならば、そのバイオ分子の抑制剤等の予防または治療剤を開発することができ、そのような抑制剤等の予防または治療剤もまた本開示の範囲内である。例えば、本開示において、有用であることが判明した生体パラメータ（特に生体分子パラメータ）の分子に対応するＲＮＡを用いて、ＤＮＡを特定しその変異から遺伝的に重症化しやすい患者を特定する方法も特定することができ、これらに基づく検査、特定、診断技術もまた本開示の範囲内である。 Once the bioparameters found to be useful (particularly biomolecular parameters) have been identified in the present disclosure, prophylactic or therapeutic agents such as inhibitors of the biomolecule can be developed, such inhibitors. Such prophylactic or therapeutic agents are also within the scope of this disclosure. For example, in the present disclosure, there is also a method of identifying DNA and identifying a patient who is genetically prone to become severe from the mutation by using RNA corresponding to a molecule of a biomolecular parameter (particularly a biomolecular parameter) that has been found to be useful. Testing, identification and diagnostic techniques based on which can be identified are also within the scope of this disclosure.
　本開示において予防を目的とする場合は、各種ワクチンを用いてもよい。ワクチンとしては、例えば、ｍＲＮＡ型ワクチン、ＤＮＡ型ワクチン、不活化タンパクワクチン、不活化ワクチン、弱毒化ワクチン、生ワクチンなど利用可能な任意にものを使用することができ、例えば、トジナメラン（Ｐｆｉｚｅｒ－ＢｉｏＮＴｅｃｈ　ｖａｃｃｉｎｅ　ＢＮＴ１６２ｂ２）、ｍＲＮＡ－１２７３（Ｍｏｄｅｒｎａ　ＣＯＶＩＤ－１９　Ｖａｃｃｉｎｅ）、バキスゼブリア（Ｏｘｆｏｒｄ／ＡｓｔｒａＺｅｎｅｃａ　ＣＯＶＩＤ－１９　ｖａｃｃｉｎｅ，　ＡＺＤ１２２２）、Ａｄ２６．ＣＯＶ２．Ｓ（Ｊａｎｓｓｅｎ　ＣＯＶＩＤ－１９　Ｖａｃｃｉｎｅ）、ＮＶＸ－ＣｏＶ２３７３（Ｎｏｖａｖａｘ　ＣＯＶＩＤ－１９　Ｖａｃｃｉｎｅ）などが含まれる。 For the purpose of prevention in this disclosure, various vaccines may be used. As the vaccine, any available vaccine such as mRNA type vaccine, DNA type vaccine, inactivated protein vaccine, inactivated vaccine, attenuated vaccine, live vaccine and the like can be used, and for example, Tojnameran (Pfizer-BioNTech) can be used. vaccine BNT162b2), mRNA-1273 (Moderna COVID-19 Vaccine), Bakiszebria (Oxford / AstraZeneca COVID-19 vaccine, AZD1222), Ad26. COV2. S (Janssen COVID-19 Vaccine), NVX-CoV2373 (Novavax COVID-19 Vaccine) and the like are included.
　本開示は、さらに本開示の予測方法を実施するための診断剤または検査薬を提供することができ、具体的には、本開示の診断剤または検査薬は、ウイルス感染症状を予測するための診断剤または検査薬であって、上述の生体パラメータから選択される少なくとも１つのパラメータを検出可能な試薬を含むことを特徴とする。 The present disclosure can further provide a diagnostic agent or test agent for carrying out the predictive method of the present disclosure, and specifically, the diagnostic agent or test agent of the present disclosure is for predicting a viral infection symptom. It is a diagnostic agent or a test agent and is characterized by containing a reagent capable of detecting at least one parameter selected from the above-mentioned biological parameters.
　１つの実施形態では、本開示の診断剤または検査薬は、被験者から採取した試料中の生体パラメータの量または濃度を測定して、ウイルス感染症状を予測するための方法に用いられる診断剤または検査薬であって、この診断剤は当該生体パラメータを検出するための試薬を含む。感染症状を実際の感染の前に予測した後は、感染症状の程度、またはその確率について適宜閾値を設定することによって、ウイルス感染を防ぐためのワクチンなどの予防薬の投与、または実際にウイルスに感染した際の治療計画の設計に用いることができる。 In one embodiment, the diagnostic agent or test agent of the present disclosure is a diagnostic agent or test agent used in a method for predicting viral infection symptoms by measuring the amount or concentration of biological parameters in a sample taken from a subject. A drug, the diagnostic agent comprises a reagent for detecting the biological parameter. After predicting the symptoms of infection before the actual infection, by setting an appropriate threshold for the degree or probability of the infection, administration of a prophylactic drug such as a vaccine to prevent viral infection, or the actual virus. It can be used to design treatment plans in the event of infection.
　一実施形態では、本明細書に記載される情報に基づいて必要な生体パラメータに対する試薬を選択して使用することができる。そのような試薬が複数ある場合は、別々に提供されていてもよく、まとめてセットとして提供されてもよく、必要な他の試薬（例えば、発色剤）とともにキットとして提供されてもよい。 In one embodiment, reagents for the required bioparameters can be selected and used based on the information described herein. When there are a plurality of such reagents, they may be provided separately, may be provided together as a set, or may be provided as a kit together with other necessary reagents (for example, a color former).
　このような生体パラメータは、ＥＬＩＳＡ等の抗原抗体反応を利用した測定系によって測定でき、生体パラメータを検出可能な試薬としては、具体的には、当該生体パラメータに特異的に結合可能な抗体、およびそのフラグメントが挙げられる。また、生体パラメータに特異的に結合可能な抗体は、適切な支持体の上に結合させて抗体アレイとして提供してもよい。 Such biological parameters can be measured by a measurement system using an antigen-antibody reaction such as ELISA, and as reagents capable of detecting biological parameters, specifically, an antibody that can specifically bind to the biological parameter, and The fragment is mentioned. Further, an antibody that can specifically bind to a biological parameter may be bound on an appropriate support and provided as an antibody array.
　（解析方法および予測モデル）
　１つの実施形態において、本開示の方法は、被験者から採取した試料から得た生体パラメータと、必要に応じて健常者における同一種類のパラメータとを比較することでウイルス感染症状、治療法適合性、および／または治療結果を予測することができる。本開示の一実施形態において、以上のようにして得た被験者毎の生体パラメータの値またその平均値をそれぞれ被験者の値または平均値と比較してその被験者の値との大小を比較することができる。また本開示の他の実施形態において、以上のようにして得た被験者毎の生体パラメータの値を対数変換することができる。これにより、より正規分布に近くすることができる。例えば、健常者における同一種類のパラメータあるいは別の参照パラメータと比較するために、健常者で現れる任意の範囲の値と、それ以外とに区別することができる。すなわち、健常者の多くで現れる値と、それを逸脱する値とに区別することができ、例えば、健常者で現れる値の１０～９０％の範囲に含まれる値を１つの群（例えば緑で色分け）とし、それより上の値（９０％以上となる値）を２つ目の群（例えば赤で色分け）、それより下の値（１０％未満となる値）を３つ目の群（例えば薄緑で色分け）とすることができる。 (Analysis method and prediction model)
In one embodiment, the method of the present disclosure is to compare biological parameters obtained from a sample taken from a subject with, if necessary, parameters of the same type in a healthy subject to indicate viral infection symptoms, therapeutic suitability, and the like. And / or treatment outcomes can be predicted. In one embodiment of the present disclosure, it is possible to compare the values of the biological parameters for each subject obtained as described above and their average values with the values or average values of the subjects, respectively, and compare the magnitude with the values of the subjects. can. Further, in another embodiment of the present disclosure, the values of the biological parameters for each subject obtained as described above can be logarithmically converted. This makes it possible to get closer to the normal distribution. For example, in order to compare with the same kind of parameter or another reference parameter in a healthy person, it is possible to distinguish between any range of values appearing in a healthy person and others. That is, it is possible to distinguish between the values that appear in many healthy subjects and the values that deviate from them. Color-coded), values above it (values above 90%) are in the second group (for example, color-coded in red), and values below it (values below 10%) are in the third group (values below 10%). For example, it can be color-coded with light green).
　本開示の一実施形態において、例えば重症者から得た生体パラメータのそれぞれについて健常者の値と比較することにより、重症者に特異的に発現が変動する生体パラメータを見出すことができ、その生体パラメータの発現の増減を基準として、被験者ウイルス感染症状、治療法適合性、および／または治療結果を予測することができる。 In one embodiment of the present disclosure, for example, by comparing each of the biological parameters obtained from a severely ill person with the value of a healthy person, it is possible to find a biological parameter whose expression changes specifically for the severely ill person, and the biological parameter thereof. Subject viral infection symptoms, treatment suitability, and / or treatment outcomes can be predicted on the basis of increased or decreased expression of.
　本開示は、別の局面において、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法であって、
　前記被験者における１または複数の生体パラメータを得る工程と、
　前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力する工程と
を含む、方法を提供し得る。特に、強化学習またはニューラルネットワークに基づいて生成された予測モデルから、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法が提供され得る。 The present disclosure is, in another aspect, a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
The step of obtaining one or more biological parameters in the subject, and
A method may be provided that comprises applying the biological parameters to a predetermined predictive model and outputting probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. In particular, prediction models generated based on reinforcement learning or neural networks may provide methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes of a subject.
　１つの実施形態では、前記生体パラメータは、生体分子パラメータ（例えば、サイトカイン量またはその変動などの生体内の反応を示す因子、ＡＣＲ－Ｒ１アイソタイプなどの生体側のウイルスの相性を決める因子を含む）、臨床データ（臨床所見、Ｘ線ＣＴ画像を含む）、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）のうち少なくとも１つに関する情報を含むことができる。１つの実施形態では、前記生体パラメータは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、血中ウイルス量、およびＡＣＲ－Ｒ１アイソタイプからなる群より選択される少なくとも１つに関する情報を含むことができる。 In one embodiment, the biomolecular parameter includes a biomolecular parameter (eg, a factor indicating an in vivo response such as the amount of cytokine or its fluctuation, a factor determining the compatibility of a virus on the biological side such as ACR-R1 isotype). , Clinical data (including clinical findings, X-ray CT images), and amount and / or type of virus in the body (including blood viral load, type of virus variant (identifiable by SNP, etc.), etc.) It can contain information about at least one. In one embodiment, the biological parameter comprises information about at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotype. Can be done.
　予測モデルは、生体分子パラメータ（サイトカイン量またはその変動など）、臨床データ（臨床所見、Ｘ線ＣＴ画像など）、履歴情報（治療履歴、状態履歴情報など）および治療法情報（治療効果情報、安全性情報など）から選択される因子を用いてもよい。他の実施形態において、前記予測モデルは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、治療履歴、治療効果情報、安全性情報、および状態履歴情報からなる群より選択される少なくとも１つに基づいて生成されることができる。一実施形態において、予測モデルを生成するために使用する臨床評価項目は特に限られるものではなく、患者の症状に応じて処置された各種治療やその効果、また患者における経時的な臨床データを含むあらゆる臨床情報を用いることができる。 Predictive models include biomolecular parameters (such as the amount of cytokines or their fluctuations), clinical data (clinical findings, X-ray CT images, etc.), historical information (treatment history, state history information, etc.) and treatment method information (therapeutic effect information, safety). Factors selected from (sex information, etc.) may be used. In another embodiment, the predictive model is at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment efficacy information, safety information, and status history information. Can be generated based on. In one embodiment, the clinical endpoints used to generate the predictive model are not particularly limited and include various treatments treated according to the patient's symptoms and their effects, as well as clinical data over time in the patient. Any clinical information can be used.
　１つの実施形態において、臨床データは、特に限られるものではなく、症状を予測する対象となるウイルスに応じて適宜選択することができる。例えば臨床データとして対象のウイルス感染患者において特に変動することが知られているものを用いることもでき、具体的には、白血球数（μｌあたり）、赤血球数（μｌあたり）、絶対好中球数（μｌあたり）、絶対リンパ球数（１μｌあたり）、血小板数（μｌあたり）、ヘモグロビン（ｇ／ｄｌ）、ヘマトクリット（％）、ナトリウム（ｍｍｏｌ／リットル）、カリウム（ｍｍｏｌ／ｌｉｔｅｒ）、塩化物（ｍｍｏｌ／ｌｉｔｅｒ）、カルシウム（ｍｇ／ｄｌ）、二酸化炭素（ｍｍｏｌ／ｌｉｔｅｒ）、陰イオンギャップ（ｍｍｏｌ／ｌｉｔｅｒ）、グルコース（ｍｍｏｌ／ｌｉｔｅｒ）、血中尿素窒素（ｍｇ／ｄｌ）、クレアチニン（ｍｇ／ｄｌ）、総タンパク質（ｇ／ｄｌ）、アルブミン（ｇ／ｄｌ）、総ビリルビン（ｍｇ／ｄｌ）、プロカルシトニン（ｎｇ／ｍｌ）、アラニンアミノトランスフェラーゼ（Ｕ／リットル）、アスパラギン酸アミノトランスフェラーゼ（Ｕ／リットル）、アルカリホスファターゼ（Ｕ／リットル）、フィブリノーゲン（ｍｇ／ｄｌ）、乳酸脱水素酵素（Ｕ／リットル）、プロトロンビン時間（秒）、国際正規化比率、クレアチンキナーゼ（Ｕ／リットル）、静脈乳酸（ミリモル／リットル）などを用いることができる。一実施形態において、臨床データとしては、発熱、鼻汁、咳嗽、倦怠、悪心、嘔吐、下痢、腹部症状などを含む臨床症状、肺Ｘ線写真、肺ＣＴ所見、ｐＯ２、鼻粘膜ウイルス検査、口腔粘膜ウイルス検査、血中ウイルス検査などを含む検査値、白血球（ＷＢＣ）、好中球（Ｎｅｕ）、リンパ球（Ｌｙｍ）、血小板（Ｐｌｔ）、血色素（Ｈｂ）、ＣＲＰｖＦｉｂ、アルブミン（Ａｌｂ）、ＡＳＴ、ＡＬＴ、乳酸脱水素酵素（ＬＤＨ）、ＡＬ－ｐｈ、クレアチニン、プロカルシトニン、プロトロンビン時間、ＦＤＰ、Ｄ－Ｄ　ｄｉｍｅｒなどの血液検査値、並びにＩｇＧ抗体およびＩｇＭ抗体を含む抗体検査値の中から選択される１または複数の臨床症状、所見、または検査値などの臨床評価変数を用いることが好ましい。 In one embodiment, the clinical data is not particularly limited and can be appropriately selected depending on the virus for which the symptom is predicted. For example, clinical data that is known to fluctuate particularly in a target virus-infected patient can be used, specifically, leukocyte count (per μl), erythrocyte count (per μl), and absolute neutrophil count. (Per μl), absolute lymphocyte count (per μl), platelet count (per μl), hemoglobin (g / dl), hematocrit (%), sodium (mmol / liter), potassium (mmol / liter), chloride (per μl) mmol / liter), calcium (mg / dl), carbon dioxide (mmol / liter), anion gap (mmol / liter), glucose (mmol / liter), blood urea nitrogen (mg / dl), creatinine (mg / liter) dl), total protein (g / dl), albumin (g / dl), total bilirubin (mg / dl), procalcitonin (ng / ml), alanine aminotransferase (U / liter), aspartate aminotransferase (U / dl) Lim), alkaline phosphatase (U / liter), fibrinogen (mg / dl), lactic acid dehydrogenase (U / liter), prothrombin time (seconds), international normalization ratio, creatin kinase (U / liter), intravenous lactic acid (U / liter) Millimole / liter) and the like can be used. In one embodiment, clinical data includes clinical symptoms including fever, nasal juice, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral cavity. Test values including mucosal virus test, blood virus test, leukocyte (WBC), neutrophil (Neu), lymphocyte (Lym), platelet (Plt), blood pigment (Hb), CRPvFib, albumin (Alb), AST , ALT, lactate dehydrogenase (LDH), AL-ph, creatinine, procalcitonin, prothrombin time, FDP, blood test values such as DD dimer, and antibody test values including IgG antibody and IgM antibody. It is preferable to use clinical evaluation variables such as one or more clinical symptoms, findings, or laboratory values.
