JP2021169991A - Method for assisting in prediction of exacerbation of respiratory infection, method for monitoring measured value of biomarker, reagent kit used for these methods, and device and computer program to assist in predicting exacerbation of respiratory infection - Google Patents

Abstract

To provide a new method for prediction of exacerbation of a respiratory infection.SOLUTION: A method for assisting in prediction of exacerbation of respiratory infection comprises measuring a biomarker in a specimen collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, where the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9, and a measured value of the biomarker is used as an index to predict exacerbation of the respiratory infection.SELECTED DRAWING: Figure 5A

Description

The present invention relates to methods that assist in predicting the severity of respiratory infections. The present invention relates to a method of monitoring biomarker measurements. The present invention relates to reagent kits used in these methods. The present invention relates to devices and computer programs that assist in predicting the severity of respiratory infections.

Patients with respiratory infections are often mild and do not require hospitalization, but some patients become severe and need to be hospitalized for treatment. Since it is necessary to give priority to treatment of severely ill patients, there is a great need for a means for predicting the aggravation of respiratory infections.

Japanese Patent No. 6081699

In view of the above circumstances, the present inventors have attempted to search for a biomarker that can predict the severity of the disease. It is an object of the present invention to use such biomarkers to provide new means for predicting the severity of respiratory infections.

Patent Document 1 describes that IL-28B in the serum of patients with chronic hepatitis C was detected by a method for specifically measuring IL (Interleukin) -28B (also called IFNλ3), which is a kind of cytokine. ing. However, Patent Document 1 does not describe that IFNλ3 was measured in order to predict the aggravation of respiratory infections.

The present inventors have IFNλ3 (Interferonλ3), CCL17 (CC chemokine ligand 17), CXCL11 (CXC chemokine ligand 11), IP-10 (Interferon-inducible Protein-10), IL-6 and CXCL9 (CXC chemokine ligand 9). However, they found that it could be used as a biomarker for predicting the aggravation of chemokines, and completed the invention. Therefore, the present invention includes a step of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and the biomarkers are IFNλ3, CCL17, Respiratory tract infection, which is at least one selected from the group consisting of CXCL11, IP-10, IL-6 and CXCL9, and whose biomarker measurement value is used as an index for predicting the severity of respiratory tract infection. Provide methods to assist in predicting the severity of the disease.

The present invention provides a method of monitoring the measured values of biomarkers in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection. This method involves obtaining biomarker measurements using samples taken from a subject at multiple time points, where the biomarkers are IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9. It is at least one selected from the group consisting of, and the measured value is used as an index for predicting the severity of respiratory tract infection.

The present invention relates to a step of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and a respiratory organ based on the measured value of the biomarker. For respiratory infections, the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9, including the step of predicting the severity of the infection. Provide a method to assist in predicting aggravation.

The present invention provides a reagent kit used in the above method, which comprises a reagent containing a substance that can specifically bind to a biomarker.

The present invention comprises a processor and a computer including a memory under the control of the processor, in which the memory is in a specimen taken from a subject suffering from or suspected of having a respiratory infection. A computer program is recorded for causing the computer to execute a step of calculating a biomarker measurement value and a step of outputting a biomarker measurement value, and the biomarker measurement value is a serious condition of respiratory infection. Prediction of aggravation of respiratory infections, including at least one biomarker selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9 Provide a device to assist.

The present invention provides a computer program to assist in predicting the severity of respiratory infections. This computer program is recorded on a computer-readable medium and calculates biomarker measurements in a sample taken from a subject with or suspected of having a respiratory infection. Have the computer perform the steps and the steps to output the biomarker measurements. The biomarker comprises at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9.

According to the present invention, it is possible to assist the subject in predicting the severity of respiratory infections.

It is the schematic which shows an example of the reagent kit of this embodiment. It is the schematic which shows an example of the reagent kit of this embodiment. It is the schematic which shows an example of the reagent kit of this embodiment. It is the schematic which shows an example of the reagent kit of this embodiment. It is the schematic which shows an example of the apparatus of this embodiment. It is a block diagram which shows the hardware configuration of the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a flowchart which shows the processing procedure by the apparatus of this embodiment. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the CXCL11 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the IP-10 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the IL-6 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the CXCL9 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 7. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 11. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 5. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 9. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 12. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 4. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 8. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of case No. 10. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of the H group. It is a graph which shows the transition of the IFNλ3 concentration in the serum of the patient of the L group. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of the H group. It is a graph which shows the transition of the CCL17 concentration in the serum of the patient of the L group. It is a graph which plotted the IFNλ3 concentration in the serum of the patient of the H group and the L group. It is a graph which plotted the CCL17 concentration in the serum of the H group and L group patients. It is a ROC (receiver operating characteristic) curve when the severity of respiratory tract infection is judged based on the IFNλ3 concentration. It is an ROC curve when the severity of respiratory tract infection is judged based on the CCL17 concentration. It is a Kaplan-Meier curve showing the relationship between the period after admission and the event-free survival rate in the group of patients whose IFNλ3 concentration is equal to or higher than the cutoff value and the group of patients whose IFNλ3 concentration is lower than the cutoff value. It is a Kaplan-Meier curve showing the relationship between the period after admission and the event-free survival rate in the group of patients whose CCL17 concentration is higher than the cutoff value and the group of patients whose CCL17 concentration is lower than the cutoff value.

In the method of assisting the prediction of the aggravation of respiratory tract infections of the present embodiment (hereinafter, also referred to as “prediction method”), first, as biomarkers in a sample collected from a subject, IFNλ3, CCL17, CXCL11, Measure at least one selected from the group consisting of IP-10, IL-6 and CXCL9. In the prediction method of the present embodiment, the measured value of the biomarker serves as an index for predicting the severity of the respiratory tract infection.

Respiratory tract infections refer to diseases caused by infection of respiratory organs such as the nasal cavity, pharynx, trachea, bronchi and alveoli with pathogens. The pathogen is not particularly limited, and examples thereof include viruses, bacteria, fungi, and parasites. Examples of the virus include coronavirus and influenza virus. The coronavirus is not particularly limited, and examples thereof include α-coronavirus, β-coronavirus, γ-coronavirus, and δ-coronavirus. Examples of β-coronavirus include SARS-CoV-2, SARS-CoV, HCoV-OC43, HCoV-HKU1, Bat SL-CoV-WIV1, BtCoV-HKU4, BtCoV-HKU5, MERS-CoV and BtCoV-HKU9. Can be mentioned.

The subjects in the present embodiment include a patient suffering from a respiratory tract infection and a person suspected of having a respiratory tract infection. A patient suffering from a respiratory tract infection is a person whose infection has been confirmed due to the detection of a pathogen and the respiratory tract infection has not yet become severe. Suspected respiratory infections include, for example, cold symptoms such as fever, cough, nasal discharge, and sore throat and / or prescribed respiratory infections such as breathlessness, shortness of breath during exertion, and abnormal taste and smell. Includes those who have specific symptoms, those who have come into contact with or suspected of having a respiratory infection. Contact with a patient with a respiratory infection includes, for example, talking with the patient within 1 m, staying in a closed space where the patient is present, and splashing saliva, coughing, etc. of the patient. An act such as taking a bath.

In one embodiment, aggravation of a respiratory infection refers to pneumonia requiring oxygen inhalation or a condition requiring intensive care management, including mechanical ventilation management.

The sample is not particularly limited as long as it is a liquid sample collected from a subject and suspected of containing the above biomarker. Examples of such liquid samples include blood samples, cerebrospinal fluid, sputum, bronchoalveolar lavage fluid, nasopharyngeal swab, lymph fluid, urine, stool, saliva and the like. Of these, blood samples are preferred. Blood samples include whole blood, plasma and serum, with plasma and serum being particularly preferred.

When the sample contains insoluble contaminants such as cells, the contaminants may be removed from the sample by known means such as centrifugation and filtration. In addition, the sample may be diluted with a suitable aqueous medium, if necessary. Such an aqueous medium is not particularly limited as long as it does not interfere with the measurement described later, and examples thereof include water, physiological saline, and a buffer solution. The buffer solution is not particularly limited as long as it has a buffering action at a pH near neutral (for example, a pH of 6 or more and 8 or less). Such buffers include, for example, Good's buffers such as HEPES, MES, Tris, PIPES, phosphate buffered saline (PBS) and the like.

The biomarker measured by the predictive method of this embodiment is one or more protein molecules selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9. IFNλ3, also called IL-28B, is a protein consisting of 200 amino acids encoded by a gene of about 1.5 Kb located on chromosome 19. IFNλ3 has 25 signal peptides at the N-terminus, and the signal peptide is cleaved when IFNλ3 is secreted extracellularly. CCL17 is also called TARC (Thymus- and activation-regulated chemokine) and is a type of Th2 type chemokine. CXCL11 is a chemokine, also called I-TAC (Interferon-inducible T-cell Chemoattractant), and is a ligand for the CXCR3 receptor. IP-10, also called CXCL10, is a type of Th1 type chemokine. IL-6 is a type of TH2 type cytokine. CXCL9, also called MIG (Monokine induced by interferon γ), is a ligand for the CXCR3 receptor and is a type of Th1 type chemokine like IP-10. These protein molecules themselves are known, and their amino acid sequences can be obtained from known databases such as NCBI (National Center for Biotechnology Information). For example, IFNλ3 may have the amino acid sequence represented by SEQ ID NO: 1 or may have the amino acid sequence represented by SEQ ID NO: 2.

In this embodiment, it is possible to obtain measurements of two or more biomarkers selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9 from the viewpoint of improving the accuracy of prediction of aggravation. preferable. Two or more biomarkers include, for example, any combination of:
--A combination of IFNλ3 and at least one selected from the group consisting of CCL17, CXCL11, IP-10, IL-6 and CXCL9;
--CCL17 in combination with at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9;
--A combination of CXCL11 and at least one selected from the group consisting of IFNλ3, CCL17, IP-10, IL-6 and CXCL9;
--A combination of IP-10 and at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IL-6 and CXCL9;
--A combination of IL-6 with at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10 and CXCL9; and
--A combination of CXCL9 and at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10 and IL-6.

In this embodiment, it is particularly preferable to obtain the measured value of IFNλ3 and the measured value of CCL17.

A known method can be used for the measurement of the biomarker, and the measurement is not particularly limited. In the present embodiment, a method of capturing the marker using a substance that can specifically bind to the biomarker is preferable. By detecting the biomarker captured by such a substance by a method known in the art, the biomarker contained in the sample can be measured. The measured value of the biomarker can be a value that reflects the amount or concentration in the sample. Further, the measured value may be a concentration or a value reflecting the concentration calculated based on the measurement result of the calibrator. Here, the "value reflecting the concentration" depends on the type of the labeling substance described later, and examples thereof include a measured value of fluorescence intensity, a measured value of emission intensity, and a measured value of radioactivity.

