WO2023275876A1 - Method and system for analyzing biological data - Google Patents

Method and system for analyzing biological data Download PDF

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Publication number
WO2023275876A1
WO2023275876A1 PCT/IL2022/050704 IL2022050704W WO2023275876A1 WO 2023275876 A1 WO2023275876 A1 WO 2023275876A1 IL 2022050704 W IL2022050704 W IL 2022050704W WO 2023275876 A1 WO2023275876 A1 WO 2023275876A1
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subject
crp
bacterial infection
concentration
infection
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PCT/IL2022/050704
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French (fr)
Inventor
Niv Steven MASTBOIM
Tanya Michelle GOTTLIEB
Eran BARASH
Roy NAVON
Annick GALETTO-LACOUR
Alain Gervaix
Laurence LACROIX
Kfir Oved
Eran Eden
Meital PAZ
Shabtai Shai ASHKENAZI
Liat ASHKENAZI HOFFNUNG
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Memed Diagnostics Ltd.
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Publication of WO2023275876A1 publication Critical patent/WO2023275876A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/04—Antibacterial agents
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G01N2333/4701—Details
    • G01N2333/4737—C-reactive protein
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • G01N2333/57—IFN-gamma
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/348—Urinary tract infections
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention in some embodiments thereof, relates to the identification of biological signatures and determinants associated with bacterial and viral infections in urine samples and methods of using such biological signatures in the screening, diagnosis, therapy and monitoring of infection.
  • Some embodiments relate to computational analysis, and, more particularly, but not exclusively, to computational analysis of biological data, e.g., for the purpose of distinguishing between bacterial infection and non-bacterial disease, and/or between a bacterial infection and viral infection, and/or between an infectious and non-infectious disease.
  • Some embodiments of the invention are particularly useful in cases in which the samples under investigation are obtained from children and/or infants.
  • Urinary tract infection is a common illness in children that may lead to renal scarring.
  • the possibility of renal damage after infection is considered to be higher than in older children; however, the diagnosis and the establishment of the severity of infection in this age group are challenging, as UTI may present with non-specific symptoms and signs such as fever, irritability, poor feeding or poor weight gain.
  • the presumptive diagnosis of UTI in children is often based on the results of urine dipstick and microscopic analysis.
  • the diagnostic accuracy of these tests is limited in young infants, with varying sensitivity and specificity according to the component analyzed.
  • the urine culture is considered the gold standard for diagnosis, but bacterial growth may be negatively influenced by transport, previous antibiotic therapy or contamination during sample collection, which is especially difficult in young ages. Furthermore, culture results are dependent on the threshold used to identify significant growth, cannot be differentiated from asymptomatic bacteriuria and are limited in their time to positivity.
  • Abx Antibiotics
  • Abx are the world's most prescribed class of drugs with a 25-30 billion $US global market. Abx are also the world's most misused drug with a significant fraction of all drugs (40-70%) being wrongly prescribed (Linder, J.A. and R.S. Stafford 2001; Scott, J. G. and D. Cohen, et al. 2001; Davey, P. and E. Brown, et al. 2006; Cadieux, G. and R. Tamblyn, et al. 2007; Pulcini,
  • Abx misuse is when the drug is administered in case of a non-bacterial disease, such as a viral infection, for which Abx is ineffective.
  • a non-bacterial disease such as a viral infection
  • Abx is ineffective.
  • the health-care and economic consequences of the Abx over prescription include: (i) the cost of antibiotics that are unnecessarily prescribed globally, estimated at >$10 billion annually; (ii) side effects resulting from unnecessary Abx treatment are reducing quality of healthcare, causing complications and prolonged hospitalization (e.g.
  • Antibiotics under-prescription is not uncommon either. For example up to 15% of adult bacterial pneumonia hospitalized patients in the US receive delayed or no Abx treatment, even though in these instances early treatment can save lives and reduce complications(Houck, P.M. and
  • Such a technology should: (i) accurately differentiate between a bacterial and viral infections; (ii) be rapid (within minutes); (iii) be able to differentiate between pathogenic and non-pathogenic bacteria that are part of the body’ s natural flora; (iv) differentiate between mixed co-infections and pure viral infections and (v) be applicable in cases where the pathogen is inaccessible (e.g. sinusitis, pneumonia, otitis-media, bronchitis, etc).
  • WO 2013/117746 teaches signatures and determinants for distinguishing between a bacterial and viral infection.
  • WO20 16/024278 and WO2018/029690 teach a method of analyzing biological data containing expression values of polypeptides in the blood of a subject. The method is based on the calculation of a distance between a segment of a curved line and an axis. The distance is calculated at a point over the curved line defined by a coordinate. The distance is correlated to the presence of, absence of, or likelihood that the subject has a bacterial infection.
  • a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is above 1.5 ⁇ g/L and/or the concentration of IP- 10 is above 2 ng/L, it is indicative of a bacterial infection, wherein the concentration of the CRP and/or the IP- 10 is creatinine normalized.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • a method of ruling out a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is below 1.5 ⁇ g/L and/or the concentration of IP- 10 is below 2 ng/L, it is indicative that the infection is not a bacterial infection.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non- infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non-infectious subject, it is indicative of a bacterial infection.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • a method of ruling in a bacterial infection in a subject under 3 months of age comprising measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIT, is below 188 pg/L it is indicative of a bacterial infection in the subject.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • TRAIT Interferon gamma-induced protein 10
  • a method of treating a bacterial infection in a subject comprising:
  • a method of treating a bacterial infection in a subject comprising:
  • the concentration of the CRP when the concentration of the CRP is above 1.7 ⁇ g/L and/or the concentration of the IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
  • the non-infectious subject is a healthy subject.
  • the subject is below 18 years of age.
  • the subject is below 3 months of age.
  • the subject exhibits symptoms of infection.
  • the symptoms comprise fever.
  • the method further comprises determining the species or strain of bacteria responsible for the bacterial infection.
  • the bacterial infection is a urinary tract infection.
  • the method further comprises measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase-associated lipocalin (NGAL), IL-1RA, IFNy, TNFa, MCP-1 and Interleukin- 18 (IL-18) in the urine sample.
  • PCT procalcitonin
  • IL-6 Interleukin-6
  • NGAL Neutrophil gelatinase-associated lipocalin
  • IL-1RA Interleukin-1RA
  • IFNy IFNy
  • TNFa TNFa
  • MCP-1 Interleukin- 18
  • IL-18 Interleukin- 18
  • the method further comprises measuring in the urine the amount of at least one additional non-protein determinant selected from the group consisting of nitrite level, white blood cell count, and pH.
  • the concentration of CRP is above 1.7 ⁇ g/L and/or the concentration of IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
  • the subject is below 18 years of age.
  • the subject is below 3 months of age.
  • the subject exhibits symptoms of fever.
  • the symptoms comprise fever.
  • the method further comprises determining the species or strain of bacteria responsible for the bacterial infection.
  • the method further comprises measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase-associated lipocalin (NGAL) and Interleukin- 18 (IL-18) in the urine sample.
  • PCT procalcitonin
  • IL-6 Interleukin-6
  • NGAL Neutrophil gelatinase-associated lipocalin
  • IL-18 Interleukin- 18
  • the bacterial infection is a urinary tract infection.
  • the measuring is carried out using an antibody that specifically binds to CRP and/or IPIO.
  • a system for analyzing biological data comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and CRP in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f( ⁇ )- ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals l/(l+exp(-
  • a system for analyzing biological data comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and IP- 10 in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f( ⁇ )- ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals 1/(1+exp(-
  • a system for analyzing biological data comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and PCT in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f( ⁇ )- ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals l/(l+exp(-
  • the sample is in a labeled cartridge, and the input circuit receives data pertaining to the age group based on the label.
  • a method of analyzing biological data comprises: obtaining biological data containing at least expression levels of TRAIL and CRP in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f( ⁇ )- ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals 1/(1+exp(- ⁇ )), wherein the coordinate d, once calculated, equals a combination of the expression levels, wherein a ratio between a coefficient of the combination of expressed TRAIL in, or once converted to, units of ml/pg and a coefficient of
  • the ratio is more than -0.4, or more than -0.4, or more than -0.3, or more than -0.2.
  • the biological data contain expression level of IP- 10, and wherein the ratio is more than -0.2.
  • the biological data contain expression level of PCT, and wherein the ratio is more than -0.2.
  • the biological data contain a count of Urine leukocytes, and wherein the ratio is more than -0.2.
  • a method of analyzing biological data comprising: obtaining biological data containing at least expression levels of TRAIT, and IP- 10 in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f( ⁇ )+ ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals l/(l+exp(- ⁇ )), wherein the coordinate d, once calculated, equals a combination of the expression
  • the biological data contain expression level of PCT.
  • the biological data contain a count of Urine leukocytes.
  • a method of analyzing biological data comprising: obtaining biological data containing at least expression levels of TRAIT, and PCT in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f( ⁇ )- ⁇ 0 and an upper bound line f( ⁇ )+ ⁇ 1 , wherein f( ⁇ ) equals 1/(1+exp(- ⁇ )), wherein the coordinate d, once calculated, equals a combination of the expression levels, wherein a ratio between a coefficient of the combination of expressed TRAIT, in, or once converted to, units of ml/pg and
  • the biological data contain expression level of CRP.
  • the biological data contain expression level of IP- 10, and wherein the ratio is more than -0.08.
  • the biological data contain a count of Urine leukocytes.
  • the subject exhibits symptoms of infection.
  • the symptoms comprise fever.
  • the method comprises obtaining background and/or clinical data pertaining to the subject, and weighing the likelihood based on the age.
  • the method comprises obtaining the likelihood based on the distance, comparing the likelihood to a predetermined threshold, and prescribing treatment to the subject based on the comparison.
  • the method comprises obtaining the likelihood based on the distance, comparing the likelihood to a predetermined threshold, and, treating the subject for the bacterial infection when the likelihood is above the predetermined threshold.
  • the method comprises generating an output of the likelihood.
  • the blood sample is whole blood.
  • the blood sample is a fraction of whole blood.
  • the blood fraction comprises serum or plasma.
  • the calculation and correlation is executed by a computer remote from the subject.
  • the calculation and the correlation is executed by a computer near the subject.
  • the calculation and correlation is executed by a cloud computing resource of a cloud computing facility.
  • a computer software product comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the hardware processor to receive expression levels of a plurality of polypeptides in the blood of a subject who has an unknown disease, and to execute the method as delineated above and optionally and preferably as further detailed below.
  • Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.
  • a data processor such as a computing platform for executing a plurality of instructions.
  • the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data.
  • a network connection is provided as well.
  • a display and/or a user input device such as a keyboard or mouse are optionally provided as well.
  • FIG. 1 is a schematic illustration of geometrical objects that can be used for determining a likelihood, according to some embodiments of the present invention
  • FIG. 2 is a flowchart diagram of a method suitable for analyzing biological data obtained from a subject, according to some embodiments of the present invention
  • FIGs. 3A-D are schematic illustrations of a procedure for obtaining a smooth version of a segment of a curved object, according to some embodiments of the present invention
  • FIG. 4 is a schematic illustration of a block diagram of a system for analyzing biological data, according to some embodiments of the present invention
  • FIGs. 5A and 5B are schematic illustrations of a block diagram of a system for analyzing biological data, in embodiments of the invention in which the system comprises a network interface (FIG. 5A) and a user interface (FIG. 5B);
  • FIGs. 6A and 6B show temporal dynamics of urine CRP (FIG. 6A) and urine IP- 10 (FIG. 6B) in bacterial patients over 90 days old;
  • FIG. 7 is a flow chart illustrating patient recruitment for urine biomarkers. UTI, urinary tract infection.
  • the present invention in some embodiments thereof, relates to the identification of biological signatures and determinants associated with bacterial and viral infections in urine samples and methods of using such biological signatures in the screening, diagnosis, therapy and monitoring of infection.
  • Some embodiments relate to computational analysis, and, more particularly, but not exclusively, to computational analysis of biological data, e.g., for the purpose of distinguishing between bacterial infection and non-bacterial disease, and/or between a bacterial infection and viral infection, and/or between an infectious and non- infectious disease.
  • Some embodiments of the invention are particularly useful in cases in which the samples under investigation are obtained from children and/or infants.
  • IP- 10 and CRP urinary levels of IP- 10 and CRP in febrile pediatric patients with suspected UTI were measured in order to check whether these biomarkers can be used to differentiate between bacterial UTI and non-bacterial etiology. It was found that the levels of IP- 10 and CRP are differentially expressed in bacterial UTI patients and may serve as a tool for detecting urinary tract infections.
  • a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the level of CRP is above 1.5 mg/L and/or the level of IP- 10 is above 2.0 ng/L, it is indicative of a bacterial infection.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non- infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non- infectious subject, it is indicative of a bacterial infection.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • the bacterial infection is ruled in when the level of CRP is at least 2 times the concentration of CRP in a control sample.
  • the bacterial infection is ruled in when the level of CRP is at least 3 times the concentration of CRP in a control sample.
  • the bacterial infection is ruled in when the level of CRP is at least 4 times the concentration of CRP in a control sample.
  • the bacterial infection is ruled in when the level of CRP is at least 5 times the concentration of CRP in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 3 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 4 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 5 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 6 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 7 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 8 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 9 times the concentration of IP- 10 in a control sample.
  • the bacterial infection is ruled in when the level of IP- 10 is at least 10 times the concentration of IP- 10 in a control sample.
  • polypeptide names presented herein are given by way of example. Many alternative names, aliases, modifications, isoforms and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all the alternative protein names, aliases, modifications isoforms and variations.
  • HGNC Human Genome Organization Naming Committee
  • NCBI National Center for Biotechnology Information
  • a protein sample is preferably prepared.
  • Creatinine a byproduct of muscle (or protein) catabolism, is typically excreted at a relatively constant rate ( ⁇ +10%) within a healthy individual but varies widely when major physiologic changes such as body building, weight loss/gain, or pregnancy are taking place. Creatinine excretion differs with respect to factors such as race/ethnicity, age, sex, lean muscle mass or body mass index (BMI), and physiologic changes in pregnancy. Creatinine correction of urine dilution may also take into account creatinine-dependent factors of the population being studied (e.g., race, age, sex).
  • Specific gravity the measure of dissolved solids in urine, is often correlated to tire creatinine concentration, but because it has less resolution, it is not as highly affected by demographic factors and so is often used instead of creatinine correction. However, it is normalized on the median specific gravity of the population rather than a constant value, and tlris may hinder the ability to compare concentrations across populations.
  • Pc is the specific gravity- corrected analyte concentration (ng/mL)
  • SGm is the median SG value among the study population
  • SG is the specific gravity of the individual urine sample. According to a specific embodiment, when the level of creatinine normalized CRP is above 1.5 ⁇ g/L, above 1.6 ⁇ g/L or above 1.7 ⁇ g/L, a bacterial infection is ruled in.
  • a bacterial infection is ruled out. Additional tests may be carried out in order to confirm that the infection is viral.
  • a bacterial infection is ruled out. Additional tests may be carried out in order to confirm that the infection is viral.
  • the ruling in takes into account the levels of additional proteins including but not limited to procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase- associated lipocalin (NGAL), IL-IRA, IRNg, TNFa, MCP-1 and Interleukin- 18 (IL-18), details of which are provided in Table 1, below.
  • PCT procalcitonin
  • IL-6 Interleukin-6
  • NGAL Neutrophil gelatinase- associated lipocalin
  • IL-IRA IL-IRA
  • IRNg IRNg
  • TNFa TNFa
  • MCP-1 Interleukin- 18
  • the ruling in takes into account additional traditional laboratory risk factors.
  • “Traditional laboratory risk factors” encompass biomarkers isolated or derived from subject samples and which are currently evaluated in the clinical laboratory and used in traditional global risk assessment algorithms, such as absolute neutrophil count (abbreviated ANC), absolute lymphocyte count (abbreviated ALC), white blood count (abbreviated WBC), neutrophil % (defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)), lymphocyte % (defined as the fraction of white blood cells that are lymphocytes and abbreviated Lym (%)), monocyte % (defined as the fraction of white blood cells that are monocytes and abbreviated Mon (%)), Sodium (abbreviated Na), Potassium (abbreviated K), Bilirubin (abbreviated Bill).
  • ANC absolute neutrophil count
  • ALC absolute lymphocyte count
  • WBC white blood count
  • neutrophil % defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)
  • lymphocyte % defined as the fraction of white blood cells that are lymphocyte
  • the laboratory risk factor includes at least one of the following: nitrite level, white blood cell count and pH.
  • the ruling in takes into account additional clinical parameters.
  • “Clinical parameters” encompass all non-sample or non-analyte biomarkers of subject health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE), gender (Sex), core body temperature (abbreviated “temperature”), maximal core body temperature since initial appearance of symptoms (abbreviated “maximal temperature”), time from initial appearance of symptoms (abbreviated “time from symptoms”) or family history (abbreviated FamHX).
  • the level of additional parameters may be analyzed such as absolute Neutrophil count (ANC), ALC, Neu (%), Lymphocyte percentage (Lym (%)), Monocyte percentage (Mono (%)), Maximal temperature, Time from symptoms, Age, Potassium (K), Pulse and Urea.
  • the bacterial infection is a chronic bacterial infection.
  • a "chronic infection” is an infection that develops slowly and lasts a long time.
  • One difference between acute and chronic infection is that during acute infection the immune system often produces IgM+ antibodies against the infectious agent, whereas the chronic phase of the infection is usually characteristic of IgM-/IgG+ antibodies.
  • acute infections cause immune mediated necrotic processes while chronic infections often cause inflammatory mediated fibrotic processes and scarring. Thus, acute and chronic infections may elicit different underlying immunological mechanisms.
  • the bacterial infection is an acute bacterial infection.
  • An "acute infection” is characterized by rapid onset of disease, a relatively brief period of symptoms, and resolution within days.
  • the infection is a urinary tract infection.
  • TP is true positive, means positive test result that accurately reflects the tested-for activity.
  • a TP is for example but not limited to, truly classifying a bacterial infection as such.
  • TN is true negative, means negative test result that accurately reflects the tested-for activity.
  • a TN is for example but not limited to, truly classifying a viral infection as such.
  • FN is false negative, means a result that appears negative but fails to reveal a situation.
  • a FN is for example but not limited to, falsely classifying abacterial infection as a viral infection.
  • FP is false positive, means test result that is erroneously classified in a positive category.
  • a FP is for example but not limited to, falsely classifying a viral infection as a bacterial infection.
  • Specificity is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.
  • Total accuracy is calculated by (TN + TP)/(TN + FP +TP + FN).
  • PSV Positive predictive value
  • NDV Neuronal predictive value
  • O’Marcaigh AS, Jacobson RM “Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results,” Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test.
  • MCC (TP * TN - FP * FN) / ⁇ (TP + FN) * (TP + FP) * (TN + FP) * (TN + FN)) 0.5
  • TP, FP, TN, FN are true- positives, false-positives,true-negatives, and false-negatives, respectively.
  • MCC values range between -1 to +1, indicating completely wrong and perfect classification, respectively.
  • An MCC of 0 indicates random classification.
  • MCC has been shown to be a useful for combining sensitivity and specificity into a single metric (Baldi, Brunak et al. 2000). It is also useful for measuring and optimizing classification accuracy in cases of unbalanced class sizes (Baldi, Brunak et al. 2000).
  • ROC Receiver Operating Characteristics
  • “Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), Matheus correlation coefficient (MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC) among other measures.
  • TP true positives
  • TN true negatives
  • FP false negatives
  • MPC Matheus correlation coefficient
  • ROC Receiver Operating Characteristic
  • a “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value”.
  • “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical- determinants, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations.
  • determinants Of particular use in combining determinants are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of determinants detected in a subject sample and the subject’s probability of having an infection or a certain type of infection.
  • structural and syntactic statistical classification algorithms, and methods of index construction utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, and Hidden Markov Models, among others.
  • PCA Principal Components Analysis
  • LogReg Logistic Regression
  • LDA Linear Discriminant Analysis
  • ELDA Eigen
  • the resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One- Out (LOO) and 10-Fold cross-validation ( 10-Fold CV).
  • LEO Leave-One- Out
  • 10-Fold CV 10-Fold cross-validation
  • false discovery rates may be estimated by value permutation according to techniques known in the art.
  • a “health economic utility function” is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care.
  • a cost and/or value measurement associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and/or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome.
  • a utility associated with each outcome
  • the sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcome’s expected utility is the total health economic utility of a given standard of care.
  • the difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention.
  • This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance.
  • Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost-effective clinical performance characteristics required of a new intervention.
  • a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures.
  • Measurement or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject’s non-analyte clinical parameters or clinical-determinants.
  • “Analytical accuracy” refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation (CV), Pearson correlation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and/or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.
  • “Performance” is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate “performance metrics,” such as AUC and MCC, time to result, shelf life, etc. as relevant.
  • Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.
  • a “subject” in the context of the present invention may be a mammal (e.g. human dog, cat, horse, cow, sheep, pig, goat).
  • the subject is a bird (e.g. chicken, turkey, duck, goose.
  • the subject is a human.
  • a subject can be male or female.
  • a subject can be one who has been previously diagnosed or identified as having an infection, and optionally has already undergone, oris undergoing, a therapeutic intervention for the infection.
  • a subject can also be one who has not been previously diagnosed as having an infection.
  • a subject can be one who exhibits one or more risk factors for having an infection.
  • the subject may be a human subject younger than 18 years old, 12 years old, 2 years old, 1 year or younger, 3, 2 and/or 1 month or younger.
  • the subject is symptomatic for an infection (e.g. has fever).
  • the subject does not have a kidney disease.
  • the subject is asymptomatic for an infection (i.e. not exhibiting the traditional signs and symptoms e.g. does not have a fever).
  • the bacterial infection may be the result of gram-positive, gram-negative bacteria or atypical bacteria.
  • Gram-positive bacteria are bacteria that are stained dark blue by Gram staining. Gram-positive organisms are able to retain the crystal violet stain because of the high amount of peptidoglycan in the cell wall.
  • Gram- negative bacteria are bacteria that do not retain the crystal violet dye in the Gram staining protocol.
  • a reference value can be relative to a number or value derived from population studies, including without limitation, such subjects having the same infection, subject having the same or similar age range, subjects in the same or similar ethnic group, or relative to the starting sample of a subject undergoing treatment for an infection.
  • Such reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of infection. Reference determinant indices can also be constructed and used using algorithms and other methods of statistical and structural classification.
  • the reference value is the amount (i.e. level) of determinants in a control sample derived from one or more subjects who do not have an infection (i.e., healthy, and or non-infectious individuals).
  • such subjects are monitored and/or periodically retested for a diagnostically relevant period of time (“longitudinal studies”) following such test to verify continued absence of infection.
  • Such period of time may be one day, two days, two to five days, five days, five to ten days, ten days, or ten or more days from the initial testing date for determination of the reference value.
  • retrospective measurement of determinants in properly banked historical subject samples may be used in establishing these reference values, thus shortening the study time required.
  • a reference value can also comprise the amounts of determinants derived from subjects who show an improvement as a result of treatments and/or therapies for the infection.
  • a reference value can also comprise the amounts of determinants derived from subjects who have confirmed infection by known techniques.
  • An example of a bacterially infected reference value index value is the mean or median concentrations of that determinant in a statistically significant number of subjects having been diagnosed as having a bacterial infection.
  • An example of a virally infected reference value is the mean or median concentrations of that determinant in a statistically significant number of subjects having been diagnosed as having a viral infection.
  • the reference value is an index value or a baseline value.
  • An index value or baseline value is a composite sample of an effective amount of determinants from one or more subjects who do not have an infection.
  • a baseline value can also comprise the amounts of determinants in a sample derived from a subject who has shown an improvement in treatments or therapies for the infection.
  • the amounts of determinants are similarly calculated and compared to the index value.
  • subjects identified as having an infection are chosen to receive a therapeutic regimen to slow the progression or eliminate the infection.
  • the amount of the determinant can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values.
  • the “normal control level” means the level of one or more determinants or combined determinant indices typically found in a subject not suffering from an infection. Such normal control level and cutoff points may vary based on whether a determinant is used alone or in a formula combining with other determinants into an index. Alternatively, the normal control level can be a database of determinant patterns from previously tested subjects.
  • the effectiveness of a treatment regimen can be monitored by detecting a determinant in an effective amount (which may be one or more) of samples obtained from a subject over time and comparing the amount of determinants detected. For example, a first sample can be obtained prior to the subject receiving treatment and one or more subsequent samples are taken after or during treatment of the subject.
  • a treatment recommendation i.e., selecting a treatment regimen for a subject is provided by identifying the type infection (i.e., bacterial, viral, mixed infection or no infection) in the subject according to the method of any of the disclosed methods and recommending that the subject receive an antibiotic treatment if the subject is identified as having bacterial infection or a mixed infection; or an anti- viral treatment is if the subject is identified as having a viral infection.
  • type infection i.e., bacterial, viral, mixed infection or no infection
  • antibiotics for the treatment of urinary tract infections include trimethoprim- sulfamethoxazole, trimethoprim, ciprofloxacin, levlfloxacin, Norflozacin, Nistofurantoin macrocrystals, Nistofurantoin monohydrate macrocrystals, fosfomycin tromethamine.
  • the methods of the invention can be used to prompt additional targeted diagnosis such as pathogen specific PCRs, chest- X-ray, cultures etc.
  • additional targeted diagnosis such as pathogen specific PCRs, chest- X-ray, cultures etc.
  • a diagnosis that indicates a viral infection according to embodiments of this invention may prompt the usage of additional viral specific multiplex- PCRs
  • a diagnosis that indicates a bacterial infection according to embodiments of this invention may prompt the usage of a bacterial specific multiplex-PCR.
  • a diagnostic test recommendation for a subject is provided by identifying the infection type (i.e., bacterial, viral, mixed infection or no infection) in the subject according to any of the disclosed methods and recommending a test to determine the source of the bacterial infection if the subject is identified as having a bacterial infection or a mixed infection; or a test to determine the source of the viral infection if the subject is identified as having a viral infection.
  • Some aspects of the present invention can also be used to screen patient or subject populations in any number of settings. For example, a health maintenance organization, public health entity or school health program can screen a group of subjects to identify those requiring interventions, as described above, or for the collection of epidemiological data.
  • Insurance companies may screen applicants in the process of determining coverage or pricing, or existing clients for possible intervention.
  • Data collected in such population screens, particularly when tied to any clinical progression to conditions like infection, will be of value in the operations of, for example, health maintenance organizations, public health programs and insurance companies.
  • Such data arrays or collections can be stored in machine-readable media and used in any number of health-related data management systems to provide improved healthcare services, costeffective healthcare, improved insurance operation, etc. See, for example, U.S. Patent Application No. 2002/0038227; U.S. Patent Application No. US 2004/0122296; U.S. Patent Application No. US 2004/ 0122297; and U.S. Patent No. 5,018,067.
  • Such systems can access the data directly from internal data storage or remotely from one or more data storage sites as further detailed herein.
  • a machine-readable storage medium can comprise a data storage material encoded with machine readable data or data arrays which, when using a machine programmed with instructions for using the data, is capable of use for a variety of purposes.
  • Measurements of effective amounts of the biomarkers of the invention and/or the resulting evaluation of risk from those biomarkers can be implemented in computer programs executing on programmable computers, comprising, inter alia, a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.
  • Program code can be applied to input data to perform the functions described above and generate output information.
  • the output information can be applied to one or more output devices, according to methods known in the art.
  • the computer may be, for example, a personal computer, microcomputer, or workstation of conventional design.
  • Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system
  • the programs can be implemented in assembly or machine language, if desired.
  • the language can be a compiled or interpreted language.
  • Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette or others as defined elsewhere in this disclosure) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein.
  • the health-related data management system used in some aspects of the invention may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform various functions described herein.
  • the determinants of the present invention in some embodiments thereof, can be used to generate a “reference determinant profile” of those subjects who do not have an infection.
  • the determinants disclosed herein can also be used to generate a “subject determinant profile” taken from subjects who have an infection.
  • the subject determinant profiles can be compared to a reference determinant profile to diagnose or identify subjects with an infection.
