Markers for diagnosing infection

Abstract

A method for diagnosing an infectious disease in a subject, comprising determining the severity of the disease, the method comprising measuring the expression level of at least one protein selected from the group consisting of TSG-14, AGER, ANG-2, and ST2 in a sample from the subject, and diagnosing the disease based on the expression level. A kit for performing the diagnosis is also disclosed.

Description

[Technical Field]

[0001] Related Applications This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 390,701, filed July 20, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] The present invention, in some embodiments, relates to the identification of signatures and determinants associated with bacterial and viral infections. [Background technology]

[0003] Disease assessment is one of the most important tasks in the management of patients with infectious diseases. As a complement to determining the etiology of infection, predicting patient prognosis can affect various aspects of patient management, including treatment, diagnostic testing (e.g., microbiology, blood chemistry, radiology, etc.), and hospitalization. Timely identification of patients who are more likely to have a poor prognosis may result in more aggressive patient management procedures, including, for example, intensive care unit (ICU) admission, advanced treatment, invasive diagnostics, or surgical intervention, which may reduce morbidity and mortality.

[0004] Further background art includes WO 2013 / 117746, WO 2016 / 024278, WO 2018 / 060998, and WO 2018 / 060999. Summary of the Invention [Means for solving the problem]

[0005] According to one aspect of the present invention, there is provided a method of diagnosing an infectious disease in a subject, comprising: (a) measuring the expression level of at least one protein selected from the group consisting of tumor necrosis factor-inducible gene 14 protein (TSG-14), advanced glycation end-products-specific receptor (AGER), angiopoietin-2 (ANG-2), and interleukin-1 receptor-like 1 (ST2) in a sample from the subject; and (b) diagnosing said disease based on said expression level. A method is provided, comprising:

[0006] According to an embodiment of the present invention, (i) if the expression level of TSG-14 is more than two times higher than the level in the control sample, it is determined to be a severe infectious disease; (ii) if the expression level of AGER is more than two times higher than that in the control sample, it is determined to be a severe infectious disease; (iii) if the expression level of ANG-2 is more than two-fold higher than the level in the control sample, the disease is determined to be severe infectious; and / or (iv) If the expression level of ST2 is three times or more higher than that in the control sample, it is determined to be a severe infectious disease.

[0007] According to an embodiment of the invention, diagnosing includes determining the severity of the infectious disease.

[0008] According to an embodiment of the present invention, the expression level of TSG-14 is less than about 930 pg / ml, which is determined to be the absence of a severe infectious disease; (ii) if the AGER expression level is less than about 960 pg / ml, it is determined that the patient does not have a severe infectious disease; (iii) if the expression level of ANG-2 is less than about 1800 pg / ml, the patient is determined not to have a severe infectious disease; and / or (iv) If the expression level of ST2 is less than about 28,000 pg / ml, it is determined that the patient does not have a severe infectious disease.

[0009] According to an embodiment of the present invention, (i) if the expression level of TSG-14 is greater than about 6000 pg / ml, it is determined to be a severe infectious disease; (ii) if the expression level of AGER is greater than approximately 3200 pg / ml, it is determined to be a severe infectious disease; (iii) if the expression level of ANG-2 is greater than about 5000 pg / ml, it is determined to be a severe infectious disease; and / or (iv) If the expression level of ST2 exceeds about 140,000 pg / ml, it is determined to be a severe infectious disease.

[0010] According to an embodiment of the present invention, the at least one protein comprises at least two proteins.

[0011] According to an embodiment of the present invention, the at least two proteins include ANG-2 and AGER; AGER and ST2; or ANG-2 and ST2.

[0012] According to an embodiment of the present invention, the method further comprises measuring the expression of at least one additional protein selected from the group consisting of IL-6, IL-10, and MR-proADM, and diagnosing the infection based on the expression level of the at least one additional protein in combination with the expression level of the at least one protein.