Examples of the substance capable of specifically binding to the biomarker include an antibody and an aptamer, and among them, an antibody is particularly preferable. The antibody against the biomarker is not particularly limited as long as it is an antibody that can specifically bind to the biomarker. Such antibodies may be monoclonal antibodies, polyclonal antibodies, and fragments thereof (eg, Fab, F (ab') 2, etc.). Moreover, you may use a commercially available antibody.

As an antibody that can specifically bind to IFNλ3, for example, a monoclonal antibody having a heavy chain variable region domain and a light chain variable region domain or a fragment thereof according to any one of (1) to (5) below is used. May be good.

(1) Heavy chain complementarity determining regions 1 containing the amino acid sequences 31 to 35 in the amino acid sequence represented by SEQ ID NO: 3, heavy chain complementarity determining regions 2 containing the amino acid sequences 50 to 66, and Heavy chain complementarity determining regions 3 containing amino acid sequences 99 to 113, and light chain complementarity including amino acid sequences 24 to 38 in the amino acid sequence represented by SEQ ID NO: 4. Determining regions 1, light chain complementarity determining regions 2 containing amino acid sequences 54-60, and light chain variable region domains containing light chain complementarity determining regions 3 containing amino acid sequences 93-101;
(2) Heavy chain complementarity determining regions 1 containing the amino acid sequences 31 to 35 in the amino acid sequence represented by SEQ ID NO: 5, heavy chain complementarity determining regions 2 containing the amino acid sequences 50 to 66, and Heavy chain complementarity determining regions 3 containing the amino acid sequences 99 to 109, and light chain complementarity including the amino acid sequences 24 to 38 in the amino acid sequence represented by SEQ ID NO: 6. Determining regions 1, light chain complementarity determining regions 2 containing amino acid sequences 54-60, and light chain variable region domains containing light chain complementarity determining regions 3 containing amino acid sequences 93-101;
(3) Heavy chain complementarity determining regions 1 containing the amino acid sequences 31 to 35 in the amino acid sequence represented by SEQ ID NO: 7, heavy chain complementarity determining regions 2 containing the amino acid sequences 50 to 66, and Heavy chain complementarity determining regions 3 containing amino acid sequences 99-106, and light chain complementarity including amino acid sequences 24-38 in the amino acid sequence represented by SEQ ID NO: 8. Determining regions 1, light chain complementarity determining regions 2 containing amino acid sequences 54-60, and light chain variable region domains containing light chain complementarity determining regions 3 containing amino acid sequences 93-101;
(4) Heavy chain complementarity determining regions 1 containing the amino acid sequences 31 to 35 in the amino acid sequence represented by SEQ ID NO: 9, heavy chain complementarity determining regions 2 containing the amino acid sequences 50 to 66, and Heavy chain complementarity determining regions 3 containing amino acid sequences 99-106, and light chain complementarity including amino acid sequences 24-38 in the amino acid sequence represented by SEQ ID NO: 10. Light chain complementary region domains containing determining regions 1, light chain complementarity determining regions 2 containing amino acid sequences 54-60, and light chain complementarity determining regions 3 containing amino acid sequences 93-101; or
(5) Heavy chain complementarity determining regions 1 containing the amino acid sequences 31 to 35 in the amino acid sequence represented by SEQ ID NO: 11, heavy chain complementarity determining regions 2 containing the amino acid sequences 50 to 66, and Heavy chain complementarity determining regions 3 containing amino acid sequences 99-106, and light chain complementarity including amino acid sequences 24-38 in the amino acid sequence represented by SEQ ID NO: 12. Finding regions 1, light chain complementarity determining regions 2 containing amino acid sequences 54-60, and light chain variable regions comprising light chain complementarity determining regions 3 containing amino acid sequences 93-101.

The method for measuring the biomarker using the antibody is not particularly limited, and can be appropriately selected from known immunoassays. In the present embodiment, the enzyme-linked immunosorbent assay (ELISA method) is preferable, and the sandwich ELISA method is particularly preferable. As an example of the measurement process, the case of measuring the biomarker in the sample by the sandwich ELISA method will be described below.

First, a complex containing a biomarker, an antibody for capturing the biomarker (hereinafter, also referred to as “capture antibody”), and an antibody for detecting the biomarker (hereinafter, also referred to as “detection antibody”). Form the body on a solid phase. When the sample contains a biomarker, a complex can be formed by mixing the sample, a capture antibody, and a detection antibody. Then, the above-mentioned complex can be formed on the solid phase by contacting the solution containing the complex with the solid phase on which the capture antibody can be immobilized. Alternatively, a solid phase in which the capture antibody is immobilized in advance may be used. That is, the above complex can be formed on the solid phase by contacting the solid phase on which the capture antibody is immobilized, the sample, and the detection antibody. When both the capture antibody and the detection antibody are monoclonal antibodies, it is preferable that their epitopes are different from each other.

The mode of fixing the capture antibody to the solid phase is not particularly limited. For example, the capture antibody and the solid phase may be directly bound, or the capture antibody and the solid phase may be indirectly bound via another substance. Examples of the direct bond include physical adsorption. Examples of the indirect bond include a bond via a combination of biotins and avidins. In this case, by modifying the capture antibody with biotin in advance and binding the avidins to the solid phase in advance, the capture antibody and the solid phase are indirectly bound to each other through the binding between the biotins and the avidins. Can be combined with. Biotins include biotin and biotin analogs such as desthiobiotin. Avidins include avidins and analogs of avidins such as streptavidin and tamavidin®.

The material of the solid phase is not particularly limited, and can be selected from, for example, an organic polymer compound, an inorganic compound, a biopolymer and the like. Examples of the organic polymer compound include latex, polystyrene, polypropylene and the like. Examples of the inorganic compound include magnetic substances (iron oxide, chromium oxide, ferrite, etc.), silica, alumina, glass, and the like. Examples of the biopolymer include insoluble agarose, insoluble dextran, gelatin, cellulose and the like. Two or more of these may be used in combination. The shape of the solid phase is not particularly limited, and examples thereof include particles, membranes, microplates, microtubes, and test tubes. Among them, particles are preferable, and magnetic particles are particularly preferable.

In the present embodiment, even if B / F (Bound / Free) separation for removing unreacted free components that do not form a complex is performed between the complex formation step and the complex detection step. good. The unreacted free component refers to a component that does not form a complex. For example, capture antibody and detection antibody that did not bind to the biomarker can be mentioned. The means for B / F separation is not particularly limited, but if the solid phase is particles, B / F separation can be performed by recovering only the solid phase in which the complex is captured by centrifugation. If the solid phase is a container such as a microplate or microtube, B / F separation can be performed by removing the liquid containing unreacted free components. When the solid phase is magnetic particles, B / F separation can be performed by suctioning and removing the liquid containing unreacted free components with a nozzle while the magnetic particles are magnetically constrained by a magnet, from the viewpoint of automation. preferable. After removing the unreacted free components, the solid phase trapped in the complex may be washed with a suitable aqueous medium such as PBS.

Then, by detecting the complex formed on the solid phase by a method known in the art, the measured value of the biomarker contained in the sample can be obtained. For example, when an antibody labeled with a labeling substance is used as the detection antibody, the measured value of the marker in the liquid sample can be obtained by detecting the signal generated by the labeling substance. Alternatively, when a labeled secondary antibody against the detection antibody is used, the measured value of the biomarker in the liquid sample can be obtained in the same manner.

Further, as an example of the method for measuring a biomarker using an antibody, the immune complex transfer method described in JP-A 1-254868 can also be used.

As used herein, "detecting a signal" includes qualitatively detecting the presence or absence of a signal, quantifying the signal intensity, and semi-quantitatively detecting the signal intensity. Semi-quantitative detection means that the signal intensity is indicated stepwise, such as "no signal generated", "weak", "medium", and "strong". In this embodiment, it is preferable to detect the signal intensity quantitatively or semi-quantitatively.

The labeling substance is not particularly limited. The labeling substance may be, for example, a substance that generates a signal by itself (hereinafter, also referred to as a “signal generating substance”), or a substance that catalyzes the reaction of another substance to generate a signal. good. Examples of the signal generating substance include a fluorescent substance and a radioisotope. Examples of substances that catalyze the reaction of other substances to generate a detectable signal include enzymes. Examples of the enzyme include alkaline phosphatase, peroxidase, β-galactosidase, luciferase and the like. Examples of the fluorescent substance include fluorescent dyes such as fluorescein isothiocyanate (FITC), rhodamine and Alexa Fluor (registered trademark), and fluorescent proteins such as GFP. Radioisotopes include ¹²⁵ I, ¹⁴ C, ³² P and the like. Among them, enzymes are preferable as labeling substances, and alkaline phosphatase and peroxidase are particularly preferable.

The method itself for detecting a signal is known in the art. In the present embodiment, the measuring method according to the type of the signal derived from the above-mentioned labeling substance may be appropriately selected. For example, when the labeling substance is an enzyme, signals such as light and color generated by reacting a substrate with the enzyme can be measured by using a known device such as a spectrophotometer.

The substrate of the enzyme can be appropriately selected from known substrates according to the type of the enzyme. For example, when alkaline phosphatase (ALP) is used as the enzyme, CDP-Star® (4-chloro-3- (methoxyspiro [1, 2-dioxetane-3, 2'-(5'-chloro)) is used as the substrate. ) Trixilo [3. 3. 1. 13, 7] decane] -4-yl) disodium phenylphosphate), CSPD® (registered trademark) (3- (4-methoxyspiro [1, 2-dioxetane-3, 2) -Chemical luminescent substrates such as (5'-chloro) tricyclo [3. 3. 1. 13, 7] decane] -4-yl) disodium phenylphosphate), 5-bromo-4-chloro-3-indolyl Color-developing substrates such as phosphoric acid (BCIP), 5-bromo-6-chloro-indrill phosphate disodium, and p-nitrophenyl phosphate can be mentioned. When peroxidase is used as the enzyme, the substrate is a chemiluminescent substrate such as luminol and its derivatives, 2, 2'-azinobis (3-ethylbenzothiazolin-6-ammonium sulfonate) (ABTS), 1, 2- Coloring substrates such as phenylenediamine (OPD), 3, 3', 5, 5'-tetramethylbenzidine (TMB) can be mentioned.

When the labeling substance is a radioisotope, the radiation as a signal can be measured using a known device such as a scintillation counter. When the labeling substance is a fluorescent substance, the fluorescence as a signal can be measured using a known device such as a fluorescent microplate reader. The excitation wavelength and the fluorescence wavelength can be appropriately determined according to the type of the fluorescent substance used.