  • the subject determinant profile of different infection types can be compared to diagnose or identify the type of infection.
  • the reference and subject determinant profiles of the present invention in some embodiments thereof, can be contained in a machine-readable medium, such as but not limited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others.
  • Such machine-readable media can also contain additional test results, such as, without limitation, measurements of clinical parameters and traditional laboratory risk factors.
  • the machine-readable media can also comprise subject information such as medical history and any relevant family history.
  • the machine-readable media can also contain information relating to other disease-risk algorithms and computed indices such as those described herein.
  • the performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above.
  • some aspects of the invention are intended to provide accuracy in clinical diagnosis and prognosis.
  • the accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects having an infection is based on whether the subjects have, a “significant alteration” (e.g., clinically significant and diagnostically significant) in the levels of a determinant.
  • a “significant alteration” e.g., clinically significant and diagnostically significant
  • effective amount it is meant that the measurement of an appropriate number of determinants (which may be one or more) to produce a “significant alteration” (e.g.
  • the difference in the level of determinant is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical, diagnostic, and clinical accuracy, may require that combinations of several determinants be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant determinant index.
  • an “acceptable degree of diagnostic accuracy” is herein defined as a test or assay (such as the test used in some aspects of the invention for determining the clinically significant presence of determinants, which thereby indicates the presence an infection type) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.
  • a “very high degree of diagnostic accuracy” it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.
  • the methods predict the presence or absence of an infection or response to therapy with at least 75% total accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.
  • the methods predict the presence of a bacterial infection or response to therapy with at least 75% sensitivity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater sensitivity.
  • the methods predict the presence of a viral infection or response to therapy with at least 75% specificity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater specificity.
  • the methods predict the presence or absence of an infection or response to therapy with an MCC larger than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8., 0.9 or 1.0.
  • the predictive value of any test depends on the sensitivity and specificity of the test, and on the prevalence of the condition in the population being tested.
  • ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon).
  • a health economic utility function is an yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each.
  • Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects.
  • As a performance measure it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.
  • diagnostic accuracy In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease.
  • measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer- Lemeshow P- value statistics and confidence intervals.
  • the degree of diagnostic accuracy i.e., cut points on a ROC curve
  • defining an acceptable AUC value determining the acceptable ranges in relative concentration of what constitutes an effective amount of the determinants of the invention allows for one of skill in the art to use the determinants to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.
  • biomarkers will be very highly correlated with the determinants (for the purpose of this application, any two variables will be considered to be “very highly correlated” when they have a Coefficient of Determination ( R 2 ) of 0.5 or greater).
  • R 2 Coefficient of Determination
  • One or more of the listed determinants can be detected in the practice of the present invention, in some embodiments thereof. For example, two (2), three (3), four (4), five (5), ten (10), fifteen (15), twenty (20), forty (40), or more determinants can be detected.
  • all determinants listed herein can be detected.
  • Preferred ranges from which the number of determinants can be detected include ranges bounded by any minimum selected from between one and, particularly two, three, four, five, six, seven, eight, nine ten, twenty, or forty. Particularly preferred ranges include two to five (2-5), two to ten (2-10), two to twenty (2- 20), or two to forty (2-40).
  • the actual measurement of levels or amounts of the determinants can be determined at the protein level using any method known in the art.
  • proteins encoded by the gene products described herein are well known in the art and include, e.g., immunoassays based on antibodies to proteins, aptamers or molecular imprints. Any biological material can be used for the detection/quantification of the protein or its activity. Alternatively, a suitable method can be selected to determine the activity of proteins encoded by the marker genes according to the activity of each protein analyzed.
  • the determinants can be detected in any suitable manner, but are typically detected by contacting a urine sample from the subject with an antibody, which binds the determinant and then detecting the presence or absence of a reaction product.
  • the antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay.
  • the antibody which specifically binds the determinant is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label.
  • Immunoassays carried out in accordance with some embodiments of the present invention may be homogeneous assays or heterogeneous assays.
  • the immunological reaction usually involves the specific antibody (e.g., anti- determinant antibody), a labeled analyte, and the sample of interest.
  • the signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte.
  • Both the immunological reaction and detection of the extent thereof can be carried out in a homogeneous solution.
  • Immunochemical labels which may be employed, include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.
  • the reagents are usually the sample, the antibody, and means for producing a detectable signal.
  • Samples as described above may be used.
  • the antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen suspected of containing the antigen in a liquid phase.
  • the support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal.
  • the signal is related to the presence of the analyte in the sample.
  • Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels.
  • an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step.
  • the presence of the detectable group on the solid support indicates the presence of the antigen in the test sample.
  • suitable immunoassays are oligonucleotides, immunoblotting, immunofluorescence methods, immunoprecipitation, chemiluminescence methods, electrochemiluminescence (ECL) or enzyme- linked immunoassays.
  • lateral flow immunoassay is used to analyze the level of the determinant. Further descriptions of LFI devices may be found in PCT Application IL2017/050697, the contents of which are incorporated herein by reference.
  • Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding.
  • Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g., 35 S, 125 I, 131 I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
  • a diagnostic assay e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene
  • Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabel
  • Antibodies can also be useful for detecting post-translational modifications of determinant proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc).
  • Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well-known to those skilled in the art, and commercially available.
  • Post- translational modifications can also be determined using metastable ions in reflector matrix- assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF) (Wirth U. and Muller D. 2002).
  • MALDI-TOF reflector matrix- assisted laser desorption ionization-time of flight mass spectrometry
  • Additional exemplary methods for analyzing the level of a protein include Western blot analysis and Enzyme linked immunosorbent assay (ELISA).
  • ELISA Enzyme linked immunosorbent assay
  • the activities can be determined in vitro using enzyme assays known in the art.
  • enzyme assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others.
  • Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant K M using known algorithms, such as the Hill plot, Michaelis-Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.
  • metabolic includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage or consumption of a biological molecule (e.g., a protein, nucleic acid, carbohydrate, or lipid).
  • a biological molecule e.g., a protein, nucleic acid, carbohydrate, or lipid.
  • Metabolites can be detected in a variety of ways known to one of skill in the art, including the refractive index spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption ionization time of flight (MALDI-TOF) combined with mass spectrometry, ion spray spectroscopy combined with mass spectrometry, capillary electrophoresis, NMR and IR detection.
  • RI refractive index spectroscopy
  • UV ultra-violet spectroscopy
  • fluorescence analysis radiochemical analysis
  • radiochemical analysis near-infrare
  • DETERMINANT analytes can be measured using the above-mentioned detection methods, or other methods known to the skilled artisan.
  • circulating calcium ions can be detected in a sample using fluorescent dyes such as the poly-amino carboxylic acid, Fluo series, Fura-2A, Rhod- 2, the ratiometric calcium indicator Indo-1, among others.
  • fluorescent dyes such as the poly-amino carboxylic acid, Fluo series, Fura-2A, Rhod- 2, the ratiometric calcium indicator Indo-1, among others.
  • Other determinant metabolites can be similarly detected using reagents that are specifically designed or tailored to detect such metabolites.
  • Suitable sources for antibodies for the detection of determinants include commercially available sources such as, for example, Abazyme, Abnova, AssayPro, Affinity Biologicals, AntibodyShop, Aviva bioscience, Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences, Chemicon International, Chemokine, Clontech, Cytolab, DAKO, Diagnostic BioSystems, eBioscience, Endocrine Technologies, Enzo Biochem, Eurogentec, Fusion Antibodies, Genesis Biotech, GloboZymes, Haematologic Technologies, Immunodetect, Immunodiagnostik, Immuno me tries, Immunostar, Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory, KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, Lee Laboratories, lifescreen, Maine Biotechnology Services, Mediclone, MicroPharm Ltd., ModiQuest, Molecular Innovations, Molecular Probes, Neoclone, Neuromics, New England Biolabs, Novocastra
  • antibodies for measuring TRAIT include without limitation: Mouse anti- Human TRAIL Monoclonal antibody (RIK-2) (12-9927-42) (Invitrogen), Goat IgG anti-Human TRAIL Polyclonal antibody (AF375) (R&D Systems), Mouse anti-Human TRAIL monoclonal antibody [2E5] (ab2219) (Abeam), Mouse anti-Human Monoclonal antibody (Clone # 75402) (MAB687) (R&D Systems),
  • antibodies for measuring CRP include without limitation: Rabbit anti-Human C-Reactive Protein/CRP polyclonal antibody (ab31156) (Abeam), Sheep anti-Human C-Reactive Protein/CRP Polyclonal antibody (AF1707) (R&D Systems), rabbit anti-Human C-Reactive Protein/CRP Polyclonal antibody (C3527) (Sigma- Aldrich), Mouse anti-Human C-Reactive Protein/CRP monoclonal antibody (C1688) (MilliporeSigma),
  • Examples of antibodies for measuring IP- 10 include without limitation: Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (Cat. No. 524401) (BioLegend), Rabbit anti-human CXCL10 (IP- 10) polyclonal Antibody (ab9807) (Abeam), Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (4D5) (MCA1693) (Bio-Rad), Goat anti-human CXCL10 (IP- 10) Monoclonal Antibody (PA5-46999) (Invitrogen), Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (MA5-23819) (Invitrogen),
  • antibodies for measuring IL-6 include without limitation: Mouse anti-human IL-6 monoclonal antibody (Clone # 1936) (MAB2061) (R&D Systems), Mouse anti-human IL-6 monoclonal antibody (ab9324) (Abeam), Rat anti-human IL-6 monoclonal antibody (MQ2-39C3) (501204) (BioLegend), Rabbit anti-human IL-6 monoclonal antibody (ab233706) (Abeam),
  • Examples of antibodies for measuring Procalcitonin include without limitation: Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (DMAB1342MH) (Creative diagnostics), Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (MAB8350) (R&D Systems), Sheep Anti-Human Procalcitonin (PCT) polyclonal antibody (PA1-75362) (Invitrogen), Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (6F10) (MAI-20888) (Invitrogen).
  • antibodies for measuring IL-18 include without limitation: Rabbit Anti- Human IL-18 polyclonal antibody (PA5-80719) (Invitrogen), Rabbit Anti-Human IL-18 recombinant monoclonal antibody (MA5-30764) (Invitrogen), Rabbit Anti-Human IL-18 polyclonal antibody (ab 191152) (Abeam).
  • antibodies for measuring NGAL include without limitation: Rabbit Anti- Human NGAL monoclonal antibody (abl25075) (Abeam), Mouse Anti-Human NGAL monoclonal antibody (ab23477) (Abeam), Rabbit Anti-Human NGAL polyclonal antibody (PA5- 79589) (Invitrogen), Rabbit Anti-Human NGAL recombinant monoclonal antibody (702248) (Invitrogen), Rat Anti-Human NGAL monoclonal antibody (MAB17571-SP) (R&D Systems),
  • Examples of antibodies for measuring IL1R/IL1R1 include without limitation: Goat Anti- Human IL1R1 Polyclonal antibody (PA5-46930) (Invitrogen), Mouse Anti-Human IL1R1 Monoclonal antibody (clone IL1 31-22.2.1) (MAI- 10857) (Invitrogen), Goat Anti-Human ILIRI Polyclonal antibody (AF269) (R&D Systems), Rabbit Anti-Human ILIRI Polyclonal antibody (abl06278) (Abeam), Mouse Anti-Human ILIRI monoclonal antibody (sc-393998) (SANTA CRUZ BIOTECHNOLOGY).
  • Goat Anti- Human IL1R1 Polyclonal antibody PA5-46930
  • Mouse Anti-Human IL1R1 Monoclonal antibody clone IL1 31-22.2.1
  • MAI- 10857 Invitrogen
  • Goat Anti-Human ILIRI Polyclonal antibody AF269) (R&D Systems
  • Examples of antibodies for measuring Serum Amyloid A1 include without limitation: Rabbit Anti-Human Serum Amyloid A1 (SAA/ SAA1) Polyclonal antibody (PA5- 112852) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (MA5- 11729) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (MBS592153) (MyBioSource), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (ab687) (Abeam), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (clone SAA 19) (MCA6030GA) (BIO-RAD).
  • Rabbit Anti-Human Serum Amyloid A1 SAA/ SAA1
  • PA5- 112852 Invitrogen
  • Mouse Anti-Human Serum Amyloid A1 SAA/ SAA1 Mono
  • antibodies for measuring TREM1 include without limitation: Mouse Anti- Human TREM-1 Monoclonal Antibody (R&D Systems), Rabbit Anti-Human TREMl Polyclonal Antibody (Merck), Rabbit Anti-Human TREMl Polyclonal Antibody (Invitrogen), Mouse Anti- Human CD354 (TREM-1) Monoclonal Antibody (BioLegend).
  • antibodies for measuring TREM2 include without limitation: Rat Anti- Human TREM2 Monoclonal antibody (Clone # 237920) (MAB 17291) (R&D Systems), Goat Anti-Human TREM2 Polyclonal antibody (AF1828) (R&D Systems), Mouse Anti-Human TREM2 Monoclonal antibody (clone 2B5) (NBP1-07101) (Novus Biologicals), Rabbit Anti- Human TREM2 Monoclonal antibody (ab209814) (Abeam).
  • antibodies for measuring IL8 include without limitation: Mouse Anti-Human IL-8 Monoclonal Antibody (abl8672) (Abeam), Mouse Anti-Human IL-8/CXCL8 Monoclonal Antibody (R&D Systems), Mouse Anti-Human IL-8 (CXCL8) Monoclonal Antibody (Invitrogen).
  • antibodies for measuring IL-15 include without limitation: Mouse Anti- Human IL-15 Monoclonal Antibody (MA5-23729) (Invitrogen), Mouse Anti-Human IL-15 Monoclonal Antibody (16-0157-82) (Invitrogen), Rabbit Anti-Human IL-15 Polyclonal Antibody (PA5- 102871) (Invitrogen), Mouse Anti-Human IL-15 Monoclonal Antibody (ab55276) (Abeam), Mouse Anti-Human IL-15 Monoclonal Antibody (Clone # 34559) (MAB2471) (R&D Systems).
  • antibodies for measuring IL-12 include without limitation: Goat Anti-Human IL-12 Polyclonal Antibody (AF-219-NA) (R&D Systems), Mouse Anti-Human IL-12 Monoclonal Antibody (Clone # 24910) (MAB219) (R&D Systems), Goat Anti-Human IL-12 Polyclonal Antibody (ab9992) (Abeam), Rat Anti-Human IL-12 Monoclonal Antibody (16-8126-85) (Invitrogen).
  • antibodies for measuring IL-10 include without limitation: Mouse Anti- Human IL-10 Monoclonal Antibody (Clone # 127107) (MAB2172) (R&D Systems), Rat Anti- Human IL-10 Monoclonal Antibody (clone JES3-9D7) (501403) (BioLegend), Rabbit Anti- Human IL-10 Polyclonal Antibody (ab34843) (Abeam), Rat Anti-Human IL-10 Monoclonal Antibody (clone JES3-12G8) (MAI-82664) (Invitrogen).
  • Examples of antibodies for measuring MCP-1 include without limitation: Rabbit anti- Human MCP-1 Polyclonal antibody (ab9669) (Abeam), Mouse anti-Human MCP-1 Monoclonal antibody (Clone # 23007) (MAB679) (R&D Systems), Mouse anti-Human MCP-1 Monoclonal antibody (Clone 2D8) (MA5- 17040) (Invitrogen), Mouse anti-Human MCP-1 Monoclonal antibody (Clone 5D3-F7) (MCA5981GA) (BIO-RAD).
  • IL-2RA antibodies for measuring IL-2R
  • examples of antibodies for measuring IL-2R include without limitation: Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (ab9496) (Abeam), Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (Clone #24212) (MAB1020) (R&D Systems), Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (Clone YNRhlL2R) (ANT- 104) (ProSpec).
  • Examples of antibodies for measuring GDF15 include without limitation: Goat anti- Human GDF15 Polyclonal antibody (AF957) (R&D Systems), Mouse anti-Human GDF15 Monoclonal antibody (Clone # 147627) (MAB957) (R&D Systems), Goat anti-Human GDF15 Polyclonal antibody (ab39999) (Abeam), Rabit anti-Human GDF15 Polyclonal antibody (abl06006) (Abeam), Rabit anti-Human GDF15 Polyclonal antibody (HPA011191) (Sigma- Aldrich).
  • Examples of antibodies for measuring MBL include without limitation: Goat anti-Human MBL Polyclonal antibody (AF2307) (R&D Systems), Mouse anti-Human MBL Monoclonal antibody (3B6) (ab23457) (Abeam), Goat anti-Human MBL Polyclonal antibody (AF2307) (Novus Biologicals).
  • Examples of antibodies for measuring CD27 include without limitation: Goat anti-Human CD27 Polyclonal antibody (AF382) (R&D Systems), Mouse anti-Human CD27 monoclonal antibody (clone 0323) (47-0279-42) (Invitrogen), Rabbit anti-Human CD27 Polyclonal antibody (PA5-83443) (Invitrogen), Mouse anti-Human CD27 monoclonal antibody (clone CLB-27/1) (MHCD2704) (Invitrogen).
  • Goat anti-Human CD27 Polyclonal antibody AF382
  • Mouse anti-Human CD27 monoclonal antibody clone 0323
  • 47-0279-42 Invitrogen
  • Rabbit anti-Human CD27 Polyclonal antibody PA5-83443
  • Mouse anti-Human CD27 monoclonal antibody clone CLB-27/1) (MHCD2704) (Invitrogen).
  • antibodies for measuring MMP2 include without limitation: Rabbit anti- Human MMP2 Polyclonal antibody (ab97779) (Abeam), Mouse anti-Human MMP2 monoclonal antibody (6E3F8) (ab86607) (Abeam), Mouse anti-Human MMP2 monoclonal antibody (Clone # 36006) (MAB902) (R&D Systems), Goat anti-Human MMP2 polyclonal antibody (AF902) (R&D Systems), Rabbit anti-Human MMP2 Polyclonal antibody (AHP2735) (BIO-RAD).
  • antibodies for measuring RESISTIN include without limitation: Goat anti- Human RESISTIN Polyclonal antibody (AF1359) (R&D Systems), Mouse anti-Human RESISTIN Monoclonal antibody (Clone # 184305) (MAB13591) (R&D Systems), Rabbit anti- Human RESISTIN Monoclonal antibody (clone EP4738) (abl24681) (Abeam), Mouse anti- Human RESISTIN Monoclonal antibody (sc-376336) (SANTA CRUZ BIOTECHNOLOGY).
  • antibodies for measuring RSAD2 include without limitation: Rabbit anti- Human RSAD2 Polyclonal antibody (HPA041160) (Sigma- Aldrich), Mouse anti-Human RSAD2 Monoclonal antibody (sc-390342) (SANTA CRUZ BIOTECHNOLOGY), Mouse anti-Human RSAD2 Monoclonal antibody (OTI4D12) (TA505799) (OriGene), Rabbit anti-Human RSAD2 Polyclonal antibody (LS-C378833) (LSBio), Rabbit anti-Human RSAD2 Polyclonal antibody (TA329507) (OriGene).
  • antibodies for measuring MX1 include without limitation: Rabbit anti-Human MX1 Polyclonal antibody (ab95926) (Abeam), Goat anti-Human MX1 Polyclonal antibody (AF7946) (R&D Systems), Mouse anti-Human MX1 monoclonal antibody (sc-271024) (SANTA
  • Examples of antibodies for measuring TIE2 include without limitation: Mouse anti-Human TIE2 Monoclonal antibody (Cl. 16) (ab24859) (Abeam), Goat anti-Human TIE2 Polyclonal antibody (AF313) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (Clone # 83715) (MAB3131) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (clone 33.1 (Ab33)) (334205) (BioLegend), Rabbit anti-Human TIE2 Polyclonal antibody
  • Examples of antibodies for measuring VCAM-1/CD106 include without limitation: Mouse anti-Human VCAM-1 Monoclonal antibody (Clone # BBIG-V1) (BBA5) (R&D Systems), Rabbit anti-Human VCAM-1 Monoclonal antibody (EPR5047) (ab 134047) (Abeam), Mouse anti-Human VCAM-1 Monoclonal antibody (clone 1.4C3) (MA5- 11447) (Invitrogen).
  • Examples of antibodies for measuring CD14 include without limitation: Mouse anti- Human CD14 Monoclonal antibody (4B4F12) (abl82032) (Abeam), Mouse anti-Human CD14 Monoclonal antibody (M5E2) (301805) (BioLegend), Mouse anti-Human CD14 Monoclonal antibody (Clone # 134620) (MAB3832) (R&D Systems), Mouse anti-Human CD14 Monoclonal antibody (clone TÜK4) (MCA1568) (Bio-Rad).
  • Examples of antibodies for measuring IGFBP-3 include without limitation: Mouse anti- Human IGFBP-3 Monoclonal antibody (Clone # 84728) (MAB305) (R&D Systems), Goat anti- Human IGFBP-3 Polyclonal antibody (AF675) (R&D Systems), Goat anti-Human IGFBP-3 Polyclonal antibody (ab77635) (Abeam), Rabbit anti-Human IGFBP-3 Polyclonal antibody (PAS- 29711 ) (Invitrogen) .
  • antibodies for measuring APR IT include without limitation: Mouse anti- Human APRIL/ TNFSF13 Monoclonal antibody (JE49-07) (MA5-34866) (Invitrogen), Mouse anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670820) (MAB5860) (R&D Systems), Mouse anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670840) (MAB8843) (R&D Systems), Rabbit anti-Human APRIL/ TNFSF13 Polyclonal antibody (ab3681) (Abeam).
  • antibodies for measuring Adiponectin include without limitation: Mouse anti- Human Adiponectin Monoclonal antibody (19F1) (ab22554) (Abeam), Rabbit anti-Human Adiponectin Polyclonal antibody (ab25891) (Abeam), Mouse anti-Human Adiponectin Monoclonal antibody (Clone # 553517) (MAB 10652) (R&D Systems), Goat anti-Human Adiponectin Polyclonal antibody (AF1065) (R&D Systems), Rabbit anti-Human Adiponectin Polyclonal antibody (A6354) (Sigma- Aldrich).
  • antibodies for measuring Angiogenin include without limitation: Goat anti- Human Angiogenin Polyclonal antibody (AF265) (R&D Systems), Goat anti-Human Angiogenin Polyclonal antibody (AB-265) (R&D Systems), Rabbit anti-Human Angiogenin Polyclonal antibody (ab 189207) (Abeam), Mouse anti-Human Angiogenin Monoclonal antibody (clone MANG-1) (0555-5008) (Bio-Rad).
  • antibodies for measuring Angiopoietin 2/ANG2 include without limitation: Rabbit anti-Human Angiopoietin 2/ANG2 Polyclonal antibody (abl53934) (Abeam), Goat anti- Human Angiopoietin 2/ANG2 Polyclonal antibody (AF623) (R&D Systems), Mouse anti-Human Angiopoietin 2/ANG2 Monoclonal antibody (Clone # 180102) (MAB0983) (R&D Systems), Mouse anti-Human Angiopoietin 2/ANG2 Monoclonal antibody (Clone # M5203F01) (682702) (BioLegend).
  • antibodies for measuring CLUSTERIN include without limitation: Rabbit anti-Human CLUSTERIN Polyclonal antibody (ab69644) (Abeam), Rabbit anti-Human CLUSTERIN Monoclonal antibody [EPR2911] (ab92548) (Abeam), Mouse anti-Human CLUSTERIN Monoclonal antibody (Clone # 350227) (MAB2937) (R&D Systems), Mouse anti- Human CLUSTERIN Monoclonal antibody (Clone # 350270) (MAB29372) (R&D Systems).
  • antibodies for measuring CD95 include without limitation: Mouse anti- Human CD95 Monoclonal antibody (clone DX2) (BioLegend), Mouse anti-Human CD95 Monoclonal antibody (clone EOS9.1) (BioLegend), Mouse anti-Human CD95 Monoclonal antibody (clone LOB 3/17) (Bio-Rad).
  • antibodies for measuring uPAR include without limitation: Mouse anti- Human uPAR Monoclonal antibody (Clone # 62022) (MAB807) (R&D Systems), Goat anti- Human uPAR Polyclonal antibody (AF807) (R&D Systems), Rabbit anti-Human uPAR Monoclonal antibody (clone 2G10) (MABC88) (Sigma- Aldrich).
  • antibodies for measuring IL7R include without limitation: Mouse anti-Human IL7R /CD127 Monoclonal antibody (Clone # 40131) (MAB306) (R&D Systems), Mouse anti- Human IL7R /CD127 Monoclonal antibody (Clone A019D5) (351303) (BioLegend), Mouse anti- Human IL7R /CD127 Monoclonal antibody (eBioRDR5) (48-1278-42) (Invitrogen), Rabbit anti- Human IL7R /CD 127 Polyclonal antibody (PA5-97870) (Invitrogen).
  • antibodies for measuring PTEN include without limitation: Mouse anti- Human PTEN Monoclonal antibody (Clone # 217702) (MAB847) (R&D Systems), Rabbit anti- Human PTEN Polyclonal antibody (ab31392) (Abeam), Mouse anti-Human PTEN Monoclonal antibody (A2bl) (ab79156) (Abeam), Mouse anti-Human PTEN Monoclonal antibody (clone 6H2.1) (Sigma- Aldrich).
  • antibodies for measuring MMP8 include without limitation: Mouse anti- Human MMP8 Monoclonal antibody (Clone # 100608) (MAB9081) (R&D Systems), Mouse anti- Human MMP8 Monoclonal antibody (Clone # 100619) (MAB908) (R&D Systems), Rabbit anti- Human MMP8 Monoclonal antibody (EP1252Y) (ab81286) (Abeam), Rabbit anti-Human MMP8 Polyclonal antibody (PA5-28246) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody (PA5-82805) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody (HPA021221) (Sigma- Aldrich).
  • Examples of antibodies for measuring Ferritin include without limitation: Mouse anti- Human Ferritin Monoclonal antibody (Clone # 962609) (MAB93541) (R&D Systems), Sheep anti-Human Ferritin Polyclonal antibody (AHP2179G) (Bio-Rad), Mouse anti-Human Ferritin Monoclonal antibody (clone F23 (7A4)) (4420-3010) (Bio-Rad), Goat anti-Human Ferritin Polyclonal antibody (PA5- 19058) (Invitrogen), Mouse anti-Human Ferritin Monoclonal antibody (clone 101) (MIF2501) (Invitrogen), Rabbit anti-Human Ferritin Monoclonal antibody (EPR3004Y) (ab75973) (Abeam).
  • Examples of antibodies for measuring D-Dimer include without limitation: Mouse anti- Human D-Dimer Monoclonal antibody (clone DD1) (MCA2523) (Bio-Rad), Mouse anti-Human D-Dimer Monoclonal antibody (3B6) (ab273889) (Abeam), Rabbit anti-Human D-Dimer Monoclonal antibody (Clone # 2609D) (MAB104712) (R&D Systems), Mouse anti-Human D- Dimer Monoclonal antibody (clone DD2) (NB 110-8376) (Novus).
  • Soluble TRAIT can be distinguished by using different measuring techniques and samples.
  • Soluble TRAIT can be measured without limitation in cell free samples such as serum or plasma, using without limitation lateral flow immunoassay (LFIA), as further described herein below.
  • LFIA lateral flow immunoassay
  • Membrane TRAIT can be measured in samples that contain cells using cell based assays including without limitation flow cytometry, ELISA, and other immunoassays.
  • the present inventors have further uncovered novel thresholds which can be used for known markers for ruling in a bacterial infection in subjects younger than 3 months of age. These thresholds provide a very high degree of sensitivity and therefore are appropriate for clinical settings.
  • a method of ruling in a bacterial infection in a subject under 3 months of age comprising measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIL is below 188 pg/L it is indicative of a bacterial infection in the subject.
  • CRP C-reactive protein
  • IP- 10 Interferon gamma-induced protein 10
  • the CRP threshold for ruling in a bacterial infection is above 10 mg/L, above 8 mg/L, above 6 mg/L or above 4 mg/L.