　一実施形態において、臨床データとして発熱、胸部ＣＴ、ＦＤＰ、フェリチン、リンパ球数の絶対値、ＣＲＰ、及び／または抗体検査値、並びに生体分子パラメータとしてＩＬ－６、ＩＬ－８、ＦＧＦ、ＩＦＮα、ＩＦＮβ、ＩＦＮγ、ＩＰ－１０、ＢＡＦＦ、ＩＬ－１１、及び／またはＰｅｎｔｒａｘｉｎを用いることが好ましい。 In one embodiment, clinical data include fever, chest CT, FDP, ferritin, absolute lymphocyte count, CRP, and / or antibody test values, and biomolecular parameters IL-6, IL-8, FGF, IFNα, It is preferred to use IFNβ, IFNγ, IP-10, BAFF, IL-11, and / or Ferraxin.
　１つの実施形態において、予測モデルを生成するための治療履歴、状態履歴情報には、過去の治療履歴（例えば、抗ウイルス薬などの投与履歴）、および対象となる被験体の任意のデータの履歴（例えば、臨床データの過去の履歴）を含み、治療履歴には、酸素吸入の有無、吸入量、期間、及び／または効果、ＥＣＭＯの有無、期間、及び／または効果、ステロイドやアクテムラの点滴の有無、投薬量、期間、及び／または効果、アビガンなどの抗ウイルス薬やヘパリン、ワーファリン、アスピリンなどの抗血栓薬などの内服薬の有無、投薬量、投与期間、及び／または効果の経時的情報を用いることができる。 In one embodiment, the treatment history for generating a predictive model, the state history information includes a history of past treatments (eg, administration history of antiviral drugs, etc.), and a history of arbitrary data of the subject. Including (eg, past history of clinical data), treatment history includes presence / absence of oxygen inhalation, inhalation volume, duration and / or effect, presence / absence of ECMO, duration and / or effect, infusion of steroids and Actemra. Presence / absence, dosage, duration, and / or effect, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or time-dependent information on the effect. Can be used.
　１つの実施形態において、予測モデルを生成するための治療効果情報、または安全性情報には、酸素吸入の有無、吸入量、期間、及び／または効果、ＥＣＭＯの有無、期間、及び／または効果、ステロイドやアクテムラの点滴の有無、投薬量、期間、及び／または効果、アビガンなどの抗ウイルス薬やヘパリン、ワーファリン、アスピリンなどの抗血栓薬などの内服薬の有無、投薬量、投与期間、及び／または効果、ならびに／あるいは安全性の情報を用いることができる。 In one embodiment, the therapeutic effect information or safety information for generating a predictive model includes the presence or absence of oxygen inhalation, the amount of inhalation, the period and / or the effect, the presence or absence of ECMO, the period and / or the effect. Presence / absence, dosage, duration, and / or effect of infusion of steroids and Actemra, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or Efficacy and / or safety information can be used.
　以下に、患者の状態を想定し、症状度や治療適合性を予測するモデルを説明する。解析のモデルを図１に示した。図１の左側の治療経路モデルが、これまでの患者の状態を整理したものである。病院には、必ずしも軽症段階から入院するとは限らないため、入院時の段階によって経路が分けてある。軽症から継続の場合は、それぞれのステップがつながった症状度予測モデルの制度を改善しつつ治療的要請を予測するモデルとなっている。 Below, we will explain a model that predicts the degree of symptoms and treatment suitability by assuming the patient's condition. The model of analysis is shown in FIG. The treatment route model on the left side of FIG. 1 summarizes the patient's condition so far. Since hospitals are not always hospitalized from the mild stage, the routes are divided according to the stage at the time of admission. In the case of continuation from mild illness, it is a model that predicts therapeutic demand while improving the system of symptom degree prediction model in which each step is connected.
　図１の右側が確立状態遷移モデルであり、各ステップが従来からの機械学習モデルを基礎とする組み合わせモデルである。症状度モデルに、初期検査データとして、約７０種のバイオ分子量変動、臨床データ、Ｘ線ＣＴ画像からの特微量血中ウイルス量、ＡＣＥ－Ｒ１アイソタイプなどのデータを節面変数として入力する。症状度は「無症状」、「軽症」、「中等症（Ｉ）」、「中等症（ＩＩ）」、「重症」などのランクに分け、それぞれのランクに該当する確率値として結果を得ることができる。 The right side of FIG. 1 is the established state transition model, and each step is a combination model based on the conventional machine learning model. Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data. The degree of symptom is divided into ranks such as "asymptomatic", "mild", "moderate (I)", "moderate (II)", and "severe", and the result is obtained as a probability value corresponding to each rank. Can be done.
　１つの実施形態において、予測モデルの生成において、ウイルス感染後、症状度ステージ毎の患者の個々の症状度再予測を求めると共に、最適治療法を予測し、いずれの感染ステージにおいても改善、軽快するアルゴリズムを確立する。一実施形態において、予測モデルは、対象患者は、無症状群（４０名）、軽症（４０名）、中等症（Ｉ）（４０名）、中等症（ＩＩ）（２０名）、重症（２０名）の症状や生体パラメータ等を取得して生成することができる。２００名未満の場合は統計学的に確立遷移モデルを用い、２００名以上の場合はＡＩのディープマニュアルネットワークによる治療効果予測モデルを用いることができる。一実施形態において、評価候補項目として、患者臨床所見、画像所見、過去の治療歴、抗体の有無、血中ウイルス量、血中バイオ分子などを用いることができ、呼吸器状態を主とした各ステージの症状度分類を目的変数とすることができる。 In one embodiment, in the generation of a predictive model, after virus infection, the patient's individual symptom degree reprediction is obtained for each symptom degree stage, the optimal treatment method is predicted, and improvement and improvement are achieved at any infection stage. Establish an algorithm. In one embodiment, the predictive model is that the target patients are asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients). It is possible to acquire and generate the symptoms and biological parameters of the name). For less than 200 people, a statistically established transition model can be used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network can be used. In one embodiment, patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. can be used as evaluation candidate items, and each of them mainly consists of respiratory condition. The stage symptom classification can be the objective variable.
　本開示の一実施形態において、血中のウイルスはＰＣＲ法、９０項目のバイオ分子測定はＢｉｏ－Ｐｌｅｘを用いて定量することができる。症状度ステージ毎に評価候補項目を特定し、再ステージ予測の一致率、症状度予測確立を算出することができる。例えば、一実施形態において、予測症状度に従って、ステージ毎の治療を仮定し、治療最適予測を行い、最適治療を適用する。これにより、無症状、軽症、中等症（Ｉ）、中等症（ＩＩ）、重症などのステージ毎に症状度を予測し、治療法適合性を予測し、最適治療が行えるようになる。以上の方法によって、いずれのステージにおいても個人毎の最適な治療法を適用し、ウイルスに感染しても症状が軽快し健全な生活が可能になるよう個別毎の治療を確立することができる。 In one embodiment of the present disclosure, the virus in blood can be quantified using the PCR method, and 90 items of biomolecular measurement can be quantified using Bio-Plex. Evaluation candidate items can be specified for each symptom degree stage, and the concordance rate of restage prediction and the establishment of symptom degree prediction can be calculated. For example, in one embodiment, treatment for each stage is assumed, optimal treatment prediction is performed, and optimal treatment is applied according to the predicted symptom degree. As a result, the degree of symptom is predicted for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe, the suitability of the treatment method is predicted, and the optimum treatment can be performed. By the above method, it is possible to apply the optimal treatment method for each individual at any stage, and to establish the treatment for each individual so that the symptoms are alleviated and a healthy life is possible even if infected with the virus.
　本開示の一実施形態において、予測モデルを生成する際に多変量解析を用いることができ、多変量解析は、LASSO(Least　Absolute　Shrinkage　and　Selection　Operator)を
使用し得る（Trevor　et.　al.:　Statistical　Learning　with　Sparsity:　The　Lasso　and　Generalizations,　CRC　Press,　2015）。これは、パラメータ推定と変数選択
を同時に行う手法（回帰推定とシステム工学の最適化理論が融合した技術）であり、説明変数がサンプル数よりも多くても計算が可能である。本当に有効な変数は数少ないという仮定が背景にある。例えば遺伝子情報解析では、説明変数が２万個に対して、サンプル数は数百程度でも適用可能である。説明変数を選択しつつ、その回帰係数を得ることができる。代表となる説明変数を自動的に選択するため、多重共線性の問題が発生しない。したがって、限られたデータの中で可能な限り過剰適合の程度を数値的に評価しながら説明変数を決定することができる。 In one embodiment of the present disclosure, multivariate analysis can be used in generating predictive models, and multivariate analysis can use LASSO (Least Absolute Shrinkage and Selection Operator) (Trevor et. Al .: Statistical). Learning with Sparsity: The Lasso and Generalizations, CRC Press, 2015). This is a method of performing parameter estimation and variable selection at the same time (a technology that combines regression estimation and optimization theory of system engineering), and it is possible to calculate even if the explanatory variables are larger than the number of samples. The background is the assumption that few variables are really valid. For example, in gene information analysis, the number of explanatory variables is 20,000, and the number of samples can be several hundred. You can get the regression coefficient while selecting the explanatory variables. Since the representative explanatory variable is automatically selected, the problem of multicollinearity does not occur. Therefore, the explanatory variables can be determined while numerically evaluating the degree of overfitting as much as possible in the limited data.
　１つの実施形態において、モデルの性能は、予め８対２に分割したデータセットの８割のデータを用いて前記ＬＡＳＳＯによる回帰モデル構築を行い、残りの２割のデータに対して得られた予測値の分布からＣｏｈｅｎ’ｓ　ｄを用いて評価することができる（Ｊ．
　Ｃｏｈｅｎ：　Ｓｔａｔｉｓｔｉｃａｌ　Ｐｏｗｅｒ　Ａｎａｌｙｓｉｓ　ｆｏｒ　ｔｈｅ　Ｂｅｈａｖｉｏｒａｌ　Ｓｃｉｅｎｃｅｓ，　ｓｅｃｏｎｄ　ｅｄｉｔｉｏｎ，　Ｐｓｙｃｏｌｏｇｙ　Ｐｒｅｓｓ．）。Ｃｏｈｅｎ’ｓ　ｄは、サンプルサイズに依存しない値である。Ｃｏｈｅｎ’ｓ　ｄは、２群の平均値の差を、両群を合わせた（プールした）標準偏差で割ったものである。予測モデルの性能は、検証用群と、学習用群との間のＣｏｈｅｎ’ｓ　ｄを用いて評価でき、医療関連の分野では、Ｃｏｈｅｎ’ｓ　ｄが１．２以上のモデルが好ましいとされる。 In one embodiment, the performance of the model is the prediction obtained for the remaining 20% of the data obtained by constructing the regression model by the LASSO using 80% of the data of the data set divided into 8 to 2 in advance. It can be evaluated using Cohen's d from the distribution of values (J.
Cohen: Statistical Power Analysis for the Behavioral Sciences, second edition, Statistics Press. ). Cohen's d is a value that does not depend on the sample size. Cohen's d is the difference between the mean values of the two groups divided by the combined (pooled) standard deviation of the two groups. The performance of the predictive model can be evaluated using Cohen's d between the verification group and the learning group, and in the medical field, a model with Cohen's d of 1.2 or more is preferable. ..
　本開示で提供される手法により、生体パラメータを適切に選択し、予測モデルに適用することによって、ウイルス感染症状、治療法適合性、および／または治療結果を予測することができる。 The techniques provided in the present disclosure allow the prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes by appropriately selecting biological parameters and applying them to predictive models.
　１つの実施形態では、本開示では、生体パラメータデータを被験体から取得し、現時点のウイルス感染状態および症状を判定し、近未来にどの程度重症化するかを予測し、症状度がどのように推移するか予測することができる。さらに、現時点のウイルス感染状態および症状を判定し、近未来にどの程度重症化するかを予測し、症状度がどのように推移するか予測することができることから、現時点でどのような治療を行うことが適切であるかあるいは最適であるかを判定し、あるいは手元にある（ａｖａｉｌａｂｌｅ）治療のうち、どれが最適かどうかを判定することができ、あるいは、治療過程において、症状度がどの程度まで悪化し、どの程度症状度を押さえることができるかを予測することができる。被験体の体力に応じて、例えば、中等症（Ｉ）までは耐えられるあるいは中等症（ＩＩ）まで耐えられる被験体である場合、中等症（ＩＩ）まで耐えられる被験者には予後はベターであるが症状度は一時期重篤になり得る治療を選択するか、中等症（Ｉ）レベルまでしか耐えられない被験者に、予後はそれほど良くないが、症状度はそれほど悪化しない治療を施すことを選択することが実現され得る。これは、本開示によって治療法適合性および治療結果を予測することができることによるものである。 In one embodiment, the present disclosure obtains biological parameter data from a subject, determines the current viral infection status and symptoms, predicts how severe it will be in the near future, and how the degree of symptoms will be. It can be predicted whether it will change. Furthermore, since it is possible to determine the current virus infection status and symptoms, predict how severe the disease will be in the near future, and predict how the degree of symptoms will change, what kind of treatment will be performed at this time. It is possible to determine whether it is appropriate or optimal, or which of the available treatments is optimal, or to what extent the symptoms are in the course of treatment. It is possible to predict how much the symptoms will worsen and the degree of symptoms can be suppressed. Depending on the physical fitness of the subject, for example, if the subject can tolerate up to moderate (I) or tolerate moderate (II), the prognosis is better for the subject tolerable to moderate (II). Choose treatment that can be severe for a period of time, or give subjects who can tolerate only moderate (I) levels a treatment that has a poor prognosis but does not worsen. Can be realized. This is due to the ability of the present disclosure to predict treatment suitability and treatment outcomes.