The signal detection result can be used as a measured value of a biomarker. For example, when the signal intensity is quantitatively detected, the measured value of the signal intensity itself or the value obtained from the measured value can be used as the measured value of the biomarker. Examples of the value obtained from the measured value of the signal intensity include a value obtained by subtracting the measured value of the negative control sample or the background value from the measured value, and a value obtained by applying the measured value to the calibration curve. Be done. The negative control sample can be appropriately selected, and examples thereof include a sample obtained from a mildly ill patient (for example, an infectious disease patient who has recovered without becoming severe), a sample obtained from a healthy person, and the like.

In this embodiment, it is preferable to measure the biomarker contained in the sample by an immunoassay such as an EIA method or an ELISA method. Biomarkers can be measured using commercially available devices and reagents such as the HISCL series (manufactured by Sysmex Corporation) and the Bio-Plex Multiplex System (manufactured by Bio-Rad).

In the present embodiment, the measured values of the above-mentioned biomarkers can be used as an index indicating whether or not the respiratory tract infection of the subject becomes severe. For example, by comparing the acquired biomarker measurement value with the threshold value corresponding to the biomarker, the biomarker measurement value is likely to aggravate the respiratory infection of the subject, or It may be an index suggesting that it is low. In the present embodiment, the possibility that the respiratory tract infection becomes severe is determined by the respiratory tract infection of the subject after a lapse of a predetermined period (for example, 1 day to 1 month) from the date when the sample is collected from the subject. There is a risk that the disease will become more severe.

As shown in the examples below, IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 were high in patients with severe respiratory infections and patients with mild respiratory infections. Showed a low value in. The measurements of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 can be used as indicators of the potential for aggravation of respiratory infections by comparison with the thresholds corresponding to each biomarker. In one embodiment, it is suggested that a subject's respiratory infection is likely to become more severe if the biomarker contains IFNλ3 and the measured value of IFNλ3 is greater than or equal to the threshold corresponding to IFNλ3. .. When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it is suggested that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, it is suggested that a subject's respiratory infection is likely to become more severe if the biomarker contains CXCL11 and the measured value of CXCL11 is greater than or equal to the threshold corresponding to CXCL11. .. When the CXCL11 reading is lower than the threshold corresponding to CXCL11, it is suggested that the subject's respiratory infection is less likely to become severe.

In one embodiment, the subject's respiratory infection may be exacerbated if the biomarker contains IP-10 and the measured value of IP-10 is greater than or equal to the threshold corresponding to IP-10. It is suggested that it is high. It is suggested that a subject's respiratory infection is less likely to become severe if the IP-10 reading is below the threshold corresponding to IP-10.

In one embodiment, the subject's respiratory infection may be exacerbated if the biomarker contains IL-6 and the measured value of IL-6 is greater than or equal to the threshold corresponding to IL-6. It is suggested that it is high. When the IL-6 reading is lower than the threshold corresponding to IL-6, it is suggested that the subject's respiratory infection is less likely to become severe.

In one embodiment, it is suggested that a subject's respiratory infection is likely to become more severe if the biomarker contains CXCL9 and the measured value of CXCL9 is greater than or equal to the threshold corresponding to CXCL9. If the CXCL9 reading is lower than the threshold corresponding to CXCL9, it is suggested that the subject's respiratory infection is less likely to become severe.

In one embodiment, the biomarker comprises at least two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and their measurements indicate that the subject has a respiratory infection. A risk of aggravation may be suggested. Specifically, when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker, the respiratory infection of the subject is likely to become severe. Is suggested. When all the measurements of the biomarkers selected from the group are lower than the threshold value corresponding to each biomarker, it is suggested that the respiratory infection of the subject is unlikely to become severe.

As an example, if the biomarkers are two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and either or both of them are above the corresponding threshold. It is suggested that the subject's respiratory tract infection is likely to become more severe. In another example, if the biomarkers are three selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and at least one of them is above the corresponding threshold, then the subject It is suggested that the examiner's respiratory infection is likely to become more severe. In another example, if the biomarkers are four selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and at least one of them is above the corresponding threshold, then the subject It is suggested that the examiner's respiratory infection is likely to become more severe. In another example, the subject's respiratory tract if there are five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, at least one of which is above the corresponding threshold. It is suggested that the infection is likely to become more severe.

In another embodiment, the biomarker comprises at least two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and their measurements indicate respiratory infections in the subject. The risk of aggravation can be classified into three stages. In particular:
-It is suggested that respiratory infections are likely to become more severe if all of the biomarker measurements selected from the group are above the threshold corresponding to each biomarker;
• It is suggested that respiratory infections are moderately likely to become severe if at least one of the biomarker measurements selected from the group is greater than or equal to the threshold corresponding to the biomarker;
-It is suggested that respiratory infections are less likely to become severe when all the measurements of biomarkers selected from the group are lower than the thresholds corresponding to each biomarker.

As an example, if the biomarkers are two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and both of them are above the corresponding threshold, the subject It is suggested that respiratory tract infections are likely to become more severe. In another example, the biomarkers are three selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are tested when they are above the corresponding threshold. It is suggested that a person's respiratory tract infection is likely to become more severe. In another example, the biomarkers are four selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are tested when they are above the corresponding threshold. It is suggested that a person's respiratory tract infection is likely to become more severe. In another example, if there are five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are above the corresponding threshold, the subject's respiratory infection. It is suggested that the disease is likely to become more severe.

In this embodiment, CCL17 in the sample may be measured. As shown in the Examples below, CCL17, unlike other biomarkers, shows low levels in patients with severe respiratory infections and high levels in patients with mild respiratory infections. Indicated. The measured value of CCL17 can be used as an index suggesting the possibility of aggravation of respiratory infections by comparison with the threshold value corresponding to CCL17. In one embodiment, it is suggested that a subject's respiratory infection is likely to be exacerbated when the biomarker contains CCL17 and the measured value of CCL17 is below the threshold corresponding to CCL17. When the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it is suggested that the respiratory infection of the subject is unlikely to become severe.

In a preferred embodiment, CCL17 is used in combination with at least one biomarker selected from IFNλ3, CXCL11, IP-10, IL-6 and CXCL9. For example, the biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, and their measurements indicate that the subject has a respiratory infection. A risk of aggravation may be suggested. Specifically, when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker and / or the measured value of CCL17 is lower than the threshold value corresponding to CCL17, the subject is infected. It is suggested that the examiner's respiratory infection is likely to become more severe. Respiratory tract infections in subjects when all of the biomarker measurements selected from the group are lower than the threshold corresponding to each biomarker and the CCL17 measurement is greater than or equal to the threshold corresponding to CCL17. It is suggested that is unlikely to become severe.

In another embodiment, the biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, and their measurements indicate the subject's respiration. The risk of aggravation of respiratory tract infections can be classified into three stages. In particular:
-Respiratory tract infection of the subject when all the measured values of the biomarkers selected from the above group are equal to or higher than the threshold value corresponding to each biomarker and the measured value of CCL17 is lower than the threshold value corresponding to CCL17. Is likely to become severe;
-Respiratory tract infection of the subject when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold corresponding to the biomarker and the measured value of CCL17 is higher than the threshold corresponding to CCL17. It has been suggested that the disease is moderately likely to become severe;
Respiratory tract infection of a subject when at least one of the biomarker measurements selected from the group is lower than the biomarker-corresponding threshold and the CCL17 measurement is lower than the CCL17-corresponding threshold. It has been suggested that the disease is moderately likely to become severe;
-Respiratory tract infection of the subject when all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17. It is suggested that the disease is unlikely to become severe.

In a more specific example, biomarkers include IFNλ3 and CCL17,
・ When the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is suggested that the respiratory infection of the subject is likely to become severe. ;
-If the measured value of IFNλ3 is greater than or equal to the threshold value corresponding to IFNλ3 and the measured value of CCL17 is greater than or equal to the threshold value corresponding to CCL17, there is a moderate possibility that the respiratory tract infection of the subject will become severe. Is suggested;
-If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, the possibility that the respiratory infection of the subject becomes severe is moderate. Is suggested;
・ If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it is suggested that the respiratory infection of the subject is unlikely to become severe. Will be done.

The threshold value corresponding to each biomarker is not particularly limited and can be set as appropriate. For example, a sample is collected from a plurality of patients with respiratory infections, and biomarkers in the sample are measured to obtain a measured value. After a lapse of a predetermined period (for example, 2 weeks) from the sample collection, it is confirmed whether or not the respiratory infection has become severe. The acquired measured value data is classified into data of a group of patients who have become severely ill and data of a group of patients who have not become severely ill. Then, for each biomarker, a value that can most accurately distinguish between the severely ill patient group and the non-severely ill patient group is obtained, and the value is set as a threshold value. In setting the threshold value, sensitivity, specificity, positive predictive value, negative predictive value, and the like can be taken into consideration.

In the present embodiment, the threshold value corresponding to IFNλ3 is set in the range of, for example, 4 pg / mL or more and 15 pg / mL or less. The threshold value corresponding to CXCL11 is set in the range of, for example, 20 pg / mL or more and 40 pg / mL or less. The threshold value corresponding to IP-10 is set in the range of, for example, 400 pg / mL or more and 1200 pg / mL or less. The threshold value corresponding to IL-6 is set in the range of, for example, 4 pg / mL or more and 6 pg / mL or less. The threshold value corresponding to CXCL9 is set in the range of, for example, 30 pg / mL or more and 40 pg / mL or less. The threshold value corresponding to CCL17 is set, for example, in the range of 40 pg / mL or more and 100 pg / mL or less.

Healthcare professionals, such as doctors, may combine biomarker measurements with other information to determine the risk of aggravation of respiratory infections. Here, "other information" includes findings of X-ray images or CT images of lungs, and other medical findings.

As an index for predicting the aggravation of the respiratory tract infection of the subject of the present embodiment, the time course of the measured value of the biomarker in the subject may be acquired. The change with time of the measured value of the biomarker is not particularly limited as long as it is information indicating the transition of the measured value of the biomarker in the sample collected from the subject a plurality of times periodically or irregularly. Such changes over time include, for example, a value calculated from a plurality of measured values (for example, the difference, ratio, etc. of the measured values of two samples collected at any two time points), and a recording of the measured values (for example,). Tables of measured values, graphs plotting measured values, etc.).