  • the TRAIT, threshold for ruling in a bacterial infection is below 190 pg/L, below 195 pg/L, below 200 pg/L, below 205 pg/L or even below 210 pg/L.
  • the IP- 10 concentration for ruling in a bacterial infection is below 300 pg/L, 310 pg/L or even 320 pg/L.
  • the present embodiments provide a method and a system suitable for analyzing biological data obtained from an infant human subject, such as, but not limited to, a human subject of less than three months of age.
  • an infant human subject such as, but not limited to, a human subject of less than three months of age.
  • the subject has been previously treated with an antibiotic, and in some embodiments of the present invention the subject has not been previously treated with an antibiotic.
  • Some embodiments are based on the use of signature of polypeptides for the diagnosis of bacterial infections, viral infections and non-bacterial, non-viral diseases.
  • the method and/or system of the present embodiments identifies the type of infection an infant subject is suffering from, which in turn allows for the selection of an appropriate treatment regimen.
  • some embodiments of the invention allow for the selection of infant subjects for whom antibiotic treatment is desired and prevent unnecessary antibiotic treatment of infant subjects having only a viral infection or a non-infectious disease.
  • Some embodiments of the invention also allow for the selection of infant subjects for whom anti-viral treatment is advantageous.
  • any of the methods described herein can be embodied in many forms. For example, it can be embodied in on a tangible medium such as a computer for performing the method operations. It can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method operations. It can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium
  • Computer programs implementing the method of the present embodiments can commonly be distributed to users on a distribution medium such as, but not limited to, CD-ROMs or flash memory media. From the distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium.
  • computer programs implementing the method of the present embodiments can be distributed to users by allowing the user to download the programs from a remote location, via a communication network, e.g., the internet.
  • the computer programs can be run by loading the computer instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of the present embodiments. All these operations are well-known to those skilled in the art of computer systems.
  • the computational operations of the method of the present embodiments can be executed by a computer, either remote from the subject or near the subject.
  • a computer When the computer is remote from the subject, it can receive the data over a network, such as a telephone network or the Internet.
  • a local computer can be used to transmit the data to the remote computer.
  • This configuration allows performing the analysis while the subject is at a different location (e.g., at home), and also allows performing simultaneous analyses for multiple subjects in multiple different locations.
  • the computational operations of the method can also be executed by a cloud computing resource of a cloud computing facility.
  • the cloud computing resource can include a computing server and optionally also a storage server, and can be operated by a cloud computing client as known in the art.
  • the method and/or system according to some embodiments may be used to “rule in” a bacterial infection.
  • the method and/or system may be used to rule out a non- bacterial (e.g., viral) infection.
  • the method and/or system according to some embodiments can be used to “rule out” a bacterial infection and “rule in” a non-bacterial disease.
  • the biological data analyzed by the method and/or system optionally and preferably contain values corresponding to concentrations or counts of a plurality of determinants in a sample of a subject, e.g., an infant subject, preferably an infant subject of less than three months of age.
  • the determinants includes polypeptides, and the biological data contain values corresponding to the expression levels of these polypeptides. More preferably, each of the determinants is a polypeptide and the biological data contain a value corresponding to an expression level for each of the polypeptides.
  • sample in the context of the present invention is a biological sample isolated from the subject, particularly an infant subject with less than three months of age, and can include, by way of example and not limitation, whole blood, serum, plasma, saliva, mucus, breath, urine, CSF, sputum, nasal mucus, sample collected by a nasal swab, sweat, stool, hair, seminal fluid, biopsy, rhinorrhea, tissue biopsy, cytological sample, platelets, reticulocytes, leukocytes, epithelial cells, or whole blood cells.
  • the sample may be fresh or frozen.
  • the sample is a blood sample, e.g., serum or a sample comprising blood cells.
  • the sample is depleted of red blood cells.
  • the sample is a urine sample.
  • the sample is not a urine sample, e.g., any of the aforementioned biological samples, excluding urine.
  • the sample is derived from the subject no more than 7 days, or no more than 6 days, or no more than 5 days, or no more than 4 days, or no more than 72 hours, no more than 60 hours, no more than 48 hours, no more than 36 hours, no more than one 24 hours or even no more than 12 hours following symptom onset.
  • the concentrations or counts of the determinants is measured within about 24 hours after the sample is obtained.
  • the concentrations or counts of the determinants is measured in a sample that was stored at 12 °C or lower, when storage begins less than 24 hours after the sample is obtained.
  • the determinant values are stored in a memory location within computer- readable medium, from which the data processor reads the data and performs the analysis as further detailed herein below.
  • the biological data can optionally include additional information, including, without limitation, preliminary diagnosis, observed clinical syndrome, suspected pathogen, age of the subject, gender of the subject, ethnicity of the subject and the like.
  • the additional information can be stored in another memory location within the same or different computer-readable medium, from which the data processor can read the additional information or a portion thereof and optionally perform the analysis based also on this information.
  • the results of the analysis can be stored in another memory location within the same or different computer- readable medium, from which it can optionally and preferably conveyed to a remote or local display, in the form of a textual or graphical output.
  • the biological data comprise values corresponding to concentrations or counts of only two polypeptides (namely a pair of polypeptide expression values), in some embodiments biological data comprise values corresponding to expression levels of only three polypeptides (namely a triple of polypeptide expression values), in some embodiments biological data comprise values corresponding to expression levels of only four polypeptides (namely a quadruple of polypeptide expression values, in some embodiments biological data comprise values corresponding to expression levels of two polypeptides and a count of one additional determinant other than a polypeptide (namely a triple of determinant values), and in some embodiments biological data comprise values corresponding to expression levels of three polypeptides and a count of one additional determinant other than a polypeptide (namely a quadruple of determinant values).
  • Use of n-tuple of determinant values, where n is more than four is also contemplated in some embodiments of the present invention.
  • pairs of polypeptides whose expression values can be measured and used as a pair of polypeptide expression values, include, without limitation, any pair of polypeptides selected from the group consisting of TNF Related Apoptosis Inducing Ligand (TRAIL), C-reactive protein (CRP), Procalcitonin (PCT), Interleukin 6 (IL-6), MX1 and Interferon gamma-induced protein 10 (IP- 10).
  • TRAIL TNF Related Apoptosis Inducing Ligand
  • CCP C-reactive protein
  • PCT Procalcitonin
  • IL-6 Interleukin 6
  • MX1 Interferon gamma-induced protein 10
  • triples of polypeptides whose expression values can be measured and used as a triple of polypeptide expression values, include, without limitation, any triple of polypeptides selected from the group consisting of TRAIL, CRP, PCT, IL-6 and IP- 10.
  • triples of determinant values in embodiments in which biological data comprise values corresponding to expression levels of two polypeptides and a count of one additional determinant other than a polypeptide, include, without limitation, triples of determinant values in which two values correspond to expression values of polypeptides selected from the group consisting of TRAIT,, CRP, PCT, IL-6, IP- 10, and one value corresponds to a count of Urine leukocytes (Uleuco).
  • a representative example of a quadruple of polypeptides whose expression values can be measured and used as a quadruple of polypeptide expression values includes, without limitation, the quadruple TRAIL, CRP, PCT and IP- 10.
  • quadruples of determinant values in embodiments in which the biological data comprise values corresponding to expression levels of three polypeptides and a count of one additional determinant other than a polypeptide, include, without limitation, quadruples of determinant values in which three values correspond to expression values of polypeptides selected from the group consisting of TRAIL, CRP, PCT, IP- 10, and Uleuco, and one value corresponds to a count of Uleuco.
  • Values that correspond to expression levels of the polypeptides can be measured in any suitable manner, but are typically detected by contacting a biological sample obtained from the infant subject with an antibody, which binds the polypeptide, and then measuring the level of a reaction product.
  • the antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, and the measurement of the level reaction product may be carried out with any suitable immunoassay.
  • the antibody which specifically binds the polypeptide is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label. Additional information about antibodies and methods of measuring determinants using same is provided herein above.
  • Urine leukocytes can be measured either by urine analysis (including microscopic examination) or through a semi-quantitative urine dipstick.
  • a protein sample is preferably prepared.
  • the subject can be one who has been previously diagnosed or identified as having an infection, and optionally has already undergone, or is undergoing, a therapeutic intervention for the infection.
  • the subject can also be one who has not been previously diagnosed as having an infection.
  • a subject can be one who exhibits one or more symptoms of having an infection.
  • a subject may also have an infection but show no symptoms of infection.
  • Exemplary symptoms which the subject may present include but are not limited to fever, nausea, headache, sore throat, runny nose, diarrhea, vomiting, rash and/or muscle soreness.
  • the subject may present with one or more of a variety of pathogens including, but not limited to Adenovirus, Coronavirus, Parainfluenza virus, Influenza A virus, Influenza B virus, Respiratory syncytial virus A/B, Chlamydophila pneumoniae, Mycoplasma pneumoniae, Legionella pneumophila, Rota Virus, Staphylococcus aureus, Streptococcus pneumoniae, Astrovirus, Enteric Adenovirus, Norovirus G I and G P, Bocavirus 1/2/3/4, Enterovirus, CMV virus, EBV virus, Group A Strep, or Escherichia coli.
  • pathogens including, but not limited to Adenovirus, Coronavirus, Parainfluenza virus, Influenza A virus, Influenza B virus, Respiratory syncytial virus A/B, Chlamydophila pneumoniae, Mycoplasma pneumoniae, Legionella pneumophila, Rota Virus, Staphylococcus aureus, Streptococcus pneumonia
  • the subject may present with a particular clinical syndrome, for example, low respiratory tract infection (LRTI) infection, upper respiratory tract infection (URTI), fever without identifiable source (FWS), UTI (urinary tract infections) or a serious bacterial infection (SBI) such as septic shock, bacteremia, pneumonia or meningitis.
  • LRTI low respiratory tract infection
  • URTI upper respiratory tract infection
  • FWS fever without identifiable source
  • UTI urinary tract infections
  • SBI serious bacterial infection
  • the subject whose disease is being diagnosed according to some embodiments of the present invention is referred to below as the “test subject”.
  • the present Inventors have collected knowledge regarding the expression pattern of polypeptides, of a plurality of subjects whose disease has already been diagnosed, and have devised the analysis technique of the present embodiments based on the collected knowledge.
  • This plurality of subjects is referred to below as “pre-diagnosed subjects” or “other subjects”.
  • the phrase “bacterial infection” refers to a condition in which a subject is infected with a bacterium.
  • the infection may be symptomatic or asymptomatic.
  • the bacterial infection may also comprise a viral component (i.e. be a mixed infection being the result of both a bacteria and a virus).
  • viral infection refers to a disease that is caused by a virus and does not comprise a bacterial component.
  • a bacterial infection may be acute or chronic.
  • An acute infection is characterized by rapid onset of disease, a relatively brief period of symptoms, and resolution within days.
  • a chronic infection is an infection that develops slowly and lasts a long time.
  • One difference between acute and chronic infection is that during acute infection the immune system often produces IgM+ antibodies against the infectious agent, whereas the chronic phase of the infection is usually characteristic of IgM-/IgG+ antibodies.
  • acute infections cause immune mediated necrotic processes while chronic infections often cause inflammatory mediated fibrotic processes and scaring. Thus, acute and chronic infections may elicit different underlying immunological mechanisms.
  • the bacterial infection may be the result of gram-positive, gram-negative bacteria or atypical bacteria, as further described herein above.
  • Some embodiments of the present invention analyze the biological data by calculating a value of a likelihood function using the values in the biological data that correspond to the concentration or counts of the determinants (e.g., the expression levels of the polypeptides) and that are obtained from the sample of the subject.
  • the value of a likelihood function as calculated using the values in the biological data, is between a lower bound SLB and an upper bound SUB, wherein each of the lower and upper bounds is calculated using a combination d (e.g. , a linear combination) of the values in the biological data
  • the value of the likelihood function can be used to provide information pertaining an infection the subject is suffering from.
  • the lower bound SLB and upper bound SUB can be viewed geometrically as two curved objects, and the combination d of the of the values in the biological data, can be viewed geometrically as a non-curved object, as illustrated schematically in FIG. 1.
  • the value of the likelihood function is represented by a distance d between the non- curved object p and a curved object S, where at least a segment S ROI of the curved object S is between the lower bound SLB and the upper bound SUB ⁇
  • each of the curved objects S, SLB and SUB is a manifold in n dimensions, where n is a positive integer, and the non-curved object p is a hyperplane in an n+ 1 dimensional space.
  • the concept of n-dimensional manifolds and hyperplanes in n+ 1 dimensions are well known to those skilled in the art of geometry.
  • the non-curved object p is a hyperplane in 2 dimensions, namely a straight line defining an axis.
  • the non-curved object p is a hyperplane in 3 dimensions, namely a flat plane, referred to below as “a plane”.
  • each of S, SLB and SUB is a curved line and p is a straight axis defined by a direction.
  • the present embodiments provide information pertaining to the infection by calculating distances between curved and non-curved geometrical objects.
  • FIG. 2 is a flowchart diagram of a method suitable for analyzing biological data obtained from an infant subject, according to various exemplary embodiments of the present invention. It is to be understood that, unless otherwise defined, the operations described hereinbelow can be executed either contemporaneously or sequentially in many combinations or orders of execution. Specifically, the ordering of the flowchart diagrams is not to be considered as limiting. For example, two or more operations, appearing in the following description or in the flowchart diagrams in a particular order, can be executed in a different order (e.g., a reverse order) or substantially contemporaneously. Additionally, several operations described below are optional and may not be executed.
  • the method begins at 10 and optionally and preferably continuous to 11 at which biological data containing values corresponding to concentrations or counts of two or more determinants (e.g., expression levels of two or more polypeptides) in a sample of the subject are obtained.
  • the biological data includes can include a pair of values, or a triple of values, or a quadruple of values, or an n-tuple of values, where n is more than four.
  • the values preferably include one of the aforementioned pairs or triples or quadruples of values that correspond to expression levels of the aforementioned pairs or triples or quadruples of polypeptides.
  • Other types of determinants are also contemplated as disclosed herein.
  • the method optionally and preferably continues to 12 at which background and/or clinical data that relate to the subject are obtained.
  • the background data includes the age of the subject, in some embodiments of the present invention the background data includes the ethnicity of the subject, in some embodiments of the present invention the background data includes the gender of the subject, in some embodiments of the present invention the clinical data includes a syndrome that the subject is experiencing, in some embodiments of the present invention the clinical data includes a pathogen suspected as being present in the subject.
  • the method proceeds to 13 at which the distance d between a segment of the curved object S ( e.g ., a curved line) and a non-curved object p ( e.g ., an axis defined by a direction) is calculated.
  • the distance d is calculated at a point R( ⁇ ) over the curved line S defined by a coordinate d along the direction.
  • the direction is denoted herein using the same Greek letters as the coordinate, except that the direction is denoted by underlined Greek letters to indicate that these are vectors.
  • the coordinate is denoted d
  • the direction is denoted d.
  • the distance d is measured from S to the point P, perpendicularly to p.
  • the segment S ROI of S is above a region-of-interest ⁇ ROI defined in the non-curved object p.
  • ⁇ ROI is a linear segment along the axis.
  • ⁇ ROI is the projection of S ROI on p.
  • S ROI is preferably a curved segment of (the curve) S.
  • the coordinate d is optionally and preferably defined by a combination of values of the biological data.
  • ⁇ can be a combination of the determinants, according to the following equation: where a 0 , a 1, ... are constant and predetermined coefficients, where each of the variables D 1 , D 2 , ... is a value that corresponds to a concentration or a count of one of the determinants (e.g., an expression level of one of the polypeptides), and where f is a function that is nonlinear with respect to at least one of the values in the biological data.
  • the function f is optional and may be set to zero (or, equivalently, not included in the calculation of the respective coordinate).
  • concentrations or counts of the determinants are all expressed with respect to the same unit volume (e.g., 1 ml).
  • different concentrations e.g., expression levels
  • the typical mass unit for CRP is mg
  • the typical mass unit for TRAIT, and IP- 10 is pg
  • the typical mass unit for PCT is ng.
  • the coefficient of the expression level of CRP is in units of ml/mg.
  • a value that is provided for a non-convectional unit mass can be converted to a value for the respective characteristic unit mass, by multiplying it by the ratio between the conventional and the non- conventional unit.
  • Di corresponds to expression level of TRAIT, in units of ng/ml (instead of the conventional unit pg/ml).
  • the coefficient a; of this variable is provided in units of ml per ng (instead of ml/pg) .
  • the value of a; is multiplied by the ratio between 1 pg and 1 ng, namely by 1/1,000.
  • a value that is provided using a volume unit other than ml can be converted to a value suitable for ml, by multiplying it by the ratio between that volume unit and 1 ml.
  • Di corresponds to expression level of TRAIT, in units of pg/m ⁇ (instead of the conventional unit pg ml).
  • the coefficient a; of this variable is provided in units of m ⁇ per pg (instead of ml/pg).
  • the value of a; is multiplied by the ratio between 1 m ⁇ and 1 ml, namely by 1/1,000.
  • the relative weight of TRAIT it is convenient to characterize the relative weight of TRAIT, using the ratio between the coefficient per pg of TRAIT, and the coefficient per mg of CRP, or the ratio between the coefficient per pg of TRAIT, and the coefficient per ng of PCT, or the ratio between the coefficient per pg of TRAIL and the coefficient per pg of IP- 10.
  • the nonlinear function f can optionally and preferably be expressed as a sum of powers of values in the biological data, for example, according to the following equations: where i is a summation index, q; and n are sets of coefficients, X i ⁇ ⁇ D 1 , D2, ... ⁇ , and ⁇ i is a numerical exponent. Note that the number of terms in the nonlinear function f does not necessarily equals the number of the determinants, and that two or more terms in the sum may correspond to the same determinant, albeit with a different numerical exponent.
  • One or more of the predetermined coefficients (a i , q i r i ) depends on the respective type of the determinant, but can also depends on the background and/or clinical data obtained at 12.
  • the calculation of the distance d can optionally and preferably be based on the background and/or clinical data, because the location of the coordinate d on p can depend on such data.
  • the coefficient a, for a particular determinant A can be different when the subject has a particular syndrome or pathogen, than when the subject does not have this particular syndrome or pathogen.
  • the location of the point R( ⁇ ) on p is different for subjects with the particular syndrome or pathogen, than for subjects without the particular syndrome or pathogen.
  • the coefficient a, (hence also the location of the point R( ⁇ ) on p) for a particular determinant can be different when the subject is of a particular age, gender and/or ethnicity, than when the subject is of a different age, gender and/or ethnicity.
  • the subject background and/or clinical data can be used for determining the coefficients, in more than one way.
  • a lookup table is used.
  • Such a lookup table can include a plurality of entries wherein each entry includes a determinant, information pertaining to the background and/or clinical data, and a coefficient that is specific to the determinant and the background and/or clinical data of the respective entry.
  • ANC absolute neutrophil count
  • ALC absolute lymphocyte count
  • WBC white blood count
  • neutrophil % defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)
  • lymphocyte % defined as the fraction of white blood cells that are lymphocytes and abbreviated Lym (%)
  • monocyte % defined as the fraction of white blood cells that are monocytes and abbreviated Mon (%)
  • Sodium abbreviated Na
  • K Potassium
  • Bill Bilirubin
  • the coefficients are initially selected based on the particular determinants, (without taking into account the background and/or clinical data), and thereafter corrected, e.g., by normalization, based on the background and/or clinical data.
  • the coefficients can be normalized according to the age of the subject.
  • the subject background and/or clinical data are used for weighing the value of the likelihood to be calculated.
  • This can also be used using a lookup table.
  • a lookup table can include a plurality of entries wherein each entry includes information pertaining to the background and/or clinical data, and a weight value that is specific to the background and/or clinical data of the respective entry, and that is to be used for weighing the likelihood to be calculated (e.g., by multiplication of by division).
  • the term “subject background” refers to the history of diseases or conditions of the subject, or which the subject is prone to.
  • the subject medical background may include conditions that affect its immune response to infection.
  • ⁇ ROI The boundaries of ⁇ ROI are denoted herein ⁇ MIN and ⁇ MAX ⁇ These boundaries preferably correspond to the physiologically possible ranges of the values in the biological data.
  • the range of the values can be set by the protocol used for obtaining the respective determinants. Suitable ranges for a preferred selection of determinants is provided in the Examples section that follows.
  • the present Inventors used the knowledge regarding the expression pattern of polypeptides of a plurality of subjects of a variety of age groups whose disease has already been diagnosed, and have employed logistic regression to obtain the coefficients for the coordinate d for these subjects. As demonstrated in the Examples section that follows (see Examples 2 and 3), the Inventors discovered a significant and unexpected difference between the coefficients obtained for infants with less than three months of age, and the coefficients obtained for older subjects. In particular, the Inventors found that when one of the determinants is TRAIL, the logistic regression accords to the value that corresponds to the expression value of TRAIT, a substantially different relative weight when it is calculated for infants than when it is calculated for older subjects.
  • the respective ratio between the coefficients that the logistic regression calculated for these determinants is much higher for infants than when it is calculated for older subjects; when one of the determinants is TRAIL, another one of the determinants is IP- 10, and the logistic regression is independent of an expression level of CRP, the coefficient (e.g., the coefficient of the linear combination) that the logistic regression calculated for IP- 10 is positive for infants and negative for older subjects; and when one of the determinants is TRAIL, and another one of the determinants is PCT, the respective ratio between the coefficients (e.g., the coefficients of the linear combination) that the logistic regression calculated for these determinants is much higher for infants than when it is calculated for older subjects.
  • the present Inventors constructed a linear combination of the polypeptide that can be used for more accurately determining the likelihood that the subject has a bacterial infection.
  • the biological data contain at least expression levels of TRAIT, and CRP in the sample, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed CRP in units of ml/mg (or a coefficient in other unit, once converted to ml/mg) is more than -0.5, or more than -0.4, or more than -0.4, or more than -0.3, or more than -0.2.
  • the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and CRP, an expression level of IP- 10 and/or an expression level of PCT and/or a count of Urine leukocytes, in which case the ratio is preferably more than -0.2.
  • the biological data contain at least expression levels of TRAIL and IP- 10 in the sample, d is independent of the expression level of CRP, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed IP- 10 in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) is positive.
  • a coefficient e.g., a coefficient of the linear combination
  • the biological data contain at least expression levels of TRAIT, and PCT in the sample, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed PCT in units of nl/pg (or a coefficient in other unit, once converted to nl/pg) is more than -0.01 or more than -0.0099 or more than -0.0098.
  • a coefficient e.g., a coefficient of the linear combination
  • the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and PCT, a count of Urine leukocytes.
  • the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and PCT, an expression level of IP- 10, in which case the ratio is preferably more than -0.08.
  • At least a major part of the segment S ROI of curved object S is between two curved objects referred to below as a lower bound curved object SLB and an upper bound curved object SUB-
  • a smooth version of the segment S ROI refers to the segment S ROI , excluding regions of S ROI at the vicinity of points at which the Gaussian curvature is above a curvature threshold, which is X times the median curvature of S ROI , where X is 1.5 or 2 or 4 or 8.
  • FIGs. 3A-D illustrate a procedure for obtaining the smooth version of S ROI .
  • S ROI is illustrated as a one dimensional segment, but the skilled person would understand that S ROI is generally an n -dimensional mathematical object.
  • the Gaussian curvature is calculated for a sufficient number of sampled points on S ROI .
  • the manifold is represented as point cloud
  • the Gaussian curvature can be calculated for the points in the point cloud.
  • the median of the Gaussian curvature is then obtained, and the curvature threshold is calculated by multiplying the obtained median by the factor X.
  • FIG. 3A illustrates S ROI before the smoothing operation. Marked is a region 320 having one or more points 322 at which the Gaussian curvature is above the curvature threshold.
  • region 320 is smoothly interpolated, e.g., via polynomial interpolation, (FIG. 3B).
  • the removal and interpolation is repeated iteratively (FIG. 3C) until the segment S ROI does not contain regions at which the Gaussian curvature is above the curvature threshold (FIG. 3D).
  • SLB is a lower bound curved line
  • SUB an upper bound curved line
  • SUB f( ⁇ +)+ ⁇ 1
  • f( ⁇ i) a probabilistic classificationfunction of the coordinate d (along the direction ⁇ ) which represents the likelihood that the test subject has an infection of a specific type, for example, a bacterial infection, or a non-bacterial infection (e.g., a viral infection).
  • the probabilistic classification function represents the likelihood that the test subject has a bacterial infection.
  • f( ⁇ ) l/(l+cxp(- ⁇ )).
  • both SLB and SUB are positive for any value of d within ⁇ ROI .
  • each of the parameters so and si is less than 0.5 or less than 0.4 or less than 0.3 or less than 0.2 or less than 0.1 or less than 0.05.
  • the method preferably proceeds to 14 at which the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a disease or condition corresponding to the type of the probabilistic function f.
  • the probabilistic function f represents the likelihood that the test subject has a bacterial infection
  • the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a bacterial infection
  • the probabilistic function f represents the likelihood that the test subject has a viral infection
  • the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a viral infection
  • the probabilistic function f represents the likelihood that the test subject has a mixed infection
  • the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a mixed infection.
  • the correlation includes determining that the distance d is the likelihood that the subject has the respective infection (bacterial, viral, mixed).
  • the likelihood is optionally and preferably compared to a predetermined threshold CO B, wherein the method can determine that it is likely that the subject has a bacterial infection when the likelihood is above co B, and that it is unlikely that the subject has a bacterial infection otherwise.
  • Typical values for ⁇ B include, without limitation, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6 and about 0.7.
  • Other likelihood thresholds are also contemplated.
  • the method proceeds to 15 at which the likelihood is corrected based on the background and/or clinical data.
  • a correction can be executed in more than one way.
  • the method can employ different predetermined thresholds co B for different ages, ethnicities, genders, syndromes, and/or suspected pathogens.
  • the method can alternatively or additionally employ different values for one or both the parameters ⁇ 0 and si for different ages, ethnicities, genders, syndromes, and/or suspected pathogens.
  • the method can alternatively or additionally normalize the value of the probabilistic classification function d, based on the age, ethnicity, gender, syndrome, and/or suspected pathogen.
  • the method optionally and preferably continues to 16 at which an output of the likelihood(s) is generated.
  • the output can be presented as text, and/or graphically and/or using a color index.
  • the output can optionally include the results of the comparison to the threshold co B ⁇ From 16 the method can optionally and preferably loops back to 13 for repeating the analysis using a different set of coefficients for the calculation of the coordinate d and/or a different probabilistic classification function f.
  • the analysis can be initially executed using a set of coefficients and probabilistic classification function f that are selected for determining the presence of, absence of, or likelihood that the subject has, a bacterial infection or a mixed infection, and then, in a subsequent execution, the analysis can use a set of coefficients and probabilistic classification function f that are selected for determining the presence of, absence of, or likelihood that the subject has, a viral infection.
  • the method determines that it is likely that the subject has a bacterial infection
  • the subject is treated (17) for the bacterial infection, as further detailed herein.
  • the method determines that it is likely that the subject has a viral infection
  • the subject is treated (17) for the viral infection, as further detailed herein. The method ends at 18.
  • the method can be carried out using a system 330, which optionally and preferably, but not necessarily, comprises a hand-held or desktop device.
  • the system can comprise two or more compartments, wherein the levels of determinants in the sample are measured in one of the compartments (e.g. using an immunohistochemical method), and wherein an analysis of the obtained levels is executed in the other compartment to provide an output relating to the diagnosis.
  • System 330 measures the values of the determinants in the sample of a subject and optionally and preferably also analyzes the measured values, according to the analysis technique described herein.
  • System 330 can comprise a first compartment 332 in which the measurement is performed, and may optionally and preferably also comprise a second compartment 334 in which the analysis is performed.
  • first compartment 332, and optionally and preferably also second compartment 334 is/are housed in a device 331 which is preferably, but not necessarily, a hand-held or a desktop device.
  • First compartment 332 can include a measuring system 333 configured to measure the values of the determinants in the sample.
  • a liquid sample of a subject e.g., blood, urine, etc.