[0013] According to an embodiment of the present invention, (i) If the IL-10 expression level is less than 0.17 pg / ml, it is determined that the patient does not have a severe infectious disease; (ii) if the IL-6 expression level is less than 9.8 pg / ml, it is determined that the patient does not have a severe infectious disease; and / or (iii) If the expression level of MR-proADM is less than 0.6 nmol / L, it is determined that the patient does not have a severe infectious disease.

[0014] According to an embodiment of the present invention, (i) If the expression level of IL-10 is greater than 68 pg / ml, it is determined to be a severe infectious disease; (ii) if the IL-6 expression level is greater than 56 pg / ml, it is determined to be a severe infectious disease; and / or (iii) If the expression level of MR-proADM exceeds 1.9 nmol / L, it is determined to be a severe infectious disease.

[0015] According to an embodiment of the present invention, (i) if the expression level of IL-10 is three times or more higher than the level in the control sample, it is determined to be a severe infectious disease; (ii) If the expression level of IL-6 is more than two times higher than the level in the control sample, it is determined to be a severe infectious disease.

[0016] According to an embodiment of the present invention, the method further comprises measuring the expression level of IP-10 and diagnosing the infection based on the expression level of IP-10 in combination with the expression level of at least one protein.

[0017] According to an embodiment of the present invention, the method further comprises measuring the expression level of IP-10 and diagnosing the infection based on the expression level of IP-10 in combination with the expression levels of at least two proteins.

[0018] According to one aspect of the present invention, there is provided a method for diagnosing an infectious disease in a subject, comprising measuring the amount of soluble urokinase plasminogen activator receptor (suPAR) and the amount of at least one determinant selected from the group consisting of interferon gamma-inducible protein 10 (IP-10) and interleukin-6 (IL-6) in a sample from the subject, wherein the total amount of suPAR and the determinant indicates the severity of the infection.

[0019] According to an embodiment of the present invention, an infection is classified as severe if the amount of suPAR exceeds a predetermined level and the amount of IP-10 exceeds a predetermined level.

[0020] According to an embodiment of the invention, if the amount of suPAR is below a predetermined level and the amount of IP-10 is below a predetermined level, the infection is classified as not severe.

[0021] According to an embodiment of the invention, if the amount of suPAR exceeds a predetermined level and the amount of IL-6 exceeds a predetermined level, the infection is classified as severe.

[0022] According to an embodiment of the invention, if the amount of suPAR is below a predetermined level and the amount of IL-6 is below a predetermined level, the infection is classified as not severe.

[0023] According to an embodiment of the present invention, the method further comprises measuring the expression levels of TRAIL and / or CRP.

[0024] According to an embodiment of the present invention, the method further comprises measuring all components of a clinical index selected from the group consisting of NEWS, NEWS 2, MEWS APACHE I, APACHE II, APACHE III, CURB-65, SMART-COP, SAPS II, SAPS III, PIM2, CMM, SOFA, qSOFA, MPM, RIFLE, CP, MODS, LODS, Rochester criteria, Philadelphia criteria, Milwaukee criteria, and Ranson score.

[0025] According to an embodiment of the invention, the method further comprises measuring the level of at least one additional protein listed in Table 5, Table 6, or Table 7.

[0026] According to an embodiment of the present invention, the infection is a viral infection.

[0027] According to an embodiment of the present invention, the infection is a bacterial infection.

[0028] According to an embodiment of the invention, the subject exhibits symptoms of an infectious disease.

[0029] According to an embodiment of the present invention, the subject does not exhibit symptoms of an infectious disease.

[0030] According to an embodiment of the present invention, the subject does not have a chronic non-communicable disease.

[0031] According to an embodiment of the present invention, the sample is whole blood or a fraction thereof.

[0032] According to an embodiment of the invention, the fraction comprises cells selected from the group consisting of lymphocytes, monocytes, and granulocytes.

[0033] According to an embodiment of the invention, the fraction comprises serum or plasma.