In the present embodiment, if the measured values of the obtained biomarkers suggest that the respiratory tract infection of the subject is likely to be aggravated, the subject is given a severe respiratory tract infection. Medical intervention for infection can be performed. Examples of medical interventions include drug administration, surgery, immunotherapy, gene therapy, oxygenation procedures, heart-lung machine procedures, and the like. The drug can be appropriately selected from known therapeutic drugs for respiratory infections or drug candidates thereof. For example, when the respiratory tract infection is a SARS-CoV-2 infection, the drug includes a drug having an antiviral action, a drug that reduces inflammation, an ACE inhibitor, and the like. Specifically, favipiravir, lopinavir, ritonavir, nafamostat, camostat, remdesivir, ribavirin, ibermectin, ciclesonide, chloroquin, hydroxychloroquin, interferon, tosirizumab, salilumab, tofacitinib, baricitinib, luxolithinib, valicitinib, tofacitinib, baricitinib, , LY3127804 and so on.

The prediction method of the present embodiment may include a step of predicting the severity of respiratory tract infection based on the measured values of biomarkers obtained from a sample collected from a subject. In this step, for example, the measured value of the acquired biomarker is compared with the threshold value corresponding to the biomarker, and based on the comparison result, it is highly possible that the respiratory tract infection of the subject becomes severe. It may be determined whether it is low or low. The details of the thresholds corresponding to the biomarkers are as described above.

In one embodiment, if the biomarker contains IFNλ3 and the measured value of IFNλ3 is greater than or equal to the threshold corresponding to IFNλ3, it can be determined that the subject's respiratory infection is likely to become more severe. When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, if the biomarker contains CXCL11 and the measured value of CXCL11 is greater than or equal to the threshold corresponding to CXCL11, it can be determined that the subject's respiratory infection is likely to be severe. When the measured value of CXCL11 is lower than the threshold value corresponding to CXCL11, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, the subject's respiratory infection may be exacerbated if the biomarker contains IP-10 and the measured value of IP-10 is greater than or equal to the threshold corresponding to IP-10. It can be judged to be high. When the measured value of IP-10 is lower than the threshold value corresponding to IP-10, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, the subject's respiratory infection may be exacerbated if the biomarker contains IL-6 and the measured value of IL-6 is greater than or equal to the threshold corresponding to IL-6. It can be judged to be high. When the measured value of IL-6 is lower than the threshold value corresponding to IL-6, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, if the biomarker contains CXCL9 and the measured value of CXCL9 is greater than or equal to the threshold corresponding to CXCL9, it can be determined that the subject's respiratory infection is likely to be severe. When the measured value of CXCL9 is lower than the threshold value corresponding to CXCL9, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, if the biomarker comprises CCL17 and the measured value of CCL17 is below the threshold corresponding to CCL17, it can be determined that the subject's respiratory infection is likely to be aggravated. When the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it can be determined that the respiratory infection of the subject is unlikely to become severe.

In one embodiment, the biomarker comprises at least two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and based on their measurements, the subject's respiratory infection. The risk of aggravation of the disease may be determined. Specifically, when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker, the respiratory infection of the subject is likely to become severe. Can be judged. When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, it can be determined that the respiratory infection of the subject is unlikely to become severe.

As an example, if the biomarkers are two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and either or both of them are above the corresponding threshold. It is determined that the respiratory tract infection of the subject is likely to become severe. In another example, if the biomarkers are three selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and at least one of them is above the corresponding threshold, then the subject It is determined that the examiner's respiratory tract infection is likely to become severe. In another example, if the biomarkers are four selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and at least one of them is above the corresponding threshold, then the subject It is determined that the examiner's respiratory tract infection is likely to become severe. In another example, the subject's respiratory tract if there are five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, at least one of which is above the corresponding threshold. It is determined that the infection is likely to become severe.

In another embodiment, the biomarker comprises at least two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and based on their measurements, the subject's respiratory tract. The risk of aggravation of the infection may be determined in three stages. In particular:
-If all the measured values of the biomarkers selected from the above group are equal to or higher than the threshold value corresponding to each biomarker, it is determined that the respiratory infection is likely to become severe;
-If at least one of the biomarker measurements selected from the group is greater than or equal to the threshold corresponding to the biomarker, it is determined that the respiratory infection is moderately likely to become severe;
-When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, it can be determined that the respiratory infection is unlikely to become severe.

As an example, if the biomarkers are two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and both of them are above the corresponding threshold, the subject It is determined that the respiratory tract infection is likely to become severe. In another example, the biomarkers are three selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are tested when they are above the corresponding threshold. It is determined that a person's respiratory tract infection is likely to become severe. In another example, the biomarkers are four selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are tested when they are above the corresponding threshold. It is determined that a person's respiratory tract infection is likely to become severe. In another example, if there are five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, all of which are above the corresponding threshold, the subject's respiratory infection. It is determined that the disease is likely to become severe.

In a further embodiment, the biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, based on those measurements of the subject. The risk of aggravation of respiratory infections may be determined. Specifically, when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker and / or the measured value of CCL17 is lower than the threshold value corresponding to CCL17. , It can be determined that the respiratory tract infection of the subject is likely to become severe. Respiratory tract infections in subjects when all of the biomarker measurements selected from the group are lower than the threshold corresponding to each biomarker and the CCL17 measurement is greater than or equal to the threshold corresponding to CCL17. Can be determined to be less likely to become severe.

In another embodiment, the biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, and the subject is based on their measurements. The risk of aggravation of respiratory infections may be determined in three stages. In particular:
-Respiratory tract infection of the subject when all the measured values of the biomarkers selected from the above group are equal to or higher than the threshold value corresponding to each biomarker and the measured value of CCL17 is lower than the threshold value corresponding to CCL17. Is likely to become severe;
-The respiratory organs of the subject when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold corresponding to the biomarker and the measured value of CCL17 is equal to or higher than the threshold corresponding to CCL17. It has been determined that the infection is moderately likely to become severe;
Respiratory tract infections in subjects when at least one of the biomarker measurements selected from the group is lower than the biomarker-corresponding threshold and the CCL17 measurement is lower than the CCL17-corresponding threshold. Is determined to have a moderate chance of becoming severe;
-Respiratory tract infection of the subject when all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17. It can be determined that the disease is unlikely to become severe.

In a more specific example, biomarkers include IFNλ3 and CCL17,
-If the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is judged that the respiratory infection of the subject is likely to become severe;
-If the measured value of IFNλ3 is greater than or equal to the threshold value corresponding to IFNλ3 and the measured value of CCL17 is greater than or equal to the threshold value corresponding to CCL17, there is a moderate possibility that the respiratory tract infection of the subject will become severe. Is determined;
・ If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is judged that the respiratory infection of the subject is moderately likely to become severe. Be;
・ When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it is judged that the respiratory infection of the subject is unlikely to become severe. obtain.

One embodiment of the present invention is a method of monitoring measured values of biomarkers in a sample collected from a subject (hereinafter, also referred to as “monitoring method”). In the monitoring method of the present embodiment, the measured values of the biomarkers are acquired using the samples collected from the subject at a plurality of time points. Here, the details of obtaining the subject, the sample, the biomarker, and the measured value thereof are the same as those described for the prediction method of the present embodiment described above.

In the present embodiment, the plurality of time points may be two or more time points different from each other. For example, the plurality of time points includes a first time point and a second time point different from the first time point. The first time point is not particularly limited and is an arbitrary time point. For example, the first time point is when the subject is found to have a respiratory infection, when the subject develops symptoms of a respiratory infection, when the subject is hospitalized, and so on. May be. The second time point is not particularly limited as long as it is different from the first time point. Preferably, the second time point is a time point within one month from the first time point. Specifically, the second time point is 0.5 hours, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours from the first time point. Hours, 15 hours, 18 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 12 days, 2 weeks, 3 weeks, 4 weeks or One month has passed.

In the present embodiment, the "sample collected from the subject at a plurality of time points" is a sample collected from the same subject at each of the plurality of time points. For example, it includes a first sample collected from a subject at a first time point and a second sample taken from the subject at a second time point different from the first time point. In the monitoring method of the present embodiment, the biomarker may be measured each time a sample is collected, or each collected sample may be stored and measured collectively.

In the monitoring method of the present embodiment, the measured values of the biomarkers in the same subject are monitored and serve as an index for predicting the aggravation of respiratory infections. In a preferred embodiment, measurements of the same biomarker at multiple time points are obtained. The measured value of the biomarker measured from each sample is compared with the threshold value corresponding to the biomarker, and based on the comparison result, the respiratory tract infection of the subject is likely to become severe, or It may be suggested that it is low. The details of the threshold value corresponding to the biomarker are the same as those described for the prediction method of the present embodiment described above.

In one embodiment, a subject's respiratory infection occurs when the biomarker comprises IFNλ3 and the measured value of IFNλ3 is greater than or equal to the threshold corresponding to IFNλ3 at at least one of a plurality of time points. It is suggested that it is likely to become severe. At any of the multiple time points, if the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it is suggested that the subject's respiratory infection is unlikely to become severe.

In one embodiment, a subject's respiratory infection occurs when the biomarker comprises CXCL11 and the measured value of CXCL11 is greater than or equal to the threshold corresponding to CXCL11 at at least one of a plurality of time points. It is suggested that it is likely to become severe. At any of the multiple time points, if the CXCL11 reading is lower than the threshold corresponding to CXCL11, it is suggested that the subject's respiratory infection is unlikely to become severe.

In one embodiment, the subject is a subject when the biomarker comprises IP-10 and the measured value of IP-10 is greater than or equal to the threshold corresponding to IP-10 at at least one of a plurality of time points. It is suggested that respiratory tract infections are likely to become more severe. It is suggested that a subject's respiratory infection is unlikely to become more severe if the IP-10 reading is lower than the IP-10-corresponding threshold at any of the multiple time points. Will be done.

In one embodiment, a subject is a subject when the biomarker comprises IL-6 and the measured value of IL-6 is greater than or equal to the threshold corresponding to IL-6 at at least one of a plurality of time points. It is suggested that respiratory tract infections are likely to become more severe. It is suggested that a subject's respiratory infection is unlikely to become more severe if the IL-6 reading is lower than the threshold corresponding to IL-6 at any of the multiple time points. Will be done.

In one embodiment, a subject's respiratory infection occurs when the biomarker comprises CXCL9 and the measured value of CXCL9 is greater than or equal to the threshold corresponding to CXCL9 at at least one of a plurality of time points. It is suggested that it is likely to become severe. At any of the multiple time points, if the CXCL9 reading is lower than the threshold corresponding to CXCL9, it is suggested that the subject's respiratory infection is unlikely to become severe.

In one embodiment, the biomarker comprises at least two selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9, and the biomarker measurements at multiple time points of the subject. The risk of aggravation of respiratory infections may be suggested. Specifically, when at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker at at least one of the plurality of time points, the subject's respiration It is suggested that organ infections are likely to become more severe. Respiratory tract infections in subjects can be exacerbated if all of the biomarker measurements selected from the group are below the threshold corresponding to each biomarker at any of the multiple time points. It is suggested that the sex is low.