　本開示は、使用するにつれて、その精度を改善することができる。ある予測式、係数などを用いて表現され得る。例えば、本開示に基づいて、感染後症状度に基づいて早期無症状、軽症、中等症(Ｉ)、中等症(ＩＩ)、重症等に分類し、症状度に応じた患者の血中ウ
イルス量、バイオ分子量、抗体有無、及び臨床所見、さらに治療歴等の初期情報及び経時的情報を評価項目とし、それぞれの段階の症状度を目的変数として、例えば、利用している予測モデルを、強化学習またはディープニューラルネットワーク（AI解析）によって、予測モデルをさらに改良し、順次利用することができる。これらの改良も出るを用いて症状度をより正確に知ることができ、各々の治療法による治療効果予測より正確に知ることができ、有効性をより正確に知ることができる。これらの予測モデルを用い、患者個々に予測することによって最適治療をさらに改善することができ、個人毎に治療効果を最適化をさらに改良することができる。 The present disclosure can improve its accuracy as it is used. It can be expressed using a certain prediction formula, coefficient, and so on. For example, based on the present disclosure, it is classified into early asymptomatic, mild, moderate (I), moderate (II), severe, etc. based on the degree of post-infection symptom, and the amount of virus in the blood of the patient according to the degree of symptom. , Biomolecular weight, presence / absence of antibody, clinical findings, initial information such as treatment history and time-dependent information are used as evaluation items, and the degree of symptom at each stage is used as the objective variable. Alternatively, deep neural networks (AI analysis) can be used to further improve the predictive model and use it sequentially. It is possible to know the degree of symptom more accurately by using these improvements, and it is possible to know more accurately than the prediction of the therapeutic effect by each treatment method, and it is possible to know the effectiveness more accurately. By using these prediction models and predicting each patient individually, the optimal treatment can be further improved, and the optimization of the therapeutic effect can be further improved for each individual.
　（他の実施の形態）
　本開示の１つまたは複数の態様に係る判定方法および解析方法について、実施の形態に基づいて説明したが、本開示は、この実施の形態に限定されるものではない。本開示の趣旨を逸脱しない限り、当業者が思いつく各種変形を本実施の形態に施したものや、異なる実施の形態における構成要素を組み合わせて構築される形態も、本開示の１つまたは複数の態様の範囲内に含まれてもよい。 (Other embodiments)
Although the determination method and the analysis method according to one or more aspects of the present disclosure have been described based on the embodiments, the present disclosure is not limited to the embodiments. As long as it does not deviate from the gist of the present disclosure, one or more of the present embodiments may be modified by those skilled in the art, or may be constructed by combining components in different embodiments. It may be included within the scope of the embodiment.
　判定方法または解析方法は、プログラムによって実行され得る。すなわち、被験者のウイルス感染症状を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムであって、前記方法は、前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程と、前記コンピュータに、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、ウイルス感染症状を予測するための指標とさせる工程とを含む、プログラムが提供され得る。あるいは、被験者における１または複数の生体パラメータを、前記被験者のウイルス感染症状を予測するための指標とする方法の処理をコンピュータに実行させるコンピュータプログラムであって、前記方法は、前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程を含み、前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、プログラムが提供され得る。あるいは、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムであって、前記方法は、前記コンピュータに、前記被験者における１または複数の生体パラメータを得る工程と、前記コンピュータに、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力させる工程とを含む、プログラムが提供され得る。このようなプログラムを格納した記録媒体もまた提供され得る。記録媒体は、非一時的な記録媒体であり得る。 The determination method or analysis method can be executed by a program. That is, it is a computer program that causes a computer to execute a process of a method for predicting a virus infection symptom of a subject, wherein the method is a step of causing the computer to obtain one or a plurality of biological parameters in the subject. A program may be provided to the computer comprising a step of analyzing the bioparameters by comparing them with reference values and using them as indicators for predicting viral infection symptoms. Alternatively, a computer program that causes a computer to perform processing of a method in which one or more biological parameters in a subject are used as an index for predicting a viral infection symptom of the subject, wherein the method causes the computer to perform the subject. The step of obtaining one or more biological parameters in the above, wherein the one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7. , IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP -1, MIP-1α, MIP-1β, PDGF-bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL -11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL -34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2 , CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM A program may be provided that includes parameters selected from the group consisting of VCAM-1. Alternatively, a computer program that causes a computer to process a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method causes the computer to perform one or more of the subjects. It comprises the steps of obtaining a plurality of bioparameters and causing the computer to apply the bioparameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. The program may be offered. A recording medium containing such a program may also be provided. The recording medium can be a non-temporary recording medium.
　システムは、本明細書に記載される方法をコンピュータに実行させるためのプログラムを備えていてよく、例えば、そのようなプログラムを格納した記録媒体を備え得る。また、プログラムによって指示される命令を実行するための計算装置（例えば、コンピュータ）を備えていてよい。計算装置は、物理的に一体としていても、あるいは、物理的に分離した複数の構成要素からなっていてもよい。これらの計算装置の内部において、学習部、計算部および取得部等に対応する機能が必要に応じて備えられ得る。 The system may include a program for causing a computer to perform the methods described herein, eg, may include a recording medium containing such a program. Further, a computer (for example, a computer) for executing the instruction instructed by the program may be provided. The arithmetic unit may be physically integrated or may consist of a plurality of physically separated components. Inside these arithmetic units, functions corresponding to a learning unit, a calculation unit, an acquisition unit, and the like can be provided as needed.
　本開示のシステムは、複数の構成部を１個のチップ上に集積して製造された超多機能ＬＳＩとして実現することができ、具体的には、マイクロプロセッサ、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）などを含んで構成されるコンピュータシステムであり得る。ＲＯＭには、コンピュータプログラムが記憶されている。前記マイクロプロセッサが、コンピュータプログラムに従って動作することにより、システムＬＳＩは、その機能を達成する。 The system of the present disclosure can be realized as an ultra-multifunctional LSI manufactured by integrating a plurality of components on one chip, and specifically, a microprocessor, a ROM (Read Only Memory), and a RAM. It may be a computer system configured to include (Random Access Memory) and the like. A computer program is stored in the ROM. When the microprocessor operates according to a computer program, the system LSI achieves its function.
　なお、ここでは、システムＬＳＩとしたが、集積度の違いにより、ＩＣ、ＬＳＩ、スーパーＬＳＩ、ウルトラＬＳＩと呼称されることもある。また、集積回路化の手法はＬＳＩに限るものではなく、専用回路または汎用プロセッサで実現してもよい。ＬＳＩ製造後に、プログラムすることが可能なＦＰＧＡ（Ｆｉｅｌｄ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｇａｔｅ　Ａｒｒａｙ）、あるいはＬＳＩ内部の回路セルの接続や設定を再構成可能なリコンフィギュラブル・プロセッサを利用してもよい。さらには、半導体技術の進歩または派生する別技術によりＬＳＩに置き換わる集積回路化の技術が登場すれば、当然、その技術を用いて機能ブロックの集積化を行ってもよい。バイオ技術の適用等が可能性としてありえる。 Although it is referred to as a system LSI here, it may be referred to as an IC, an LSI, a super LSI, or an ultra LSI due to the difference in the degree of integration. Further, the method of making an integrated circuit is not limited to the LSI, and may be realized by a dedicated circuit or a general-purpose processor. An FPGA (Field Programmable Gate Array) that can be programmed after the LSI is manufactured, or a reconfigurable processor that can reconfigure the connection and settings of the circuit cells inside the LSI may be used. Furthermore, if an integrated circuit technology that replaces an LSI appears due to advances in semiconductor technology or another technology derived from it, it is naturally possible to integrate functional blocks using that technology. The application of biotechnology may be possible.
　また、本開示の一態様は、このようなウイルス感染症状、治療法適合性、および／または治療結果の予測またはウイルス感染症状の予測、治療法適合性、および／または治療結果の予測のための指標の特定のためのシステムだけではなく、ウイルス感染症状の予測またはウイルス感染症状の予測のための指標の特定のためのシステムに含まれる特徴的な構成部をステップとするウイルス感染症状の予測またはウイルス感染症状の予測のための指標、値生成、判別・分類方法であってもよい。また、本開示は、特徴量縮約、特徴量抽出、疼痛判別・推定モデル生成、疼痛判別・推定に含まれる特徴的な各ステップをコンピュータに実行させるコンピュータプログラムであってもよい。また、本開示の一実施形態は、ウイルス感染症状、治療法適合性、および／または治療結果の予測またはウイルス感染症状、治療法適合性、および／または治療結果の予測のための指標の特定のための方法に含まれる特徴的な各ステップをコンピュータに実行させるコンピュータプログラムであってもよい。また、本開示の一態様は、そのようなコンピュータプログラムが記録された、コンピュータ読み取り可能な非一時的な記録媒体であってもよい。 Also, one aspect of the present disclosure is for such viral infection symptoms, treatment suitability, and / or prediction of treatment outcomes or prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes. Not only the system for identifying indicators, but also the prediction of virus infection symptoms or the prediction of virus infection symptoms, or the prediction of virus infection symptoms, which steps through the characteristic components included in the system for identifying indicators for predicting virus infection symptoms. It may be an index for predicting a virus infection symptom, a value generation, a discrimination / classification method. Further, the present disclosure may be a computer program that causes a computer to execute each characteristic step included in feature amount reduction, feature amount extraction, pain discrimination / estimation model generation, and pain discrimination / estimation. Also, one embodiment of the present disclosure identifies indicators for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes or for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. It may be a computer program that causes the computer to perform each of the characteristic steps included in the method. Also, one aspect of the present disclosure may be a computer-readable, non-temporary recording medium on which such a computer program is recorded.
　本開示は、クラウド、ＩｏＴおよびＡＩを用いた実施形態として実現してもよい、本開示の予測技術は、１つのシステムまたは装置として、すべてを含む形で提供され得る。あるいは、予測装置として生体パラメータの測定および結果の表示を主に行い、計算や判別モデルの計算は、サーバやクラウドで行う形態も想定され得る。これらの一部または全部は、ＩｏＴ（Ｉｎｔｅｒｎｅｔ　ｏｆ　Ｔｈｉｎｇｓ）および／または人工知能（ＡＩ）を用いて実施され得る。 The present disclosure may be realized as an embodiment using the cloud, IoT and AI, and the prediction technology of the present disclosure may be provided as an all-inclusive system or device. Alternatively, it can be assumed that the biological parameters are mainly measured and the results are displayed as a prediction device, and the calculation and the calculation of the discrimination model are performed by a server or the cloud. Some or all of these can be performed using IoT (Internet of Things) and / or artificial intelligence (AI).
　あるいは、本開示の装置が予測モデルも格納し、その場で判別を行うが、判別モデルの計算などの主要な計算は、サーバやクラウドで行う形態である半スタンドアローン型の形態も想定され得る。病院等の一部の実施場所では、送受信が常にできると限らないことから、遮蔽した場合でも使えるモデルを想定したものである。 Alternatively, the apparatus of the present disclosure also stores a prediction model and makes a discrimination on the spot, but a semi-standalone type, which is a form in which the main calculation such as the calculation of the discrimination model is performed on a server or the cloud, can be assumed. .. Since it is not always possible to send and receive at some places such as hospitals, a model that can be used even when shielded is assumed.
　したがって、1つの局面では、本開示は、被験体のウイルス感染症状、治療法適合性、
および／または治療結果を予測する方法をコンピュータに実行させるプログラムであって、該方法はａ）モデル被験者から生体パラメータを得るステップと、ｂ）該生体パラメータに基づいて予測モデルを生成するステップと、ｃ）被験体からの生体パラメータを予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果を予測するステップとを含む、プログラム、ならびにそれを格納した記録媒体、システムおよび装置を提供する。ここで、クラウドなどを利用する場合は、予測モデルの生成は、臨床現場とは離れた場所にあるサーバで実現し、生体パラメータの取得のみ、臨床現場またはそこから近い臨床検査の現場で入力するような形でもよい。場合によっては、簡易な生体パラメータ取得装置を提供し、それによって、瞬時に計算結果を臨床現場に変換して、瞬時の判断をできるようにしてもよい。 Therefore, in one aspect, the present disclosure describes a subject's viral infection symptoms, therapeutic suitability,
And / or a program that causes a computer to execute a method of predicting a treatment result, the method of which is a) a step of obtaining a biological parameter from a model subject, and b) a step of generating a predictive model based on the biological parameter. c) A program, and recording media, systems and equipment containing it, including steps to apply biological parameters from the subject to a predictive model to predict viral infection symptoms, treatment suitability, and / or treatment outcomes. offer. Here, when using the cloud etc., the generation of the prediction model is realized on the server located away from the clinical site, and only the acquisition of biometric parameters is input at the clinical site or the clinical test site near it. It may be in the form of. In some cases, a simple biological parameter acquisition device may be provided, whereby the calculation result may be instantly converted into a clinical setting so that an instant judgment can be made.
　このようなクラウドサービスとしては、おおむね、「Ｓｏｆｔｗａｒｅ　ａｓ　ｓｅｒｖｉｃｅ　（ＳａａＳ）」が該当する。実施例４で例示されるモデルのデータから作られた予測モデルを搭載していると考えられることから、本明細書に記載される実施形態の２つまたは３つあるいはそれ以上の特徴を備えるシステムとして提供されてもよい。 As such a cloud service, "Software as a service (SaaS)" is generally applicable. A system comprising two or three or more features of the embodiments described herein, as it is believed to include a predictive model made from the data of the model exemplified in Example 4. May be provided as.
　好ましい実施形態では、予測モデル改善を行う機能が備わっていてもよい。１つの実施形態では、予測精度が最も高くなる最適なパラメータを選ぶことは、例えば、ＭＡＴＬＡＢのＬＡＳＳＯ関数、ＳＶＭ関数等の当該分野において公知の任意のものを利用することができることで実現することができる。この機能は予測モデルを生成する部分にあってもよく、別個のモジュールとして備えられてもよい。この予測モデル改善機能は、例えば、オプション１（期間１年、年１～２回）、オプション２（期間１年、１、２ヶ月に１回）、オプション３（期間延長、年１～２回）、オプション４（期間延長＋１、２ヶ月に１回）などのオプションを備えていてもよい。 In a preferred embodiment, a function for improving the prediction model may be provided. In one embodiment, selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can. This function may be in the part that generates the predictive model, or it may be provided as a separate module. This predictive model improvement function is, for example, option 1 (period 1 year, once or twice a year), option 2 (period 1 year, once every 1 or 2 months), option 3 (period extension, once or twice a year). ), Option 4 (extension of period +1, once every two months) and other options may be provided.
　データ保存も必要に応じてなされ得る。データ保存は通常サーバ側に備えられるが、全装備型の場合はもとより、クラウド型の場合でも端末側にあってもよい。クラウドでサービスを提供する場合、データ保存は、標準（例えば、クラウドに１０Ｇバイトまで）、オプション１（例えば、クラウドに１Ｔバイト増量）、オプション２（クラウドにパラメータ設定して分割保存）、オプション３（クラウドに判別モデル別に保存）のオプションを提供し得る。データを保存して、販売されたすべての装置からデータを吸い上げてビッグデータを作り、判別モデルを継時的に更新したり、新たなモデルを構築して、例えば、ＣＯＶＩＤ－１９以外の新たなパンデミックなどのように新たな判別モデルソフトウェアを提供することができる。データ解析オプションを有していてもよい。ここでは、患者のパターン分類（判別精度や特徴量のパターン変化に基づき、患者クラスターを探索する）などを提供することができる。 Data can be saved as needed. Data storage is usually provided on the server side, but it may be on the terminal side as well as the cloud type as well as the all-equipped type. When providing services in the cloud, data storage is standard (for example, up to 10 GB in the cloud), option 1 (for example, 1 TB increase in the cloud), option 2 (setting parameters in the cloud and split storage), option 3. It may offer the option of (save in the cloud by discriminant model). Save data, download data from all sold devices to create big data, update discriminant models over time, build new models, for example, new ones other than COVID-19 It is possible to provide new discrimination model software such as pandemics. It may have a data analysis option. Here, it is possible to provide patient pattern classification (searching for patient clusters based on discrimination accuracy and pattern change of feature amount).