  • a cartridge 360 containing reagents for detecting the determinants e.g., TRAIL, CRP, PCT, IP- 10, IL-6, and/or Uleuco
  • the cartridge 360 can then be loaded to compartment 332, e.g., into a cartridge holder or socket 362 being sized and shaped to receive cartridge 360.
  • Cartridge 360 is preferably labeled) with a label 361, such as a barcode or the like, encoding information describing the subject (e.g., I.D. No., name, age, etc.).
  • the information can be encodes by the label itself, or the label can encode a unique identification string that can be searched for in a database in order to extract the information.
  • Measuring system 333 can perform at least one assay selected from the group consisting of an immunoassay such as ELISA or LFIA, and a functional assay. In some embodiments of the present invention measuring system 333 uses chemiluminescence or florescence for measuring the expression value of the determinants.
  • System 330 can also comprise a second compartment 334 comprising a hardware processor 336 having a computer-readable medium 338 for storing computer program instructions for executing the operations described herein.
  • Hardware processor 336 is configured to receive measured values of the determinants from first compartment 332 and execute the program instructions responsively to the measured values and output the processed data to a display device 340.
  • hardware processor 336 is also configured to receive input pertaining to the age group of the subject, e.g., whether the subject is less than three months of age, or older, in which case the program instructions are executed by processor 336 also responsively to the input age group.
  • system 330 comprises a label reader 363 that reads the information encodes by label 361.
  • hardware processor 336 receives information pertaining to the age group based on signal received from the reader 363.
  • processor 336 receives from the reader 363 a signal pertaining to the age group.
  • label 361 encodes a unique identification string
  • processor 336 receives from the reader 363 the unique identification string and searches a database stored in medium 338 for the age or age group of the subject.
  • processor 336 can transmit, by means of communication interface 350, the unique identification string over a communication network 352 to a remote server (not shown) that is associated with a database storing user information.
  • the remote server can search the database for the for the age or age group of the subject based on the unique identification, and transmit the information back to professor 336.
  • the program instructions when the subject is less than three months of age the program instructions accords more weight to the expression level of TRAIL, and/or use a positive coefficient for the expression level of IP- 10, as further detailed hereinabove.
  • the input age group can be received from a user (not shown, see FIG. 5B) by means a user interface 354, or via communication network 352 by means of communication interface 350.
  • hardware processor 336 receives over network 352, via communication interface 350, measured values of the determinants from a measuring system, such as, but not limited to, measuring system 333, and executes the computer program instructions in computer-readable medium 338, responsively to the received measurements.
  • hardware processor 336 is also configured to receive input pertaining to the age group of the subject and execute the program instructions also responsively to the input age group as further detailed hereinabove.
  • hardware processor 336 can receive over the network 352 a unique identification string read from a label attached to a cartridge (not shown), and search a database stored in medium 338 for the age or age group of the subject.
  • Hardware processor 336 can then output the processed data to display device 340.
  • system 330 communicates with a user, as schematically illustrated in the block diagram of FIG. 5B.
  • system 330 can comprise computer-readable medium 338, as further detailed hereinabove, and a hardware processor, such as, but not limited to, processor 336.
  • Hardware processor 336 comprises a user interface 354 that communicates with a user 356. Via interface 350, hardware processor 336 receives measured values of the determinants from user 356.
  • User 356 can obtain the measured values from an external source, or by executing at least one assay selected from the group consisting of an immunoassay and a functional assay, or by operating system 333 (not shown, see FIGs. 4 and 5A).
  • Hardware processor 336 executes the computer program instructions in computer-readable medium 338, responsively to the received measurements.
  • hardware processor 336 is also configured to receive input pertaining to the age group of the subject and execute the program instructions also responsively to the input age group as further detailed hereinabove.
  • Hardware processor 336 can then output the processed data to display device 340.
  • the subject may be treated with an antibiotic agent.
  • antibiotic agents include, but are not limited to Daptomycin; Gemifloxacin ; Telavancin; Ceftaroline; Fidaxomicin; Amoxicillin; Ampicillin; Bacampicillin; Carbenicillin; Cloxacillin; Dicloxacillin; Flucloxacillin; Mezlocillin; Nafcillin; Oxacillin; Penicillin G; Penicillin V; Piperacillin; Pivampicillin; Pivmecillinam; Ticarcillin; Aztreonam; Imipenem; Doripenem; Meropenem; Ertapenem; Clindamycin; lincomycin; Pristinamycin; Quinupristin; Cefacetrile (cephacetrile); Cefadroxil (cefadroxyl); Cefalexin (cephalexin); Cefaloglycin (cephaloglycin) ; Cefalonium (cephalonium); Cefaloridine (cephaloradine); Cef
  • antiviral agents include, but are not limited to Abacavir; Aciclovir; Acyclovir; Adefovir; Amantadine; Amprenavir; Ampligen; Arbidol; Atazanavir; Atripla; Balavir; Boceprevirertet; Cidofovir; Combivir; Dolutegravir; Darunavir; Delavirdine; Didanosine; Docosanol; Edoxudine; Efavirenz; Emtricitabine; Enfuvirtide; Entecavir; Ecoliever; Famciclovir; Fomivirsen; Fosamprenavir; Foscarnet; Fosfonet; Fusion inhibitor; Ganciclovir; Ibacitabine; Imunovir; Idoxuridine; Imiquimod; Indinavir; Inosine; Integrase inhibitor; Interferon type IP; Interfer
  • the information gleaned using the methods described herein may aid in additional patient management options. For example, the information may be used for determining whether a patient should or should not be admitted to hospital. It may also affect whether or not to prolong hospitalization duration. It may also affect the decision whether additional tests need to be performed or may save performing unnecessary tests such as CT and/or X-rays and/or MRI and/or culture and/or serology and/or PCR assay for specific bacteria and/or PCR assays for viruses and/or perform procedures such as lumbar puncture.
  • compositions, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
  • a compound or “at least one compound” may include a plurality of compounds, including mixtures thereof.
  • range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
  • a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range.
  • the phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.
  • method refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.
  • treating includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.
  • sequences that substantially correspond to its complementary sequence as including minor sequence variations, resulting from, e.g., sequencing errors, cloning errors, or other alterations resulting in base substitution, base deletion or base addition, provided that the frequency of such variations is less than 1 in 50 nucleotides, alternatively, less than 1 in 100 nucleotides, alternatively, less than 1 in 200 nucleotides, alternatively, less than 1 in 500 nucleotides, alternatively, less than 1 in 1000 nucleotides, alternatively, less than 1 in 5,000 nucleotides, alternatively, less than 1 in 10,000 nucleotides.
  • Exclusion criteria were therapeutic antibiotic use during the preceding two weeks, congenital or acquired immune-deficiency, including treatment with high-dose corticosteroids >1 mg/kg/day prednisone or equivalent in the preceding two weeks, monoclonal antibodies, anti-tumor necrosis factor agents, intravenous immunoglobulin, and chronic severe illnesses affecting life expectancy or quality of life.
  • the non- infectious control group were hospitalized afebrile children, such as cases of elective admission for a surgical procedure.
  • the reference standard for determining bacterial versus non-bacterial etiology was based on the adjudication of two senior pediatricians, each with more than 10 years of working experience as specialists in pediatric infectious diseases. Confirmation of UTI diagnosis was according to the AAP criteria (AAP 2016). These include pyuria (positive leukocyte esterase or nitrite on dipstick or >5 WBC/high power field on centrifuged urine microscopy) and/or bacteriuria on urinalysis, and >50,000 CFUs/ml growth of an uropathogen cultured from supra pubic aspiration (SPA), bladder catheterization or midstream urine specimen.
  • SPA supra pubic aspiration
  • Serum CRP was measured using either one of the following kits: Cobas-6000, Cobas- Integra-400/800, or Modular- Analytics-P800 (Roche).
  • Urinary CRP was measured by commercial high sensitivity enzyme-linked immunosorbent assays (ELISAs) (Immundiagnostik AG, Bensheim, Germany). Serum and urinary TRAIT, and IP- 10 were measured using commercial ELISA kits (MeMed Diagnostics). Pending analysis, samples were stored at -70°C.
  • the urinary creatinine concentration was used to normalize biomarker measurements and account for the influence of urinary dilution.
  • the laboratory technicians conducting biomarker tests were blinded to clinical data and the adjudication label.
  • Urinary CRP, IP- 10 and TRAIL levels in bacterial UTI versus non-bacterial etiology There was no difference in urinary CRP, IP- 10 and TRAIT, levels in healthy versus viral children and so these subjects were grouped as ‘non-bacterial’ for further analyses.
  • AUC receiver operator curve
  • Table 3 lists minimal and maximal values suitable for the determinants TRAIL, CRP, IP- 10, and Uleco, according to some embodiments of the present invention.
  • Table 3 Lor a given set of coefficients, the values ⁇ MIN and ⁇ MAX (see PIG. 1) are optionally and preferably obtained by selecting the values in Table 3 that respectively minimize and maximize the value of the coordinate d.
  • Example 3
  • Exemplary sets of coefficients for the d coordinate, in the cases in which the biological data comprise pairs, triples and quadruple of determinant values, are provided in Table 4, and 5, where Table 4 corresponds to subjects with less than three months of age, and Table 5 corresponds to older subjects.
  • the coefficients are named by the respective determinant.
  • the column TRAIT lists coefficients of the expression levels of TRAIL.
  • the coefficients of the expression levels of TRAIL, CRP, IP- 10, and PCT are suitable for use when the expression levels of TRAIL, CRP, IP- 10, and PCT are expressed in units of pg/ml, mg/L, pg/ml, and ng/ml, respectively, and the coefficients of the count of urine leukocytes are suitable for use when the counts are expressed as 1, 2 or 3, where 1 indicates about 70 leukocytes per pL, 2 indicates about 125 leukocytes per pL, and 3 indicates about 500 leukocytes per pL. Also provided are values of area under the receiver operating curve (AUC) that correspond to each set of coefficients.
  • AUC area under the receiver operating curve
  • the sets of coefficients provided in Example 2 were calculated according to some embodiments of the present invention by logistic regression, also for the case in which the values of the determinants (and the corresponding coefficients) are dimensionless. Conversion of the measured values of the determinants to dimensionless values was employed by means of the minimal and maximal values provided in Table 5, above, according to the following formula: Tables 6 and 7, below, provide sets of dimensionless coefficients for the ⁇ coordinate, in the cases in which the biological data comprise pairs, triples and quadruple of determinant values, where Table 6 corresponds to subjects with less than three months of age, and Table 7 corresponds to older subjects.
  • Table 8 depicts a comparison between urinary biomarker levels in viral and healthy patients
  • Table 9, below lists Serum expression levels of CRP, IP- 10 and TRAIL
  • FIGs. 6A and 6B show temporal dynamics of urine CRP and IP- 10 in bacterial patients over 90 days old, where gray dots denote mean level, thick lines denote median level, and boxes indicate patients with values between the 25 and 75 percentiles.

Abstract

A method of ruling in a bacterial infection in a subject, comprises measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is above 1.5 mg/L and/or the concentration of IP- 10 is above 2 ng/L, it is indicative of a bacterial infection.

Description

METHOD AND SYSTEM FOR ANALYZING BIOLOGICAL DATA
RELATED APPLICATION S
This application claims the benefit of priority of U.S. Provisional Patent Application No. 63/216,679 filed on June 30, 2021, and U.S. Provisional Patent Application No. 63/229,623 filed on August 5, 2021, the contents of which are incorporated herein by reference in their entirety.
FIELD AND BACKGROUND OF THE INVENTION
The present invention, in some embodiments thereof, relates to the identification of biological signatures and determinants associated with bacterial and viral infections in urine samples and methods of using such biological signatures in the screening, diagnosis, therapy and monitoring of infection. Some embodiments relate to computational analysis, and, more particularly, but not exclusively, to computational analysis of biological data, e.g., for the purpose of distinguishing between bacterial infection and non-bacterial disease, and/or between a bacterial infection and viral infection, and/or between an infectious and non-infectious disease. Some embodiments of the invention are particularly useful in cases in which the samples under investigation are obtained from children and/or infants.
Urinary tract infection (UTI) is a common illness in children that may lead to renal scarring. In infants and young children, the possibility of renal damage after infection is considered to be higher than in older children; however, the diagnosis and the establishment of the severity of infection in this age group are challenging, as UTI may present with non-specific symptoms and signs such as fever, irritability, poor feeding or poor weight gain. Since the clinical manifestations are insufficient for diagnosis, the presumptive diagnosis of UTI in children is often based on the results of urine dipstick and microscopic analysis. However, the diagnostic accuracy of these tests is limited in young infants, with varying sensitivity and specificity according to the component analyzed. The urine culture is considered the gold standard for diagnosis, but bacterial growth may be negatively influenced by transport, previous antibiotic therapy or contamination during sample collection, which is especially difficult in young ages. Furthermore, culture results are dependent on the threshold used to identify significant growth, cannot be differentiated from asymptomatic bacteriuria and are limited in their time to positivity.
This diagnostic challenge contributes to undertreatment or delayed treatment of UTI with its potential complications, on the one hand, and on the other - to overtreatment, antimicrobial adverse effects, and increasing antibiotic resistance. Of the small number of studies that have investigated the role of urine markers in the diagnosis of UTI in children, urinary retinol-binding protein (RBP), Clara cell protein (CC16), N- acetyl-beta-glucosaminidase (NAG) and neutrophil gelatinase- associated lipocalin (NGAL) and kidney injury molecule- 1 (KIM-1) showed potential in differentiating between UTI and other sources of fever; however most results were either contradictory or showed relatively low diagnostic accuracy. Indeed, these markers did not make the transition to the routine clinical arena.
Antibiotics (Abx) are the world's most prescribed class of drugs with a 25-30 billion $US global market. Abx are also the world's most misused drug with a significant fraction of all drugs (40-70%) being wrongly prescribed (Linder, J.A. and R.S. Stafford 2001; Scott, J. G. and D. Cohen, et al. 2001; Davey, P. and E. Brown, et al. 2006; Cadieux, G. and R. Tamblyn, et al. 2007; Pulcini,
C. and E. Cua, et al. 2007), (“CDC - Get Smart: Fast Facts About Antibiotic Resistance” 2011).
One type of Abx misuse is when the drug is administered in case of a non-bacterial disease, such as a viral infection, for which Abx is ineffective. For example, according to the USA center for disease control and prevention CDC, over 60 Million wrong Abx prescriptions are given annually to treat flu in the US. The health-care and economic consequences of the Abx over prescription include: (i) the cost of antibiotics that are unnecessarily prescribed globally, estimated at >$10 billion annually; (ii) side effects resulting from unnecessary Abx treatment are reducing quality of healthcare, causing complications and prolonged hospitalization (e.g. allergic reactions, Abx associated diarrhea, intestinal yeast etc.) and (iii) the emergence of resistant strains of bacteria as a result of the overuse (the CDC has declared the rise in antibiotic resistance of bacteria as “one of the world’s most pressing health problems in the 21st century” (Arias, C.A. and B.E. Murray 2009; “CDC - About Antimicrobial Resistance” 2011).
Antibiotics under-prescription is not uncommon either. For example up to 15% of adult bacterial pneumonia hospitalized patients in the US receive delayed or no Abx treatment, even though in these instances early treatment can save lives and reduce complications(Houck, P.M. and
D. W. Bratzler, et al. 2002).
Technologies for infectious disease diagnosis have the potential to reduce the associated health and financial burden associated with Abx misuse. Ideally, such a technology should: (i) accurately differentiate between a bacterial and viral infections; (ii) be rapid (within minutes); (iii) be able to differentiate between pathogenic and non-pathogenic bacteria that are part of the body’ s natural flora; (iv) differentiate between mixed co-infections and pure viral infections and (v) be applicable in cases where the pathogen is inaccessible (e.g. sinusitis, pneumonia, otitis-media, bronchitis, etc). WO 2013/117746 teaches signatures and determinants for distinguishing between a bacterial and viral infection.
WO20 16/024278 and WO2018/029690 teach a method of analyzing biological data containing expression values of polypeptides in the blood of a subject. The method is based on the calculation of a distance between a segment of a curved line and an axis. The distance is calculated at a point over the curved line defined by a coordinate. The distance is correlated to the presence of, absence of, or likelihood that the subject has a bacterial infection.
SUMMARY OF THE INVENTION
According to an aspect of the present invention there is provided a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is above 1.5 μg/L and/or the concentration of IP- 10 is above 2 ng/L, it is indicative of a bacterial infection, wherein the concentration of the CRP and/or the IP- 10 is creatinine normalized.
According to an aspect of the present invention there is provided a method of ruling out a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is below 1.5 μg/L and/or the concentration of IP- 10 is below 2 ng/L, it is indicative that the infection is not a bacterial infection.
According to an aspect of the present invention there is provided a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non- infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non-infectious subject, it is indicative of a bacterial infection.
According to another aspect, there is provided a method of ruling in a bacterial infection in a subject under 3 months of age, comprising measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIT, is below 188 pg/L it is indicative of a bacterial infection in the subject. According to still another aspect there is provided a method of treating a bacterial infection in a subject under 3months of age comprising:
(a) confirming with a specificity above 90 % and a selectivity above 90 % that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIL in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIL is below 188 pg/L it is indicative of a bacterial infection in the subject; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
According to an aspect of the present invention there is provided a method of treating a bacterial infection in a subject comprising:
(a) confirming that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the level of CRP is above 1.5 μg/L and/or the level of IP - 10 is above 2 ng/L, it is indicative of a bacterial infection, wherein the concentration of the CRP and/or the IP- 10 is creatinine normalized; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
According to an aspect of the present invention there is provided a method of treating a bacterial infection in a subject comprising:
(a) confirming that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non-infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non-infectious subject, it is indicative of a bacterial infection; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
According to embodiments of the present invention, when the concentration of the CRP is above 1.7 μg/L and/or the concentration of the IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
According to embodiments of the present invention, the non-infectious subject is a healthy subject. According to embodiments of the present invention, the subject is below 18 years of age.
According to embodiments of the present invention, the subject is below 3 months of age.
According to embodiments of the present invention, the subject exhibits symptoms of infection.
According to embodiments of the present invention, the symptoms comprise fever.
According to embodiments of the present invention, the method further comprises determining the species or strain of bacteria responsible for the bacterial infection.
According to embodiments of the present invention, the bacterial infection is a urinary tract infection.
According to embodiments of the present invention, the method further comprises measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase-associated lipocalin (NGAL), IL-1RA, IFNy, TNFa, MCP-1 and Interleukin- 18 (IL-18) in the urine sample.
According to embodiments of the present invention, the method further comprises measuring in the urine the amount of at least one additional non-protein determinant selected from the group consisting of nitrite level, white blood cell count, and pH.
According to embodiments of the present invention, the concentration of CRP is above 1.7 μg/L and/or the concentration of IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
According to embodiments of the present invention, the subject is below 18 years of age.
According to embodiments of the present invention, the subject is below 3 months of age.
According to embodiments of the present invention, the subject exhibits symptoms of fever.
According to embodiments of the present invention, the symptoms comprise fever.
According to embodiments of the present invention, the method further comprises determining the species or strain of bacteria responsible for the bacterial infection.
According to embodiments of the present invention, the method further comprises measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase-associated lipocalin (NGAL) and Interleukin- 18 (IL-18) in the urine sample.
According to embodiments of the present invention, the bacterial infection is a urinary tract infection.
According to embodiments of the present invention, the measuring is carried out using an antibody that specifically binds to CRP and/or IPIO.
According to an aspect of some embodiments of the present invention there is provided a system for analyzing biological data. The system comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and CRP in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals l/(l+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels; and wherein a ratio between a coefficient of the combination of expressed TRAIT, and a coefficient of the combination of expressed CRP has a first ratio value when the age group is defined for ages less than three months, and a second ratio value, lower than the first ratio value, than when the age group is for older ages.
According to an aspect of some embodiments of the present invention there is provided a system for analyzing biological data. The system comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and IP- 10 in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals 1/(1+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels and is independent of an expression level of CRP; and wherein a coefficient of the combination of expressed IP- 10 is positive when the age group is defined for ages less than three months, and negative when the age group is for older ages.
According to an aspect of some embodiments of the present invention there is provided a system for analyzing biological data. The system comprises: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIL and PCT in a sample extracted from the subject; a data processor having a circuit configured to access a computer- readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; correlate the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on the age; and generate output pertaining to the correlation; wherein at least 90% of the segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals l/(l+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels; and wherein a ratio between a coefficient of the combination of expressed TRAIT, and a coefficient of the combination of expressed PCT has a first ratio value when the age group is defined for ages less than three months, and a second ratio value, lower than the first ratio value, than when the age group is for older ages.
According to some embodiments of the invention the the sample is in a labeled cartridge, and the input circuit receives data pertaining to the age group based on the label.
According to an aspect of some embodiments of the present invention there is provided a method of analyzing biological data. The method comprises: obtaining biological data containing at least expression levels of TRAIL and CRP in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals 1/(1+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels, wherein a ratio between a coefficient of the combination of expressed TRAIL in, or once converted to, units of ml/pg and a coefficient of the combination expressed CRP in, or once converted to, units of ml/mg is more than -0.5.
According to some embodiments of the invention the ratio is more than -0.4, or more than -0.4, or more than -0.3, or more than -0.2.
According to some embodiments of the invention the biological data contain expression level of IP- 10, and wherein the ratio is more than -0.2.
According to some embodiments of the invention the biological data contain expression level of PCT, and wherein the ratio is more than -0.2.
According to some embodiments of the invention the biological data contain a count of Urine leukocytes, and wherein the ratio is more than -0.2. According to an aspect of some embodiments of the present invention there is provided a method of analyzing biological data, the method comprising: obtaining biological data containing at least expression levels of TRAIT, and IP- 10 in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f(δ)+ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals l/(l+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels and is independent of an expression level of CRP, wherein a coefficient of the combination of expressed TRAIL is negative and a coefficient of the combination of expressed IP- 10 is positive.
According to some embodiments of the invention the biological data contain expression level of PCT.
According to some embodiments of the invention the biological data contain a count of Urine leukocytes.
According to an aspect of some embodiments of the present invention there is provided a method of analyzing biological data, the method comprising: obtaining biological data containing at least expression levels of TRAIT, and PCT in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate d along the direction; and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of the segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein f(δ) equals 1/(1+exp(-δ)), wherein the coordinate d, once calculated, equals a combination of the expression levels, wherein a ratio between a coefficient of the combination of expressed TRAIT, in, or once converted to, units of ml/pg and a coefficient of the combination of expressed PCT in, or once converted to, units of ml/ng is more than -0.01.
According to some embodiments of the invention the biological data contain expression level of CRP.
According to some embodiments of the invention the biological data contain expression level of IP- 10, and wherein the ratio is more than -0.08.
According to some embodiments of the invention the biological data contain a count of Urine leukocytes. According to some embodiments of the invention the subject exhibits symptoms of infection.
According to some embodiments of the invention the symptoms comprise fever.
According to some embodiments of the invention the method comprises obtaining background and/or clinical data pertaining to the subject, and weighing the likelihood based on the age.
According to some embodiments of the invention the method comprises obtaining the likelihood based on the distance, comparing the likelihood to a predetermined threshold, and prescribing treatment to the subject based on the comparison.
According to some embodiments of the invention the method comprises obtaining the likelihood based on the distance, comparing the likelihood to a predetermined threshold, and, treating the subject for the bacterial infection when the likelihood is above the predetermined threshold.
According to some embodiments of the invention the method comprises generating an output of the likelihood.
According to some embodiments of the invention the blood sample is whole blood. According to some embodiments of the invention the blood sample is a fraction of whole blood. According to some embodiments of the invention the blood fraction comprises serum or plasma.
According to some embodiments of the invention the calculation and correlation is executed by a computer remote from the subject.
According to some embodiments of the invention the calculation and the correlation is executed by a computer near the subject.
According to some embodiments of the invention the calculation and correlation is executed by a cloud computing resource of a cloud computing facility.
According to an aspect of some embodiments of the present invention there is provided a computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the hardware processor to receive expression levels of a plurality of polypeptides in the blood of a subject who has an unknown disease, and to execute the method as delineated above and optionally and preferably as further detailed below.
Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.
Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.
For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.
In the drawings:
FIG. 1 is a schematic illustration of geometrical objects that can be used for determining a likelihood, according to some embodiments of the present invention;
FIG. 2 is a flowchart diagram of a method suitable for analyzing biological data obtained from a subject, according to some embodiments of the present invention;
FIGs. 3A-D are schematic illustrations of a procedure for obtaining a smooth version of a segment of a curved object, according to some embodiments of the present invention; FIG. 4 is a schematic illustration of a block diagram of a system for analyzing biological data, according to some embodiments of the present invention;
FIGs. 5A and 5B are schematic illustrations of a block diagram of a system for analyzing biological data, in embodiments of the invention in which the system comprises a network interface (FIG. 5A) and a user interface (FIG. 5B);
FIGs. 6A and 6B show temporal dynamics of urine CRP (FIG. 6A) and urine IP- 10 (FIG. 6B) in bacterial patients over 90 days old; and
FIG. 7 is a flow chart illustrating patient recruitment for urine biomarkers. UTI, urinary tract infection.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
The present invention, in some embodiments thereof, relates to the identification of biological signatures and determinants associated with bacterial and viral infections in urine samples and methods of using such biological signatures in the screening, diagnosis, therapy and monitoring of infection. Some embodiments relate to computational analysis, and, more particularly, but not exclusively, to computational analysis of biological data, e.g., for the purpose of distinguishing between bacterial infection and non-bacterial disease, and/or between a bacterial infection and viral infection, and/or between an infectious and non- infectious disease. Some embodiments of the invention are particularly useful in cases in which the samples under investigation are obtained from children and/or infants.
Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.
The urinary levels of IP- 10 and CRP in febrile pediatric patients with suspected UTI were measured in order to check whether these biomarkers can be used to differentiate between bacterial UTI and non-bacterial etiology. It was found that the levels of IP- 10 and CRP are differentially expressed in bacterial UTI patients and may serve as a tool for detecting urinary tract infections.
Thus, according to a first aspect of the present invention, there is provided a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the level of CRP is above 1.5 mg/L and/or the level of IP- 10 is above 2.0 ng/L, it is indicative of a bacterial infection. According to another aspect of the present invention there is provided a method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non- infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non- infectious subject, it is indicative of a bacterial infection.
In one embodiment, the bacterial infection is ruled in when the level of CRP is at least 2 times the concentration of CRP in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of CRP is at least 3 times the concentration of CRP in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of CRP is at least 4 times the concentration of CRP in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of CRP is at least 5 times the concentration of CRP in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 3 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 4 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 5 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 6 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 7 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 8 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 9 times the concentration of IP- 10 in a control sample.
In one embodiment, the bacterial infection is ruled in when the level of IP- 10 is at least 10 times the concentration of IP- 10 in a control sample.
It will be appreciated that the polypeptide names presented herein are given by way of example. Many alternative names, aliases, modifications, isoforms and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all the alternative protein names, aliases, modifications isoforms and variations.
Gene products, are identified based on the official letter abbreviation or gene symbol assigned by the international Human Genome Organization Naming Committee (HGNC) and fisted at the date of this fifing at the US National Center for Biotechnology Information (NCBI) web site also known as Entrez Gene.
For measuring a polypeptide, a protein sample is preferably prepared.
Traditional approaches for correcting for urine dilution factor are creatinine correction (see for example Love et al., Journal of Analytical Toxicology, Volume 40, Issue 8, 2016), specific gravity correction, calculation of urinary excretion rate, using creatinine or specific gravity as an independent variable in a model that evaluates toxicant exposure and outcome, or use of directed acyclic graphs.
Creatinine, a byproduct of muscle (or protein) catabolism, is typically excreted at a relatively constant rate (~+10%) within a healthy individual but varies widely when major physiologic changes such as body building, weight loss/gain, or pregnancy are taking place. Creatinine excretion differs with respect to factors such as race/ethnicity, age, sex, lean muscle mass or body mass index (BMI), and physiologic changes in pregnancy. Creatinine correction of urine dilution may also take into account creatinine-dependent factors of the population being studied (e.g., race, age, sex). Specific gravity, the measure of dissolved solids in urine, is often correlated to tire creatinine concentration, but because it has less resolution, it is not as highly affected by demographic factors and so is often used instead of creatinine correction. However, it is normalized on the median specific gravity of the population rather than a constant value, and tlris may hinder the ability to compare concentrations across populations.