The measured value of CCL17 in each of the above samples may be obtained. In one embodiment, a subject's respiratory infection is severe if the biomarker comprises CCL17 and the measured value of CCL17 is below the threshold corresponding to CCL17 at at least one of a plurality of time points. It is suggested that there is a high possibility of becoming. It is suggested that the respiratory tract infection of the subject is unlikely to become severe when the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17 at any of the plurality of time points.

In a further embodiment, the biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, according to the biomarker measurements at multiple time points. The risk of aggravation of respiratory infections in the examiner may be suggested. Specifically, at least at least one of the plurality of time points, at least one of the measured values of the biomarker selected from the group is equal to or higher than the threshold value corresponding to the biomarker, and / or a plurality of times. It is suggested that a subject's respiratory infection is likely to be aggravated if the CCL17 reading is below the threshold corresponding to CCL17 at at least one of the time points. At any time point among the plurality of time points, all the measured values of the biomarkers selected from the group are lower than the threshold value corresponding to each biomarker, and at any time point among the plurality of time points, CCL17 When the measured value is above the threshold corresponding to CCL17, it is suggested that the subject's respiratory infection is unlikely to become severe.

The conditions for terminating the monitoring method of the present embodiment are not particularly limited, and a medical worker such as a doctor may appropriately determine the conditions. For example, when it is suggested that the measured values of biomarkers obtained from the samples collected from the subject at a plurality of time points are likely to aggravate the respiratory infection of the subject, the present embodiment You may end the monitoring method of. In this case, it is preferable that the subject is given medical intervention for the aggravation of respiratory infections. Details of the medical intervention are as described above. Alternatively, measurements of biomarkers obtained from specimens collected from the subject at multiple time points suggest that the subject's respiratory tract infection is unlikely to become more severe, and the subject If the person does not have symptoms of respiratory infection, the monitoring method of the present embodiment may be terminated.

In each of the embodiments described above, the subject's respiratory infection is exacerbated when the measurements of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are the same as the thresholds corresponding to each biomarker. Although it was suggested that it is likely, it may be suggested that the subject's respiratory tract infection is unlikely to become severe. In addition, in each of the above-described embodiments, if the measured value of CCL17 is the same as the threshold value corresponding to CCL17, it is suggested that the respiratory infection of the subject is unlikely to become severe. It may be suggested that the subject's respiratory tract infection is likely to become more severe.

One embodiment of the present invention is a reagent kit for use in the above-mentioned prediction method and / or monitoring method of the present embodiment. The reagent kit of this embodiment includes a reagent containing a substance that can specifically bind to IFNλ3, a reagent containing a substance that can specifically bind to CCL17, a reagent containing a substance that can specifically bind to CXCL11, and IP-10. Includes at least one reagent selected from reagents containing substances that can specifically bind to IL-6, reagents that contain substances that can specifically bind to IL-6, and reagents that contain substances that can specifically bind to CXCL9. .. In a further embodiment, the reagent kit includes a reagent containing a substance that can specifically bind to IFNλ3, a reagent that contains a substance that can specifically bind to CXCL11, and a reagent that contains a substance that can specifically bind to IP-10. A reagent containing a substance that can specifically bind to IL-6, and at least one selected from a reagent containing a substance that can specifically bind to CXCL9, and a reagent containing a substance that can specifically bind to CCL17. It may be included. In a preferred embodiment, the reagent kit comprises a reagent comprising a substance specifically binding to IFNλ3 and a reagent comprising a substance specifically binding to CCL17. Examples of substances that can specifically bind to each biomarker include antibodies and aptamers. Among them, antibodies are particularly preferable.

An example of the reagent kit of this embodiment is shown in FIG. 1A. In FIG. 1A, 11 shows a reagent kit, 12 shows a first container containing a reagent containing a substance that can specifically bind to IFNλ3, and 13 shows a substance that can specifically bind to CCL17. A second container containing the reagent is shown, 14 is a packing box, and 15 is a package insert. The package insert may describe the composition, usage, storage method, etc. of each reagent. The reagent kit of this example contains a reagent containing a substance that can specifically bind to IFNλ3 and a reagent that contains a substance that can specifically bind to CCL17, but in place of these reagents with other biomarkers. Reagents containing specifically bindable substances may be included.

In a preferred embodiment, the reagent kit of this embodiment comprises a biomarker capture antibody and a detection antibody. The detection antibody may be labeled with a labeling substance. The details of the capture antibody, the detection antibody, and the labeling substance are the same as those described for the prediction method of the present embodiment described above. In addition, the reagent kit may include a solid phase and a substrate. The details of the solid phase and the substrate are the same as those described for the prediction method of the present embodiment described above.

An example of a reagent kit of a further embodiment is shown in FIG. 1B. In FIG. 1B, 21 shows a reagent kit, 22 shows a first container containing a reagent containing an antibody for capturing IFNλ3, and 23 indicates a second container containing a reagent containing a labeled antibody for detecting IFNλ3. 24 indicates a third container containing a reagent containing a capture antibody for CCL17, 25 indicates a fourth container containing a reagent containing a labeling antibody for detection of CCL17, and 26 indicates an attachment. Shown, 27 indicates a packing box. The reagent kit of this example contains reagents containing each of the capture antibody and detection labeled antibody of IFNλ3 and reagents containing each of the capture antibody and detection labeled antibody of CCL17, but instead of these reagents. , Reagents containing each of the capture antibody for other biomarkers and the detection labeled antibody may be included.

It is preferable to include a calibrator in any of the above reagent kits. Examples of the calibrator include a calibrator for quantifying IFNλ3 (calibrator for IFNλ3), a calibrator for quantifying CXCL11 (calibrator for CXCL11), a calibrator for quantifying IP-10 (calibrator for IP-10), and IL-. Examples include a calibrator for quantification of 6 (calibrator for IL-6), a calibrator for quantification of CXCL9 (calibrator for CXCL9), and a calibrator for quantification of CCL17 (calibrator for CCL17). The IFNλ3 calibrator may include, for example, a buffer solution that does not contain IFNλ3 (negative control) and a buffer solution that contains IFNλ3 at a known concentration. The calibrator for CXCL11 may include, for example, a buffer solution containing no CXCL11 (negative control) and a buffer solution containing CXCL11 at a known concentration. The IP-10 calibrator may include, for example, a buffer solution containing no IP-10 (negative control) and a buffer solution containing IP-10 at a known concentration. The IL-6 calibrator may include, for example, a buffer solution containing no IL-6 (negative control) and a buffer solution containing IL-6 at a known concentration. The calibrator for CXCL9 may include, for example, a buffer solution containing no CXCL9 (negative control) and a buffer solution containing CXCL9 at a known concentration. The calibrator for CCL17 may include, for example, a buffer solution containing no CCL17 (negative control) and a buffer solution containing CCL17 at a known concentration.

An example of a reagent kit of a further embodiment is shown in FIG. 1C. In FIG. 1C, 31 indicates a reagent kit, 32 indicates a first container containing a reagent containing an antibody for capturing IFNλ3, and 33 indicates a second container containing a reagent containing a labeling antibody for IFNλ3. 34 indicates a third container containing a reagent containing a capture antibody for CCL17, 35 indicates a fourth container containing a reagent containing a labeling antibody for detection of CCL17, and 36 indicates IFNλ3 and CCL17. Indicates a fifth container containing a buffer containing none of the above, 37 indicates a sixth container containing a buffer containing IFNλ3 and CCL17 at predetermined concentrations, 38 indicates a packing box, 39. Indicates an attached document. A buffer solution containing neither IFNλ3 nor CCL17 and a buffer solution containing IFNλ3 and CCL17 at predetermined concentrations can be used as a calibrator for the quantification of IFNλ3 and CCL17. The reagent kit of this example is a reagent containing each of the capture antibody and detection-labeled antibody of IFNλ3, a reagent containing each of the capture antibody and detection-labeled antibody of CCL17, and a calibrator for quantification of IFNλ3 and CCL17. However, in place of these reagents, reagents and calibrators containing each of the capture antibody and the detection labeled antibody of other biomarkers may be included.

In a further embodiment, a reagent containing a substance that can specifically bind to IFNλ3, a reagent containing a substance that can specifically bind to CCL17, a reagent containing a substance that can specifically bind to CXCL11, and a reagent specific to IP-10. Box containing one reagent selected from reagents containing substances that can specifically bind to IL-6, reagents that contain substances that can specifically bind to IL-6, and reagents that contain substances that can specifically bind to CXCL9. It may be provided to the user as a reagent kit by packing it in a reagent kit. The package insert may be included in the box. The package insert may describe the composition of the reagent, how to use it, how to store it, and the like. An example of the reagent kit is shown in FIG. 1D. In FIG. 1D, 41 represents a reagent kit, 42 represents a container containing a reagent containing a substance that can specifically bind IFNλ3, 43 represents a packaging box, and 44 represents a package insert. The reagent kit of this example contains a reagent containing a substance that can specifically bind to IFNλ3, but this reagent may be replaced with a reagent that contains a substance that can specifically bind to other biomarkers. ..

One embodiment of the present invention is an apparatus for carrying out the above-mentioned prediction method and / or monitoring method of the present embodiment. Such a device is a device (hereinafter, also simply referred to as “device”) that assists in predicting the severity of respiratory infections. Another embodiment of the present invention is a computer program for causing a computer to execute the prediction method and / or the monitoring method of the present embodiment described above. Such computer programs are computer programs to assist in predicting the severity of respiratory infections.

Hereinafter, an example of an apparatus for carrying out the prediction method of the present embodiment will be described with reference to the drawings. FIG. 2 is a schematic view of the apparatus of this embodiment. The device 10 shown in FIG. 2 includes an immunoassay device 20 and a computer system 30 connected to the immunoassay device 20.

In the present embodiment, the type of the immunoassay device is not particularly limited, and can be appropriately selected depending on the method for measuring the biomarker. In the example shown in FIG. 2, the immunoassay device 20 is a commercially available automatic immunoassay device capable of detecting a chemiluminescent signal generated by a sandwich ELISA method using magnetic particles on which a capture antibody is immobilized and an enzyme-labeled detection antibody. Is. The immunoassay device 20 is not particularly limited as long as it can detect a signal based on the labeling substance used, and can be appropriately selected depending on the type of labeling substance.

When a reagent containing magnetic particles on which a capture antibody is immobilized, a reagent containing an enzyme-labeled detection antibody, and a sample collected from a subject are set in an immunoassay device 20, the immunoassay device 20 uses each reagent. The antigen-antibody reaction is executed, a chemiluminescence signal is acquired as optical information based on the enzyme-labeled antibody specifically bound to the biomarker, and the obtained optical information is transmitted to the computer system 30.