[0034] According to an embodiment of the present invention, the levels of 10 or fewer proteins are used to diagnose infection.

[0035] According to an embodiment of the invention, six or fewer proteins are measured to diagnose infection.

[0036] According to an embodiment of the present invention, diagnosing an infection includes determining the severity of the infection.

[0037] According to one aspect of the present invention, there is provided a kit for diagnosing infection, comprising a detection reagent that specifically detects a first determinant selected from the group consisting of IP-10, MR-proADM, IL-6, and IL-10, and a second determinant selected from the group consisting of TSG-14, AGER, ANG-2, and ST2.

[0038] According to one aspect of the present invention, there is provided a kit for diagnosing infection, comprising a detection reagent that specifically detects at least two determinants selected from the group consisting of TSG-14, AGER, ANG-2, and ST2.

[0039] According to an embodiment of the present invention, the determinant is IP-10.

[0040] According to one aspect of the present invention, (i) an antibody that specifically binds to a determinant selected from the group consisting of IP-10 and IL-6; and (ii) an antibody that specifically binds to suPAR; A kit for determining the severity of an infection, comprising: The kit comprises 10 or fewer antibodies, The kit is provided.

[0041] According to an embodiment of the present invention, the kit further comprises a detection reagent that specifically detects IP-10.

[0042] According to an embodiment of the present invention, the kit further comprises a detection reagent for specifically detecting TRAIL.

[0043] According to an embodiment of the present invention, the kit further comprises a detection reagent that specifically detects CRP.

[0044] According to an embodiment of the present invention, the detection reagent is an antibody.

[0045] According to an embodiment of the invention, at least one of the antibodies is conjugated to a detectable moiety.

[0046] According to an embodiment of the invention, at least one of the antibodies is a monoclonal antibody.

[0047] According to an embodiment of the invention, at least one of the antibodies is bound to a solid support.

[0048] According to an embodiment of the present invention, the kit comprises detection reagents that specifically detect 10 or fewer protein markers.

[0049] According to an embodiment of the present invention, the kit comprises detection reagents that specifically detect six or fewer protein markers.

[0050] According to one aspect of the present invention, (a) diagnosing an infection according to any one of claims 1 to 29; and (b) treating said subject in response to diagnosis of said infection; Methods of treating a subject with an infectious disease are provided.

[0051] According to embodiments of the present invention, if a severe infection is determined, at least one of the following treatments is used: hospitalization; placement in an intensive care unit; mechanical ventilation; non-invasive ventilation, ECMO, renal replacement therapy, cardiac catheterization, antibiotic treatment, vasopressor therapy, and / or treatments of last resort.

[0052] According to an embodiment of the invention, the subject exhibits symptoms of an infectious disease.

[0053] According to an embodiment of the present invention, the symptoms include fever.

[0054] According to one aspect of the invention, there is provided a method of distinguishing between a viral infection and a bacterial infection in a subject, comprising: (a) measuring the expression level of a combination of proteins in a blood sample of a subject, wherein the combination belongs to Group 1 or Group 2; and (b) determining whether the infection is bacterial or viral based on the expression level. A method is provided, comprising:

[0055] According to one aspect of the present invention, there is provided a method for determining the severity of an infectious disease in a subject, comprising: (a) measuring the expression level of a combination of proteins in a blood sample of the subject, wherein the combination belongs to Group 3 or Group 4; and (b) determining the severity based on the expression level; A method is provided.

[0056] According to one aspect of the present invention, there is provided a method of diagnosing an infectious disease in a subject, comprising: (a) measuring the expression level of a combination of proteins in a blood sample of a subject, wherein the combination belongs to Group 5 or Group 6; and (b) determining whether the disease is infectious or non-infectious based on said expression level.

[0057] According to an embodiment of the invention, the method further comprises measuring the level of at least one additional protein listed in Table 5 or Table 7.