　なお、上記各実施の形態において、各構成要素は、専用のハードウェアで構成されるか、各構成要素に適したソフトウェアプログラムを実行することによって実現されてもよい。各構成要素は、ＣＰＵまたはプロセッサなどのプログラム実行部が、ハードディスクまたは半導体メモリなどの記録媒体に記録されたソフトウェアプログラムを読み出して実行することによって実現されてもよい。 In each of the above embodiments, each component may be configured by dedicated hardware or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.
　本明細書において「プログラム」は、当該分野で使用される通常の意味で用いられ、コンピュータが行うべき処理を順序立てて記述したものであり、法律上「物」として扱われるものである。すべてのコンピュータはプログラムに従って動作している。現代のコンピュータではプログラムはデータとして表現され、記録媒体または記憶装置に格納される。 In this specification, "program" is used in the usual meaning used in the relevant field, and describes the processes to be performed by a computer in order, and is treated as a "thing" by law. All computers operate according to the program. In modern computers, programs are represented as data and stored in recording media or storage devices.
　本明細書において「記録媒体」は、本開示を実行させるプログラムを格納した記録媒体であり、記録媒体は、プログラムを記録できる限り、どのようなものであってもよい。例えば、内部に格納され得るＲＯＭやＨＤＤ、磁気ディスク、ＵＳＢメモリ等のフラッシュメモリなどの外部記憶装置でありうるがこれらに限定されない。 In the present specification, the "recording medium" is a recording medium in which the program for executing the present disclosure is stored, and the recording medium may be any as long as the program can be recorded. For example, it may be an external storage device such as a ROM, an HDD, a magnetic disk, or a flash memory such as a USB memory that can be stored internally, but the present invention is not limited thereto.
　本明細書において「システム」とは、本発明の方法またはプログラムを実行する構成をいい、本来的には、目的を遂行するための体系や組織を意味し、複数の要素が体系的に構成され、相互に影響するものであり、コンピュータの分野では、ハードウェア、ソフトウェア、ＯＳ、ネットワークなどの、全体の構成をいう。 As used herein, the term "system" refers to a configuration for executing the method or program of the present invention, and originally means a system or organization for accomplishing an object, and a plurality of elements are systematically configured. In the field of computers, it refers to the entire configuration of hardware, software, OS, network, etc.
　本開示を種々の実施形態を用いて説明してきた。本明細書において本開示の説明のために引用した特許、特許出願および文献は、その内容自体が具体的に本明細書に記載されているのと同様にその内容が本明細書に対する参考として援用される。 This disclosure has been described using various embodiments. The patents, patent applications and documents cited herein for the purposes of this disclosure are incorporated herein by reference in their content as they are specifically described herein. Will be done.
　以下に、理解の容易のために実施例を挙げて、本開示を具体的に説明する。しかしながら、提供される実施例は、例示の目的のみに提供され、本開示を限定する目的で提供したのではない。したがって、本開示の範囲は、本明細書に具体的に記載された実施形態にも実施例にも限定されるものではなく、特許請求の範囲によってのみ限定される。 Hereinafter, the present disclosure will be specifically described with reference to examples for ease of understanding. However, the examples provided are provided for purposes of illustration only and not for the purpose of limiting the present disclosure. Therefore, the scope of the present disclosure is not limited to the embodiments and examples specifically described in the present specification, but is limited only by the scope of claims.
（実施例１－１：サイトカイン／ケモカイン／可溶性レセプターアッセイ（１））
　サイトカインおよびバイマーカーは、マルチプレックスサイトカインアレイシステムであるＢｉｏ－Ｐｌｅｘ　２００（Ｂｉｏ－ＲａｄＬａｂｏｒａｔｏｒｉｅｓ、ＣＡ、ＵＳＡ）を用いて、製造者の指示に従って定量した。治療前のＣＯＶＩＤ－１９重症患者１３名および健常者４２名から血清を回収し、１６００×ｇで１０分遠心した。血清サンプルは分析するまで－８０℃で凍結させた。当該患者におけるサイトカイン／ケモカイン／可溶性レセプターを同時に定量した。以下のキットまたは装置：Ｔｈｅ　Ｂｉｏ－ＰｌｅｘＨｕｍａｎ　Ｃｙｔｏｋｉｎｅ　２７－Ｐｌｅｘ　Ｐａｎｅｌ（２７項目）（ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ）、Ｉｎｆｌａｍｍａｔｉｏｎ１　ｋｉｔ（３７項目）（ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ：Ｂｉｏ－ＲａｄＬａｂｏｒａｔｏｒｉｅｓ、ＣＡ、ＵＳＡ）、およびＲ＆Ｄ　ＬｕｍｉｎｉｘＳｃｒｅｅｎｉｎｇ　Ａｓｓａｙ　２１ｐｌｅｘ（２１項目）（ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１：Ｒ＆Ｄ　Ｓｙｓｔｅｍｓ、Ｉｎｃ．Ｍｉｎｎｅａｐｏｌｉｓ、ＭＮ　５５４１３　Ｔｏｌｌ　Ｆｒｅｅ　ＵＳＡ、Ｃａｎａｄａ）を用いて、示される各サイトカイン／ケモカイン／可溶性レセプターの定量を行った。これらのマルチプレックスサイトカインアレイは、製造者の指示に従って測定を行った。データ取得および分析は、Ｂｉｏ－ＰｌｅｘＭａｎａｇｅｒソフトウェアｖｅｒｓｉｏｎ５．０を用いて行った。 (Example 1-1: Cytokine / chemokine / soluble receptor assay (1))
Cytokines and bimarkers were quantified using the multiplex cytokine array system Bio-Plex 200 (Bio-Rad Laboratories, CA, USA) according to the manufacturer's instructions. Serum was collected from 13 severely ill patients with COVID-19 and 42 healthy subjects before treatment and centrifuged at 1600 xg for 10 minutes. Serum samples were frozen at −80 ° C. until analysis. Cytokines / chemokines / soluble receptors in the patient were simultaneously quantified. The following kits or devices: The Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL- 8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb, RANTES, TNF-α, VEGF), Information1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Cytokine, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK: Bio-RadLaboratories, CA, USA), and R & D LuminixScreening Assay 21plex (21 items) (ADAMATS13, Angiopoietin-2, GIPoietin-2, , L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, VCAM-1: R & D Systems, Inc. MN 55413 Doll Free USA (Canda) was used to quantify each of the indicated cytokines / chemokines / soluble receptors. These multiplex cytokine arrays were measured according to the manufacturer's instructions. Data acquisition and analysis were performed using Bio-Plex Manager software version 5.0.
　健常者（Ｈｅ）およびＣＯＶＩＤ－１９重症患者のそれぞれについて、各生体パラメータの平均値を算出し、健常者とＣＯＶＩＤ－１９重症患者とでその発現を比較した。結果を表２に示す。表中、健常者と比べてＣＯＶＩＤ－１９重症患者で発現が増加していたパラメータを、判定欄において「+」の数で示した。また「２７」はＴｈｅ　Ｂｉｏ－Ｐｌ
ｅｘＨｕｍａｎ　Ｃｙｔｏｋｉｎｅ　２７－Ｐｌｅｘ　Ｐａｎｅｌに、「Ｉｎｆｌａｍ」はＩｎｆｌａｍｍａｔｉｏｎ１　ｋｉｔに、「Ｒ＆Ｄ」はＲ＆Ｄ　ＬｕｍｉｎｉｘＳｃｒｅｅｎｉｎｇ　Ａｓｓａｙ　２１ｐｌｅｘに含まれるバイオマーカーである。丸印はｇｐ１３０をシグナル伝達因子とするものである。 For each of the healthy subject (He) and the COVID-19 severely ill patient, the average value of each biological parameter was calculated, and the expression was compared between the healthy subject and the COVID-19 severely ill patient. The results are shown in Table 2. In the table, the parameters whose expression was increased in COVID-19 severely ill patients as compared with healthy subjects are indicated by the number of "+" in the judgment column. "27" is The Bio-Pl
"Inflam" is a biomarker contained in exHuman Cytokine 27-Plex Panel, "R &D" is a biomarker contained in R & D Luminix Creating Assay 21plex. The circles use gp130 as a signal transduction factor.
Figure JPOXMLDOC01-appb-T000002
Figure JPOXMLDOC01-appb-T000002
Figure JPOXMLDOC01-appb-T000003
Figure JPOXMLDOC01-appb-T000003
　この表からわかるとおり、ＣＯＶＩＤ－１９重症患者で発現が増加していた生体パラメータは、炎症関連のパラメータだけではなく、ＣＯＶＩＤ１９特異的肺症状、血管内皮の活性による血栓形成関与のパラメータも増加していることがわかった。 As can be seen from this table, the biological parameters whose expression was increased in COVID-19 severely ill patients increased not only the inflammation-related parameters but also the COVID19-specific lung symptoms and the parameters involved in thrombus formation due to the activity of the vascular endothelium. It turned out that there was.
　（実施例１－２：サイトカイン／ケモカイン／可溶性レセプターアッセイ（２））
　実施例１－１と同様に、Ｂｉｏ－Ｐｌｅｘ　Ｐｒｏ　ヒトサイトカインスクリーニング４８－ＰｌｅｘパネルおよびＢｉｏ－Ｐｌｅｘ　ＰｒｏヒトＩｎｆｌａｍｍａｔｉｏｎ１，３７－Ｐｌｅｘパネルを用いて示される各サイトカイン／ケモカイン／可溶性レセプターの定量を行った。これらのマルチプレックスサイトカインアレイは、製造者の指示に従って測定を行った。データ取得および分析は、Ｂｉｏ－ＰｌｅｘＭａｎａｇｅｒソフトウェアｖｅｒｓｉｏｎ５．０を用いて行った。Ｂｉｏ－Ｐｌｅｘ　Ｐｒｏ　ヒトサイトカインスクリーニング４８－ＰｌｅｘパネルではFGF basic、Eotaxin、G-CSF、GM-CSF、IFN-γ、IL-1β、IL-1ra、IL-1α、IL-2Rα、IL-3、IL-12(p40)、IL-16、IL-2、IL-4、IL-5、IL-6、IL-7、IL-8、IL-9、GRO-α、HGF、IFN-α2、LIF、MCP-3、IL-10、IL-12(p70)、IL-13、IL-15、IL-17、IP-10、MCP-1 (MCAF) 、MIG、β-NGF、SCF、SCGF-β、SDF-1α、MIP-1α、MIP-1β、PDGF-BB、RANTES、TNF-α、VEGF、CTACK、MIF、TRAIL、IL-18、M-CSF、TNF-βが含まれる。Ｂｉｏ－Ｐｌｅｘ　ＰｒｏヒトＩｎｆｌａｍｍａｔｉｏｎ１，３７－ＰｌｅｘパネルではAPRIL/TNFSF13、BAFF/TNFSF13B、sCD30/TNFRSF8、sCD163、Chitinase-3-like1、gp130/sIL-6Rβ、IFN-α2、IFN-β、IFN-γ、IL-2、sIL-6Rα、IL-8、IL-10、IL-11、IL-12 (p40)、IL-12(p70)、IL-19、IL-20、IL-22、IL-26、IL-27 (p28) 、IL-28A / IFN-λ2、IL-29/IFN-λ1、IL-32、IL-34、IL-35、LIGHT/ TNFSF1、MMP-1、MMP-2、MMP-3、Osteocalcin、Osteopontin、Pentraxin-3、sTNF-R1、sTNF-R2、TSLP、TWEAK/TNFSF12が含まれる。 (Example 1-2: Cytokine / chemokine / soluble receptor assay (2))
Similar to Example 1-1, each cytokine / chemokine / soluble receptor shown was quantified using the Bio-Plex Pro Human Cytokine Screening 48-Plex Panel and the Bio-Plex Pro Human Inflammation 1,37-Plex Panel. These multiplex cytokine arrays were measured according to the manufacturer's instructions. Data acquisition and analysis were performed using Bio-Plex Manager software version 5.0. Bio-Plex Pro Human Cytokine Screening 48-Plex panel in FGF basic, Eotaxin, G-CSF, GM-CSF, IFN-γ, IL-1β, IL-1ra, IL-1α, IL-2Rα, IL-3, IL -12 (p40), IL-16, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, GRO-α, HGF, IFN-α2, LIF, MCP-3, IL-10, IL-12 (p70), IL-13, IL-15, IL-17, IP-10, MCP-1 (MCAF), MIG, β-NGF, SCF, SCGF-β, Includes SDF-1α, MIP-1α, MIP-1β, PDGF-BB, RANTES, TNF-α, VEGF, CTACK, MIF, TRAIL, IL-18, M-CSF, TNF-β. Bio-Plex Pro Human Inflammation 1,37-Plex panel APRIL / TNFSF13, BAFF / TNFSF13B, sCD30 / TNFRSF8, sCD163, Chitinase-3-like1, gp130 / sIL-6Rβ, IFN-α2, IFN-β, IFN-γ, IL-2, sIL-6Rα, IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27 (p28), IL-28A / IFN-λ2, IL-29 / IFN-λ1, IL-32, IL-34, IL-35, LIGHT / TNFSF1, MMP-1, MMP-2, MMP-3 , Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK / TNFSF12.
　健常者、ＣＯＶＩＤ－１９の軽症患者、中等症I患者、中等症ＩＩ患者、及び重症患者、並びにインフルエンザ患者のそれぞれについて、各生体パラメータの平均値を算出し、各患者でその発現を比較した。結果を表３に示す。表中、「４８ｐｌｅｘ」は　「Ｉｎｆｌａｍ」はＩｎｆｌａｍｍａｔｉｏｎ１　ｋｉｔに、「４８ｐｌｅｘ」はＢｉｏ－Ｐｌｅｘ　Ｐｒｏ　ヒトサイトカインスクリーニング４８－Ｐｌｅｘパネルに、「Ｉｎｆｌａｍ」はＢｉｏ－Ｐｌｅｘ　ＰｒｏヒトＩｎｆｌａｍｍａｔｉｏｎ１，３７－Ｐｌｅｘパネルに含まれるバイオマーカーである。
Figure JPOXMLDOC01-appb-T000004
Figure JPOXMLDOC01-appb-T000005
 Mean values of each biological parameter were calculated for each of healthy subjects, COVID-19 mild patients, moderate I patients, moderate II patients, and severe patients, and influenza patients, and their expression was compared in each patient. The results are shown in Table 3. In the table, "48 plex" is included in the Inflamation 1 kit, "48 plex" is included in the Bio-Plex Pro human cytokine screening 48-Plex panel, and "Inflam" is included in the Bio-Plex Pro human Inflammation 1,37-Plex panel. It is a biomarker.