The computer system 30 includes a computer main body 300, an input unit 301, and a display unit 302 that displays sample information, determination results, and the like. The computer system 30 receives optical information from the immunoassay device 20. Then, the processor of the computer system 30 executes a computer program installed on the hard disk 313 to assist in predicting the severity of the respiratory tract infection based on the optical information. As shown in FIG. 2, the computer system 30 may be a device separate from the immunoassay device 20 or a device including the immunoassay device 20. In the latter case, the computer system 30 may itself be the predictive aid 10. A commercially available automated immunoassay may be equipped with a computer program to assist in predicting the severity of respiratory infections. The device 10 may be a device in which the immunoassay device 20 and the computer system 30 are integrally configured.

With reference to FIG. 3, the computer main body 300 reads the CPU (Central Processing Unit) 310, the ROM (Read Only Memory) 311, the RAM (Random Access Memory) 312, the hard disk 313, the input / output interface 314, and the like. It includes a device 315, a communication interface 316, and an image output interface 317. The CPU 310, ROM 311, RAM 312, hard disk 313, input / output interface 314, reader 315, communication interface 316, and image output interface 317 are connected by a bus 318 so that data can be communicated. Further, the immunoassay device 20 is communicably connected to the computer system 30 by the communication interface 316.

The CPU 310 can execute a program stored in the ROM 311 or the hard disk 313 and a program loaded in the RAM 312. The CPU 310 calculates the measured value of the biomarker and displays it on the display unit 302.

The ROM 311 is composed of a mask ROM, a PROM, an EPROM, an EEPROM and the like. As described above, the computer program executed by the CPU 310 and the data used for executing the computer program are recorded in the ROM 311. The computer program recorded in the ROM 311 includes a BIOS (Basic Input Output System).

The RAM 312 is composed of SRAM, DRAM, and the like. The RAM 312 is used to read the programs recorded in the ROM 311 and the hard disk 313. The RAM 312 is also used as a work area for the CPU 310 when executing these programs.

The hard disk 313 is installed with a computer program such as an operating system and an application program for the CPU 310 to execute, and data used for executing the computer program.

The reading device 315 is composed of a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, a USB port, an SD card reader, a CF card reader, a memory stick reader, a solid state drive, and the like. The reading device 315 can read the program or data recorded on the portable recording medium 400.

The input / output interface 314 is composed of, for example, a serial interface such as USB, IEEE1394, RS-232C, a parallel interface such as SCSI, IDE, IEEE1284, and an analog interface including a D / A converter and an A / D converter. It is configured. An input unit 301 such as a keyboard and a mouse is connected to the input / output interface 314. The operator can input various commands to the computer main body 300 by the input unit 301.

The communication interface 316 is, for example, an Ethernet (registered trademark) interface. The computer body 300 can also transmit print data to a printer or the like by using the communication interface 316.

The image output interface 317 is connected to a display unit 302 composed of an LCD, a CRT, or the like. As a result, the display unit 302 can output a video signal corresponding to the image data given by the CPU 310. The display unit 302 displays an image (screen) according to the input video signal.

The processing procedure executed by the apparatus 10 of the present embodiment will be described with reference to FIG. 4A. Here, a case where the measured value of IFNλ3 is acquired and output from the chemiluminescent signal generated by the sandwich ELISA method using the magnetic particles on which the capture antibody is immobilized and the enzyme-labeled detection antibody will be described as an example. Instead of the measured value of IFNλ3, the measured value of CCL17, CXCL11, IP-10, IL-6 or CXCL9 may be obtained.

In step S101, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S102, the CPU 310 calculates the measured value of IFNλ3 from the acquired optical information and stores it in the hard disk 313. In step S103, the CPU 310 outputs the measured value of IFNλ3 and displays it on the display unit 302 or prints it on the printer. When outputting the measured value of IFNλ3, the threshold value corresponding to INFλ3 may also be displayed on the display unit 302 as reference information. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

Other processing procedures executed by the apparatus 10 of the present embodiment will be described with reference to FIG. 4B. Here, a case where the measured values of IFNλ3 and CCl17 are acquired and output will be described as an example. In step S201, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S202, the CPU 310 calculates the measured values of IFNλ3 and CCL17 from the acquired optical information and stores them in the hard disk 313. In step S203, the CPU 310 outputs the measured value and displays it on the display unit 302 or prints it on the printer. When outputting the measured values of IFNλ3 and CCL17, the threshold values corresponding to each of IFNλ3 and CCL17 may also be displayed on the display unit 302 as reference information. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

With reference to FIG. 4C, a flow in the case of predicting the severity of respiratory infection based on the measured value of IFNλ3 will be described. In step S301, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S302, the CPU 310 calculates the measured value of IFNλ3 from the acquired optical information and stores it in the hard disk 313. In step S303, the CPU 310 compares the calculated measured value of IFNλ3 with the threshold value corresponding to IFNλ3 stored in the hard disk 313. When the measured value of IFNλ3 is equal to or greater than the threshold value, the process proceeds to step S304. In step S304, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is likely to become serious.

On the other hand, in step S303, when the measured value of IFNλ3 is lower than the threshold value, the process proceeds to step S305. In step S305, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is unlikely to become serious. In step S306, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

With reference to FIG. 4D, a flow in the case of predicting the severity of respiratory infection based on the measured values of IFNλ3 and CXCL11 will be described. In FIG. 4D, the threshold value corresponding to the measured value of IFNλ3 is referred to as “first threshold value”, and the threshold value corresponding to the measured value of CXCL11 is referred to as “second threshold value”.

In step S401, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S402, the CPU 310 calculates the measured values of IFNλ3 and CXCL11 from the acquired optical information and stores them in the hard disk 313. In step S403, the CPU 310 compares the calculated measured value of IFNλ3 with the first threshold value stored in the hard disk 313. When the measured value of IFNλ3 is equal to or greater than the first threshold value, the process proceeds to step S404. In step S404, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is likely to become serious.

In step S403, when the measured value of IFNλ3 is lower than the first threshold, the process proceeds to step S405. In step S405, the CPU 310 compares the calculated measured value of CXCL11 with the second threshold value stored in the hard disk 313. When the measured value of CXCL11 is equal to or higher than the second threshold value, the process proceeds to step S404, and the CPU 310 stores the determination result that the respiratory infection of the subject is likely to become severe in the hard disk 313. do. In step S405, when the measured value of CXCL11 is lower than the second threshold, the process proceeds to step S406. In step S406, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is unlikely to become serious. In step S407, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

With reference to FIG. 4E, a flow in the case of predicting the severity of respiratory infection based on the measured values of IFNλ3 and CCL17 will be described. In FIG. 4E, the threshold value corresponding to the measured value of IFNλ3 is referred to as “first threshold value”, and the threshold value corresponding to the measured value of CCL17 is referred to as “third threshold value”.

In step S501, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S502, the CPU 310 calculates the measured values of IFNλ3 and CCL17 from the acquired optical information and stores them in the hard disk 313. In step 503, the CPU 310 compares the calculated measured value of IFNλ3 with the first threshold value stored in the hard disk 313. When the measured value of IFNλ3 is equal to or greater than the first threshold value, the process proceeds to step S504. In step S504, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is likely to become serious. In step S503, when the measured value of IFNλ3 is lower than the first threshold value, the process proceeds to step S505. In step S505, the CPU 310 compares the calculated measured value of CCL17 with the third threshold value stored in the hard disk 313. When the measured value of CCL17 is equal to or higher than the third threshold value, the process proceeds to step S506. In step S506, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is unlikely to become serious.

In step S505, when the measured value of CCL17 is lower than the third threshold value, the process proceeds to step S504, and the CPU 310 determines that the respiratory infection of the subject is likely to become severe. Store in 313. In step S507, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

With reference to FIG. 4F, a flow in the case of predicting the severity of respiratory tract infection based on the measured value of CCL17 will be described. In step S601, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S602, the CPU 310 calculates the measured value of CCL17 from the acquired optical information and stores it in the hard disk 313. In step S603, the CPU 310 compares the calculated measured value of CCL17 with the threshold value corresponding to CCL17 stored in the hard disk 313. When the measured value of CCL17 is lower than the threshold value, the process proceeds to step S604. In step S604, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is likely to become serious.

On the other hand, in step S603, when the measured value of CCL17 is equal to or greater than the threshold value, the process proceeds to step S605. In step S605, the CPU 310 stores in the hard disk 313 the determination result that the respiratory tract infection of the subject is unlikely to become serious. In step S606, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

In another embodiment, three biomarkers selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these three biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured value of each biomarker with the corresponding threshold value, and if the measured value of at least one biomarker is equal to or higher than the threshold value, the respiratory infection of the subject is likely to become severe. The determination result of is stored in the hard disk 313. When the measured values of all the biomarkers are lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

In another embodiment, three biomarkers selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these three biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured values of each biomarker with the corresponding threshold values, and if the measured values of all the biomarkers are equal to or higher than the threshold values, it is highly likely that the respiratory tract infection of the subject becomes severe. The determination result of is stored in the hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

In another embodiment, four biomarkers selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these four biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured value of each biomarker with the corresponding threshold value, and if the measured value of at least one biomarker is equal to or higher than the threshold value, the respiratory infection of the subject is likely to become severe. The determination result of is stored in the hard disk 313. When the measured values of all the biomarkers are lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

In another embodiment, four biomarkers selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these four biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured values of each biomarker with the corresponding threshold values, and if the measured values of all the biomarkers are equal to or higher than the threshold values, it is highly likely that the respiratory tract infection of the subject becomes severe. The determination result of is stored in the hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

In another embodiment, five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these five biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured value of each biomarker with the corresponding threshold value, and if the measured value of at least one biomarker is equal to or higher than the threshold value, the respiratory infection of the subject is likely to become severe. The determination result of is stored in the hard disk 313. When the measured values of all the biomarkers are lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

In another embodiment, five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used. In this case, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20 for these five biomarkers. The CPU 310 calculates the measured value of each biomarker from the acquired optical information and stores it in the hard disk 313. The CPU 310 compares the measured values of each biomarker with the corresponding threshold values, and if the measured values of all the biomarkers are equal to or higher than the threshold values, it is highly likely that the respiratory tract infection of the subject becomes severe. The determination result of is stored in the hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the determination result that the respiratory infection of the subject is unlikely to become severe is stored in the hard disk 313. The CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer.