To calculate creatinine-corrected analyte concentration, divide the analyte concentration in ng/mL (μg/L) units by creatinine in mg/dL units and multiply by 100 to give units μg/g creatinine units.
To calculate specific gravity-corrected analyte concentration, the following formula may be used (final units are ng/mL):
Pc = P[(SGm - 1)/(SG- 1)], where
Pc is the specific gravity- corrected analyte concentration (ng/mL),
P is the observed analyte concentration (ng/mL),
SGm is the median SG value among the study population, and
SG is the specific gravity of the individual urine sample. According to a specific embodiment, when the level of creatinine normalized CRP is above 1.5 μg/L, above 1.6 μg/L or above 1.7 μg/L, a bacterial infection is ruled in.
Alternatively, or additionally, when the level of creatinine normalized IP- 10 is above 2.0 ng/L or above 2.1 ng/L, a bacterial infection is ruled in. According to a specific embodiment, when the level of creatinine normalized CRP is below
1.5 μg/L, a bacterial infection is ruled out. Additional tests may be carried out in order to confirm that the infection is viral.
Alternatively, or additionally, when the level of creatinine normalized IP- 10 is below 2.0 ng/L, a bacterial infection is ruled out. Additional tests may be carried out in order to confirm that the infection is viral.
In some embodiments, the ruling in (or ruling out) takes into account the levels of additional proteins including but not limited to procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase- associated lipocalin (NGAL), IL-IRA, IRNg, TNFa, MCP-1 and Interleukin- 18 (IL-18), details of which are provided in Table 1, below.
Table 1
Figure imgf000016_0001
Figure imgf000017_0001
In some embodiments, the ruling in (or ruling out) takes into account additional traditional laboratory risk factors.
“Traditional laboratory risk factors” encompass biomarkers isolated or derived from subject samples and which are currently evaluated in the clinical laboratory and used in traditional global risk assessment algorithms, such as absolute neutrophil count (abbreviated ANC), absolute lymphocyte count (abbreviated ALC), white blood count (abbreviated WBC), neutrophil % (defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)), lymphocyte % (defined as the fraction of white blood cells that are lymphocytes and abbreviated Lym (%)), monocyte % (defined as the fraction of white blood cells that are monocytes and abbreviated Mon (%)), Sodium (abbreviated Na), Potassium (abbreviated K), Bilirubin (abbreviated Bill).
According to a specific embodiment, the laboratory risk factor includes at least one of the following: nitrite level, white blood cell count and pH.
In some embodiments, the ruling in takes into account additional clinical parameters.
“Clinical parameters” encompass all non-sample or non-analyte biomarkers of subject health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE), gender (Sex), core body temperature (abbreviated "temperature"), maximal core body temperature since initial appearance of symptoms (abbreviated "maximal temperature"), time from initial appearance of symptoms (abbreviated "time from symptoms") or family history (abbreviated FamHX).
In some embodiments the level of additional parameters may be analyzed such as absolute Neutrophil count (ANC), ALC, Neu (%), Lymphocyte percentage (Lym (%)), Monocyte percentage (Mono (%)), Maximal temperature, Time from symptoms, Age, Potassium (K), Pulse and Urea.
According to one embodiment, the bacterial infection is a chronic bacterial infection.
A "chronic infection" is an infection that develops slowly and lasts a long time. One difference between acute and chronic infection is that during acute infection the immune system often produces IgM+ antibodies against the infectious agent, whereas the chronic phase of the infection is usually characteristic of IgM-/IgG+ antibodies. In addition, acute infections cause immune mediated necrotic processes while chronic infections often cause inflammatory mediated fibrotic processes and scarring. Thus, acute and chronic infections may elicit different underlying immunological mechanisms.
According to another embodiment, the bacterial infection is an acute bacterial infection.
An "acute infection" is characterized by rapid onset of disease, a relatively brief period of symptoms, and resolution within days.
According to another embodiment, the infection is a urinary tract infection.
By “ruling in” an infection it is meant that the subject has that type of infection.
By “ruling out” an infection it is meant that the subject does not have that type of infection.
“TP” is true positive, means positive test result that accurately reflects the tested-for activity. For example in the context of the present invention a TP, is for example but not limited to, truly classifying a bacterial infection as such.
“TN” is true negative, means negative test result that accurately reflects the tested-for activity. For example in the context of the present invention a TN, is for example but not limited to, truly classifying a viral infection as such.
“FN” is false negative, means a result that appears negative but fails to reveal a situation. For example in the context of the present invention a FN, is for example but not limited to, falsely classifying abacterial infection as a viral infection.
“FP” is false positive, means test result that is erroneously classified in a positive category. For example in the context of the present invention a FP, is for example but not limited to, falsely classifying a viral infection as a bacterial infection.
“Sensitivity” is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.
“Specificity” is calculated by TN/(TN+FP) or the true negative fraction of non-disease or normal subjects.
"Total accuracy" is calculated by (TN + TP)/(TN + FP +TP + FN).
“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.
“Negative predictive value” or “NPV” is calculated by TN/(TN + FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested. See, e.g., O’Marcaigh AS, Jacobson RM, “Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results,” Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test. "MCC" (Mathwes Correlation coefficient) is calculated as follows: MCC = (TP * TN - FP * FN) / {(TP + FN) * (TP + FP) * (TN + FP) * (TN + FN))
Figure imgf000019_0001
0.5 where TP, FP, TN, FN are true- positives, false-positives,true-negatives, and false-negatives, respectively. Note that MCC values range between -1 to +1, indicating completely wrong and perfect classification, respectively. An MCC of 0 indicates random classification. MCC has been shown to be a useful for combining sensitivity and specificity into a single metric (Baldi, Brunak et al. 2000). It is also useful for measuring and optimizing classification accuracy in cases of unbalanced class sizes (Baldi, Brunak et al. 2000).
Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by a Receiver Operating Characteristics (ROC) curve according to Pepe et al., “Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker,” Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, “Clinical Interpretation Of Laboratory Procedures,” chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4th edition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., “ROC Curve Analysis: An Example Showing The Relationships Among Serum lipid And Apolipoprotein Concentrations In Identifying Subjects With Coronory Artery Disease,” Clin. Chem., 1992, 38(8): 1425-1428. An alternative approach using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction,” Circulation 2007, 115: 928-935.
“Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), Matheus correlation coefficient (MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC) among other measures.
A “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value”. Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical- determinants, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining determinants are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of determinants detected in a subject sample and the subject’s probability of having an infection or a certain type of infection. In panel and combination construction, of particular interest are structural and syntactic statistical classification algorithms, and methods of index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a determinant selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One- Out (LOO) and 10-Fold cross-validation ( 10-Fold CV). At various steps, false discovery rates may be estimated by value permutation according to techniques known in the art. A “health economic utility function” is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care. It encompasses estimates of the accuracy, effectiveness and performance characteristics of such intervention, and a cost and/or value measurement (a utility) associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and/or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome. The sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcome’s expected utility is the total health economic utility of a given standard of care. The difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention. This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance. Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost-effective clinical performance characteristics required of a new intervention.
For diagnostic (or prognostic) interventions of the invention, as each outcome (which in a disease classifying diagnostic test may be a TP, FP, TN, or FN) bears a different cost, a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures. These different measurements and relative trade-offs generally will converge only in the case of a perfect test, with zero error rate (a.k.a., zero predicted subject outcome misclassifications or FP and FN), which all performance measures will favor over imperfection, but to differing degrees.
“Measuring” or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject’s non-analyte clinical parameters or clinical-determinants.
“Analytical accuracy” refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation (CV), Pearson correlation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and/or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.
“Performance” is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate “performance metrics,” such as AUC and MCC, time to result, shelf life, etc. as relevant.
By “statistically significant”, it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a “false positive”). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.
A “subject” in the context of the present invention may be a mammal (e.g. human dog, cat, horse, cow, sheep, pig, goat). According to another embodiment, the subject is a bird (e.g. chicken, turkey, duck, goose. According to a particular embodiment, the subject is a human. A subject can be male or female. A subject can be one who has been previously diagnosed or identified as having an infection, and optionally has already undergone, oris undergoing, a therapeutic intervention for the infection. Alternatively, a subject can also be one who has not been previously diagnosed as having an infection. For example, a subject can be one who exhibits one or more risk factors for having an infection.
The subject may be a human subject younger than 18 years old, 12 years old, 2 years old, 1 year or younger, 3, 2 and/or 1 month or younger.
In one embodiment, the subject is symptomatic for an infection (e.g. has fever).
According to a specific embodiment, the subject does not have a kidney disease.
In another embodiment, the subject is asymptomatic for an infection (i.e. not exhibiting the traditional signs and symptoms e.g. does not have a fever).
The bacterial infection may be the result of gram-positive, gram-negative bacteria or atypical bacteria.
The term "Gram-positive bacteria" are bacteria that are stained dark blue by Gram staining. Gram-positive organisms are able to retain the crystal violet stain because of the high amount of peptidoglycan in the cell wall.
The term "Gram- negative bacteria" are bacteria that do not retain the crystal violet dye in the Gram staining protocol.
The term "Atypical bacteria" are bacteria that do not fall into one of the classical “Gram” groups. They are usually, though not always, intracellular bacterial pathogens. They include, without limitations, Mycoplasmas spp., Legionella spp. Rickettsiae spp., and Chlamydiae spp. A reference value can be relative to a number or value derived from population studies, including without limitation, such subjects having the same infection, subject having the same or similar age range, subjects in the same or similar ethnic group, or relative to the starting sample of a subject undergoing treatment for an infection. Such reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of infection. Reference determinant indices can also be constructed and used using algorithms and other methods of statistical and structural classification.
In one embodiment of the present invention, the reference value is the amount (i.e. level) of determinants in a control sample derived from one or more subjects who do not have an infection (i.e., healthy, and or non-infectious individuals). In a further embodiment, such subjects are monitored and/or periodically retested for a diagnostically relevant period of time (“longitudinal studies”) following such test to verify continued absence of infection. Such period of time may be one day, two days, two to five days, five days, five to ten days, ten days, or ten or more days from the initial testing date for determination of the reference value. Furthermore, retrospective measurement of determinants in properly banked historical subject samples may be used in establishing these reference values, thus shortening the study time required.
A reference value can also comprise the amounts of determinants derived from subjects who show an improvement as a result of treatments and/or therapies for the infection. A reference value can also comprise the amounts of determinants derived from subjects who have confirmed infection by known techniques.
An example of a bacterially infected reference value index value is the mean or median concentrations of that determinant in a statistically significant number of subjects having been diagnosed as having a bacterial infection.
An example of a virally infected reference value is the mean or median concentrations of that determinant in a statistically significant number of subjects having been diagnosed as having a viral infection.
In another embodiment, the reference value is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of determinants from one or more subjects who do not have an infection. A baseline value can also comprise the amounts of determinants in a sample derived from a subject who has shown an improvement in treatments or therapies for the infection. In this embodiment, to make comparisons to the subject-derived sample, the amounts of determinants are similarly calculated and compared to the index value. Optionally, subjects identified as having an infection, are chosen to receive a therapeutic regimen to slow the progression or eliminate the infection. Additionally, the amount of the determinant can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values. The “normal control level” means the level of one or more determinants or combined determinant indices typically found in a subject not suffering from an infection. Such normal control level and cutoff points may vary based on whether a determinant is used alone or in a formula combining with other determinants into an index. Alternatively, the normal control level can be a database of determinant patterns from previously tested subjects.
The effectiveness of a treatment regimen can be monitored by detecting a determinant in an effective amount (which may be one or more) of samples obtained from a subject over time and comparing the amount of determinants detected. For example, a first sample can be obtained prior to the subject receiving treatment and one or more subsequent samples are taken after or during treatment of the subject.
In a specific embodiment of the invention a treatment recommendation (i.e., selecting a treatment regimen) for a subject is provided by identifying the type infection (i.e., bacterial, viral, mixed infection or no infection) in the subject according to the method of any of the disclosed methods and recommending that the subject receive an antibiotic treatment if the subject is identified as having bacterial infection or a mixed infection; or an anti- viral treatment is if the subject is identified as having a viral infection.
Exemplary antibiotics for the treatment of urinary tract infectionsinclude trimethoprim- sulfamethoxazole, trimethoprim, ciprofloxacin, levlfloxacin, Norflozacin, Nistofurantoin macrocrystals, Nistofurantoin monohydrate macrocrystals, fosfomycin tromethamine.
In another embodiment, the methods of the invention can be used to prompt additional targeted diagnosis such as pathogen specific PCRs, chest- X-ray, cultures etc. For example, a diagnosis that indicates a viral infection according to embodiments of this invention, may prompt the usage of additional viral specific multiplex- PCRs, whereas a diagnosis that indicates a bacterial infection according to embodiments of this invention may prompt the usage of a bacterial specific multiplex-PCR. Thus, one can reduce the costs of unwarranted expensive diagnostics.
In a specific embodiment, a diagnostic test recommendation for a subject is provided by identifying the infection type (i.e., bacterial, viral, mixed infection or no infection) in the subject according to any of the disclosed methods and recommending a test to determine the source of the bacterial infection if the subject is identified as having a bacterial infection or a mixed infection; or a test to determine the source of the viral infection if the subject is identified as having a viral infection. Some aspects of the present invention can also be used to screen patient or subject populations in any number of settings. For example, a health maintenance organization, public health entity or school health program can screen a group of subjects to identify those requiring interventions, as described above, or for the collection of epidemiological data. Insurance companies (e.g., health, life or disability) may screen applicants in the process of determining coverage or pricing, or existing clients for possible intervention. Data collected in such population screens, particularly when tied to any clinical progression to conditions like infection, will be of value in the operations of, for example, health maintenance organizations, public health programs and insurance companies. Such data arrays or collections can be stored in machine-readable media and used in any number of health-related data management systems to provide improved healthcare services, costeffective healthcare, improved insurance operation, etc. See, for example, U.S. Patent Application No. 2002/0038227; U.S. Patent Application No. US 2004/0122296; U.S. Patent Application No. US 2004/ 0122297; and U.S. Patent No. 5,018,067. Such systems can access the data directly from internal data storage or remotely from one or more data storage sites as further detailed herein.
A machine-readable storage medium can comprise a data storage material encoded with machine readable data or data arrays which, when using a machine programmed with instructions for using the data, is capable of use for a variety of purposes. Measurements of effective amounts of the biomarkers of the invention and/or the resulting evaluation of risk from those biomarkers can be implemented in computer programs executing on programmable computers, comprising, inter alia, a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code can be applied to input data to perform the functions described above and generate output information. The output information can be applied to one or more output devices, according to methods known in the art. The computer may be, for example, a personal computer, microcomputer, or workstation of conventional design.
Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system However, the programs can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language. Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette or others as defined elsewhere in this disclosure) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The health-related data management system used in some aspects of the invention may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform various functions described herein.
The determinants of the present invention, in some embodiments thereof, can be used to generate a “reference determinant profile” of those subjects who do not have an infection. The determinants disclosed herein can also be used to generate a “subject determinant profile” taken from subjects who have an infection. The subject determinant profiles can be compared to a reference determinant profile to diagnose or identify subjects with an infection. The subject determinant profile of different infection types can be compared to diagnose or identify the type of infection. The reference and subject determinant profiles of the present invention, in some embodiments thereof, can be contained in a machine-readable medium, such as but not limited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others. Such machine-readable media can also contain additional test results, such as, without limitation, measurements of clinical parameters and traditional laboratory risk factors. Alternatively or additionally, the machine-readable media can also comprise subject information such as medical history and any relevant family history. The machine-readable media can also contain information relating to other disease-risk algorithms and computed indices such as those described herein.
Performance and Accuracy Measures of the Invention.
The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, some aspects of the invention are intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects having an infection is based on whether the subjects have, a “significant alteration” (e.g., clinically significant and diagnostically significant) in the levels of a determinant. By “effective amount” it is meant that the measurement of an appropriate number of determinants (which may be one or more) to produce a “significant alteration” (e.g. level of expression or activity of a determinant) that is different than the predetermined cut-off point (or threshold value) for that determinant (s) and therefore indicates that the subject has an infection for which the determinant (s) is a determinant. The difference in the level of determinant is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical, diagnostic, and clinical accuracy, may require that combinations of several determinants be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant determinant index.
In the categorical diagnosis of a disease state, changing the cut point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject’s condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. One way to achieve this is by using the MCC metric, which depends upon both sensitivity and specificity. Use of statistics such as AUC, encompassing all potential cut point values, is preferred for most categorical risk measures when using some aspects of the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred.
By predetermined level of predictability it is meant that the method provides an acceptable level of clinical or diagnostic accuracy. Using such statistics, an “acceptable degree of diagnostic accuracy”, is herein defined as a test or assay (such as the test used in some aspects of the invention for determining the clinically significant presence of determinants, which thereby indicates the presence an infection type) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.
By a “very high degree of diagnostic accuracy”, it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.
Alternatively, the methods predict the presence or absence of an infection or response to therapy with at least 75% total accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.
Alternatively, the methods predict the presence of a bacterial infection or response to therapy with at least 75% sensitivity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater sensitivity.
Alternatively, the methods predict the presence of a viral infection or response to therapy with at least 75% specificity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater specificity. Alternatively, the methods predict the presence or absence of an infection or response to therapy with an MCC larger than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8., 0.9 or 1.0. The predictive value of any test depends on the sensitivity and specificity of the test, and on the prevalence of the condition in the population being tested. This notion, based on Bayes’ theorem, provides that the greater the likelihood that the condition being screened for is present in an individual or in the population (pre-test probability), the greater the validity of a positive test and the greater the likelihood that the result is a true positive. Thus, the problem with using a test in any population where there is a low likelihood of the condition being present is that a positive result has limited value (i.e., more likely to be a false positive). Similarly, in populations at very high risk, a negative test result is more likely to be a false negative.
As a result, ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon).
A health economic utility function is an yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each. Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects. As a performance measure, it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.
In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer- Lemeshow P- value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% Cl) based on a historical observed cohort’ s predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City, California).
In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the determinants of the invention allows for one of skill in the art to use the determinants to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.
Furthermore, other unlisted biomarkers will be very highly correlated with the determinants (for the purpose of this application, any two variables will be considered to be “very highly correlated” when they have a Coefficient of Determination ( R 2) of 0.5 or greater). Some aspects of the present invention encompass such functional and statistical equivalents to the aforementioned determinants. Furthermore, the statistical utility of such additional determinants is substantially dependent on the cross-correlation between multiple biomarkers and any new biomarkers will often be required to operate within a panel in order to elaborate the meaning of the underlying biology.
One or more of the listed determinants can be detected in the practice of the present invention, in some embodiments thereof. For example, two (2), three (3), four (4), five (5), ten (10), fifteen (15), twenty (20), forty (40), or more determinants can be detected.
In some aspects, all determinants listed herein can be detected. Preferred ranges from which the number of determinants can be detected include ranges bounded by any minimum selected from between one and, particularly two, three, four, five, six, seven, eight, nine ten, twenty, or forty. Particularly preferred ranges include two to five (2-5), two to ten (2-10), two to twenty (2- 20), or two to forty (2-40).
Measurement of determinants
The actual measurement of levels or amounts of the determinants can be determined at the protein level using any method known in the art.
For example, by measuring the levels of proteins encoded by the gene products described herein, or subcellular localization or activities thereof. Such methods are well known in the art and include, e.g., immunoassays based on antibodies to proteins, aptamers or molecular imprints. Any biological material can be used for the detection/quantification of the protein or its activity. Alternatively, a suitable method can be selected to determine the activity of proteins encoded by the marker genes according to the activity of each protein analyzed.
The determinants can be detected in any suitable manner, but are typically detected by contacting a urine sample from the subject with an antibody, which binds the determinant and then detecting the presence or absence of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay. In one embodiment, the antibody which specifically binds the determinant is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label.
Immunoassays carried out in accordance with some embodiments of the present invention may be homogeneous assays or heterogeneous assays. In a homogeneous assay the immunological reaction usually involves the specific antibody (e.g., anti- determinant antibody), a labeled analyte, and the sample of interest. The signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte. Both the immunological reaction and detection of the extent thereof can be carried out in a homogeneous solution. Immunochemical labels, which may be employed, include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.
In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and means for producing a detectable signal. Samples as described above may be used. The antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal. The signal is related to the presence of the analyte in the sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if the antigen to be detected contains a second binding site, an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays are oligonucleotides, immunoblotting, immunofluorescence methods, immunoprecipitation, chemiluminescence methods, electrochemiluminescence (ECL) or enzyme- linked immunoassays.
Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which may be useful for carrying out the method disclosed herein. According to a particular embodiment, lateral flow immunoassay is used to analyze the level of the determinant. Further descriptions of LFI devices may be found in PCT Application IL2017/050697, the contents of which are incorporated herein by reference.
See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al., titled “Methods for Modulating Ligand- Receptor Interactions and their Application,” U.S. Pat. No. 4,659,678 to Forrest et al., titled “Immunoassay of Antigens,” U.S. Pat. No. 4,376,110 to David etal., titled “hnrnuno metric Assays Using Monoclonal Antibodies,” U.S. Pat. No. 4,275,149 to litman et al., titled “Macromolecular Environment Control in Specific Receptor Assays,” U.S. Pat. No. 4,233,402 to Maggio etal., titled “Reagents and Method Employing Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled “Heterogenous Specific Binding Assay Employing a Coenzyme as Label.” The determinant can also be detected with antibodies using flow cytometry. Those skilled in the art will be familiar with flow cytometric techniques which may be useful in carrying out the methods disclosed herein (Shapiro 2005). These include, without limitation, Cytokine Bead Array (Becton Dickinson) and Luminex technology.
Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g., 35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
Antibodies can also be useful for detecting post-translational modifications of determinant proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well-known to those skilled in the art, and commercially available. Post- translational modifications can also be determined using metastable ions in reflector matrix- assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF) (Wirth U. and Muller D. 2002).
Additional exemplary methods for analyzing the level of a protein include Western blot analysis and Enzyme linked immunosorbent assay (ELISA).
For determinant-proteins, polypeptides, mutations, and polymorphisms known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art. Such assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis-Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.
The term "metabolite" includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage or consumption of a biological molecule (e.g., a protein, nucleic acid, carbohydrate, or lipid). Metabolites can be detected in a variety of ways known to one of skill in the art, including the refractive index spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption ionization time of flight (MALDI-TOF) combined with mass spectrometry, ion spray spectroscopy combined with mass spectrometry, capillary electrophoresis, NMR and IR detection. In this regard, other DETERMINANT analytes can be measured using the above-mentioned detection methods, or other methods known to the skilled artisan. For example, circulating calcium ions (Ca 2+ ) can be detected in a sample using fluorescent dyes such as the poly-amino carboxylic acid, Fluo series, Fura-2A, Rhod- 2, the ratiometric calcium indicator Indo-1, among others. Other determinant metabolites can be similarly detected using reagents that are specifically designed or tailored to detect such metabolites.
Suitable sources for antibodies for the detection of determinants include commercially available sources such as, for example, Abazyme, Abnova, AssayPro, Affinity Biologicals, AntibodyShop, Aviva bioscience, Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences, Chemicon International, Chemokine, Clontech, Cytolab, DAKO, Diagnostic BioSystems, eBioscience, Endocrine Technologies, Enzo Biochem, Eurogentec, Fusion Antibodies, Genesis Biotech, GloboZymes, Haematologic Technologies, Immunodetect, Immunodiagnostik, Immuno me tries, Immunostar, Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory, KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, Lee Laboratories, lifescreen, Maine Biotechnology Services, Mediclone, MicroPharm Ltd., ModiQuest, Molecular Innovations, Molecular Probes, Neoclone, Neuromics, New England Biolabs, Novocastra, Novus Biologicals, Oncogene Research Products, Orbigen, Oxford Biotechnology, Panvera, PerkinElmer Life Sciences, Pharmingen, Phoenix Pharmaceuticals, Pierce Chemical Company, Polymun Scientific, Polysiences, Inc., Promega Corporation, Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D Systems, Repligen, Research Diagnostics, Roboscreen, Santa Cruz Biotechnology, Seikagaku America, Serological Corporation, Serotec, SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH, Technopharm, Terra Nova Biotechnology, TiterMax, Trillium Diagnostics, Upstate Biotechnology, US Biological, Vector Laboratories, Wako Pure Chemical Industries, and Zeptometrix. However, the skilled artisan can routinely make antibodies, against any of the polypeptide determinants described herein.
Examples of antibodies for measuring TRAIT, include without limitation: Mouse anti- Human TRAIL Monoclonal antibody (RIK-2) (12-9927-42) (Invitrogen), Goat IgG anti-Human TRAIL Polyclonal antibody (AF375) (R&D Systems), Mouse anti-Human TRAIL monoclonal antibody [2E5] (ab2219) (Abeam), Mouse anti-Human Monoclonal antibody (Clone # 75402) (MAB687) (R&D Systems),
Examples of antibodies for measuring CRP include without limitation: Rabbit anti-Human C-Reactive Protein/CRP polyclonal antibody (ab31156) (Abeam), Sheep anti-Human C-Reactive Protein/CRP Polyclonal antibody (AF1707) (R&D Systems), rabbit anti-Human C-Reactive Protein/CRP Polyclonal antibody (C3527) (Sigma- Aldrich), Mouse anti-Human C-Reactive Protein/CRP monoclonal antibody (C1688) (MilliporeSigma),
Examples of antibodies for measuring IP- 10 include without limitation: Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (Cat. No. 524401) (BioLegend), Rabbit anti-human CXCL10 (IP- 10) polyclonal Antibody (ab9807) (Abeam), Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (4D5) (MCA1693) (Bio-Rad), Goat anti-human CXCL10 (IP- 10) Monoclonal Antibody (PA5-46999) (Invitrogen), Mouse anti-human CXCL10 (IP- 10) Monoclonal Antibody (MA5-23819) (Invitrogen),
Examples of antibodies for measuring IL-6 include without limitation: Mouse anti-human IL-6 monoclonal antibody (Clone # 1936) (MAB2061) (R&D Systems), Mouse anti-human IL-6 monoclonal antibody (ab9324) (Abeam), Rat anti-human IL-6 monoclonal antibody (MQ2-39C3) (501204) (BioLegend), Rabbit anti-human IL-6 monoclonal antibody (ab233706) (Abeam),
Examples of antibodies for measuring Procalcitonin (PCT) include without limitation: Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (DMAB1342MH) (Creative diagnostics), Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (MAB8350) (R&D Systems), Sheep Anti-Human Procalcitonin (PCT) polyclonal antibody (PA1-75362) (Invitrogen), Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (6F10) (MAI-20888) (Invitrogen).
Examples of antibodies for measuring IL-18 include without limitation: Rabbit Anti- Human IL-18 polyclonal antibody (PA5-80719) (Invitrogen), Rabbit Anti-Human IL-18 recombinant monoclonal antibody (MA5-30764) (Invitrogen), Rabbit Anti-Human IL-18 polyclonal antibody (ab 191152) (Abeam).
Examples of antibodies for measuring NGAL include without limitation: Rabbit Anti- Human NGAL monoclonal antibody (abl25075) (Abeam), Mouse Anti-Human NGAL monoclonal antibody (ab23477) (Abeam), Rabbit Anti-Human NGAL polyclonal antibody (PA5- 79589) (Invitrogen), Rabbit Anti-Human NGAL recombinant monoclonal antibody (702248) (Invitrogen), Rat Anti-Human NGAL monoclonal antibody (MAB17571-SP) (R&D Systems),
Examples of antibodies for measuring IL1R/IL1R1 include without limitation: Goat Anti- Human IL1R1 Polyclonal antibody (PA5-46930) (Invitrogen), Mouse Anti-Human IL1R1 Monoclonal antibody (clone IL1 31-22.2.1) (MAI- 10857) (Invitrogen), Goat Anti-Human ILIRI Polyclonal antibody (AF269) (R&D Systems), Rabbit Anti-Human ILIRI Polyclonal antibody (abl06278) (Abeam), Mouse Anti-Human ILIRI monoclonal antibody (sc-393998) (SANTA CRUZ BIOTECHNOLOGY).