Figure JPOXMLDOC01-appb-T000004
Figure JPOXMLDOC01-appb-T000005
　（実施例２：統計解析）
　Ｂｉｏ－Ｐｌｅｘ　２００を用いて得た被験者毎の生体パラメータの発現量を比較した。本実施例においては、健常者およびＣＯＶＩＤ－１９重症患者に加えて、リウマチ患者から得た血清を用いてサイトカイン／ケモカイン／可溶性レセプターを同時に定量した。すべてのパラメータは、より分布が正規化することから、対数変換して用いた。その後、健常者で現れる値の１０～９０％の範囲に含まれる値（緑）と、それ以上の値（赤）と、それ以下の値（薄緑）とに分類した。その結果を図２～５に示した。図２及び５において、「ＣＯＶＩＤ－１９－０」はアクテムラ治療前の患者、「ＣＯＶＩＤ－１９－１ｗ未満」はアクテムラ治療から１～５日前後の患者、「ＣＯＶＩＤ－１９－１ｗ」はアクテムラ治療から７日前後の患者、「ＣＯＶＩＤ－１９－２ｗ」はアクテムラ治療から１０～１４日前後の患者をそれぞれ表す。 (Example 2: statistical analysis)
The expression levels of biological parameters for each subject obtained using Bio-Plex 200 were compared. In this example, cytokines / chemokines / soluble receptors were simultaneously quantified using sera obtained from rheumatoid arthritis patients in addition to healthy subjects and COVID-19 severely ill patients. All parameters were logarithmically converted and used because the distribution is more normalized. After that, the values were classified into values (green) included in the range of 10 to 90% of the values appearing in healthy subjects, higher values (red), and lower values (light green). The results are shown in FIGS. 2 to 5. In FIGS. 2 and 5, "COVID-19-0" is a patient before Actemra treatment, "COVID-19-1w" is a patient about 1 to 5 days after Actemra treatment, and "COVID-19-1w" is Actemra treatment. Patients about 7 days after, and "COVID-19-2w" represent patients about 10 to 14 days after Actemra treatment, respectively.
　以上の結果から、試験した生体パラメータのうち、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、またはＴＳＬＰを用いることによってＣＯＶＩＤ－１９感染症状を予測することができると考えられる。また、その中でも、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、またはＰｅｎｔｒａｘｉｎ－３を用いることで重症ＣＯＶＩＤ－１９感染を極めて高感度に予測することができると考えられる。 From the above results, among the biological parameters tested, IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL- 12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF, APLIL, BAFF, CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27 , IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, or TSLP. It is considered that COVID-19 infection symptoms can be predicted. Among them, IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, Alternatively, it is considered that severe COVID-19 infection can be predicted with extremely high sensitivity by using Pentraxin-3.
　（実施例３：無症候者、軽症者、中等症者の予測）
　鼻粘膜汁ＣＯＶＩＤ－１９陽性者のうち、抗体検査済み呼吸器症状の症状度によるステージ確定者を対象患者として、インフォームドコンセントを取得する。４０名の無症候者：症状なし；４０名の軽症者：軽度症状（咳のみ）、ＰＳＯ２＞＝９６％；４０名の中等症（Ｉ）者：軽度呼吸器症状（息切れ、一部酸素マスク）、９３％＜ＳＯ２＜９６％と＝＜９３％；２０名の中等症（ＩＩ）者：酸素吸入、人工呼吸器管理：肺炎所見（Ｌ型）ＰＳＯ２＝＜９３％；２０名の重症者：ＥＣＭＯ：重症肺炎（Ｈ型）、肺水腫とに分類する。規定の採血管にて採血し、血清分離後分注し凍結保存する。血中ウイルス量をＣＯＶＩＤ－１９特異ＰＣＲにて定量する。血清をＵＶ照射しウイルス不活後、ＢｉｏＲａｄのＢｉｏＰｌｅｘ、Ｍｉｌｌｉｐｏｒｅ、Ｒ＆Ｄ社のサイトカイン測定キットを用いて９０種のバイオ分子を定量測定する。ステージ事にウイルス量、バイオ分子量、抗体有無、臨床所見、加えて過去の治療法を評価項目とし、そこから症状度を予測する評価項目を特定する。
　以上により、重症患者に加えて、無症候者、軽症者、中等症者を予測するための生体パラメータを取得することができ、これを用いてＣＯＶＩＤ－１９感染症状を予測することができると考えられる。 (Example 3: Prediction of asymptomatic, mild, and moderately ill persons)
Informed consent will be obtained for those who are positive for nasal mucosal juice COVID-19 and whose stage is confirmed by the degree of signification of respiratory symptoms that have been tested for antibodies. 40 asymptomatic: no symptoms; 40 mild: mild symptoms (cough only), PSO 2 > = 96%; 40 moderate (I): mild respiratory symptoms (shortness of breath, partial oxygen) Mask), 93% <SO 2 <96% and = <93%; 20 moderate (II) individuals: oxygen inhalation, ventilator management: pneumonia sign (L-type) PSO 2 = <93%; 20 Severely ill: ECMO: Severe pneumonia (type H), pulmonary edema. Blood is collected from the specified blood collection tube, and after serum separation, it is dispensed and stored frozen. The amount of virus in the blood is quantified by COVID-19-specific PCR. After UV irradiation of serum and virus inactivation, 90 kinds of biomolecules are quantitatively measured using BioPlex, Millipore, and R & D cytokine measurement kits of BioRad. For each stage, the viral load, biomolecular weight, presence / absence of antibody, clinical findings, and past treatment methods are used as evaluation items, and the evaluation items for predicting the degree of symptoms are specified.
Based on the above, it is possible to acquire biological parameters for predicting asymptomatic, mild, and moderately ill patients in addition to severely ill patients, and it is considered that COVID-19 infection symptoms can be predicted using these parameters. Be done.
　（実施例４：予測モデルの生成）
　症状度の異なる患者の臨床所見、ウイルス量、バイオ分子量の測定結果を評価項目として、それぞれの症状度を再判定し、機械学習の確率遷移モデル又はＡＩによるディープ・ニューラル・ネットワーク統計解析法を用いて最適な治療薬及び治療法を選択する。 (Example 4: Generation of prediction model)
Using the clinical findings, virus amount, and biomolecular weight measurement results of patients with different symptom levels as evaluation items, each symptom level is re-determined, and a machine learning probabilistic transition model or deep neural network statistical analysis method using AI is used. Select the most suitable therapeutic agent and treatment method.
　解析のモデルを図１に示した。図１の左側の治療経路モデルにおいて、患者の症状や経時的な臨床データを学習する。軽症から継続の場合は、それぞれのステップがつながった症状度予測モデルの制度を改善しつつ治療的要請を予測するモデルとしている。図１の右側が確立状態遷移モデルであり、各ステップが従来からの機械学習モデルを基礎とする組み合わせモデルである。症状度モデルに、初期検査データとして、約７０種のバイオ分子量変動、臨床データ、Ｘ線ＣＴ画像からの特微量血中ウイルス量、ＡＣＥ－Ｒ１アイソタイプなどのデータを節面変数として入力する。症状度は「無症状」、「軽症」、「中等症（Ｉ）」、「中等症（ＩＩ）」、「重症」などのランクに分け、それぞれのランクに該当する確率値として結果を得ることができる。 The analysis model is shown in Fig. 1. In the treatment route model on the left side of FIG. 1, the patient's symptoms and clinical data over time are learned. In the case of continuation from mild illness, the model is to predict the therapeutic request while improving the system of the symptom degree prediction model in which each step is connected. The right side of FIG. 1 is an established state transition model, and each step is a combination model based on a conventional machine learning model. Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data. The degree of symptom is divided into ranks such as "asymptomatic", "mild", "moderate (I)", "moderate (II)", and "severe", and the result is obtained as a probability value corresponding to each rank. Can be done.
　予測モデルの生成においては、ウイルス感染後、症状度ステージ毎の患者の個々の症状度再予測を求めると共に、最適治療法を予測し、いずれの感染ステージにおいても改善、軽快するアルゴリズムを確立する。対象患者は、無症状群（４０名）、軽症（４０名）、中等症（Ｉ）（４０名）、中等症（ＩＩ）（２０名）、重症（２０名）の症状や生体パラメータ等を取得する。２００名未満の場合は統計学的に確立遷移モデルを用い、２００名以上の場合はＡＩのディープマニュアルネットワークによる治療効果予測モデルを用いる。評価候補項目として、患者臨床所見、画像所見、過去の治療歴、抗体の有無、血中ウイルス量、血中バイオ分子などを用いる。呼吸器状態を主とした各ステージの症状度分類を目的変数とする。 In the generation of the prediction model, after virus infection, the patient's individual symptom degree reprediction is obtained for each symptom degree stage, the optimal treatment method is predicted, and an algorithm for improvement and improvement at any infection stage is established. Target patients include asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients) symptoms and biological parameters. get. For less than 200 people, a statistically established transition model is used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network is used. Patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. are used as evaluation candidate items. The objective variable is the symptom classification of each stage, mainly the respiratory condition.
　血中のウイルスはＰＣＲ法、９０項目のバイオ分子測定は実施例１のようなＢｉｏ－Ｐｌｅｘを用いて定量する。症状度ステージ毎に評価候補項目を特定し、再ステージ予測の一致率、症状度予測確立を算出する。予測症状度に従って、ステージ毎の治療を仮定し、治療最適予測を行い、最適治療を適用する。これにより、治療法適合性を予測し、最適治療が行えるようになる。以上の方法によって、いずれのステージにおいても個人毎の最適な治療法を適用し、ウイルスに感染しても症状が軽快し健全な生活が可能になるよう個別毎の治療を確立することができる。 The virus in blood is quantified using the PCR method, and 90 items of biomolecule measurement are quantified using Bio-Plex as in Example 1. The evaluation candidate items are specified for each symptom degree stage, and the concordance rate of the restage prediction and the establishment of the symptom degree prediction are calculated. According to the predicted symptom degree, treatment for each stage is assumed, optimal treatment is predicted, and optimal treatment is applied. This makes it possible to predict the suitability of the treatment method and perform optimal treatment. By the above method, it is possible to apply the optimal treatment method for each individual at any stage, and to establish the treatment for each individual so that the symptoms are alleviated and a healthy life is possible even if infected with the virus.
　臨床データとしては、発熱、鼻汁、咳嗽、倦怠、悪心、嘔吐、下痢、腹部症状などを含む臨床症状、肺Ｘ線写真、肺ＣＴ所見、ｐＯ２、鼻粘膜ウイルス検査、口腔粘膜ウイルス検査、血中ウイルス検査などを含む検査値、ＷＢＣ、Ｎｅｕ、Ｌｙｍ、Ｐｌｔ、Ｈｂ、ＣＲＰ、Ｆｌｂ、Ａｌｂ、ＡＳＴ、ＡＬＴ，ＬＤＨ、ＡＬ－ｐｈ、Ｃｒｅａｔｉｎｉｎ、Ｐｒｏｃａｌｃｉｔｏｎｉｎ、Ｐｒｏｔｈｒｏｍｂｉｎ　ｔｉｍｅ、ＦＤＰ、Ｄ－Ｄ　ｄｉｍｅｒなどの血液検査値、並びにＩｇＧ抗体およびＩｇＭ抗体を含む抗体検査値の中から選択される１または複数の臨床症状、所見、または検査値などの臨床評価変数を用いる。 Clinical data includes clinical symptoms including fever, nasal discharge, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral mucosa virus test, blood. Test values including medium virus test, WBC, Neu, Lym, Plt, Hb, CRP, Flb, Alb, AST, ALT, LDH, AL-ph, Creatinin, Procalcitonin, Prothrombin time, FDP, D-D dimer, etc. Use clinical endpoints such as blood test values and one or more clinical symptoms, findings, or test values selected from antibody test values including IgG and IgM antibodies.
　また予測モデルを生成するための治療履歴、状態履歴情報には、過去の治療履歴（例えば、抗ウイルス薬などの投与履歴）、および対象となる被験体の任意のデータの履歴（例えば、臨床データの過去の履歴）を含み、治療履歴には、酸素吸入の有無、吸入量、期間、及び／または効果、ＥＣＭＯの有無、期間、及び／または効果、ステロイドやアクテムラの点滴の有無、投薬量、期間、及び／または効果、アビガンなどの抗ウイルス薬やヘパリン、ワーファリン、アスピリンなどの抗血栓薬などの内服薬の有無、投薬量、投与期間、及び／または効果の経時的情報を用いる。 In addition, the treatment history and status history information for generating a predictive model include past treatment history (for example, administration history of antiviral drugs and the like), and history of arbitrary data of the target subject (for example, clinical data). The treatment history includes the presence / absence of oxygen inhalation, inhalation amount, duration and / or effect, presence / absence of ECMO, duration and / or effect, presence / absence of infusion of steroids and Actemra, dosage, etc. Use time-course information on duration and / or efficacy, presence / absence of oral medications such as antivirals such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, duration of administration, and / or efficacy.
　（実施例５：予測モデルの利用：ウイルス感染性・症状度予測）
　実施例４で生成される予測モデルを用いて、本実施例では、ウイルス感染性・症状度予測を行う。 (Example 5: Use of prediction model: virus infectivity / symptom degree prediction)
In this example, virus infectivity / symptom degree prediction is performed using the prediction model generated in Example 4.
　得られた予測モデルに対して、被験体から入手する選択される生体パラメータを当てはめ、得られた確率から、無症状、軽症、中等症（Ｉ）、中等症（ＩＩ）、重症などのステージ毎に症状度に分類するか、あるいは、症状度を数値（例えば、１００％あるいは１００点満点で表示）として表示することができる。 The selected biological parameters obtained from the subject are applied to the obtained predictive model, and the obtained probabilities are used for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe. The degree of symptom can be classified into the degree of symptom, or the degree of symptom can be displayed as a numerical value (for example, displayed on a 100% or 100-point scale).
　予測された症状度に基づいて、今後取り得るべき行動計画を立てることができる。
　例えば、ウイルス感染が陽性と出た場合（例えば、ＰＣＲ、ＬＡＭＰ法、あるいは抗原法）でも、本開示の方法により無症状または軽症と判断される場合は、病院ではなく簡易宿泊施設または自宅にて隔離両方を行うという判断を行うことができ、重症化するリスクがあるあるいはその確率が高い被験者に対しては、重症者用の施設（ＩＣＵを含む病院や施設）に配置することができる。あるいは、中等症でとどまると判定される者中等症を収容し得る病院に振り分けることができる。 Based on the predicted degree of symptoms, it is possible to formulate an action plan that should be taken in the future.
For example, even if the virus infection is positive (for example, PCR, LAMP method, or antigen method), if it is judged to be asymptomatic or mild by the method of the present disclosure, it is not in the hospital but in a simple accommodation facility or at home. It is possible to make a decision to perform both isolation, and for subjects who are at risk of becoming severe or have a high probability of becoming severe, they can be placed in facilities for the severely ill (hospitals and facilities including ICU). Alternatively, those who are determined to remain in moderate illness can be assigned to a hospital that can accommodate moderate illness.
　また、症状度を参考にして、当該被験者に対して、主治医がその時点で判明しているまたは利用可能な適切な治療を選択し、施すことができる。 Also, with reference to the degree of symptoms, the attending physician can select and administer appropriate treatment that is known or available at that time to the subject.
　（実施例６：予測モデルの利用：治療適合性および治療結果予測）
　実施例４で生成される予測モデルを用いて、本実施例では、治療適合性および治療結果予測を行う。 (Example 6: Use of prediction model: treatment suitability and treatment result prediction)
Using the prediction model generated in Example 4, treatment suitability and treatment result prediction are performed in this example.
　得られた予測モデルに対して、被験体から入手する選択される生体パラメータを当てはめ、治療法適合性およびまたは治療結果予測に関する項目を算出することができる。 The selected bioparameters obtained from the subject can be applied to the obtained prediction model to calculate items related to treatment suitability and / or treatment result prediction.
　例えば、ある治療剤（例えば、レムデシビル、アクテムラ、ステロイド剤等）あるいは治療法（ＥＣＭＯ，酸素吸入療法等）について、適合性を数値で表現することができる。このような治療法適合性については、数値で表すことができ、あるいは文言で表現することができる。 For example, the compatibility of a certain therapeutic agent (eg, remdesivir, actemra, steroid agent, etc.) or therapeutic method (ECMO, oxygen inhalation therapy, etc.) can be expressed numerically. Such therapeutic suitability can be expressed numerically or verbal.