One embodiment of the present invention relates to a method of treating a respiratory infection. The method for treating a respiratory tract infection of the present embodiment includes a step of measuring a biomarker in a sample collected from a subject suffering from a respiratory tract infection or a subject suspected of having a respiratory tract infection, and a biomarker. Includes a step of predicting the severity of respiratory tract infection based on the measured values of, and a step of performing medical intervention for a subject predicted to become severe of respiratory tract infection in the prediction step. .. Examples of "medical intervention" include drug administration, surgery, immunotherapy, gene therapy, oxygen supply treatment, treatment using a heart-lung machine, and the like. The drug can be appropriately selected from known therapeutic drugs for respiratory infections or drug candidates thereof. For example, when the respiratory tract infection is a SARS-CoV-2 infection, the drug includes a drug having an antiviral action, a drug that reduces inflammation, an ACE inhibitor, and the like. Specifically, favipiravir, lopinavir, ritonavir, nafamostat, camostat, remdesivir, ribavirin, ibermectin, ciclesonide, chloroquin, hydroxychloroquin, interferon, tosirizumab, salilumab, tofacitinib, baricitinib, luxolithinib, valicitinib, tofacitinib, baricitinib, , LY3127804 and so on.

Hereinafter, the present invention will be described in detail with reference to Examples. Hereinafter, "HISCL" is a registered trademark of Sysmex Corporation.

Example 1
(1) Biological sample
Serum obtained from 8 patients whose SARS-CoV-2 infection was confirmed by PCR was used as a biological sample. Serum was prepared from blood collected at multiple time points from the day the patient was hospitalized. Information on each patient is shown in Table 1. In the table, "onset date" indicates the day when cold symptoms such as fever and cough appeared. "Severity" indicates the final medical condition of each patient after admission. “Mild” indicates a case without pneumonia. “Moderate” indicates a case with pneumonia without oxygen demand. “Severe” refers to cases with pneumonia with oxygen demand. “Critical” indicates cases requiring intensive care management, including mechanical ventilation management.

(2) Measurement of biomarkers
(2.1) Measurement of chemokines and cytokines Concentrations of various chemokines and cytokines, bio-Plex Pro ™ human chemokine 40-Plex panel (# 171AK99MR2, BIO-RAD) and Bio-Plex Pro ™ human cytokine screening Measurements were made using a 48-Plex panel (# 12007283, BIO-RAD). A Bio-Plex MAGPIX system (BIO-RAD) was used as the measuring device. The specific operation was performed according to the package insert of the kit and the package insert of the measuring device.

(2.2) Measurement of INFλ3 (IL-28B) The concentration of INFλ3 as a cytokine not included in the measurement items of the above kit was measured by the fully automatic immunoassay device HISCL-5000 (Sysmex Corporation) using the following R1 to R5 reagents. It was measured. The R1 reagent (capture antibody reagent) is 1% bovine serum albumin (BSA) obtained by labeling the anti-hIL-28B antibody (clone name: Hyb-TA2650B) provided by the National Center for Global Health and Medicine with biotin by a conventional method. ) And 0.5% casein-containing buffer. As the R2 reagent (solid phase), a HISCL R2 reagent (Sysmex Corporation) containing streptavidin-bound magnetic particles was used. For the R3 reagent (detection antibody reagent), the anti-rhIL-28B antibody (clone name: Hyb-TA2664) provided by the National Center for Global Health and Medicine was used as a Fab'fragment by a conventional method, and this Fab'fragment was used as a conventional method. Labeled with ALP and dissolved in 1% BSA and 0.5% casein-containing buffer. HISCL R4 reagent (Sysmex Corporation) was used as the R4 reagent (buffer solution for measurement). HISCL R5 reagent (Sysmex Corporation) was used as the R5 reagent (ALP substrate solution). A method for producing an anti-hIL-28B antibody and an anti-rhIL-28B antibody is described in Patent Document 1.

The measurement procedure by HISCL-5000 was as follows. After mixing serum (30 μL) and R1 reagent (100 μL), R2 reagent (30 μL) was added. The magnetic particles in the obtained mixed solution were collected to remove the supernatant, and HISCL cleaning solution (300 μL) was added to wash the magnetic particles. The supernatant was removed, and R3 reagent (100 μL) was added to the magnetic particles and mixed. The magnetic particles in the obtained mixed solution were collected to remove the supernatant, and HISCL cleaning solution (300 μL) was added to wash the magnetic particles. The supernatant was removed, R4 reagent (50 μL) and R5 reagent (100 μL) were added to the magnetic particles, and the chemiluminescence intensity was measured. As a calibrator (antigen for preparing a calibration curve), hIL-28B (10-046) provided by the National Center for Global Health and Medicine was used. A calibration curve was prepared by measuring the calibrator in the same manner as serum. The chemiluminescence intensity obtained from each serum measurement was applied to the calibration curve to determine the concentration of INFλ3.

(3) Measurement results From the measurement results of various chemokines and cytokines, IFNλ3, CXCL11, IP-10, IL-6, CXCL9 and CCL17 were found as biomarkers that can predict the prognosis of SARS-CoV-2 infection. .. Graphs plotting the measured values of each patient for IFNλ3, CXCL11, IP-10, IL-6, CXCL9 and CCL17 are shown in FIGS. 5 to 10. In the figure, "elapsed days" indicates the number of days from the day when the patient was hospitalized (elapsed days 0). The arrows in the figure indicate the time when the patient was treated with an oxygen inhaler or a heart-lung machine. In the figure, "IFNL3" means IFNλ3.

As can be seen from A to E in FIGS. 5-9, patients in cases Nos. 5, 7, 9, 11 and 12 were infected after high levels of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9. The disease tended to become more severe. In case No. 9, the measured value of IFNλ3 was relatively low in the patient with severe illness, but the measured values of CXCL11, IP-10, IL-6 and CXCL9 tended to be high. rice field. On the other hand, as can be seen from F, G and H in FIGS. 5-9, the measured values of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 became severe in the patients of cases Nos. 4, 8 and 10. It tended to show lower values than patients. These results suggest that IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 can be used as biomarkers to predict the severity of respiratory infections.

As can be seen from FIGS. 10A to 10H, the measured value of CCL17 tended to be higher in mildly ill patients than in severely ill patients. From this result, CCL17 can be used as a biomarker to find patients who are unlikely to have severe respiratory infections when used in combination with IFNλ3, CXCL11, IP-10, IL-6 or CXCL9. It was suggested.

Example 2
(1) Biological sample
Serum obtained from 20 patients whose SARS-CoV-2 infection was confirmed by PCR was used as a biological sample. Serum was prepared from blood collected at multiple time points from the day the patient was hospitalized. The severity of the 20 patients was mild in 2, moderate in 11, severe in 2, and critical in 5.

(2) Measurement of biomarkers The concentrations of IFNλ3 and CCL17 in the plasma of each patient were measured in the same manner as in Example 1. Graphs 11 and 12 plotting the measurements of 28 patients, including 20 patients in Example 2 and 8 patients in Example 1, for IFNλ and CCL17. FIG. 11A shows IFNλ3 measurements of critical and severe patients, and FIG. 11B shows IFNλ3 measurements of moderate and mild patients. FIG. 12A shows CCL17 measurements for critical and severe patients, and FIG. 12B shows CCL17 measurements for moderate and mild patients. In the figure, "Days after hospitalization" indicates the number of days from the day when the patient was hospitalized (0 day).

(3) Analysis of measured values of biomarkers The analysis was performed on the measured values of 28 patients including 20 patients of Example 2 and 8 patients of Example 1. Of the 28 patients, those with mild or moderate severity were classified as "low risk group" (hereinafter referred to as "L group"), and those with severe or critical severity were classified as "high risk group". It was classified into (hereinafter referred to as "H group"), and the concentrations of IFNλ3 and CCL17 in each group were plotted. The results are shown in FIGS. 13A and 13B. The horizontal lines in the figure indicate the first quartile, median and third quartile of the biomarker concentration of each group.

The biomarker concentrations of 28 patients were analyzed by ROC, and the optimum cutoff value (threshold value) for discriminating between the L group and the H group was set. For 28 patients, the set cutoff values were used to calculate the sensitivity, specificity, and area under the curve (AUC) when determining whether respiratory infections were likely to become more severe. bottom. The obtained ROC curves are shown in FIGS. 14A and 14B. Table 2 shows the cutoff values of IFNλ3 and CCL17, the sensitivity, specificity, AUC and p values of the judgment using the cutoff values. The Kaplan-Meier method was used to determine post-hospital event-free survival (EFS) for two patient groups classified based on the above cutoff values. The obtained Kaplan-Meier curves are shown in FIGS. 15A and 15B.

Table 2 shows that IFNλ3 and CCL17 are biomarkers that make it possible to predict the severity of respiratory infections. FIG. 15A shows that respiratory infections are more likely to become more severe when the IFNλ3 measurement is higher than the cutoff value, and respiratory infections when the IFNλ3 measurement is less than or equal to the cutoff value. It was shown that the disease is unlikely to become severe. FIG. 15B shows that respiratory infections are more likely to become more severe when the CCL17 measurement is lower than the cutoff value, and when the CCL17 measurement is greater than or equal to the cutoff value, the respiratory tract. It has been shown that the infection is unlikely to become severe.

11, 21, 31, 41: Reagent kit 12, 22, 32, 42: 1st container 13, 23, 33: 2nd container 24, 34: 3rd container 25, 35: 4th container 36: 5th container 37 : 6th container 14, 27, 38, 43: Packing box 15, 26, 39, 44: Attachment 10: Judgment device 20: Immunometric measuring device 30: Computer system 40: Recording medium 300: Computer main body 301: Input unit 302 : Display unit 310: CPU
311: ROM
312: RAM
313: Hard disk 314: Input / output interface 315: Reader 316: Communication interface 317: Image output interface 318: Bus

The present inventors have IFNλ3 (Interferonλ3), CCL17 (CC chemokine ligand 17), CXCL11 (CXCchemokine ligand 11), IP-10 (Interferon-inducible Protein-10), IL-6 and CXCL9 (CXC chemokine ligand 9). The invention was completed by finding that it can be used as a biomarker for predicting the severity of respiratory infections. Therefore, the present invention includes a step of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and the biomarkers are IFNλ3 , C XCL11. , IP-10, IL-6 and CXCL9, which is at least one selected from the group and uses biomarker measurements as indicators for predicting the severity of respiratory tract infections. Provide a method to assist in predicting the severity of the disease.

The present invention provides a method of monitoring the measured values of biomarkers in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection. This method involves obtaining biomarker measurements using specimens taken from a subject at multiple time points, with biomarkers from IFNλ3 , C XCL11, IP-10, IL-6 and CXCL9. It is at least one selected from the group, and the measured value is used as an index for predicting the severity of respiratory tract infection.

The present invention relates to a step of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and a respiratory organ based on the measured value of the biomarker. Severity of respiratory infections, including the step of predicting the severity of the infection, where the biomarker is at least one selected from the group consisting of IFNλ3, C XCL11, IP-10, IL-6 and CXCL9. It provides a method to assist in the prediction of infection.