Examples of antibodies for measuring Serum Amyloid A1 (SAA / SAA1) include without limitation: Rabbit Anti-Human Serum Amyloid A1 (SAA/ SAA1) Polyclonal antibody (PA5- 112852) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (MA5- 11729) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (MBS592153) (MyBioSource), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (ab687) (Abeam), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal antibody (clone SAA 19) (MCA6030GA) (BIO-RAD).
Examples of antibodies for measuring TREM1 include without limitation: Mouse Anti- Human TREM-1 Monoclonal Antibody (R&D Systems), Rabbit Anti-Human TREMl Polyclonal Antibody (Merck), Rabbit Anti-Human TREMl Polyclonal Antibody (Invitrogen), Mouse Anti- Human CD354 (TREM-1) Monoclonal Antibody (BioLegend).
Examples of antibodies for measuring TREM2 include without limitation: Rat Anti- Human TREM2 Monoclonal antibody (Clone # 237920) (MAB 17291) (R&D Systems), Goat Anti-Human TREM2 Polyclonal antibody (AF1828) (R&D Systems), Mouse Anti-Human TREM2 Monoclonal antibody (clone 2B5) (NBP1-07101) (Novus Biologicals), Rabbit Anti- Human TREM2 Monoclonal antibody (ab209814) (Abeam).
Examples of antibodies for measuring IL8 include without limitation: Mouse Anti-Human IL-8 Monoclonal Antibody (abl8672) (Abeam), Mouse Anti-Human IL-8/CXCL8 Monoclonal Antibody (R&D Systems), Mouse Anti-Human IL-8 (CXCL8) Monoclonal Antibody (Invitrogen).
Examples of antibodies for measuring IL-15 include without limitation: Mouse Anti- Human IL-15 Monoclonal Antibody (MA5-23729) (Invitrogen), Mouse Anti-Human IL-15 Monoclonal Antibody (16-0157-82) (Invitrogen), Rabbit Anti-Human IL-15 Polyclonal Antibody (PA5- 102871) (Invitrogen), Mouse Anti-Human IL-15 Monoclonal Antibody (ab55276) (Abeam), Mouse Anti-Human IL-15 Monoclonal Antibody (Clone # 34559) (MAB2471) (R&D Systems). Examples of antibodies for measuring IL-12 include without limitation: Goat Anti-Human IL-12 Polyclonal Antibody (AF-219-NA) (R&D Systems), Mouse Anti-Human IL-12 Monoclonal Antibody (Clone # 24910) (MAB219) (R&D Systems), Goat Anti-Human IL-12 Polyclonal Antibody (ab9992) (Abeam), Rat Anti-Human IL-12 Monoclonal Antibody (16-8126-85) (Invitrogen).
Examples of antibodies for measuring IL-10 include without limitation: Mouse Anti- Human IL-10 Monoclonal Antibody (Clone # 127107) (MAB2172) (R&D Systems), Rat Anti- Human IL-10 Monoclonal Antibody (clone JES3-9D7) (501403) (BioLegend), Rabbit Anti- Human IL-10 Polyclonal Antibody (ab34843) (Abeam), Rat Anti-Human IL-10 Monoclonal Antibody (clone JES3-12G8) (MAI-82664) (Invitrogen).
Examples of antibodies for measuring MCP-1 include without limitation: Rabbit anti- Human MCP-1 Polyclonal antibody (ab9669) (Abeam), Mouse anti-Human MCP-1 Monoclonal antibody (Clone # 23007) (MAB679) (R&D Systems), Mouse anti-Human MCP-1 Monoclonal antibody (Clone 2D8) (MA5- 17040) (Invitrogen), Mouse anti-Human MCP-1 Monoclonal antibody (Clone 5D3-F7) (MCA5981GA) (BIO-RAD).
Examples of antibodies for measuring IL-2R (IL-2RA) include without limitation: Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (ab9496) (Abeam), Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (Clone #24212) (MAB1020) (R&D Systems), Mouse anti-Human IL-2R (IL-2RA) Monoclonal antibody (Clone YNRhlL2R) (ANT- 104) (ProSpec).
Examples of antibodies for measuring GDF15 include without limitation: Goat anti- Human GDF15 Polyclonal antibody (AF957) (R&D Systems), Mouse anti-Human GDF15 Monoclonal antibody (Clone # 147627) (MAB957) (R&D Systems), Goat anti-Human GDF15 Polyclonal antibody (ab39999) (Abeam), Rabit anti-Human GDF15 Polyclonal antibody (abl06006) (Abeam), Rabit anti-Human GDF15 Polyclonal antibody (HPA011191) (Sigma- Aldrich).
Examples of antibodies for measuring MBL include without limitation: Goat anti-Human MBL Polyclonal antibody (AF2307) (R&D Systems), Mouse anti-Human MBL Monoclonal antibody (3B6) (ab23457) (Abeam), Goat anti-Human MBL Polyclonal antibody (AF2307) (Novus Biologicals).
Examples of antibodies for measuring CD27 include without limitation: Goat anti-Human CD27 Polyclonal antibody (AF382) (R&D Systems), Mouse anti-Human CD27 monoclonal antibody (clone 0323) (47-0279-42) (Invitrogen), Rabbit anti-Human CD27 Polyclonal antibody (PA5-83443) (Invitrogen), Mouse anti-Human CD27 monoclonal antibody (clone CLB-27/1) (MHCD2704) (Invitrogen). Examples of antibodies for measuring MMP2 include without limitation: Rabbit anti- Human MMP2 Polyclonal antibody (ab97779) (Abeam), Mouse anti-Human MMP2 monoclonal antibody (6E3F8) (ab86607) (Abeam), Mouse anti-Human MMP2 monoclonal antibody (Clone # 36006) (MAB902) (R&D Systems), Goat anti-Human MMP2 polyclonal antibody (AF902) (R&D Systems), Rabbit anti-Human MMP2 Polyclonal antibody (AHP2735) (BIO-RAD).
Examples of antibodies for measuring RESISTIN include without limitation: Goat anti- Human RESISTIN Polyclonal antibody (AF1359) (R&D Systems), Mouse anti-Human RESISTIN Monoclonal antibody (Clone # 184305) (MAB13591) (R&D Systems), Rabbit anti- Human RESISTIN Monoclonal antibody (clone EP4738) (abl24681) (Abeam), Mouse anti- Human RESISTIN Monoclonal antibody (sc-376336) (SANTA CRUZ BIOTECHNOLOGY).
Examples of antibodies for measuring RSAD2 include without limitation: Rabbit anti- Human RSAD2 Polyclonal antibody (HPA041160) (Sigma- Aldrich), Mouse anti-Human RSAD2 Monoclonal antibody (sc-390342) (SANTA CRUZ BIOTECHNOLOGY), Mouse anti-Human RSAD2 Monoclonal antibody (OTI4D12) (TA505799) (OriGene), Rabbit anti-Human RSAD2 Polyclonal antibody (LS-C378833) (LSBio), Rabbit anti-Human RSAD2 Polyclonal antibody (TA329507) (OriGene).
Examples of antibodies for measuring MX1 include without limitation: Rabbit anti-Human MX1 Polyclonal antibody (ab95926) (Abeam), Goat anti-Human MX1 Polyclonal antibody (AF7946) (R&D Systems), Mouse anti-Human MX1 monoclonal antibody (sc-271024) (SANTA
CRUZ BIOTECHNOLOGY), Rabbit anti-Human MX1 Polyclonal antibody (PA5-56590) (Invitrogen), Mouse anti-Human MX1 Monoclonal antibody (OTI2G12) (MA5-24914) (Invitrogen), Mouse anti-Human MXl Monoclonal antibody (GT4812) (MA5-31483) (Invitrogen).
Examples of antibodies for measuring TIE2 include without limitation: Mouse anti-Human TIE2 Monoclonal antibody (Cl. 16) (ab24859) (Abeam), Goat anti-Human TIE2 Polyclonal antibody (AF313) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (Clone # 83715) (MAB3131) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (clone 33.1 (Ab33)) (334205) (BioLegend), Rabbit anti-Human TIE2 Polyclonal antibody
(SAB4502942) (Sigma- Aldrich).
Examples of antibodies for measuring VCAM-1/CD106 include without limitation: Mouse anti-Human VCAM-1 Monoclonal antibody (Clone # BBIG-V1) (BBA5) (R&D Systems), Rabbit anti-Human VCAM-1 Monoclonal antibody (EPR5047) (ab 134047) (Abeam), Mouse anti-Human VCAM-1 Monoclonal antibody (clone 1.4C3) (MA5- 11447) (Invitrogen). Examples of antibodies for measuring CD14 include without limitation: Mouse anti- Human CD14 Monoclonal antibody (4B4F12) (abl82032) (Abeam), Mouse anti-Human CD14 Monoclonal antibody (M5E2) (301805) (BioLegend), Mouse anti-Human CD14 Monoclonal antibody (Clone # 134620) (MAB3832) (R&D Systems), Mouse anti-Human CD14 Monoclonal antibody (clone TÜK4) (MCA1568) (Bio-Rad).
Examples of antibodies for measuring IGFBP-3 include without limitation: Mouse anti- Human IGFBP-3 Monoclonal antibody (Clone # 84728) (MAB305) (R&D Systems), Goat anti- Human IGFBP-3 Polyclonal antibody (AF675) (R&D Systems), Goat anti-Human IGFBP-3 Polyclonal antibody (ab77635) (Abeam), Rabbit anti-Human IGFBP-3 Polyclonal antibody (PAS- 29711 ) (Invitrogen) .
Examples of antibodies for measuring APR IT, include without limitation: Mouse anti- Human APRIL/ TNFSF13 Monoclonal antibody (JE49-07) (MA5-34866) (Invitrogen), Mouse anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670820) (MAB5860) (R&D Systems), Mouse anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670840) (MAB8843) (R&D Systems), Rabbit anti-Human APRIL/ TNFSF13 Polyclonal antibody (ab3681) (Abeam).
Examples of antibodies for measuring Adiponectin include without limitation: Mouse anti- Human Adiponectin Monoclonal antibody (19F1) (ab22554) (Abeam), Rabbit anti-Human Adiponectin Polyclonal antibody (ab25891) (Abeam), Mouse anti-Human Adiponectin Monoclonal antibody (Clone # 553517) (MAB 10652) (R&D Systems), Goat anti-Human Adiponectin Polyclonal antibody (AF1065) (R&D Systems), Rabbit anti-Human Adiponectin Polyclonal antibody (A6354) (Sigma- Aldrich).
Examples of antibodies for measuring Angiogenin include without limitation: Goat anti- Human Angiogenin Polyclonal antibody (AF265) (R&D Systems), Goat anti-Human Angiogenin Polyclonal antibody (AB-265) (R&D Systems), Rabbit anti-Human Angiogenin Polyclonal antibody (ab 189207) (Abeam), Mouse anti-Human Angiogenin Monoclonal antibody (clone MANG-1) (0555-5008) (Bio-Rad).
Examples of antibodies for measuring Angiopoietin 2/ANG2 include without limitation: Rabbit anti-Human Angiopoietin 2/ANG2 Polyclonal antibody (abl53934) (Abeam), Goat anti- Human Angiopoietin 2/ANG2 Polyclonal antibody (AF623) (R&D Systems), Mouse anti-Human Angiopoietin 2/ANG2 Monoclonal antibody (Clone # 180102) (MAB0983) (R&D Systems), Mouse anti-Human Angiopoietin 2/ANG2 Monoclonal antibody (Clone # M5203F01) (682702) (BioLegend). Examples of antibodies for measuring CLUSTERIN include without limitation: Rabbit anti-Human CLUSTERIN Polyclonal antibody (ab69644) (Abeam), Rabbit anti-Human CLUSTERIN Monoclonal antibody [EPR2911] (ab92548) (Abeam), Mouse anti-Human CLUSTERIN Monoclonal antibody (Clone # 350227) (MAB2937) (R&D Systems), Mouse anti- Human CLUSTERIN Monoclonal antibody (Clone # 350270) (MAB29372) (R&D Systems).
Examples of antibodies for measuring CD95 include without limitation: Mouse anti- Human CD95 Monoclonal antibody (clone DX2) (BioLegend), Mouse anti-Human CD95 Monoclonal antibody (clone EOS9.1) (BioLegend), Mouse anti-Human CD95 Monoclonal antibody (clone LOB 3/17) (Bio-Rad).
Examples of antibodies for measuring uPAR include without limitation: Mouse anti- Human uPAR Monoclonal antibody (Clone # 62022) (MAB807) (R&D Systems), Goat anti- Human uPAR Polyclonal antibody (AF807) (R&D Systems), Rabbit anti-Human uPAR Monoclonal antibody (clone 2G10) (MABC88) (Sigma- Aldrich).
Examples of antibodies for measuring IL7R include without limitation: Mouse anti-Human IL7R /CD127 Monoclonal antibody (Clone # 40131) (MAB306) (R&D Systems), Mouse anti- Human IL7R /CD127 Monoclonal antibody (Clone A019D5) (351303) (BioLegend), Mouse anti- Human IL7R /CD127 Monoclonal antibody (eBioRDR5) (48-1278-42) (Invitrogen), Rabbit anti- Human IL7R /CD 127 Polyclonal antibody (PA5-97870) (Invitrogen).
Examples of antibodies for measuring PTEN include without limitation: Mouse anti- Human PTEN Monoclonal antibody (Clone # 217702) (MAB847) (R&D Systems), Rabbit anti- Human PTEN Polyclonal antibody (ab31392) (Abeam), Mouse anti-Human PTEN Monoclonal antibody (A2bl) (ab79156) (Abeam), Mouse anti-Human PTEN Monoclonal antibody (clone 6H2.1) (Sigma- Aldrich).
Examples of antibodies for measuring MMP8 include without limitation: Mouse anti- Human MMP8 Monoclonal antibody (Clone # 100608) (MAB9081) (R&D Systems), Mouse anti- Human MMP8 Monoclonal antibody (Clone # 100619) (MAB908) (R&D Systems), Rabbit anti- Human MMP8 Monoclonal antibody (EP1252Y) (ab81286) (Abeam), Rabbit anti-Human MMP8 Polyclonal antibody (PA5-28246) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody (PA5-82805) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody (HPA021221) (Sigma- Aldrich).
Examples of antibodies for measuring Ferritin include without limitation: Mouse anti- Human Ferritin Monoclonal antibody (Clone # 962609) (MAB93541) (R&D Systems), Sheep anti-Human Ferritin Polyclonal antibody (AHP2179G) (Bio-Rad), Mouse anti-Human Ferritin Monoclonal antibody (clone F23 (7A4)) (4420-3010) (Bio-Rad), Goat anti-Human Ferritin Polyclonal antibody (PA5- 19058) (Invitrogen), Mouse anti-Human Ferritin Monoclonal antibody (clone 101) (MIF2501) (Invitrogen), Rabbit anti-Human Ferritin Monoclonal antibody (EPR3004Y) (ab75973) (Abeam).
Examples of antibodies for measuring D-Dimer include without limitation: Mouse anti- Human D-Dimer Monoclonal antibody (clone DD1) (MCA2523) (Bio-Rad), Mouse anti-Human D-Dimer Monoclonal antibody (3B6) (ab273889) (Abeam), Rabbit anti-Human D-Dimer Monoclonal antibody (Clone # 2609D) (MAB104712) (R&D Systems), Mouse anti-Human D- Dimer Monoclonal antibody (clone DD2) (NB 110-8376) (Novus).
Soluble TRAIT, and membrane TRAIT, can be distinguished by using different measuring techniques and samples. For example, Soluble TRAIT, can be measured without limitation in cell free samples such as serum or plasma, using without limitation lateral flow immunoassay (LFIA), as further described herein below. Membrane TRAIT, can be measured in samples that contain cells using cell based assays including without limitation flow cytometry, ELISA, and other immunoassays.
Based on the data provided in Table 9 herein below, the present inventors have further uncovered novel thresholds which can be used for known markers for ruling in a bacterial infection in subjects younger than 3 months of age. These thresholds provide a very high degree of sensitivity and therefore are appropriate for clinical settings.
Thus, according to yet another aspect of the present invention there is provided a method of ruling in a bacterial infection in a subject under 3 months of age, comprising measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIL is below 188 pg/L it is indicative of a bacterial infection in the subject.
In one embodiment, the CRP threshold for ruling in a bacterial infection is above 10 mg/L, above 8 mg/L, above 6 mg/L or above 4 mg/L.
In another embodiment, the TRAIT, threshold for ruling in a bacterial infection is below 190 pg/L, below 195 pg/L, below 200 pg/L, below 205 pg/L or even below 210 pg/L.
In still another embodiment, the IP- 10 concentration for ruling in a bacterial infection is below 300 pg/L, 310 pg/L or even 320 pg/L.
Additional contemplated levels of specificity and sensitivity are provided herein above.
The present inventors previously identified novel sets of biomarkers whose pattern of expression significantly correlates with infection type, as documented in International Patent Applications WO2011132086, WO2013/117746, WO 2016/024278, WO 2016/092554, and WO20 18/029690 all of which are incorporated herein by reference.
The present embodiments provide a method and a system suitable for analyzing biological data obtained from an infant human subject, such as, but not limited to, a human subject of less than three months of age. In some embodiments of the present invention the subject has been previously treated with an antibiotic, and in some embodiments of the present invention the subject has not been previously treated with an antibiotic.
Some embodiments are based on the use of signature of polypeptides for the diagnosis of bacterial infections, viral infections and non-bacterial, non-viral diseases. The method and/or system of the present embodiments identifies the type of infection an infant subject is suffering from, which in turn allows for the selection of an appropriate treatment regimen. Thus, some embodiments of the invention allow for the selection of infant subjects for whom antibiotic treatment is desired and prevent unnecessary antibiotic treatment of infant subjects having only a viral infection or a non-infectious disease. Some embodiments of the invention also allow for the selection of infant subjects for whom anti-viral treatment is advantageous.
Any of the methods described herein can be embodied in many forms. For example, it can be embodied in on a tangible medium such as a computer for performing the method operations. It can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method operations. It can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium
Computer programs implementing the method of the present embodiments can commonly be distributed to users on a distribution medium such as, but not limited to, CD-ROMs or flash memory media. From the distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium In some embodiments of the present invention, computer programs implementing the method of the present embodiments can be distributed to users by allowing the user to download the programs from a remote location, via a communication network, e.g., the internet. The computer programs can be run by loading the computer instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of the present embodiments. All these operations are well-known to those skilled in the art of computer systems.
The computational operations of the method of the present embodiments can be executed by a computer, either remote from the subject or near the subject. When the computer is remote from the subject, it can receive the data over a network, such as a telephone network or the Internet. To this end, a local computer can be used to transmit the data to the remote computer. This configuration allows performing the analysis while the subject is at a different location (e.g., at home), and also allows performing simultaneous analyses for multiple subjects in multiple different locations.
The computational operations of the method can also be executed by a cloud computing resource of a cloud computing facility. The cloud computing resource can include a computing server and optionally also a storage server, and can be operated by a cloud computing client as known in the art.
The method and/or system according to some embodiments may be used to “rule in” a bacterial infection. Alternatively, the method and/or system may be used to rule out a non- bacterial (e.g., viral) infection. The method and/or system according to some embodiments can be used to “rule out” a bacterial infection and “rule in” a non-bacterial disease.
The biological data analyzed by the method and/or system optionally and preferably contain values corresponding to concentrations or counts of a plurality of determinants in a sample of a subject, e.g., an infant subject, preferably an infant subject of less than three months of age.
In a preferred embodiments, at least a portion of the determinants includes polypeptides, and the biological data contain values corresponding to the expression levels of these polypeptides. More preferably, each of the determinants is a polypeptide and the biological data contain a value corresponding to an expression level for each of the polypeptides.
A “sample” in the context of the present invention is a biological sample isolated from the subject, particularly an infant subject with less than three months of age, and can include, by way of example and not limitation, whole blood, serum, plasma, saliva, mucus, breath, urine, CSF, sputum, nasal mucus, sample collected by a nasal swab, sweat, stool, hair, seminal fluid, biopsy, rhinorrhea, tissue biopsy, cytological sample, platelets, reticulocytes, leukocytes, epithelial cells, or whole blood cells. The sample may be fresh or frozen.
In a particular embodiment, the sample is a blood sample, e.g., serum or a sample comprising blood cells. In a particular embodiment, the sample is depleted of red blood cells.
In a particular embodiment of the invention, the sample is a urine sample. Alternatively, and preferably, the sample is not a urine sample, e.g., any of the aforementioned biological samples, excluding urine.
According to this aspect of the present invention, the sample is derived from the subject no more than 7 days, or no more than 6 days, or no more than 5 days, or no more than 4 days, or no more than 72 hours, no more than 60 hours, no more than 48 hours, no more than 36 hours, no more than one 24 hours or even no more than 12 hours following symptom onset.
Preferably, the concentrations or counts of the determinants is measured within about 24 hours after the sample is obtained. Alternatively, the concentrations or counts of the determinants is measured in a sample that was stored at 12 °C or lower, when storage begins less than 24 hours after the sample is obtained.
In some embodiments of the present invention the determinant values are stored in a memory location within computer- readable medium, from which the data processor reads the data and performs the analysis as further detailed herein below. The biological data can optionally include additional information, including, without limitation, preliminary diagnosis, observed clinical syndrome, suspected pathogen, age of the subject, gender of the subject, ethnicity of the subject and the like. The additional information can be stored in another memory location within the same or different computer-readable medium, from which the data processor can read the additional information or a portion thereof and optionally perform the analysis based also on this information. The results of the analysis can be stored in another memory location within the same or different computer- readable medium, from which it can optionally and preferably conveyed to a remote or local display, in the form of a textual or graphical output.
In some embodiments the biological data comprise values corresponding to concentrations or counts of only two polypeptides (namely a pair of polypeptide expression values), in some embodiments biological data comprise values corresponding to expression levels of only three polypeptides (namely a triple of polypeptide expression values), in some embodiments biological data comprise values corresponding to expression levels of only four polypeptides (namely a quadruple of polypeptide expression values, in some embodiments biological data comprise values corresponding to expression levels of two polypeptides and a count of one additional determinant other than a polypeptide (namely a triple of determinant values), and in some embodiments biological data comprise values corresponding to expression levels of three polypeptides and a count of one additional determinant other than a polypeptide (namely a quadruple of determinant values). Use of n-tuple of determinant values, where n is more than four is also contemplated in some embodiments of the present invention.
Representative examples of pairs of polypeptides whose expression values can be measured and used as a pair of polypeptide expression values, include, without limitation, any pair of polypeptides selected from the group consisting of TNF Related Apoptosis Inducing Ligand (TRAIL), C-reactive protein (CRP), Procalcitonin (PCT), Interleukin 6 (IL-6), MX1 and Interferon gamma-induced protein 10 (IP- 10). These examples include 15 possible pairs: (i) TRAIT, and CRP, (ii) TRAIL and PCT, (iii) TRAIL and IL-6, (iv) TRAIL and MX1, (v) TRAIL and IP- 10, (vi) CRP and PCT, (vii) CRP and IL-6, (viii) CRP and MX1, (ix) CRP and IP- 10, (x) PCT and IL- 6, (xi) PCT and MX1, (xii) PCT and IP- 10, (xii) IL-6 and MX1, (xiv) IL-6 and IP- 10, and (xv) MX1 and IP- 10.
Representative examples of triples of polypeptides whose expression values can be measured and used as a triple of polypeptide expression values, include, without limitation, any triple of polypeptides selected from the group consisting of TRAIL, CRP, PCT, IL-6 and IP- 10. These examples include ten possible triples: (i) TRAIL, CRP and PCT, (ii) TRAIL, CRP and IL- 6, (iii) TRAIL, CRP and IP- 10, (iv) TRAIL, PCT and IL-6, (v) TRAIL, PCT and IP- 10, (vi) TRAIL, IL-6 and IP- 10, (vii) CRP, PCT and IL-6, (viii) CRP, PCT and IP- 10, (ix) CRP, IL-6 and IP- 10, and (x) PCT, IL-6 and IP- 10,
Representative examples of triples of determinant values, in embodiments in which biological data comprise values corresponding to expression levels of two polypeptides and a count of one additional determinant other than a polypeptide, include, without limitation, triples of determinant values in which two values correspond to expression values of polypeptides selected from the group consisting of TRAIT,, CRP, PCT, IL-6, IP- 10, and one value corresponds to a count of Urine leukocytes (Uleuco). These examples include ten possible triples: (i) TRAIL, CRP and Uleuco, (ii) TRAIL, PCT and Uleuco, (iii) TRAIL, IL-6 and Uleuco, (iv) TRAIL, IP- 10 and Uleuco, (v) CRP, PCT and Uleuco, (vi) CRP, IL-6 and Uleuco, (vii) CRP, IP- 10 and Uleuco, (viii) PCT, IL-6 and Uleuco, (ix) PCT, IP- 10 and Uleuco, and (x) IL-6, IP- 10 and Uleuco.
A representative example of a quadruple of polypeptides whose expression values can be measured and used as a quadruple of polypeptide expression values, includes, without limitation, the quadruple TRAIL, CRP, PCT and IP- 10.
Representative examples of quadruples of determinant values in embodiments in which the biological data comprise values corresponding to expression levels of three polypeptides and a count of one additional determinant other than a polypeptide, include, without limitation, quadruples of determinant values in which three values correspond to expression values of polypeptides selected from the group consisting of TRAIL, CRP, PCT, IP- 10, and Uleuco, and one value corresponds to a count of Uleuco. These example includes four possible quadruples: (i) TRAIL, IP- 10, PCT and Uleuco, (ii) TRAIL, CRP, PCT and Uleuco, (iii) TRAIL, CRP, IP- 10 and Uleuco, and (iv) CRP, IP- 10, PCT and Uleuco.
Values that correspond to expression levels of the polypeptides can be measured in any suitable manner, but are typically detected by contacting a biological sample obtained from the infant subject with an antibody, which binds the polypeptide, and then measuring the level of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, and the measurement of the level reaction product may be carried out with any suitable immunoassay. In an embodiment, the antibody which specifically binds the polypeptide is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label. Additional information about antibodies and methods of measuring determinants using same is provided herein above.
Urine leukocytes can be measured either by urine analysis (including microscopic examination) or through a semi-quantitative urine dipstick.
For measuring a polypeptide a protein sample is preferably prepared.
The subject, as stated, can be one who has been previously diagnosed or identified as having an infection, and optionally has already undergone, or is undergoing, a therapeutic intervention for the infection. Alternatively, the subject can also be one who has not been previously diagnosed as having an infection. For example, a subject can be one who exhibits one or more symptoms of having an infection. A subject may also have an infection but show no symptoms of infection.
Exemplary symptoms which the subject may present include but are not limited to fever, nausea, headache, sore throat, runny nose, diarrhea, vomiting, rash and/or muscle soreness.
The subject may present with one or more of a variety of pathogens including, but not limited to Adenovirus, Coronavirus, Parainfluenza virus, Influenza A virus, Influenza B virus, Respiratory syncytial virus A/B, Chlamydophila pneumoniae, Mycoplasma pneumoniae, Legionella pneumophila, Rota Virus, Staphylococcus aureus, Streptococcus pneumoniae, Astrovirus, Enteric Adenovirus, Norovirus G I and G P, Bocavirus 1/2/3/4, Enterovirus, CMV virus, EBV virus, Group A Strep, or Escherichia coli.
The subject may present with a particular clinical syndrome, for example, low respiratory tract infection (LRTI) infection, upper respiratory tract infection (URTI), fever without identifiable source (FWS), UTI (urinary tract infections) or a serious bacterial infection (SBI) such as septic shock, bacteremia, pneumonia or meningitis.