　別の例としては、治療剤（例えば、レムデシビル、アクテムラ、ステロイド剤等）あるいは治療法（ＥＣＭＯ，酸素吸入療法等）について、治療結果を算出することもできる。治療結果は、例えば、ＹＥＳ－ＮＯすなわち、奏功するかしないか、あるいは、改善度合い（例えば、症状度が治療前から治療後にどのように変化するか、あるいはどのような経過を取るか）を算出することができる。 As another example, the treatment result can be calculated for a therapeutic agent (for example, remdesivir, actemra, steroid agent, etc.) or a therapeutic method (ECMO, oxygen inhalation therapy, etc.). The treatment result is, for example, YES-NO, that is, whether it responds or not, or the degree of improvement (for example, how the degree of symptom changes from before treatment to after treatment, or how it takes) is calculated. can do.
　単剤あるいは単治療に対しての予測を行うこともできるし、併用した場合の結果を予測することができる。 It is possible to make predictions for single agents or single treatments, and it is possible to predict the results when used in combination.
　これらを、手持ちまたは利用可能な治療またはその組み合わせに対して予測モデルを適用して改善を比較して、その中から最適な手法を選択することができる。 Predictive models can be applied to these on hand or available treatments or combinations thereof to compare improvements and select the optimal method from among them.
　（実施例７：自習モデル）
　実施例４でいったん生成した予測モデルは、実際に利用しながら、改善することができる。 (Example 7: Self-study model)
The prediction model once generated in Example 4 can be improved while actually using it.
　このような自習モデルは、当該分野で公知の任意の手法（機械学習等）を用いて、改善を常に行うモデルを構築することができる。 For such a self-study model, it is possible to construct a model that constantly improves by using an arbitrary method (machine learning, etc.) known in the field.
　これらのモデルは、特定の病院内でのクローズド環境で行ってもよく、オープンとして施設外のデータを取り込んでビッグデータとして取り込んで学習してもよい。その際には、取り込むべきデータについて、データを規格化して機械学習させることができる。 These models may be performed in a closed environment in a specific hospital, or may be learned by taking in data outside the facility as open and taking in as big data. In that case, the data to be captured can be standardized and machine-learned.
　予測モデル改善を行う機能が備わっていてもよい。１つの実施形態では、予測精度が最も高くなる最適なパラメータを選ぶことは、例えば、ＭＡＴＬＡＢのＬＡＳＳＯ関数、ＳＶＭ関数等の当該分野において公知の任意のものを利用することができることで実現することができる。 It may be equipped with a function to improve the predictive model. In one embodiment, selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can.
　（実施例８：アクテムラおよびアビガン治療における生体パラメータの変動）
　ＣＯＶＩＤ－１９感染患者における生体パラメータが治療前後においてどのような変動を示すのかを確認するため、ＣＯＶＩＤ－１９感染重症患者から得た血清を用いて、以下の表に示す生体パラメータの治療前後での変動を調べた。 (Example 8: Changes in biological parameters in Actemra and Avigan treatment)
In order to confirm how the biological parameters in COVID-19 infected patients change before and after treatment, serum obtained from severely infected COVIDEO-19 infected patients was used before and after treatment of the biological parameters shown in the table below. I investigated the fluctuation.
Figure JPOXMLDOC01-appb-T000006
Figure JPOXMLDOC01-appb-T000006
Figure JPOXMLDOC01-appb-T000007
Figure JPOXMLDOC01-appb-T000007
　表中、治療前に「上昇」、「低下」、「正常」とあるのは、治療前において健常者と比較して、各生体パラメータの発現が、それぞれ上昇していたか、低下していたか、または同等であったかを示す。また治療後に「減少」、「不変」、「増加」とあるのは、治療後に、各生体パラメータの発現がそれぞれ減少したか、変わらなかったか、または増加したのかを示す。 In the table, "increased", "decreased", and "normal" before the treatment indicate whether the expression of each biological parameter was increased or decreased, respectively, as compared with the healthy subject before the treatment. Or it indicates whether it was equivalent. Further, "decreased", "unchanged", and "increased" after the treatment indicate whether the expression of each biological parameter decreased, did not change, or increased after the treatment, respectively.
　以上の結果からわかるとおり、ＣＯＶＩＤ－１９感染重症患者では、治療前にはＩＬ－６、ＩＰ－１０、ＩＬ－１９、Ｐｅｎｔｒａｘｉｎ－３、ＢＡＦＦ、ＡＰＲＩＬ、ＴＮＦ－α、ＭＭＰ－１、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－１７、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＦＧＦ、ＩＦＮ－γ、ＰＤＧＦ、ＩＦＮ－α２、ＶＥＧＦ、ＣＤ３０、ＩＦＮ－β、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＴＮＦ－αＲ１、ＴＳＬＰ、ＩＬ－７、２７ｐ　ＩＦＮ－γ、２７ｐ　ＩＬ－２、２７ｐ　ＩＬ－８、２７ｐ　ＩＬ－１０、ＩＬ－２６、ＩＬ－８、ＭＩＰ－１α、およびＴＮＦ－αＲ２が上昇しており、ＭＩＰ－１βおよびＯｓｔｅｏｃａｌｃｉｎが低下しており、ＩＬ－１β、Ｏｓｔｅｏｐｏｎｔｉｎ、ＭＣＰ－１、ＩＬ－６Ｒ、Ｇ－ＣＳＦ、ＣＤ１６３、ｅｏｔａｘｉｎ、ＭＭＰ－２、ＭＭＰ－３、ＩＬ－４、ＩＬ－５、ＩＬ－９、ＧＭ－ＣＳＦ、ＲＡＮＴＥＳ、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、およびＴＷＥＡＫが健常者と同等であることがわかった。 As can be seen from the above results, in severely ill patients with COVID-19 infection, IL-6, IP-10, IL-19, Pentraxin-3, BAFF, APRIL, TNF-α, MMP-1, IL-1Ra before treatment. , IL-2, IL-17, IL-10, IL-12, IL-13, IL-15, FGF, IFN-γ, PDGF, IFN-α2, VEGF, CD30, IFN-β, IL-11, IL -20, IL-22, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, TNF-αR1, TSLP, IL-7, 27p IFN-γ, 27p IL -2, 27p IL-8, 27p IL-10, IL-26, IL-8, MIP-1α, and TNF-αR2 are increasing, MIP-1β and Osteocalcin are decreasing, IL-1β, Osteopontin, MCP-1, IL-6R, G-CSF, CD163, eotaxin, MMP-2, MMP-3, IL-4, IL-5, IL-9, GM-CSF, RANTES, Chitinase-3, gp130, It was found that IL-12 (p40), IL-12 (p70), and TWEAK were equivalent to healthy subjects.
　またアクテムラおよびアビガンで治療すると、ＩＬ－６、ＩＰ－１０、ＩＬ－１９、Ｐｅｎｔｒａｘｉｎ－３、ＢＡＦＦ、ＡＰＲＩＬ、ＩＬ－１β、Ｏｓｔｅｏｐｏｎｔｉｎ、およびＭＣＰ－１はその発現量が減少し、ＡＰＲＩＬ、ＴＮＦ－α、ＭＭＰ－１、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－１７、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＦＧＦ、ＩＦＮ－γ、ＰＤＧＦ、ＩＦＮ－α２、ＶＥＧＦ、ＣＤ３０、ＩＦＮ－β、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＴＮＦ－αＲ１、ＴＳＬＰ、ＩＬ－７、２７ｐ　ＩＦＮ－γ、２７ｐ　ＩＬ－２、２７ｐ　ＩＬ－８、２７ｐ　ＩＬ－１０、ＩＬ－２６、ＭＩＰ－１β、Ｏｓｔｅｏｃａｌｃｉｎ、ｅｏｔａｘｉｎ、ＭＭＰ－２、ＭＭＰ－３、ＩＬ－４、ＩＬ－５、ＩＬ－９、ＧＭ－ＣＳＦ、ＲＡＮＴＥＳ、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、およびＴＷＥＡＫはその発現量は変わらず、ＩＬ－８、ＭＩＰ－１α、ＴＮＦ－αＲ２、ＩＬ－６Ｒ、Ｇ－ＣＳＦ、およびＣＤ１６３はその発現量が増加したことがわかった。 Treatment with Actemra and Avigan reduced the expression levels of IL-6, IP-10, IL-19, Pentraxin-3, BAFF, APRIL, IL-1β, Osteopontin, and MCP-1, and APRIL, TNF- α, MMP-1, IL-1Ra, IL-2, IL-17, IL-10, IL-12, IL-13, IL-15, FGF, IFN-γ, PDGF, IFN-α2, VEGF, CD30, IFN-β, IL-11, IL-20, IL-22, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, TNF-αR1, TSLP, IL- 7,27p IFN-γ, 27p IL-2, 27p IL-8, 27p IL-10, IL-26, MIP-1β, Osteocalcin, eotaxin, MMP-2, MMP-3, IL-4, IL-5, The expression levels of IL-9, GM-CSF, RANTES, Chitinase-3, gp130, IL-12 (p40), IL-12 (p70), and TWEAK did not change, and IL-8, MIP-1α, TNF- It was found that the expression levels of αR2, IL-6R, G-CSF, and CD163 were increased.
　（実施例９：トシリズマブまたはデクスメデトミジン治療における生体パラメータの変動）
　ＣＯＶＩＤ－１９感染患者における生体パラメータがトシリズマブ（ＴＣＺ）またはデクスメデトミジン（ＤＸ）の治療前後においてどのような変動を示すのかを確認するため、ＣＯＶＩＤ－１９感染重症患者から得た血清を用いて、種々の生体パラメータの治療前後での変動を調べた。結果を図６～２２に示す。生体パラメータとしては、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、およびＴＷＥＡＫを用いた。 (Example 9: Changes in biological parameters in treatment with tocilizumab or dexmedetomidine)
In order to confirm how the biological parameters in COVID-19 infected patients fluctuate before and after treatment with tocilizumab (TCZ) or dexmedetomidine (DX), various serums obtained from severely infected COVID-19 infected patients were used. The changes in the biological parameters of Tocilizumab before and after treatment were investigated. The results are shown in FIGS. 6 to 22. Bioparameters include IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130. , IL12 (p40), IL-6Ra, IL-10, and TWEAK.
　これらの図からわかるとおり、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－ａ２などは治療後にその発現量が低下し、ｓｇｐ１３０、ＩＬ－６Ｒａ、ＴＷＥＡＫなどは治療後にその発現量が上昇した。 As can be seen from these figures, the expression levels of IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-a2, etc. decreased after treatment, and the expression levels of sgp130, IL-6Ra, TWEAK, etc. decreased after treatment. The amount has risen.
　（注記）
　以上のように、本開示の好ましい実施形態を用いて本開示を例示してきたが、本開示は、特許請求の範囲によってのみ、その範囲が解釈されるべきであることが理解される。本明細書において引用した特許、特許出願および他の文献は、その内容自体が具体的に本明細書に記載されているのと同様に、その内容が本明細書に対する参考として援用されるべきであることが理解される。本願は、日本国特許庁に２０２０年８月７日に出願された特願２０２０－１３５１７８に対して優先権主張をするものであり、その内容はその全体があたかも本願の内容を構成するのと同様に参考として援用される。 (Note)
As described above, the present disclosure has been exemplified by using the preferred embodiments of the present disclosure, but it is understood that the scope of the present disclosure should be interpreted only by the scope of claims. The patents, patent applications and other documents cited herein should be incorporated herein by reference in their content, just as the content itself is specifically described herein. It is understood that there is. This application claims priority to Japanese Patent Application No. 2020-135178 filed with the Japan Patent Office on August 7, 2020, and the content thereof constitutes the content of the present application as a whole. It is also used as a reference.
　本開示は、ウイルス感染患者の症状度を個別に予測し、その症状度に応じた最適な治療法を適用することによって重症患者をなくす技術を提供し、医薬品産業において利用可能である。 This disclosure provides a technique for individually predicting the degree of symptom of a virus-infected patient and applying an optimal treatment method according to the degree of symptom to eliminate severely ill patients, and is available in the pharmaceutical industry.

Claims (100)

  1. 　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法であって、
    　前記被験者における１または複数の生体パラメータを得る工程と、
    　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程と
    を含む、方法。     A method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
    The step of obtaining one or more biological parameters in the subject, and
    A method comprising predicting the viral infection symptom, therapeutic suitability, and / or treatment outcome based on the biological parameters.
  2. 　前記ウイルス感染症状、治療法適合性、および／または治療結果が、ウイルス感染症状であり、
    　前記予測する工程は、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、ウイルス感染症状を予測するための指標とする工程を含む、請求項１に記載の方法。     The viral infection symptom, treatment suitability, and / or treatment result is a viral infection symptom.
    The method according to claim 1, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
  3. 　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、請求項１または２に記載の方法。 The method according to claim 1 or 2, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  4. 　前記生体パラメータは、炎症性サイトカインではない少なくとも１種のマーカーを含む、請求項１～３のいずれか一項に記載の方法。 The method according to any one of claims 1 to 3, wherein the biological parameter contains at least one marker that is not an inflammatory cytokine.
  5. 　前記生体パラメータは、遺伝子産物（タンパク質）である、請求項１～４のいずれか一項に記載の方法。 The method according to any one of claims 1 to 4, wherein the biological parameter is a gene product (protein).
  6. 　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、請求項１～５のいずれか一項に記載の方法。 The method according to any one of claims 1 to 5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  7. 　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、請求項１～６のいずれか一項に記載の方法。 13. the method of.
  8. 　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、請求項１～７のいずれか一項に記載の方法。 The method according to any one of claims 1 to 7, wherein the virus infection is SARS-CoV-2 infection.
  9. 　前記症状の予測が症状度の予測を含む、請求項１～８のいずれか一項に記載の方法。 The method according to any one of claims 1 to 8, wherein the prediction of the symptom includes a prediction of the degree of symptom.
  10. 　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、請求項１～９のいずれか一項に記載の方法。 The method according to any one of claims 1 to 9, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
  11. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、ＶＣＡＭ－１、ＩＬ－１α、ＩＬ－２Ｒα、ＩＬ－３、ＩＬ－１６、ＧＲＯ－α、ＭＣＰ－３、ＭＩＧ、β－ＮＧＦ、ＳＣＦ、ＳＣＧＦ－β、ＳＤＦ－１α、ＣＴＡＣＫ、ＭＩＦ、Ｍ－ＣＳＦ、及びＴＮＦ－βからなる群から選択されるパラメータを含む、請求項１～１０のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, VCAM-1, IL-1α, IL-2Rα, IL-3. , IL-16, GRO-α, MCP-3, MIG, β-NGF, SCF, SCGF-β, SDF-1α, CTACK, MIF, M-CSF, and TNF-β. The method according to any one of claims 1 to 10, including.
  12. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The method according to any one of claims 1 to 11.
  13. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、請求項１～１２のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL. -13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF, APRIL, BAFF , CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL- Selected from the group consisting of 28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of claims 1 to 12, which comprises the above-mentioned parameters.
  14. 　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、請求項１～１３のいずれか一項に記載の方法。 The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, The method according to any one of claims 1 to 13, comprising a parameter selected from the group consisting of MMP-1 and Pentraxin-3.