The present invention comprises a processor and a computer including a memory under the control of the processor, in which the memory is in a specimen taken from a subject suffering from or suspected of having a respiratory infection. A computer program is recorded for causing the computer to execute a step of calculating a biomarker measurement value and a step of outputting a biomarker measurement value, and the biomarker measurement value is a serious condition of respiratory infection. Prediction of aggravation of respiratory infections, including at least one biomarker selected from the group consisting of IFNλ3, C XCL11, IP-10, IL-6 and CXCL9, which serves as an indicator for predicting illness. Provide an auxiliary device.

The present invention provides a computer program to assist in predicting the severity of respiratory infections. This computer program is recorded on a computer-readable medium and calculates biomarker measurements in a sample taken from a subject with or suspected of having a respiratory infection. Have the computer perform the steps and the steps to output the biomarker measurements. The biomarker comprises at least one selected from the group consisting of IFNλ3, C XCL11, IP-10, IL-6 and CXCL9.

In step S101, the CPU 310 acquires optical information (chemiluminescence signal) from the immunoassay device 20. In step S102, the CPU 310 calculates the measured value of IFNλ3 from the acquired optical information and stores it in the hard disk 313. In step S103, the CPU 310 outputs the measured value of IFNλ3 and displays it on the display unit 302 or prints it on the printer. When outputting the measured value of IFNramuda3, threshold value corresponding to the I FN [lambda] 3 as the reference information may also be displayed on the display unit 302. This makes it possible to provide doctors and the like with an index for assisting in predicting the severity of respiratory infections.

(2.2) Measurement of I FN λ3 (IL-28B) Fully automatic immunoassay device HISCL-5000 (Sysmex) using the following R1 to R5 reagents for the concentration of I FN λ3 as a cytokine not included in the measurement items of the above kit. Co., Ltd.). The R1 reagent (capture antibody reagent) is 1% bovine serum albumin (BSA) obtained by labeling the anti-hIL-28B antibody (clone name: Hyb-TA2650B) provided by the National Center for Global Health and Medicine with biotin by a conventional method. ) And 0.5% casein-containing buffer. As the R2 reagent (solid phase), a HISCL R2 reagent (Sysmex Corporation) containing streptavidin-bound magnetic particles was used. For the R3 reagent (detection antibody reagent), the anti-rhIL-28B antibody (clone name: Hyb-TA2664) provided by the National Center for Global Health and Medicine was used as a Fab'fragment by a conventional method, and this Fab'fragment was used as a conventional method. Labeled with ALP and dissolved in 1% BSA and 0.5% casein-containing buffer. HISCL R4 reagent (Sysmex Corporation) was used as the R4 reagent (buffer solution for measurement). HISCL R5 reagent (Sysmex Corporation) was used as the R5 reagent (ALP substrate solution). A method for producing an anti-hIL-28B antibody and an anti-rhIL-28B antibody is described in Patent Document 1.

The measurement procedure by HISCL-5000 was as follows. After mixing serum (30 μL) and R1 reagent (100 μL), R2 reagent (30 μL) was added. The magnetic particles in the obtained mixed solution were collected to remove the supernatant, and HISCL cleaning solution (300 μL) was added to wash the magnetic particles. The supernatant was removed, and R3 reagent (100 μL) was added to the magnetic particles and mixed. The magnetic particles in the obtained mixed solution were collected to remove the supernatant, and HISCL cleaning solution (300 μL) was added to wash the magnetic particles. The supernatant was removed, R4 reagent (50 μL) and R5 reagent (100 μL) were added to the magnetic particles, and the chemiluminescence intensity was measured. As a calibrator (antigen for preparing a calibration curve), hIL-28B (10-046) provided by the National Center for Global Health and Medicine was used. A calibration curve was prepared by measuring the calibrator in the same manner as serum. The chemiluminescence intensity obtained by the measurement of the serum by applying the calibration curve to determine the concentration of I FN [lambda] 3.

Claims (33)

The biomarkers include IFNλ3, CCL17, CXCL11, IP-10, which comprises the step of measuring biomarkers in a sample collected from a subject suffering from a respiratory tract infection or a subject suspected of having a respiratory tract infection. , IL-6 and CXCL9, and the measured value of the biomarker is used as an index for predicting the aggravation of the respiratory tract infection.
A method to help predict the severity of respiratory infections. When the biomarker contains IFNλ3 and the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3, it is suggested that the respiratory infection of the subject is likely to be aggravated. Item 1. The method according to Item 1. Claim that it is suggested that the subject's respiratory infection is less likely to become severe if the biomarker contains IFNλ3 and the measured value of IFNλ3 is lower than the threshold corresponding to IFNλ3. The method according to 1 or 2. A claim suggests that if the biomarker comprises CCL17 and the measured value of CCL17 is below the threshold corresponding to CCL17, the respiratory infection of the subject is likely to be aggravated. The method according to any one of 1 to 3. When the biomarker contains CCL17 and the measured value of the CCL17 is equal to or higher than the threshold value corresponding to CCL17, it is suggested that the respiratory infection of the subject is unlikely to be aggravated. Item 8. The method according to any one of Items 1 to 4. When the biomarker contains CXCL11 and the measured value of the CXCL11 is equal to or higher than the threshold value corresponding to the CXCL11, it is suggested that the respiratory infection of the subject is likely to be aggravated. Item 5. The method according to any one of Items 1 to 5. Claim that it is suggested that the subject's respiratory infection is less likely to become severe if the biomarker comprises CXCL11 and the measured value of CXCL11 is below the threshold corresponding to CXCL11. The method according to any one of 1 to 6. When the biomarker contains IP-10 and the measured value of the IP-10 is equal to or higher than the threshold value corresponding to the IP-10, the respiratory infection of the subject is likely to become severe. The method according to any one of claims 1 to 7, which is suggested. When the biomarker contains IP-10 and the measured value of the IP-10 is lower than the threshold corresponding to the IP-10, it is unlikely that the respiratory infection of the subject will be aggravated. Suggested method according to any one of claims 1-8. When the biomarker contains IL-6 and the measured value of IL-6 is equal to or higher than the threshold value corresponding to IL-6, the respiratory infection of the subject is likely to become severe. The method according to any one of claims 1 to 9, which is suggested. When the biomarker contains IL-6 and the measured value of IL-6 is lower than the threshold corresponding to IL-6, it is unlikely that the respiratory infection of the subject will be aggravated. Suggested method according to any one of claims 1-10. When the biomarker contains CXCL9 and the measured value of the CXCL9 is equal to or higher than the threshold value corresponding to the CXCL9, it is suggested that the respiratory infection of the subject is likely to be aggravated. Item 2. The method according to any one of Items 1 to 11. Claim that it is suggested that the subject's respiratory infection is less likely to become severe if the biomarker contains CXCL9 and the measured value of CXCL9 is lower than the threshold corresponding to CXCL9. The method according to any one of 1 to 12. The biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, and at least one of the measured values of the biomarker selected from the group. Claim 1 suggests that the subject's respiratory infection is likely to become more severe when it is greater than or equal to the corresponding threshold and the measured value of CCL17 is lower than the threshold corresponding to CCL17. The method described in. The method according to claim 1, wherein the index is a change over time in the measured value of the biomarker in the subject. The method according to any one of claims 1 to 15, wherein the sample is whole blood, plasma or serum. The method according to any one of claims 1 to 16, wherein the respiratory tract infection is an infectious disease caused by a virus. 17. The method of claim 17, wherein the virus is a coronavirus or an influenza virus. The method according to claim 18, wherein the coronavirus is α-coronavirus, β-coronavirus, γ-coronavirus or δ-coronavirus. The β-coronavirus is any of SARS-CoV-2, HCoV-OC43, HCoV-HKU1, SARS-CoV, Bat SL-CoV-WIV1, BtCoV-HKU4, BtCoV-HKU5, MERS-CoV and BtCoV-HKU9. One kind,
19. The method of claim 19. A method of monitoring biomarker measurements in specimens collected from subjects with or suspected respiratory infections.
Includes obtaining measurements of the biomarker using specimens collected from the subject at multiple time points, the biomarker from IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9. The method, which is at least one selected from the group, and uses the measured value as an index for predicting the severity of the respiratory tract infection. When the biomarker contains IFNλ3 and the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3 at at least one of the plurality of time points, the respiratory infection of the subject becomes severe. 21. The method of claim 21, which is suggested to be more likely to occur. When the biomarker contains IFNλ3 and the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 at any of the plurality of time points, the respiratory infection of the subject can be aggravated. The method of claim 21 or 22, which is suggested to be less sexual. When the biomarker contains CCL17 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17 at at least one of the plurality of time points, the respiratory infection of the subject becomes severe. The method according to any one of claims 21 to 23, which is suggested to be highly probable. When the biomarker contains CCL17 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17 at any of the plurality of time points, the respiratory infection of the subject becomes severe. The method according to any one of claims 21 to 24, which is suggested to be unlikely. The biomarker comprises at least one selected from the group consisting of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 and CCL17, and at least one of the plurality of time points, from the group. When at least one of the measured values of the selected biomarker is equal to or higher than the corresponding threshold value, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17 at at least one of the plurality of time points. 21. The method of claim 21, which suggests that the subject's respiratory tract infection is likely to become more severe. Claimed that the sample collected at the plurality of time points includes a first sample collected at the first time point and a second sample collected at a second time point different from the first time point. The method according to any one of 21 to 26. The plurality of time points include a first time point and a second time point different from the first time point, and the second time point is a time point within one month from the first time point. The method according to any one of claims 21 to 27. The process of measuring biomarkers in specimens collected from subjects with or suspected respiratory infections, and
A step of predicting the severity of the respiratory tract infection based on the measured values of the biomarker, and
Including
The biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9.
A method to help predict the severity of respiratory infections. The reagent kit used in the method according to any one of claims 1 to 29, which comprises a reagent containing a substance that can specifically bind to the biomarker. The reagent kit according to claim 30, wherein the substance is an antibody. It comprises a processor and a computer containing memory under the control of the processor.
In the memory, the following steps:
Steps to calculate biomarker measurements in specimens collected from subjects with or suspected respiratory infections, and
A computer program for causing the computer to execute a step of outputting the measured value of the biomarker is recorded.
The measured value of the biomarker serves as an index for predicting the severity of the respiratory tract infection.
A device that assists in predicting the severity of respiratory infections, wherein the biomarker comprises at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9. A computer program recorded on a computer-readable medium, wherein the computer program is described in the following steps:
Steps to calculate biomarker measurements in specimens collected from subjects with or suspected respiratory infections, and
It is a computer program for causing the computer to execute the step of outputting the measured value of the biomarker.
The biomarker comprises at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9.
A computer program to help predict the severity of respiratory infections.

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