The subject whose disease is being diagnosed according to some embodiments of the present invention is referred to below as the “test subject”. The present Inventors have collected knowledge regarding the expression pattern of polypeptides, of a plurality of subjects whose disease has already been diagnosed, and have devised the analysis technique of the present embodiments based on the collected knowledge. This plurality of subjects is referred to below as “pre-diagnosed subjects” or “other subjects”. As used herein, the phrase “bacterial infection” refers to a condition in which a subject is infected with a bacterium. The infection may be symptomatic or asymptomatic. In the context of this invention, the bacterial infection may also comprise a viral component (i.e. be a mixed infection being the result of both a bacteria and a virus).
The phrase “viral infection” as used herein refers to a disease that is caused by a virus and does not comprise a bacterial component.
A bacterial infection may be acute or chronic.
An acute infection is characterized by rapid onset of disease, a relatively brief period of symptoms, and resolution within days. A chronic infection is an infection that develops slowly and lasts a long time. One difference between acute and chronic infection is that during acute infection the immune system often produces IgM+ antibodies against the infectious agent, whereas the chronic phase of the infection is usually characteristic of IgM-/IgG+ antibodies. In addition, acute infections cause immune mediated necrotic processes while chronic infections often cause inflammatory mediated fibrotic processes and scaring. Thus, acute and chronic infections may elicit different underlying immunological mechanisms.
The bacterial infection may be the result of gram-positive, gram-negative bacteria or atypical bacteria, as further described herein above.
Some embodiments of the present invention analyze the biological data by calculating a value of a likelihood function using the values in the biological data that correspond to the concentration or counts of the determinants (e.g., the expression levels of the polypeptides) and that are obtained from the sample of the subject. When the value of a likelihood function, as calculated using the values in the biological data, is between a lower bound SLB and an upper bound SUB, wherein each of the lower and upper bounds is calculated using a combination d (e.g. , a linear combination) of the values in the biological data, the value of the likelihood function can be used to provide information pertaining an infection the subject is suffering from.
The lower bound SLB and upper bound SUB can be viewed geometrically as two curved objects, and the combination d of the of the values in the biological data, can be viewed geometrically as a non-curved object, as illustrated schematically in FIG. 1. In this geometrical representation, the value of the likelihood function is represented by a distance d between the non- curved object p and a curved object S, where at least a segment SROI of the curved object S is between the lower bound SLB and the upper bound SUB·
Generally, each of the curved objects S, SLB and SUB is a manifold in n dimensions, where n is a positive integer, and the non-curved object p is a hyperplane in an n+ 1 dimensional space. The concept of n-dimensional manifolds and hyperplanes in n+ 1 dimensions are well known to those skilled in the art of geometry. For example, when n= 1 the first curved object is a curved line, and the non-curved object p is a hyperplane in 2 dimensions, namely a straight line defining an axis. When n= 2, the first curved object is a curved surface, and the non-curved object p is a hyperplane in 3 dimensions, namely a flat plane, referred to below as “a plane”.
In the simplest case, each of S, SLB and SUB is a curved line and p is a straight axis defined by a direction.
Thus, the present embodiments provide information pertaining to the infection by calculating distances between curved and non-curved geometrical objects.
FIG. 2 is a flowchart diagram of a method suitable for analyzing biological data obtained from an infant subject, according to various exemplary embodiments of the present invention. It is to be understood that, unless otherwise defined, the operations described hereinbelow can be executed either contemporaneously or sequentially in many combinations or orders of execution. Specifically, the ordering of the flowchart diagrams is not to be considered as limiting. For example, two or more operations, appearing in the following description or in the flowchart diagrams in a particular order, can be executed in a different order (e.g., a reverse order) or substantially contemporaneously. Additionally, several operations described below are optional and may not be executed.
The method begins at 10 and optionally and preferably continuous to 11 at which biological data containing values corresponding to concentrations or counts of two or more determinants (e.g., expression levels of two or more polypeptides) in a sample of the subject are obtained. The biological data includes can include a pair of values, or a triple of values, or a quadruple of values, or an n-tuple of values, where n is more than four. The values preferably include one of the aforementioned pairs or triples or quadruples of values that correspond to expression levels of the aforementioned pairs or triples or quadruples of polypeptides. Other types of determinants are also contemplated as disclosed herein.
The method optionally and preferably continues to 12 at which background and/or clinical data that relate to the subject are obtained. In some embodiments of the present invention the background data includes the age of the subject, in some embodiments of the present invention the background data includes the ethnicity of the subject, in some embodiments of the present invention the background data includes the gender of the subject, in some embodiments of the present invention the clinical data includes a syndrome that the subject is experiencing, in some embodiments of the present invention the clinical data includes a pathogen suspected as being present in the subject. The method proceeds to 13 at which the distance d between a segment of the curved object S ( e.g ., a curved line) and a non-curved object p ( e.g ., an axis defined by a direction) is calculated. The distance d is calculated at a point R(δ) over the curved line S defined by a coordinate d along the direction. The direction is denoted herein using the same Greek letters as the coordinate, except that the direction is denoted by underlined Greek letters to indicate that these are vectors. Thus, when the coordinate is denoted d, the direction is denoted d.
The distance d is measured from S to the point P, perpendicularly to p. The segment SROI of S is above a region-of-interest πROI defined in the non-curved object p. For example, when p is an axis, πROI is a linear segment along the axis. Thus, πROI is the projection of SROI on p. For n= 1, SROI is preferably a curved segment of (the curve) S.
The coordinate d is optionally and preferably defined by a combination of values of the biological data. For example, δ can be a combination of the determinants, according to the following equation:
Figure imgf000047_0001
where a0, a1,... are constant and predetermined coefficients, where each of the variables D1, D2, ... is a value that corresponds to a concentration or a count of one of the determinants (e.g., an expression level of one of the polypeptides), and where f is a function that is nonlinear with respect to at least one of the values in the biological data.
The function f is optional and may be set to zero (or, equivalently, not included in the calculation of the respective coordinate). When f=0 the coordinate d is a linear combination of the determinants.
The coefficients a (i=1, 2, ...) of the linear combination of the determinant concentrations are expressed in units that are multiplicative inverse of concentration, e.g., units of volume per mass or count of the respective determinant. Typically, the concentrations or counts of the determinants are all expressed with respect to the same unit volume (e.g., 1 ml). On the other hand, different concentrations (e.g., expression levels) are oftentimes expressed, for convenience, using different mass units that are selected based on the normal concentrations of the respective determinants in healthy subjects. Thus, for example, the typical mass unit for CRP is mg, the typical mass unit for TRAIT, and IP- 10 is pg, and the typical mass unit for PCT is ng. Below, the value of each of the coefficients a (i=1, 2, ...) are in units of ml per the characteristic unit mass of the respective determinant. For example, the coefficient of the expression level of CRP is in units of ml/mg.
It is appreciated that for any of the coefficients of the combination (e.g., a coefficient of the linear combination), a value that is provided for a non-convectional unit mass (namely unit mass other than the above characteristic unit masses), can be converted to a value for the respective characteristic unit mass, by multiplying it by the ratio between the conventional and the non- conventional unit. For example, suppose that a particular variable Di corresponds to expression level of TRAIT, in units of ng/ml (instead of the conventional unit pg/ml). In this case, the coefficient a; of this variable is provided in units of ml per ng (instead of ml/pg) . In order to convert the value of a; to units of pg/ml, the value of a; is multiplied by the ratio between 1 pg and 1 ng, namely by 1/1,000.
Similarly, for any of the coefficients (e.g., a coefficient of the linear combination), a value that is provided using a volume unit other than ml, can be converted to a value suitable for ml, by multiplying it by the ratio between that volume unit and 1 ml. For example, suppose that a particular variable Di corresponds to expression level of TRAIT, in units of pg/mΐ (instead of the conventional unit pg ml). In this case, the coefficient a; of this variable is provided in units of mΐ per pg (instead of ml/pg). In order to convert the value of a; to a value in units of pg/ml, the value of a; is multiplied by the ratio between 1 mΐ and 1 ml, namely by 1/1,000.
Since the expression levels are typically expressed with respect to the same unit volume (e.g., ml), it is convenient to observe the ratio between the various coefficients, for example, the ratio between the coefficients a, (i=l, 2, ...) of the linear combination, even though they are not expressed using identical mass units. Such ratio can be used to characterize the relative weight of a particular determinant within the combination (e.g., the linear combination). For example, it is convenient to characterize the relative weight of TRAIT, using the ratio between the coefficient per pg of TRAIT, and the coefficient per mg of CRP, or the ratio between the coefficient per pg of TRAIT, and the coefficient per ng of PCT, or the ratio between the coefficient per pg of TRAIL and the coefficient per pg of IP- 10.
It is also convenient to convert the values of the determinants to dimensionless values, in which case the coefficients a (i=1, 2, ...) of the linear combination are also dimensionless. A mathematical procedure suitable for such a conversion, and exemplary dimensionless coefficients, are provided in the Examples section that follows (see Example 3).
The nonlinear function f can optionally and preferably be expressed as a sum of powers of values in the biological data, for example, according to the following equations:
Figure imgf000048_0001
where i is a summation index, q; and n are sets of coefficients, Xi ∈ { D1, D2, ...}, and γi is a numerical exponent. Note that the number of terms in the nonlinear function f does not necessarily equals the number of the determinants, and that two or more terms in the sum may correspond to the same determinant, albeit with a different numerical exponent. One or more of the predetermined coefficients (ai, qi ri) depends on the respective type of the determinant, but can also depends on the background and/or clinical data obtained at 12. Thus, the calculation of the distance d can optionally and preferably be based on the background and/or clinical data, because the location of the coordinate d on p can depend on such data. For example, the coefficient a, for a particular determinant A, can be different when the subject has a particular syndrome or pathogen, than when the subject does not have this particular syndrome or pathogen. In this case, the location of the point R(δ) on p is different for subjects with the particular syndrome or pathogen, than for subjects without the particular syndrome or pathogen. Since the location is different, the distance d can also be different. Similarly, the coefficient a, (hence also the location of the point R(δ) on p) for a particular determinant can be different when the subject is of a particular age, gender and/or ethnicity, than when the subject is of a different age, gender and/or ethnicity.
The subject background and/or clinical data can be used for determining the coefficients, in more than one way. In some embodiments of the present invention, a lookup table is used. Such a lookup table can include a plurality of entries wherein each entry includes a determinant, information pertaining to the background and/or clinical data, and a coefficient that is specific to the determinant and the background and/or clinical data of the respective entry. Relevant clinical data includes but is not limited to absolute neutrophil count (abbreviated ANC), absolute lymphocyte count (abbreviated ALC), white blood count (abbreviated WBC), neutrophil % (defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)), lymphocyte % (defined as the fraction of white blood cells that are lymphocytes and abbreviated Lym (%)), monocyte % (defined as the fraction of white blood cells that are monocytes and abbreviated Mon (%)), Sodium (abbreviated Na), Potassium (abbreviated K), Bilirubin (abbreviated Bill). Other clinical parameters are described herein below.
In some embodiments of the present invention, the coefficients are initially selected based on the particular determinants, (without taking into account the background and/or clinical data), and thereafter corrected, e.g., by normalization, based on the background and/or clinical data. For example, the coefficients can be normalized according to the age of the subject.
In some embodiments of the present invention, the subject background and/or clinical data are used for weighing the value of the likelihood to be calculated. This can also be used using a lookup table. Such a lookup table can include a plurality of entries wherein each entry includes information pertaining to the background and/or clinical data, and a weight value that is specific to the background and/or clinical data of the respective entry, and that is to be used for weighing the likelihood to be calculated (e.g., by multiplication of by division). As used herein the term “subject background” refers to the history of diseases or conditions of the subject, or which the subject is prone to. For example, the subject medical background may include conditions that affect its immune response to infection.
The boundaries of πROI are denoted herein δMIN and δMAX· These boundaries preferably correspond to the physiologically possible ranges of the values in the biological data. The range of the values can be set by the protocol used for obtaining the respective determinants. Suitable ranges for a preferred selection of determinants is provided in the Examples section that follows.
The present Inventors used the knowledge regarding the expression pattern of polypeptides of a plurality of subjects of a variety of age groups whose disease has already been diagnosed, and have employed logistic regression to obtain the coefficients for the coordinate d for these subjects. As demonstrated in the Examples section that follows (see Examples 2 and 3), the Inventors discovered a significant and unexpected difference between the coefficients obtained for infants with less than three months of age, and the coefficients obtained for older subjects. In particular, the Inventors found that when one of the determinants is TRAIL, the logistic regression accords to the value that corresponds to the expression value of TRAIT, a substantially different relative weight when it is calculated for infants than when it is calculated for older subjects.
For example, when one of the determinants is TRAIL, and another one of the determinants is CRP, the respective ratio between the coefficients that the logistic regression calculated for these determinants is much higher for infants than when it is calculated for older subjects; when one of the determinants is TRAIL, another one of the determinants is IP- 10, and the logistic regression is independent of an expression level of CRP, the coefficient (e.g., the coefficient of the linear combination) that the logistic regression calculated for IP- 10 is positive for infants and negative for older subjects; and when one of the determinants is TRAIL, and another one of the determinants is PCT, the respective ratio between the coefficients (e.g., the coefficients of the linear combination) that the logistic regression calculated for these determinants is much higher for infants than when it is calculated for older subjects.
Based on this observation, the present Inventors constructed a linear combination of the polypeptide that can be used for more accurately determining the likelihood that the subject has a bacterial infection. According to some embodiments of the present invention the biological data contain at least expression levels of TRAIT, and CRP in the sample, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed CRP in units of ml/mg (or a coefficient in other unit, once converted to ml/mg) is more than -0.5, or more than -0.4, or more than -0.4, or more than -0.3, or more than -0.2. Preferably, the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and CRP, an expression level of IP- 10 and/or an expression level of PCT and/or a count of Urine leukocytes, in which case the ratio is preferably more than -0.2.
According to some embodiments of the present invention the biological data contain at least expression levels of TRAIL and IP- 10 in the sample, d is independent of the expression level of CRP, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed IP- 10 in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) is positive.
According to some embodiments of the present invention the biological data contain at least expression levels of TRAIT, and PCT in the sample, and the ratio between a coefficient (e.g., a coefficient of the linear combination) of expressed TRAIT, in units of ml/pg (or a coefficient in other unit, once converted to ml/pg) and a coefficient (e.g., a coefficient of the linear combination) of the expressed PCT in units of nl/pg (or a coefficient in other unit, once converted to nl/pg) is more than -0.01 or more than -0.0099 or more than -0.0098. Preferably, the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and PCT, a count of Urine leukocytes. Also preferably, the biological data also contain, in addition to values corresponding to the expression levels of TRAIT, and PCT, an expression level of IP- 10, in which case the ratio is preferably more than -0.08.
At least a major part of the segment SROI of curved object S is between two curved objects referred to below as a lower bound curved object SLB and an upper bound curved object SUB-
As used herein “major part of the segment SROI” refers to a part of a smoothed version SROI whose length (when n= 1), surface area (when n= 2) or volume (when n³ 3) is 60% or 70% or 80% or 90% or 95% or 99% of a smoothed version of the length, surface area or volume of SROI, respectively.
As used herein, “a smooth version of the segment SROI” refers to the segment SROI, excluding regions of SROI at the vicinity of points at which the Gaussian curvature is above a curvature threshold, which is X times the median curvature of SROI, where X is 1.5 or 2 or 4 or 8.
The following procedure can be employed for the purpose of determining whether the major part of the segment SROI is between SLB and SUB- Firstly, a smoothed version of the segment SROI is obtained. Secondly, the length (when n= 1), surface area (when n= 2) or volume (when n³ 3) Ai of the smoothed version of the segment SROI is calculated. Thirdly, the length (when n= 1) surface area (when n= 2) or volume (when n³ 3) A2 of the part of the smoothed version of the segment SROI that is between SLB and SUB is calculated. Fourthly, the percentage of A2 relative to Ai is calculated. FIGs. 3A-D illustrate a procedure for obtaining the smooth version of SROI.
For clarity of presentation, SROI is illustrated as a one dimensional segment, but the skilled person would understand that SROI is generally an n -dimensional mathematical object. The Gaussian curvature is calculated for a sufficient number of sampled points on SROI. For example, when the manifold is represented as point cloud, the Gaussian curvature can be calculated for the points in the point cloud. The median of the Gaussian curvature is then obtained, and the curvature threshold is calculated by multiplying the obtained median by the factor X. FIG. 3A illustrates SROI before the smoothing operation. Marked is a region 320 having one or more points 322 at which the Gaussian curvature is above the curvature threshold. The point or points at which with the Gaussian curvature is maximal within region 320 is removed and region 320 is smoothly interpolated, e.g., via polynomial interpolation, (FIG. 3B). The removal and interpolation is repeated iteratively (FIG. 3C) until the segment SROI does not contain regions at which the Gaussian curvature is above the curvature threshold (FIG. 3D).
When n= 1 (namely when S is a curved line), SLB is a lower bound curved line, and SUB an upper bound curved line. In these embodiments, SLB and SUB can be written in the form;
SLB = f(δ-)-ε 0,
SUB = f(δ+)+ε1 where f(δ i)s a probabilistic classificationfunction of the coordinate d (along the direction δ) which represents the likelihood that the test subject has an infection of a specific type, for example, a bacterial infection, or a non-bacterial infection (e.g., a viral infection). Typically, the probabilistic classification function represents the likelihood that the test subject has a bacterial infection. In some embodiments of the invention f(δ) =l/(l+cxp(-δ)). In some embodiments of the invention both SLB and SUB are positive for any value of d within πROI.
In any of the above embodiments each of the parameters so and si is less than 0.5 or less than 0.4 or less than 0.3 or less than 0.2 or less than 0.1 or less than 0.05.
The method preferably proceeds to 14 at which the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a disease or condition corresponding to the type of the probabilistic function f. For example, when the probabilistic function f represents the likelihood that the test subject has a bacterial infection, the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a bacterial infection, when the probabilistic function f represents the likelihood that the test subject has a viral infection, the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a viral infection, and when the probabilistic function f represents the likelihood that the test subject has a mixed infection, the calculated distance d is correlated to the presence of, absence of, or likelihood that the subject has, a mixed infection.
In various exemplary embodiments of the invention the correlation includes determining that the distance d is the likelihood that the subject has the respective infection (bacterial, viral, mixed). The likelihood is optionally and preferably compared to a predetermined threshold CO B, wherein the method can determine that it is likely that the subject has a bacterial infection when the likelihood is above co B, and that it is unlikely that the subject has a bacterial infection otherwise. Typical values for ω B include, without limitation, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6 and about 0.7. Other likelihood thresholds are also contemplated.
In some embodiments of the present invention the method proceeds to 15 at which the likelihood is corrected based on the background and/or clinical data. Such a correction can be executed in more than one way. For example, the method can employ different predetermined thresholds co B for different ages, ethnicities, genders, syndromes, and/or suspected pathogens. The method can alternatively or additionally employ different values for one or both the parameters ε0 and si for different ages, ethnicities, genders, syndromes, and/or suspected pathogens. The method can alternatively or additionally normalize the value of the probabilistic classification function d, based on the age, ethnicity, gender, syndrome, and/or suspected pathogen.
The method optionally and preferably continues to 16 at which an output of the likelihood(s) is generated. The output can be presented as text, and/or graphically and/or using a color index. The output can optionally include the results of the comparison to the threshold co B· From 16 the method can optionally and preferably loops back to 13 for repeating the analysis using a different set of coefficients for the calculation of the coordinate d and/or a different probabilistic classification function f. For example, the analysis can be initially executed using a set of coefficients and probabilistic classification function f that are selected for determining the presence of, absence of, or likelihood that the subject has, a bacterial infection or a mixed infection, and then, in a subsequent execution, the analysis can use a set of coefficients and probabilistic classification function f that are selected for determining the presence of, absence of, or likelihood that the subject has, a viral infection.
In some embodiments of the present invention, when the method determines that it is likely that the subject has a bacterial infection, the subject is treated (17) for the bacterial infection, as further detailed herein. In some embodiments of the present invention, when the method determines that it is likely that the subject has a viral infection, the subject is treated (17) for the viral infection, as further detailed herein. The method ends at 18.
In some embodiments, the method can be carried out using a system 330, which optionally and preferably, but not necessarily, comprises a hand-held or desktop device. The system can comprise two or more compartments, wherein the levels of determinants in the sample are measured in one of the compartments (e.g. using an immunohistochemical method), and wherein an analysis of the obtained levels is executed in the other compartment to provide an output relating to the diagnosis.
A block diagram of representative example of system 330 is illustrated in FIG. 4. System 330 measures the values of the determinants in the sample of a subject and optionally and preferably also analyzes the measured values, according to the analysis technique described herein. System 330 can comprise a first compartment 332 in which the measurement is performed, and may optionally and preferably also comprise a second compartment 334 in which the analysis is performed. In some embodiments of the present invention, first compartment 332, and optionally and preferably also second compartment 334, is/are housed in a device 331 which is preferably, but not necessarily, a hand-held or a desktop device.
First compartment 332 can include a measuring system 333 configured to measure the values of the determinants in the sample. For example, a liquid sample of a subject (e.g., blood, urine, etc.) can be loaded onto a cartridge 360 containing reagents for detecting the determinants (e.g., TRAIL, CRP, PCT, IP- 10, IL-6, and/or Uleuco), and the cartridge 360 can then be loaded to compartment 332, e.g., into a cartridge holder or socket 362 being sized and shaped to receive cartridge 360.
Cartridge 360 is preferably labeled) with a label 361, such as a barcode or the like, encoding information describing the subject (e.g., I.D. No., name, age, etc.). The information can be encodes by the label itself, or the label can encode a unique identification string that can be searched for in a database in order to extract the information.
Measuring system 333 can perform at least one assay selected from the group consisting of an immunoassay such as ELISA or LFIA, and a functional assay. In some embodiments of the present invention measuring system 333 uses chemiluminescence or florescence for measuring the expression value of the determinants.
System 330 can also comprise a second compartment 334 comprising a hardware processor 336 having a computer-readable medium 338 for storing computer program instructions for executing the operations described herein. Hardware processor 336 is configured to receive measured values of the determinants from first compartment 332 and execute the program instructions responsively to the measured values and output the processed data to a display device 340. In some embodiments of the present invention hardware processor 336 is also configured to receive input pertaining to the age group of the subject, e.g., whether the subject is less than three months of age, or older, in which case the program instructions are executed by processor 336 also responsively to the input age group.
In some embodiments of the present invention system 330 comprises a label reader 363 that reads the information encodes by label 361. In some embodiments of the present invention hardware processor 336 receives information pertaining to the age group based on signal received from the reader 363. When the subject information is encoded on the label itself, processor 336 receives from the reader 363 a signal pertaining to the age group. When label 361 encodes a unique identification string, processor 336 receives from the reader 363 the unique identification string and searches a database stored in medium 338 for the age or age group of the subject. Alternatively, processor 336 can transmit, by means of communication interface 350, the unique identification string over a communication network 352 to a remote server (not shown) that is associated with a database storing user information. The remote server can search the database for the for the age or age group of the subject based on the unique identification, and transmit the information back to professor 336.
Preferably, when the subject is less than three months of age the program instructions accords more weight to the expression level of TRAIL, and/or use a positive coefficient for the expression level of IP- 10, as further detailed hereinabove. The input age group can be received from a user (not shown, see FIG. 5B) by means a user interface 354, or via communication network 352 by means of communication interface 350.
In some embodiments, schematically illustrated in the block diagram of FIG. 5A, hardware processor 336 receives over network 352, via communication interface 350, measured values of the determinants from a measuring system, such as, but not limited to, measuring system 333, and executes the computer program instructions in computer-readable medium 338, responsively to the received measurements. In some embodiments of the present invention hardware processor 336 is also configured to receive input pertaining to the age group of the subject and execute the program instructions also responsively to the input age group as further detailed hereinabove. For example, hardware processor 336 can receive over the network 352 a unique identification string read from a label attached to a cartridge (not shown), and search a database stored in medium 338 for the age or age group of the subject.
Hardware processor 336 can then output the processed data to display device 340.
In some embodiments of the present invention system 330 communicates with a user, as schematically illustrated in the block diagram of FIG. 5B. In these embodiments, system 330 can comprise computer-readable medium 338, as further detailed hereinabove, and a hardware processor, such as, but not limited to, processor 336. Hardware processor 336 comprises a user interface 354 that communicates with a user 356. Via interface 350, hardware processor 336 receives measured values of the determinants from user 356. User 356 can obtain the measured values from an external source, or by executing at least one assay selected from the group consisting of an immunoassay and a functional assay, or by operating system 333 (not shown, see FIGs. 4 and 5A). Hardware processor 336 executes the computer program instructions in computer-readable medium 338, responsively to the received measurements. In some embodiments of the present invention hardware processor 336 is also configured to receive input pertaining to the age group of the subject and execute the program instructions also responsively to the input age group as further detailed hereinabove. Hardware processor 336 can then output the processed data to display device 340.
Once the diagnosis has been made, it will be appreciated that a number of actions may be taken.
Thus, for example, if a bacterial infection is ruled in, then the subject may be treated with an antibiotic agent.
Examples of antibiotic agents include, but are not limited to Daptomycin; Gemifloxacin ; Telavancin; Ceftaroline; Fidaxomicin; Amoxicillin; Ampicillin; Bacampicillin; Carbenicillin; Cloxacillin; Dicloxacillin; Flucloxacillin; Mezlocillin; Nafcillin; Oxacillin; Penicillin G; Penicillin V; Piperacillin; Pivampicillin; Pivmecillinam; Ticarcillin; Aztreonam; Imipenem; Doripenem; Meropenem; Ertapenem; Clindamycin; lincomycin; Pristinamycin; Quinupristin; Cefacetrile (cephacetrile); Cefadroxil (cefadroxyl); Cefalexin (cephalexin); Cefaloglycin (cephaloglycin) ; Cefalonium (cephalonium); Cefaloridine (cephaloradine); Cefalotin (cephalothin); Cefapirin (cephapirin); Cefatrizine; Cefazaflur; Cefazedone; Cefazolin (cephazolin);Cefradine (cephradine); Cefroxadine; Ceftezole; Cefaclor; Cefamandole; Cefmetazole; Cefonicid; Cefotetan; Cefoxitin; Cefprozil (cefproxil); Cefuroxime; Cefuzonam; Cefcapene; Cefdaloxime; Cefdinir; Cefditoren; Cefetamet; Cefrxime; Cefmenoxime; Cefodizime; Cefotaxime; Cefpimizole; Cefpodoxime; Cefteram; Ceftibuten; Ceftiofur; Ceftiolene; Ceftizoxime; Ceftriaxone; Cefoperazone; Ceftazidime; Cefclidine; Cefepime; Cefluprenam; Cefoselis; Cefozopran; Cefpirome; Cefquinome; Fifth Generation; Ceftobiprole; Ceftaroline; Not Classified; Cefaclomezine; Cefaloram; Cefaparole; Cefcanel; Cefedrolor; Cefempidone; Cefetrizole; Cefrvitril; Cefmatilen; Cefmepidium; Cefovecin; Cefoxazole; Cefrotil; Cefsumide; Cefuracetime; Ceftioxide; Azithromycin; Erythromycin; Clarithromycin; Dirithromycin; Roxithromycin; Telithromycin; Amikacin; Gentamicin; Kanamycin; Neomycin; Netilmicin; Paromomycin; Streptomycin; Tobramycin; Flumequine; Nalidixic acid; Oxolinic acid; Piromidic acid; Pipemidic acid; Rosoxacin; Ciprofloxacin; Enoxacin; Lomefloxacin; Nadifloxacin; Norfloxacin; Ofloxacin; Pefloxacin; Rufloxacin; Balofloxacin; Gatifloxacin; Grepafloxacin; Levofloxacin; Moxifloxacin; Pazufloxacin; Sparfloxacin; Temafloxacin; Tosufloxacin; Besifloxacin; Clinafloxacin; Gemifloxacin; Sitafloxacin; Trovafloxacin; Prulifloxacin; Sulfamethizole; Sulfamethoxazole; Sulfisoxazole; Trimethoprim-Sulfamethoxazole; Demeclocycline; Doxycycline; Minocycline; Oxytetracycline; Tetracycline; Tigecycline; Chloramphenicol; Metronidazole; Tinidazole; Nitrofurantoin; Vancomycin; Teicoplanin; Telavancin; Linezolid; Cycloserine 2; Rifampin; Rifabutin; Rifapentine; Bacitracin; Polymyxin B; Viomycin; Capreomycin.