  15. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-13. , IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL-28A, IL -8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and any one of claims 1-11, comprising parameters selected from the group consisting of LIGHT. The method described in the section.
  16. 　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-β. , IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Leptin, OncostatinM, and VCAM- The method of any one of claims 1-11, comprising a parameter selected from the group consisting of 1.
  17. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3. , Gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL -28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-SELect , IL-18, Leptin, OncostatinM, and VCAM-1, the method according to any one of claims 1 to 11.
  18. 　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10. , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32. , IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP -2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, Oncostin , And the method of any one of claims 1-11, comprising a parameter selected from the group consisting of VCAM-1.
  19. 　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、ＭＣＰ－３、Ｍ－ＣＳＦ、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＴＲＡＩＬ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ａ２、ＩＦＮ－ｇ、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２０、ＩＬ－２２、ＩＬ－２８Ａ、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF -B, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method of any one of claims 1-11, comprising a parameter selected from the group consisting of TWEAK.
  20. 　前記１または複数の生体パラメータが、ＣＴＡＣＫ、ＧＭ－ＣＳＦ、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１７Ａ、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＳＣＧＦ－ｂ、ＳＤＦ－１ａ、ＢＡＦＦ、ｓＣＤ３０、ｓＣＤ１６３、Ｃｈｉｔｉｎａｓｅ　３－ｌｉｋｅ　１、ＩＦＮ－ｇ、ＩＬ－６Ｒａ、ＩＬ－２０、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. The method according to any one of claims 1 to 11.
  21. 　前記１または複数の生体パラメータが、ＨＧＦ、ＩＦＮ－ａ２、ＩＬ－１ｒａ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＩＬ－１８、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、Ｍ－ＣＳＦ、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、ＩＦＮ－ａ２、ＩＬ－２、ＩＬ－６Ｒａ、ＩＬ－１０、ＩＬ－１１、ＩＬ－２８Ａ、ＩＬ－３４、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL 6. Any of claims 1-11, comprising parameters selected from the group consisting of -6Ra, IL-10, IL-11, IL-28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. The method described in paragraph 1.
  22. 　前記１または複数の生体パラメータが、ＨＧＦ、ＩＬ－２Ｒａ、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１２（ｐ４０）、ＩＬ－１６、ＭＣＰ－１（ＭＣＡＦ）、ＭＩＧ、ｂ－ＮＧＦ、ＰＤＧＦ－ｂｂ、ＢＡＦＦ、ｓＣＤ３０、ＩＬ－６Ｒａ、ＩＬ－３４、ｓＴＮＦ－Ｒ１、及びｓＴＮＦ－Ｒ２からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. The method described in.
  23. 　前記１または複数の生体パラメータが、ＭＣＰ－３、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method of any one of claims 1-11, comprising a parameter selected from the group consisting of.
  24. 　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method of any one of claims 1-11, comprising the selected parameter.
  25. 　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１ｒａ、ＩＬ－６、ＩＰ－１０、ＭＣＰ－１（ＭＣＡＦ）、TNF-b、ＡＰＲＩＬ、ＢＡＦＦ、ｓＣＤ３０、IFN-a2、IFN-ｂ、ＩＬ－１２（ｐ４０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２８Ａ、ＩＬ－２９、及びＩＬ－３５からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APLIL, BAFF, sCD30, IFN-a2, IFN-b, The invention according to any one of claims 1 to 11, which comprises a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35. the method of.
  26. 　前記１または複数の生体パラメータが、ＩＦＮ－ｇ、ＩＬ－１２（ｐ４０）、ＩＬ－２０、ＩＬ－３２、ＩＬ－３５、ＭＭＰ－１、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、ＴＳＬＰ、及びＴＷＥＡＫからなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK. The method of any one of claims 1-11, comprising the selected parameter.
  27. 　前記１または複数の生体パラメータが、ＩＬ－６、ＩＬ－１Ｒａ、ＩＰ－１０、ＢＡＦＦ、ＡＰＲＩＬ、ＶＣＡＭ－１、ＩＦＮ－２８Ａ、ＩＬ－２９、ＩＦＮ－ａ２、ＩＦＮ－ｂ、ＩＦＮ－ｇ、ＴＮＦ－ａ、ｓｇｐ１３０、ＩＬ１２（ｐ４０）、ＩＬ－６Ｒａ、ＩＬ－１０、ＴＷＥＡＫ、及びＩＬ－８からなる群から選択されるパラメータを含む、請求項１～１１のいずれか一項に記載の方法。 The one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF. The method of any one of claims 1-11, comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  28. 　前記基準値が健常者における前記生体パラメータの値である、請求項２～２７のいずれか一項に記載の方法。 The method according to any one of claims 2 to 27, wherein the reference value is a value of the biological parameter in a healthy person.
  29. 　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、請求項１～２８のいずれか一項に記載の方法。 The method according to any one of claims 1 to 28, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  30. 　被験者においてウイルス感染を予防または治療する方法であって、
    　前記被験者における１または複数の生体パラメータを得る工程と、
    　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する工程と
    　前記ウイルス感染症状、治療法適合性、および／または治療結果の予測に基づいて、前記被験者を予防または治療する工程と
    を含む、方法。     A method of preventing or treating a viral infection in a subject.
    The step of obtaining one or more biological parameters in the subject, and
    The subject based on the step of predicting the virus infection symptom, treatment suitability, and / or treatment result based on the biological parameters and the prediction of the virus infection symptom, treatment suitability, and / or treatment result. A method that includes the steps of preventing or treating.
  31. 　前記予測する工程は、前記生体パラメータを基準値と比較することで前記生体パラメータを分析し、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測するための指標とする工程を含み、
    　前記予防または治療する工程は、前記被験者が重症患者と予測された場合に、前記被験者に治療薬を投与する工程を含む、請求項３０に記載の方法。     The predictive step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting the virus infection symptom, treatment suitability, and / or treatment result. ,
    30. The method of claim 30, wherein the prophylactic or therapeutic step comprises the step of administering a therapeutic agent to the subject when the subject is predicted to be a critically ill patient.
  32. 　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、請求項３０または３１に記載の方法。 The method of claim 30 or 31, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  33. 　前記炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含む、請求項３０～３２のいずれか一項に記載の方法。 The method according to any one of claims 30 to 32, wherein the inflammatory marker contains at least one marker that is not an inflammatory cytokine.
  34. 　前記生体パラメータは、遺伝子産物（タンパク質）である、請求項３０～３３のいずれか一項に記載の方法。 The method according to any one of claims 30 to 33, wherein the biological parameter is a gene product (protein).
  35. 　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、請求項３０～３４のいずれか一項に記載の方法。 The method according to any one of claims 30 to 34, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  36. 　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、請求項３０～３５のいずれか一項に記載の方法。 30-35 according to any one of claims 30 to 35, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. the method of.
  37. 　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、請求項３０～３６のいずれか一項に記載の方法。 The method according to any one of claims 30 to 36, wherein the virus infection is SARS-CoV-2 infection.
  38. 　前記治療薬が、レムデシビル、ファビピラビル、シクレソニド、ナファモスタット、カモスタット、イベルメクチン、ステロイド剤、トシリズマブ、サリルマブ、トファシチニブ、バリシチニブ、ルキソリチニブ、アカラブルチニブ、ラブリズマブ、エリトラン、イブジラスト、ＬＹ３１２７８０４、オチリマブ、ＨＬＣＭ０５１、及びＡＤＲ－００１からなる群から選択される１または複数の薬剤である、請求項３０～３７のいずれか一項に記載の方法。 The therapeutic agents include remdesivir, favipiravir, ciclesonide, nafamostat, camostat, ivermectin, steroids, tocilizumab, sarilumab, tofacitinib, baricitinib, ruxolitinib, acalabrutinib, rubrismab, erythran, ibudilast 30. The method of any one of claims 30-37, which is one or more agents selected from the group.
  39. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃ　ＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項３０～３８のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF- bb, RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (P70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF Contains parameters selected from the group consisting of Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostinM, and VCAM-1. , The method according to any one of claims 30 to 38.
  40. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、請求項３０～３９のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL. -13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF, APRIL, BAFF , CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL- Selected from the group consisting of 28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of claims 30 to 39, which comprises the above-mentioned parameters.
  41. 　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、請求項３０～４０のいずれか一項に記載の方法。 The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, The method of any one of claims 30-40, comprising a parameter selected from the group consisting of MMP-1 and Pentraxin-3.
  42. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、請求項３０～３９のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-13. , IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL-28A, IL -8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and any one of claims 30-39 comprising parameters selected from the group consisting of LIGHT. The method described in the section.
  43. 　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項３０～３９のいずれか一項に記載の方法。 The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-β. , IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Leptin, OncostatinM, and VCAM- The method of any one of claims 30-39, comprising a parameter selected from the group consisting of 1.
  44. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項３０～３９のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3. , Gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL -28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-SELect The method of any one of claims 30-39, comprising a parameter selected from the group consisting of IL-18, Leptin, OncostatinM, and VCAM-1.
  45. 　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項３０～３９のいずれか一項に記載の方法。 The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10. , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32. , IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP -2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, Oncostin , And the method of any one of claims 30-39, comprising a parameter selected from the group consisting of VCAM-1.
  46. 　前記基準値が健常者における前記生体パラメータの値である、請求項３１～４５のいずれか一項に記載の方法。 The method according to any one of claims 31 to 45, wherein the reference value is a value of the biological parameter in a healthy person.
  47. 　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、請求項３０～４６のいずれか一項に記載の方法。 The method according to any one of claims 30 to 46, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  48. 　被験者における１または複数の生体パラメータを、前記被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための指標とする方法。 A method in which one or more biological parameters in a subject are used as an index for predicting the viral infection symptom, treatment suitability, and / or treatment result of the subject.
  49. 　前記被験者における１または複数の生体パラメータを得る工程を含む、請求項４８に記載の方法。 The method of claim 48, comprising the step of obtaining one or more biological parameters in the subject.
  50. 　前記１または複数の生体パラメータが、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）を含む、請求項４８または４９に記載の方法。 The one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (identifiable by SNP, etc.), etc.). 28. The method of claim 48 or 49, including.
  51. 前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項４８～５０のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The method according to any one of claims 48 to 50.
  52. 　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、請求項４８～５１のいずれか一項に記載の方法。 The method according to any one of claims 48 to 51, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  53. 　前記生体パラメータは、炎症性サイトカインではない少なくとも１種のマーカーを含む、請求項４８～５２のいずれか一項に記載の方法。 The method according to any one of claims 48 to 52, wherein the biological parameter comprises at least one marker that is not an inflammatory cytokine.
  54. 　前記生体パラメータは、遺伝子産物（タンパク質）である、請求項４８～５３のいずれか一項に記載の方法。 The method according to any one of claims 48 to 53, wherein the biological parameter is a gene product (protein).
  55. 　さらに、前記生体パラメータを基準値と比較することで前記生体パラメータを分析する工程を含む、請求項４８～５４のいずれか一項に記載の方法。 The method according to any one of claims 48 to 54, further comprising a step of analyzing the biological parameter by comparing the biological parameter with a reference value.
  56. 　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、請求項４８～５５のいずれか一項に記載の方法。 The method according to any one of claims 48 to 55, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  57. 　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、請求項４８～５６のいずれか一項に記載の方法。 The invention according to any one of claims 48 to 56, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. the method of.
  58. 　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、請求項４８～５７のいずれか一項に記載の方法。 The method according to any one of claims 48 to 57, wherein the virus infection is SARS-CoV-2 infection.
  59. 　前記症状の予測が症状度の予測を含む、請求項４８～５８のいずれか一項に記載の方法。 The method according to any one of claims 48 to 58, wherein the symptom prediction includes a symptom degree prediction.
  60. 　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、請求項４８～５９のいずれか一項に記載の方法。 The method according to any one of claims 48 to 59, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
  61. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、請求項４８～６０のいずれか一項に記載の方法。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL. -13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF, APRIL, BAFF , CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL- Selected from the group consisting of 28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The method according to any one of claims 48 to 60, which comprises the above-mentioned parameters.
  62. 　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、請求項４８～６１のいずれか一項に記載の方法。 The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, The method of any one of claims 48-61, comprising a parameter selected from the group consisting of MMP-1 and Pentraxin-3.
  63. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、請求項４８～６０のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-13. , IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL-28A, IL -8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and any one of claims 48-60, comprising parameters selected from the group consisting of LIGHT. The method described in the section.
  64. 　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項４８～６０のいずれか一項に記載の方法。 The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-β. , IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Leptin, OncostatinM, and VCAM- The method of any one of claims 48-60, comprising a parameter selected from the group consisting of 1.
  65. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項４８～６０のいずれか一項に記載の方法。 The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3. , Gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL -28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-SELect The method of any one of claims 48-60, comprising a parameter selected from the group consisting of IL-18, Leptin, OncostatinM, and VCAM-1.
  66. 　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択されるパラメータを含む、請求項４８～６０のいずれか一項に記載の方法。 The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10. , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32. , IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP -2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, Oncostin , And the method of any one of claims 48-60, comprising a parameter selected from the group consisting of VCAM-1.
  67. 　前記基準値が健常者における前記生体パラメータの値である、請求項５５～６６のいずれか一項に記載の方法。 The method according to any one of claims 55 to 66, wherein the reference value is a value of the biological parameter in a healthy person.
  68. 　前記１または複数の生体パラメータが前記被験者の末梢血に由来する、請求項４８～６７のいずれか一項に記載の方法。 The method according to any one of claims 48 to 67, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  69. 　１または複数の生体パラメータを含む、被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための生体マーカー。 A biomarker for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, including one or more bioparameters.
  70. 　前記１または複数の生体パラメータが、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）を含む、請求項６９に記載の生体マーカー。 The one or more bioparameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (identifiable by SNP, etc.), etc.). 69. The biomarker of claim 69.
  71. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、請求項６９または７０に記載の生体マーカー。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. Or the biomarker according to 70.
  72. 　前記生体パラメータは、少なくとも１種のサイトカイン類および少なくとも１種の炎症性マーカーを含む、請求項６９～７１のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 71, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  73. 　前記炎症性マーカーは、炎症性サイトカインではない少なくとも１種のマーカーを含む、請求項６９～７２のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 72, wherein the inflammatory marker contains at least one marker that is not an inflammatory cytokine.
  74. 　前記生体パラメータは、遺伝子産物（タンパク質）である、請求項６９～７３のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 73, wherein the biological parameter is a gene product (protein).
  75. 　前記ウイルス感染がコロナウイルス科に属するウイルス感染である、請求項６９～７４のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 74, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  76. 　前記ウイルス感染がＨＣｏＶ－ＨＫＵ１、ＨＣｏＶ－ＯＣ４３、ＳＡＲＳ－ＣｏＶ、ＭＥＲＳ－ＣｏＶ、およびＳＡＲＳ－ＣｏＶ－２からなる群から選択されるウイルス感染である、請求項６９～７５のいずれか一項に記載の生体マーカー。 13. Biomarker.
  77. 　前記ウイルス感染がＳＡＲＳ－ＣｏＶ－２感染である、請求項６９～７６のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 76, wherein the virus infection is SARS-CoV-2 infection.
  78. 　前記症状の予測が症状度の予測を含む、請求項６９～７７のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 77, wherein the prediction of the symptom includes a prediction of the degree of symptom.
  79. 　前記症状の予測が、前記被験者のウイルス感染症状が重症化するかどうかの予測を含む、請求項６９～７８のいずれか一項に記載の生体マーカー。 The biological marker according to any one of claims 69 to 78, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
  80. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＰＤＧＦ－ｂｂ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、およびＴＳＬＰからなる群から選択されるパラメータを含む、請求項６９～７９のいずれか一項に記載の生体マーカー。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL. -13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, PDGF-bb, TNF-α, VEGF, APRIL, BAFF , CD30, CD163, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL- Selected from the group consisting of 28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP. The biomarker according to any one of claims 69 to 79, comprising the parameters.
  81. 　前記１または複数の生体パラメータが、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－７、ＩＬ－８、ＩＬ－１０、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃ　ＦＧＦ、ＩＰ－１０、ＴＮＦ－α、ＢＡＦＦ、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－１１、ＩＬ－１９、ＩＬ－２０、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、およびＰｅｎｔｒａｘｉｎ－３からなる群から選択されるパラメータを含む、請求項６９～８０のいずれか一項に記載の生体マーカー。 The one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-α, BAFF, IFN-α2, IFN-β, IFN-γ, IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, The biomarker according to any one of claims 69-80, comprising a parameter selected from the group consisting of MMP-1 and Pentraxin-3.
  82. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ＩＬ－２２、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＴＮＦ－α、ＧＭ－ＣＳＦ、ＩＬ－１Ｒａ、ＩＦＮ－α２、ＩＦＮ－γ、ＩＬ－２８Ａ、ＩＬ－８、ＩＰ－１０、ＭＣＰ－１、ｂａｓｉｃ　ＦＧＦ、ＶＥＧＦ、ＶＣＡＭ－１、ＣＤ３０、ＢＡＦＦ、Ｐｅｎｔｒａｘｉｎ－３、およびＬＩＧＨＴからなる群から選択されるパラメータを含む、請求項６９～７９のいずれか一項に記載の生体マーカー。 The one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-13. , IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF-α, GM-CSF, IL-1Ra, IFN-α2, IFN-γ, IL-28A, IL -8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and any one of claims 69-79 comprising parameters selected from the group consisting of LIGHT. The biomarker described in the section.
  83. 　前記１または複数の生体パラメータが、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＩＬ－１５、ｂａｓｉｃＦＧＦ、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、請求項６９～７９のいずれか一項に記載の生体マーカー。 The one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-β. , IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29. , IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, Leptin, OncostatinM, and VCAM- The biomarker according to any one of claims 69 to 79, selected from the group consisting of 1.
  84. 　前記１または複数の生体パラメータが、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－１２、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＲＡＮＴＥＳ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、請求項６９～７９のいずれか一項に記載の生体マーカー。 The one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1α, MIP-1β, RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3. , Gp130, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL -28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-SELect , IL-18, Leptin, OncostatinM, and VCAM-1, the biomarker of any one of claims 69-79.
  85. 　前記１または複数の生体パラメータが、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＦＮ－γ、ＩＬ－２、ＩＬ－６Ｒα、ＩＬ－８、ＩＬ－１０、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、請求項６９～７９のいずれか一項に記載の生体マーカー。 The one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IFN-γ, IL-2, IL-6Rα, IL-8, IL-10. , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32. , IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP -2, CD40Ligand, CX3CL1, HGF, IFN-γR1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, Oncostin , And the biomarker of any one of claims 69-79, selected from the group consisting of VCAM-1.
  86. 　請求項１～８５のいずれか一項に記載の方法に使用するための試薬、キット、もしくはデバイス、またはそれらの組み合わせ。 A reagent, kit, or device for use in the method according to any one of claims 1 to 85, or a combination thereof.
  87. 被験者の１または複数の生体パラメータからのウイルス感染症状、治療法適合性、および／または治療結果の予測に使用するための試薬、キット、もしくはデバイス、またはそれらの組み合わせ。 Reagents, kits, or devices, or combinations thereof, for use in predicting viral infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
  88. 前記生体パラメータを測定するための手段、薬剤またはデバイスを含む、請求項８６または８７に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。 The reagent, kit, or device, or combination thereof, according to claim 86 or 87, comprising means, agents or devices for measuring said bioparameters.
  89. 前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を行う計算ユニットをさらに備える、請求項８６～８８のいずれか一項に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。 The reagent, kit according to any one of claims 86-88, further comprising a calculation unit that performs calculations for predicting the viral infection symptoms, treatment suitability, and / or treatment results based on the biological parameters. , Or devices, or a combination thereof.
  90. 　前記１または複数の生体パラメータが、ＩＬ－１β、ＩＬ－１Ｒａ、ＩＬ－２、ＩＬ－４、ＩＬ－５、ＩＬ－６、ＩＬ－７、ＩＬ－８、ＩＬ－９、ＩＬ－１０、ＩＬ－１２、ＩＬ－１３、ＩＬ－１５、ＩＬ－１７、ｂａｓｉｃＦＧＦ、ｅｏｔａｘｉｎ、Ｇ－ＣＳＦ、ＧＭ－ＣＳＦ、ＩＦＮ－γ、ＩＰ－１０、ＭＣＰ－１、ＭＩＰ－１α、ＭＩＰ－１β、ＰＤＧＦ－ｂｂ、ＲＡＮＴＥＳ、ＴＮＦ－α、ＶＥＧＦ、ＡＰＲＩＬ、ＢＡＦＦ、ＣＤ３０、ＣＤ１６３、Ｃｈｉｔｉｎａｓｅ－３、ｇｐ１３０、ＩＦＮ－α２、ＩＦＮ－β、ＩＬ－６Ｒα、ＩＬ－１１、ＩＬ－１２（ｐ４０）、ＩＬ－１２（ｐ７０）、ＩＬ－１９、ＩＬ－２０、ＩＬ－２２、ＩＬ－２６、ＩＬ－２７、ＩＬ－２８Ａ、ＩＬ－２９、ＩＬ－３２、ＩＬ－３４、ＩＬ－３５、ＬＩＧＨＴ、ＭＭＰ－１、ＭＭＰ－２、ＭＭＰ－３、Ｏｓｔｅｏｃａｌｃｉｎ、Ｏｓｔｅｏｐｏｎｔｉｎ、Ｐｅｎｔｒａｘｉｎ－３、ｓＴＮＦ－Ｒ１、ｓＴＮＦ－Ｒ２、ＴＳＬＰ、ＴＷＥＡＫ、ＡＤＡＭＴＳ１３、Ａｎｇｉｏｐｏｉｅｔｉｎ－２、ＢＭＰ－２、ＣＤ４０Ｌｉｇａｎｄ、ＣＸ３ＣＬ１、ＨＧＦ、ＩＦＮ－γＲ１、Ｌ－Ｓｅｌｅｃｔｉｎ、ＬＩＦ、ＴＲＡＩＬ、ＶＥＧＦＲ２／ＫＤＲ、Ａｇｇｒｅｃａｎ、Ｂ７Ｈ１／ＰＤＬ１、ＣＤ４０、ＣＤ４４、Ｅ－Ｓｅｌｅｃｔｉｎ、ＩＣＡＭ－１、ＩＬ－１８、Ｌｅｐｔｉｎ、ＯｎｃｏｓｔａｔｉｎＭ、及びＶＣＡＭ－１からなる群から選択される、請求項８６～８９のいずれか一項に記載の試薬、キット、もしくはデバイス、またはそれらの組み合わせ。 The one or more biological parameters are IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-bb , RANTES, TNF-α, VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN-α2, IFN-β, IL-6Rα, IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin-2, BMP-2, CD40Ligand, CX3CL1, HGF1L , LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18, Leptin, OncostatinM, and VCAM-1. The reagent, kit, or device according to any one of 89 to, or a combination thereof.
  91. 　前記予測する工程は、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力する工程を含む、請求項１に記載の方法。 The method according to claim 1, wherein the prediction step includes a step of applying the biological parameters to a predetermined prediction model and outputting a probability regarding virus infection symptom, treatment suitability, and / or prediction of treatment result.
  92. 　前記生体パラメータは、生体分子パラメータ、臨床データ、および体内ウイルスの量および／または種類（血中ウイルス量、ウイルスの変異体の種類（ＳＮＰなどで特定可能。）などを含む）からなる群より選択される少なくとも１つに関する情報を含み、
    　前記予測モデルは、生体分子パラメータ、臨床データ、履歴情報および治療法情報からなる群より選択される少なくとも１つに基づいて作出されたものである、請求項９１に記載の方法。     The bioparameters are selected from the group consisting of biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (specified by SNP, etc.), etc.). Contains information about at least one that is
    The method of claim 91, wherein the predictive model was created based on at least one selected from the group consisting of biomolecular parameters, clinical data, historical information and treatment information.
  93. 　前記生体パラメータは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、血中ウイルス量、およびＡＣＲ－Ｒ１アイソタイプからなる群より選択される少なくとも１つに関する情報を含み、
    　前記予測モデルは、サイトカイン量またはその変動、臨床データ、Ｘ線ＣＴ画像、治療履歴、治療効果情報、安全性情報、および状態履歴情報からなる群より選択される少なくとも１つに基づいて作出されたものである、請求項９１または９２に記載の方法。     The bioparameters include information about at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotypes.
    The predictive model was created based on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment effect information, safety information, and status history information. The method of claim 91 or 92.
  94. 　前記予測モデルは、強化学習またはニューラルネットワークに基づいて生成されたものである、請求項９１～９３のいずれか一項に記載の方法。 The method according to any one of claims 91 to 93, wherein the prediction model is generated based on reinforcement learning or a neural network.
  95. 　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムであって、前記方法は、
    　前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程と、
    　前記コンピュータに、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力させる工程と
    　を含む、プログラム。     A computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, said method.
    A step of causing the computer to obtain one or more biological parameters in the subject.
    A program comprising the steps of having the computer apply the biological parameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  96. 　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法の処理をコンピュータに実行させるコンピュータプログラムを格納する記録媒体であって、前記方法は、
    　前記コンピュータに、前記被験者における１または複数の生体パラメータを得させる工程と、
    　前記コンピュータに、前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力させる工程と
    　を含む、記録媒体。     A recording medium containing a computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
    A step of causing the computer to obtain one or more biological parameters in the subject.
    A recording medium comprising the steps of causing the computer to apply the biological parameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  97. 　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するためのシステムであって、
    　前記被験者における１または複数の生体パラメータを得る手段と、
    　前記生体パラメータを所定の予測モデルに当てはめてウイルス感染症状、治療法適合性、および／または治療結果の予測に関する確率を出力する手段と
    　を含む、システム。     A system for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
    A means for obtaining one or more biological parameters in the subject,
    A system comprising the means of applying the biological parameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  98. 被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法をコンピュータに実行させるコンピュータプログラムであって、前記方法は、
    　前記コンピュータに、前記被験者における１または複数の生体パラメータを入力する工程と、
    　前記コンピュータに、前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行させる工程と
    を含む、プログラム。     A computer program that causes a computer to perform a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, said method.
    The step of inputting one or more biological parameters in the subject into the computer,
    A program comprising the steps of causing the computer to perform calculations to predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
  99. 被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するための方法をコンピュータに実行させるコンピュータプログラムを格納する記録媒体であって、前記方法は、
    　前記コンピュータに、前記被験者における１または複数の生体パラメータを入力する工程と、
    　前記コンピュータに、前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行させる工程と
    を含む、記録媒体。     A recording medium containing a computer program that causes a computer to execute a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, said method.
    The step of inputting one or more biological parameters in the subject into the computer,
    A recording medium comprising a step of causing the computer to perform a calculation for predicting the viral infection symptom, treatment suitability, and / or treatment outcome based on the biological parameters.
  100. 　被験者のウイルス感染症状、治療法適合性、および／または治療結果を予測するためのシステムであって、
    　前記被験者における１または複数の生体パラメータを入力する手段と、
    　前記生体パラメータに基づいて、前記ウイルス感染症状、治療法適合性、および／または治療結果を予測する計算を実行する手段と
    を含む、システム。     A system for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
    A means for inputting one or more biological parameters in the subject,
    A system comprising the means of performing calculations to predict said viral infection symptoms, treatment suitability, and / or treatment outcomes based on said bioparameters.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006524996A (en) * 2003-05-02 2006-11-09 ホンコン・ディエヌエイ・チップス・リミテッド Nucleic acid detection
JP2013516400A (en) * 2009-12-30 2013-05-13 パラファーム カンパニー リミテッド Materials and methods for prevention and treatment of viral infections
JP2020507746A (en) * 2017-01-08 2020-03-12 ザ ヘンリー エム．ジャクソン ファンデーション フォー ザ アドバンスメント オブ ミリタリー メディシン，インコーポレーテッド Systems and methods for using supervised learning to predict subject-specific bacteremia transcription
JP6933834B1 (en) * 2020-06-18 2021-09-08 国立研究開発法人国立国際医療研究センター Test method for aggravation risk of new coronavirus infected person, its test kit, companion diagnostic drug and its aggravation risk marker

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006524996A (en) * 2003-05-02 2006-11-09 ホンコン・ディエヌエイ・チップス・リミテッド Nucleic acid detection
JP2013516400A (en) * 2009-12-30 2013-05-13 パラファーム カンパニー リミテッド Materials and methods for prevention and treatment of viral infections
JP2020507746A (en) * 2017-01-08 2020-03-12 ザ ヘンリー エム．ジャクソン ファンデーション フォー ザ アドバンスメント オブ ミリタリー メディシン，インコーポレーテッド Systems and methods for using supervised learning to predict subject-specific bacteremia transcription
JP6933834B1 (en) * 2020-06-18 2021-09-08 国立研究開発法人国立国際医療研究センター Test method for aggravation risk of new coronavirus infected person, its test kit, companion diagnostic drug and its aggravation risk marker

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MANDEL MATHILDA; HARARI GIL; GUREVICH MICHAEL; ACHIRON ANAT: "Cytokine prediction of mortality in COVID19 patients", CYTOKINE, ACADEMIC PRESS LTD, PHILADELPHIA, PA, US, vol. 134, 10 July 2020 (2020-07-10), US , XP086244100, ISSN: 1043-4666, DOI: 10.1016/j.cyto.2020.155190 *
RUSINOVICH O., MORA A., MUÑEZ E., DELGADO TELLEZ DE CEPEDA L., DE LA TORRE N., PAVÍA M., SANZ J., ESPINOSA M., ANDRÉU SÁNCHEZ J. L: "POS1218 SAFETY AND EFFICACY OF ANAKINRA IN SEVERE SARS-COV2 INFECTION (COVID19) AT A TERTIARY HOSPITAL", ANNALS OF THE RHEUMATIC DISEASES, BRITISH MEDICAL ASSOCIATION, GB, vol. 80, no. Suppl 1, 1 June 2021 (2021-06-01), GB , pages 892.2 - 893, XP055894664, ISSN: 0003-4967, DOI: 10.1136/annrheumdis-2021-eular.2590 *
SKEVAKI CHRYSANTHI; FRAGKOU PARASKEVI C.; CHENG CHONGSHENG; XIE MIN; RENZ HARALD: "Laboratory characteristics of patients infected with the novel SARS-CoV-2 virus", JOURNAL OF INFECTION., ACADEMIC PRESS, LONDON., GB, vol. 81, no. 2, 21 June 2020 (2020-06-21), GB , pages 205 - 212, XP086228509, ISSN: 0163-4453, DOI: 10.1016/j.jinf.2020.06.039 *

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