If a viral infection is ruled in, the subject may be treated with an antiviral agent. Examples of antiviral agents include, but are not limited to Abacavir; Aciclovir; Acyclovir; Adefovir; Amantadine; Amprenavir; Ampligen; Arbidol; Atazanavir; Atripla; Balavir; Boceprevirertet; Cidofovir; Combivir; Dolutegravir; Darunavir; Delavirdine; Didanosine; Docosanol; Edoxudine; Efavirenz; Emtricitabine; Enfuvirtide; Entecavir; Ecoliever; Famciclovir; Fomivirsen; Fosamprenavir; Foscarnet; Fosfonet; Fusion inhibitor; Ganciclovir; Ibacitabine; Imunovir; Idoxuridine; Imiquimod; Indinavir; Inosine; Integrase inhibitor; Interferon type IP; Interferon type P; Interferon type I; Interferon; Lamivudine; Lopinavir; Loviride; Maraviroc; Moroxydine; Methisazone; Nelfinavir; Nevirapine; Nexavir; Oseltamivir; Peginterferon alfa-2a; Penciclovir; Peramivir; Pleconaril; Podophyllotoxin; Raltegravir; Reverse transcriptase inhibitor; Ribavirin; Rimantadine; Ritonavir; Pyramidine; Saquinavir; Sofosbuvir; StavudineTelaprevir; Tenofovir; Tenofovir disoproxil; Tipranavir; Trifluridine; Trizivir; Tromantadine; Truvada; traporved; Valaciclovir; Valganciclovir; Vicriviroc; Vidarabine; Viramidine; Zalcitabine; Zanamivir; Zidovudine; RNAi antivirals; inhaled rhibovirons; monoclonal antibody respigams; neuriminidase blocking agents.
The information gleaned using the methods described herein may aid in additional patient management options. For example, the information may be used for determining whether a patient should or should not be admitted to hospital. It may also affect whether or not to prolong hospitalization duration. It may also affect the decision whether additional tests need to be performed or may save performing unnecessary tests such as CT and/or X-rays and/or MRI and/or culture and/or serology and/or PCR assay for specific bacteria and/or PCR assays for viruses and/or perform procedures such as lumbar puncture.
As used herein the term “about” refers to ± 10 %
The terms "comprises", "comprising", "includes", "including", “having” and their conjugates mean "including but not limited to". The term “consisting of’ means “including and limited to”.
The term "consisting essentially of' means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" may include a plurality of compounds, including mixtures thereof.
Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.
As used herein the term "method" refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.
As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.
When reference is made to particular sequence listings, such reference is to be understood to also encompass sequences that substantially correspond to its complementary sequence as including minor sequence variations, resulting from, e.g., sequencing errors, cloning errors, or other alterations resulting in base substitution, base deletion or base addition, provided that the frequency of such variations is less than 1 in 50 nucleotides, alternatively, less than 1 in 100 nucleotides, alternatively, less than 1 in 200 nucleotides, alternatively, less than 1 in 500 nucleotides, alternatively, less than 1 in 1000 nucleotides, alternatively, less than 1 in 5,000 nucleotides, alternatively, less than 1 in 10,000 nucleotides.
It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.
Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.
EXAMPLES
Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non limiting fashion.
Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, "Molecular Cloning: A laboratory Manual" Sambrook et al., (1989); "Current Protocols in Molecular Biology" Volumes I-PI Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols in Molecular Biology", John Wiley and Sons, Baltimore, Maryland (1989); Perbal, "A Practical Guide to Molecular Cloning", John Wiley & Sons, New York (1988); Watson et al., "Recombinant DNA", Scientific American Books, New York; Birren et al. (eds) "Genome Analysis: A Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; "Cell Biology: A Laboratory Handbook", Volumes I-PI Cellis, J. E., ed. (1994); "Culture of Animal Cells - A Manual of Basic Technique" by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; "Current Protocols in Immunology" Volumes I-PI Coligan J. E., ed. (1994); Stites et al. (eds), "Basic and Clinical Immunology" (8th Edition), Appleton & Lange, Norwalk, CT (1994); Mishell and Shiigi (eds), "Selected Methods in Cellular Immunology", W. H. Freeman and Co., New York (1980); available immunoassays are extensively described in the patent and scientific literature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521; "Oligonucleotide Synthesis" Gait, M. J., ed. (1984); “Nucleic Acid Hybridization" Hames, B. D., and Higgins S. J., eds. (1985); "Transcription and Translation" Hames, B. D., and Higgins S. J., eds. (1984); "Animal Cell Culture" Freshney, R. L, ed. (1986); "Immobilized Cells and Enzymes" IRL Press, (1986); "A Practical Guide to Molecular Cloning" Perbal, B., (1984) and "Methods in Enzymology" Vol. 1-317, Academic Press; "PCR Protocols: A Guide To Methods And Applications", Academic Press, San Diego, CA (1990); Marshak et al., "Strategies for Protein Purification and Characterization - A Laboratory Course Manual" CSHL Press (1996); all of which are incorporated by reference as if fully setforth herein. Other general references are provided throughout this document. The procedures therein are believed to be well known in the art and are provided for the convenience of the reader. All the information contained therein is incorporated herein by reference.
Example 1
MATERIALS AND METHODS
Study design and population
Participants were recruited prospectively from April 2016 to January 2018 at the Schneider Children’s Medical Center, a tertiary-care 270-bed pediatric hospital located in Israel. Eligible infectious participants were hospitalized febrile children with a suspected viral infection or with a presumptive diagnosis of UTI based on an abnormal urinalysis. Inclusion criteria were age under 18 years, documented temperature >38°C (100.4°F), presumptive diagnosis of UTI or suspected viral infection, and symptom duration < 7 days. Exclusion criteria were therapeutic antibiotic use during the preceding two weeks, congenital or acquired immune-deficiency, including treatment with high-dose corticosteroids >1 mg/kg/day prednisone or equivalent in the preceding two weeks, monoclonal antibodies, anti-tumor necrosis factor agents, intravenous immunoglobulin, and chronic severe illnesses affecting life expectancy or quality of life. The non- infectious control group were hospitalized afebrile children, such as cases of elective admission for a surgical procedure.
Patients were categorized according to age (under 3 months versus older age) and the appearance of complications, such as, lobar nephronia, defined based on ultrasound of the urinary tract. Data and sample collection
For each patient, the following baseline variables were recorded: demographics, medical history, physical examination, complete blood count, chemistry panel and urinalysis. Additional testing was performed as deemed appropriate by the treating physician, e.g., multiplex-PCR diagnostic assays for viral pathogens and radiological tests (e.g., chest X-ray or ultrasound of the urinary tract).
Study- specific blood and urine samples were collected at enrollment, during hospital admission (day 2/3) and on discharge for measurement of the biomarkers: CRP, TRAIT, and IP- 10. Among controls, only urine samples were collected for biomarker measurement. Disease course was recorded until hospital discharge.
Reference Standard
The reference standard for determining bacterial versus non-bacterial etiology was based on the adjudication of two senior pediatricians, each with more than 10 years of working experience as specialists in pediatric infectious diseases. Confirmation of UTI diagnosis was according to the AAP criteria (AAP 2016). These include pyuria (positive leukocyte esterase or nitrite on dipstick or >5 WBC/high power field on centrifuged urine microscopy) and/or bacteriuria on urinalysis, and >50,000 CFUs/ml growth of an uropathogen cultured from supra pubic aspiration (SPA), bladder catheterization or midstream urine specimen. After reviewing the microbiological results of urine cultures and hospitalization course, the experts independently classified each of the infectious participants to one of the following: (a) confirmed bacterial UTI according to the American Academy of Pediatrics (AAP) criteria (b) viral infection, (c) indeterminate or (d) mixed viral and bacterial infection. “Bacterial UTI” and “viral infection” adjudication labels required both experts to assign the same label. An “indeterminate” adjudication label was given in case of a discrepancy between the assigned diagnoses, or an assigned indeterminate diagnosis, or an assigned mixed viral and bacterial infection.
Host-protein measurement and analysis
Serum CRP was measured using either one of the following kits: Cobas-6000, Cobas- Integra-400/800, or Modular- Analytics-P800 (Roche). Urinary CRP was measured by commercial high sensitivity enzyme-linked immunosorbent assays (ELISAs) (Immundiagnostik AG, Bensheim, Germany). Serum and urinary TRAIT, and IP- 10 were measured using commercial ELISA kits (MeMed Diagnostics). Pending analysis, samples were stored at -70°C.
The urinary creatinine concentration was used to normalize biomarker measurements and account for the influence of urinary dilution. The laboratory technicians conducting biomarker tests were blinded to clinical data and the adjudication label. Statistical analysis
Statistical analysis was performed using MATLAB (MathWorks). The p-values were calculated as follows: for the mean and standard deviation (SD), t-test. p<0.05 was deemed statistically significant. RESULTS
Patient characterization
Sixty-one children were recruited with fever and suspected UTI and 12 healthy children, constituting a study population of 73 children aged < 18 years (FIG. 7). Two experts adjudicated the etiology of the 61 febrile patients: 40 were assigned as bacterial UTI infections; 10 as viral infections and the remaining 11 cases were assigned an indeterminate adjudication label. Patient characteristics are shown in Table 2.
Table 2
Figure imgf000062_0001
Urinary CRP, IP- 10 and TRAIL levels in bacterial UTI versus non-bacterial etiology There was no difference in urinary CRP, IP- 10 and TRAIT, levels in healthy versus viral children and so these subjects were grouped as ‘non-bacterial’ for further analyses. The mean urinary levels of CRP were significantly higher in bacterial UTI as compared to non-bacterial etiology across the study population (n=54; p<0.001), 9.6 (SD 12.4) versus 0.9 (SD 1.8), respectively. Elevated CRP levels were observed in infants aged under 3 months with bacterial UTI (n=17; p=0.001), 14.0 (SD 16.9) versus 0.4 (SD 0.4), respectively, and also in children aged 3 months or older (n=37; p=0.001), 7.2 (SD 9.0) versus 1.0 (SD 2.1), respectively. Similarly, the mean urinary levels of IP- 10 were significantly higher in bacterial UTI as compared to non-bacterial etiology across the study population (n=54; p<0.001), 13.0 (SD 12.9) versus 2.2 (SD 6.2), respectively. Elevated IP- 10 levels were observed both in infants aged under 3 months with bacterial UTI (n=17; p=0.05), 19.0 (SD 15.8) versus 5.4 (SD 11.4), respectively, and also in children aged 3 months or older (n=37; p<0.001), 9.9 (SD 10.0) versus 1.0 (SD 2.4), respectively.
Urinary TRAIT, levels were not significantly different in bacterial UTI versus non-bacterial etiology (p=0.1 for all ages; p=0.1 for < 3 months; and p=0.5 for > 3 months).
To examine if these urinary biomarkers differentiate between bacterial UTI and non- bacterial etiology, area under the receiver operator curve (AUC) analysis was performed, with bacterial UTI considered as positive. In line with the expression levels, CRP displayed discriminatory potential across children of all ages, with an AUC of 0.85 (95% Cl, 0.75-0.95); in children aged 3 months or older with an AUC of 0.82 (95% 0=0.68-0.95); and in infants under the age of 3 months with an AUC of 0.98 (95% 0=0.93-1.00). Similarly, IP-10 displayed discriminatory potential, with an AUC of 0.87 (95% Cl, 0.78-0.96) across children of all ages; 0.90 (95% Cl, 0.80-1.00) for children aged 3 months or older; and 0.80 (95% 0=0.59-1.00) for infants aged under 3 months.
Example 2
Exemplary Ranees of Determinants
Table 3 below lists minimal and maximal values suitable for the determinants TRAIL, CRP, IP- 10, and Uleco, according to some embodiments of the present invention.
Table 3
Figure imgf000063_0001
Lor a given set of coefficients, the values δMIN and δMAX (see PIG. 1) are optionally and preferably obtained by selecting the values in Table 3 that respectively minimize and maximize the value of the coordinate d. Example 3
Exemplary Coefficients
Exemplary sets of coefficients for the d coordinate, in the cases in which the biological data comprise pairs, triples and quadruple of determinant values, are provided in Table 4, and 5, where Table 4 corresponds to subjects with less than three months of age, and Table 5 corresponds to older subjects.
The coefficients are named by the respective determinant. Thus, for example, the column TRAIT, lists coefficients of the expression levels of TRAIL. The coefficients of the expression levels of TRAIL, CRP, IP- 10, and PCT, are suitable for use when the expression levels of TRAIL, CRP, IP- 10, and PCT are expressed in units of pg/ml, mg/L, pg/ml, and ng/ml, respectively, and the coefficients of the count of urine leukocytes are suitable for use when the counts are expressed as 1, 2 or 3, where 1 indicates about 70 leukocytes per pL, 2 indicates about 125 leukocytes per pL, and 3 indicates about 500 leukocytes per pL. Also provided are values of area under the receiver operating curve (AUC) that correspond to each set of coefficients.
Table 4
Figure imgf000064_0001
Figure imgf000065_0001
Table 5
Figure imgf000065_0002
Figure imgf000066_0001
Example 4
Exemplary Normalized Coefficients
The sets of coefficients provided in Example 2 were calculated according to some embodiments of the present invention by logistic regression, also for the case in which the values of the determinants (and the corresponding coefficients) are dimensionless. Conversion of the measured values of the determinants to dimensionless values was employed by means of the minimal and maximal values provided in Table 5, above, according to the following formula:
Figure imgf000067_0001
Tables 6 and 7, below, provide sets of dimensionless coefficients for the δ coordinate, in the cases in which the biological data comprise pairs, triples and quadruple of determinant values, where Table 6 corresponds to subjects with less than three months of age, and Table 7 corresponds to older subjects.
Table 6
Figure imgf000067_0002
Figure imgf000068_0001
Table 7
Figure imgf000068_0002
Figure imgf000069_0001
Example 5
Differential serum and urine CRP. IP- 10 and TRAIL levels in pediatric urinary tract infection Hospitalized febrile children aged under 18 years with suspected UTI based on abnormal urinalysis were recruited prospectively over period of about two years. Also, non- febrile controls were recruited. Following urine culture results and hospitalization course, participants were divided into three groups based on AAP 2016 criteria and expert adjudication: UTI, viral infection and non- febrile controls.
Study- specific blood and urine samples were collected at enrollment, during hospital admission (day 2/3) and on discharge for measurement of the biomarkers CRP, TRAIL and IP- 10. Among controls, only urine samples were collected for biomarker measurement. TRAIL, IP- 10 and CRP serum measurements, were measured using Immuno Xpert™ kit. The urinary creatinine concentration was used to normalize biomarker measurements and account for the influence of urinary dilution. Urinary creatinine concentration ( mg/dL) was measured and the ratio of urinary CRP (ng/mL), urinary IP- 10 (pg/mL) and urinary TRAIL (pg/mL) to urine creatinine was calculated.
Table 8, below, depicts a comparison between urinary biomarker levels in viral and healthy patients, Table 9, below, lists Serum expression levels of CRP, IP- 10 and TRAIL, and FIGs. 6A and 6B show temporal dynamics of urine CRP and IP- 10 in bacterial patients over 90 days old, where gray dots denote mean level, thick lines denote median level, and boxes indicate patients with values between the 25 and 75 percentiles.
Table 8
Figure imgf000070_0001
Table 9
Figure imgf000070_0002
REFERENCES
(1) Oved, K.; Cohen, A.; Boico, O.; Navon, R.; Friedman, T.; Etshtein, L.; Kriger, O.; Bamberger, E.; Fonar, Y.; Yacobov, R.; Wolchinsky, R.; Denkberg, G.; Dotan, Y.; Hochberg, A.; Reiter, Y.; Grupper, M.; Srugo, L; Feigin, P.; Gorfine, M.; Chistyakov, L; Dagan, R.; Klein, A.; Potasman, L; Eden, E. A Novel Host-Proteome Signature for Distinguishing between Acute Bacterial and Viral Infections. PLoS ONE 2015, 10 (3), e0120012. w w w (dot) doi(dot)org/ 10.1371/j ournal .pone .0120012.
(2) van Houten, C. B.; de Groot, J. A. H; Klein, A.; Srugo, L; Chistyakov, L; de Waal, W.; Meijssen, C. B.; Avis, W.; Wolfs, T. F. W.; Shachor-Meyouhas, Y.; Stein, M.; Sanders, E. A. M.; Bont, L. J. A Host-Protein Based Assay to Differentiate between Bacterial and Viral Infections in Preschool Children (OPPORTUNITY): A Double-Blind, Multicentre, Validation Study. Lancet Infect Dis 2017, 17 (4), 431-440. www(dot)/doi(dot)org/10.1016/S1473-3099(16)30519-9.
(3) Srugo, L; Klein, A.; Stein, M.; Golan-Shany, O.; Kerem, N.; Chistyakov, L; Genizi, J.; Glazer, O.; Yaniv, L.; German, A.; Miron, D.; Shachor-Meyouhas, Y.; Bamberger, E.; Oved, K.; Gottlieb, T. M.; Navon, R.; Paz, M.; Etshtein, L.; Boico, O.; Kronenfeld, G.; Eden, E.; Cohen, R.; Chappuy, H.; Angoulvant, F.; Lacroix, L.; Gervaix, A. Validation of a Novel Assay to Distinguish Bacterial and Viral Infections. Pediatrics 2017. www(dot)doi(dot)org/10.1542/peds.2016-3453.
Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.
It is the intent of the applicants) that all publications, patents and patent applications referred to in this specification are to be incorporated in their entirety by reference into the specification, as if each individual publication, patent or patent application was specifically and individually noted when referenced that it is to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority documents) of this application is/are hereby incorporated herein by reference in its/their entirety.

Claims

WHAT IS CLAIMED IS:
1. A method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is above 1.5 μg/L and/or the concentration of IP- 10 is above 2 ng/L, it is indicative of a bacterial infection, wherein the concentration of said CRP and/or said IP- 10 is creatinine normalized.
2. The method of claim 1, wherein when the concentration of said CRP is above 1.7 μg/L and/or the concentration of said IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
3. A method of ruling out a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is below 1.5 μg/L and/or the concentration of IP- 10 is below 2 ng/L, it is indicative that the infection is not a bacterial infection.
4. A method of ruling in a bacterial infection in a subject comprising measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non-infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non-infectious subject, it is indicative of a bacterial infection.
5. A method of ruling in a bacterial infection in a subject under 3 months of age, comprising measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma- induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 mg/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIT, is below 188 pg/L it is indicative of a bacterial infection in the subject.
6. The method of claim 4, wherein the non-infectious subject is a healthy subject.
7. The method of any one of claims 1-3, wherein the subject is below 18 years of age.
8. The method of any one of claims 1-3, wherein the subject is below 3 months of age.
9. The method of any one of claims 1-5, wherein the subject exhibits symptoms of infection.
10. The method of claim 9, wherein said symptoms comprise fever.
11. The method of any one of claims 1-10, further comprising determining the species or strain of bacteria responsible for said bacterial infection.
12. The method of any one of claims 1-11, wherein the bacterial infection is a urinary tract infection.
13. The method of any one of claims 1-12, further comprising measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase- associated lipocalin (NGAL), IL-1RA, IFNy, TNFa, MCP-1 and Interleukin- 18 (IL-18) in said urine sample.
14. The method of any one of claims 1-13, further comprising measuring in the urine the amount of at least one additional non-protein determinant selected from the group consisting of nitrite level, white blood cell count and pH.
15. A method of treating a bacterial infection in a subject comprising:
(a) confirming that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the level of CRP is above 1.5 μg/L and/or the level of IP - 10 is above 2 ng/L, it is indicative of a bacterial infection, wherein the concentration of said CRP and/or said IP- 10 is creatinine normalized; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
16. The method of claim 15, wherein when the concentration of CRP is above 1.7 μg/L and/or the concentration of IP- 10 is above 2.1 ng/L, it is indicative of a bacterial infection.
17. A method of treating a bacterial infection in a subject comprising:
(a) confirming that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP) and/or Interferon gamma-induced protein 10 (IP- 10) in a urine sample of the subject, wherein when the concentration of CRP is at least three times the concentration of CRP in a control sample of the same dilution of a non-infectious subject, and/or when the concentration of IP- 10 is at least five times the concentration of IP- 10 in a control sample of the same dilution of a non-infectious subject, it is indicative of a bacterial infection; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
18. A method of treating a bacterial infection in a subject under 3months of age comprising:
(a) confirming that the subject has a bacterial infection by measuring the concentration of C-reactive protein (CRP), and/or Interferon gamma-induced protein 10 (IP- 10) and/or TRAIT, in a blood sample of the subject, wherein when the concentration of CRP is above 12.8 ing/L and/or the concentration of IP- 10 is below 293 pg/L, and/or the concentration of TRAIT, is below 188 pg/L it is indicative of a bacterial infection in the subject; and
(b) treating the subject with a therapeutically effective amount of an antibiotic, thereby treating the bacterial infection of the subject.
19. The method of claims 15 or 17, wherein the subject is below 18 years of age.
20. The method of claims 15 or 17, wherein the subject is below 3 months of age.
21. The method of any one of claims 15, 17 or 18, wherein the subject exhibits symptoms of fever.
22. The method of claim 21, wherein said symptoms comprise fever.
23. The method of any one of claims 15, 17 or 18, further comprising determining the species or strain of bacteria responsible for said bacterial infection.
24. The method of any one of claims 15-23, further comprising measuring the amount of at least one additional protein determinant selected from the group consisting of procalcitonin (PCT), Interleukin-6 (IL-6), Neutrophil gelatinase- associated lipocalin (NGAL) and Interleukin- 18 (IL-18) in said urine sample.
25. The method of any one of claims 15-23, further comprising measuring the amount of at least one additional non-protein determinant selected from the group consisting of nitrite level, white blood cell count and pH.
26. The method of any one of claims 15-23, wherein the bacterial infection is a urinary tract infection.
27. The method of any one of claims 1-26, wherein said measuring is carried out using an antibody that specifically binds to CRP and/or IPIO.
28. A system for analyzing biological data, the system comprising: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIT, and CRP in a sample extracted from the subject; a data processor having a circuit configured to access a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; correlate said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on said age; and generate output pertaining to said correlation; wherein at least 90% of said segment is between a lower bound line f(δ-)So and an upper bound line f(δ+)si, wherein said f(δ) equals l/(l+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels; and wherein a ratio between a coefficient of said combination of expressed TRAIT, and a coefficient of said combination of expressed CRP has a first ratio value when said age group is defined for ages less than three months, and a second ratio value, lower than said first ratio value, than when said age group is for older ages.
29. A system for analyzing biological data, the system comprising: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIT, and IP- 10 in a sample extracted from the subject; a data processor having a circuit configured to access a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; correlate said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on said age; and generate output pertaining to said correlation; wherein at least 90% of said segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein said f(δ) equals 1/(1+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels and is independent of an expression level of CRP; and wherein a coefficient of said combination of expressed IP- 10 is positive when said age group is defined for ages less than three months, and negative when said age group is for older ages.
30. A system for analyzing biological data, the system comprising: input circuit for receiving data pertaining to an age group of a subject, and biological data containing expression levels of TRAIT, and PCT in a sample extracted from the subject; a data processor having a circuit configured to access a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the data processor to: calculate a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; correlate said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection, based on said age; and generate output pertaining to said correlation; wherein at least 90% of said segment is between a lower bound line f(δ-)So and an upper bound line f(δ+)si, wherein said f(δ) equals l/(l+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels; and wherein a ratio between a coefficient of said combination of expressed TRAIT, and a coefficient of said combination of expressed PCT has a first ratio value when said age group is defined for ages less than three months, and a second ratio value, lower than said first ratio value, than when said age group is for older ages.
31. The system according to any of claims 28-30, wherein said sample is in a labeled cartridge, and said input circuit receives data pertaining to said age group based on said label.
32. A method of analyzing biological data, the method comprising: obtaining biological data containing at least expression levels of TRAIL and CRP in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; and correlating said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of said segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein said f(δ) equals 1/(1+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels, wherein a ratio between a coefficient of said combination of expressed TRAIL in, or once converted to, units of ml/pg and a coefficient of said combination expressed CRP in, or once converted to, units of ml/mg is more than -0.5.
33. The method according to claim 32, wherein said ratio is more than -0.4, or more than -0.4, or more than -0.3, or more than -0.2.
34. The method according to claim 32, wherein said biological data contain expression level of IP- 10, and wherein said ratio is more than -0.2.
35. The method according to any of claims 32-34, wherein said biological data contain expression level of PCT, and wherein said ratio is more than -0.2.
36. The method according to any of claims 32-35, wherein said biological data contain a count of Urine leukocytes, and wherein said ratio is more than -0.2.
37. A method of analyzing biological data, the method comprising: obtaining biological data containing at least expression levels of TRAIT, and IP- 10 in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; and correlating said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of said segment is between a lower bound line f(δ-)So and an upper bound line f(δ+)si, wherein said f(δ) equals l/(l+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels and is independent of an expression level of CRP, wherein a coefficient of said combination of expressed TRAIL is negative and a coefficient of said combination of expressed IP- 10 is positive.
38. The method according to claim 37, wherein said biological data contain expression level of PCT.
39. The method according to any of claims 37-38, wherein said biological data contain a count of Urine leukocytes.
40. A method of analyzing biological data, the method comprising: obtaining biological data containing at least expression levels of TRAIT, and PCT in a sample of an infant human subject with less than three months of age; calculating a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated at a point over said curved line defined by a coordinate d along said direction; and correlating said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; wherein at least 90% of said segment is between a lower bound line f(δ)-ε0 and an upper bound line f(δ)+ε1, wherein said f(δ) equals 1/(1+exp(-δ)), wherein said coordinate d, once calculated, equals a combination of said expression levels, wherein a ratio between a coefficient of said combination of expressed TRAIL in, or once converted to, units of ml/pg and a coefficient of said combination of expressed PCT in, or once converted to, units of ml/ng is more than -0.01.
41. The method according to claim 40, wherein said biological data contain expression level of CRP.
42. The method according to any of claims 40-41, wherein said biological data contain expression level of IP- 10, and wherein said ratio is more than -0.08.
43. The method according to any of claims 40-42, wherein said biological data contain a count of Urine leukocytes.
44. The system or method according to any of claims 28-43, wherein the subject exhibits symptoms of infection.
45. The system or method of claim 44, wherein said symptoms comprise fever.
46. The system or method according to any of claims 28-45, further comprising obtaining background and/or clinical data pertaining to the subject, and weighing said likelihood based on said age.
47. The system or method according to any of claims 28-46, further comprising obtaining said likelihood based on said distance, comparing said likelihood to a predetermined threshold, and prescribing treatment to said subject based on said comparison.
48. The system or method according to any of claims 28-47, further comprising obtaining said likelihood based on said distance, comparing said likelihood to a predetermined threshold, and, treating the subject for said bacterial infection when said likelihood is above said predetermined threshold.
49. The system or method according to any of claims 28-47, further comprising generating an output of said likelihood.
50. The system or method according to any of claims 28-49, wherein said blood sample is whole blood.
51. The system or method according to any of claims 28-49, wherein said blood sample is a fraction of whole blood.
52. The system or method according to claim 51 , wherein said blood fraction comprises serum or plasma.
53. The system or method according to any of claims 28-52, wherein said calculating and said correlating is executed by a computer remote from the subject.
54. The system or method according to any of claims 28-52, wherein said calculating and said correlating is executed by a computer near the subject.
55. The system or method according to any of claims 28-52, wherein said calculating and said correlating is executed by a cloud computing resource of a cloud computing facility.
56. A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a hardware processor, cause the hardware processor to receive expression levels of a plurality of polypeptides in the blood of a subject who has an unknown disease, and to execute the method according to any of claims 32-55.
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