[0058] According to an embodiment of the invention, the method further comprises measuring the level of at least one additional protein listed in Table 6 or Table 7.

[0059] According to an embodiment of the invention, the method further comprises determining the severity of the infection.

[0060] According to an embodiment of the present invention, determining the severity of the infection is carried out by measuring the level of at least one protein listed in Table 6.

[0061] According to an embodiment of the invention, the method further comprises measuring the level of at least one additional protein listed in Table 5.

[0062] According to an embodiment of the invention, the subject exhibits symptoms of an infectious disease.

[0063] According to an embodiment of the present invention, the subject does not exhibit symptoms of an infectious disease.

[0064] According to an embodiment of the present invention, the subject does not have a chronic non-communicable disease.

[0065] According to an embodiment of the present invention, the sample is whole blood or a fraction thereof.

[0066] According to an embodiment of the invention, the fraction comprises cells selected from the group consisting of lymphocytes, monocytes, and granulocytes.

[0067] According to an embodiment of the invention, the fraction comprises serum or plasma.

[0068] According to an embodiment of the present invention, the levels of 10 or fewer proteins are used to classify the infection.

[0069] According to an embodiment of the present invention, no more than five proteins are measured to determine the type of infection.

[0070] According to one aspect of the present invention, there is provided a kit for diagnosing an infection type, comprising a detection reagent for specifically detecting each of the proteins in the combinations described in Groups 1 to 6.

[0071] According to an embodiment of the present invention, the detection reagent is an antibody.

[0072] According to an embodiment of the invention, at least one of the antibodies is conjugated to a detectable moiety.

[0073] According to an embodiment of the invention, at least one of the antibodies is a monoclonal antibody.

[0074] According to an embodiment of the invention, at least one of the antibodies is bound to a solid support.

[0075] According to an embodiment of the present invention, the kit comprises detection reagents that specifically detect 10 or fewer protein markers.

[0076] According to an embodiment of the present invention, the kit comprises detection reagents that specifically detect six or fewer protein markers.

[0077] According to one aspect of the invention, there is provided a method of treating a subject having an infectious disease, comprising: (a) classifying the infection type according to any one of claims 1 to 16; and (b) treating said subject according to said classification of infection; If a severe viral infection is determined, at least one of the following treatments is used: treatment with antiviral agents; hospitalization; placement in an intensive care unit; mechanical ventilation; and / or treatment of last resort.

[0078] According to an embodiment of the present invention, the antiviral agent is selected from the group consisting of molnupiravir, paxlovid, and remdesivir.

[0079] According to an embodiment of the invention, the subject exhibits symptoms of an infectious disease.

[0080] According to an embodiment of the present invention, the symptoms include fever.

[0081] 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 this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of this invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. Furthermore, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting. DETAILED DESCRIPTION OF THE INVENTION

[0082] The present invention, in some embodiments, relates to the identification of signatures and determinants associated with bacterial and viral infections.

[0083] Before describing at least one embodiment of the present invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details set forth in the following description or illustrated in the examples. The invention is capable of other embodiments and of being practiced or applied in various ways.

[0084] Distinguishing between bacterial and viral infections is a routine clinical challenge. Recent publications have shown that the host response exhibits an inflammatory "bacterial" pattern to severe infections, regardless of the underlying infectious etiology, even if the underlying infection is viral (Tang, BM, Nature Communications 10, 3422. doi:10.1038 / s41467-019-11249-y; Dunning, J., et al Nature Immunology 19(6):625-635. doi:10.1038 / s41590-018-0111-5).

[0085] By conducting large-scale clinical trials, the inventors have discovered specific proteins in the blood that act as markers of the severity of infection, and propose to diagnose subjects and determine appropriate treatments based on the expression levels of such markers.

[0086] Furthermore, in the course of practicing this invention, the inventors have identified combinations of such markers that can classify infections with very high accuracy in terms of severity. These proteins can be combined with additional protein determinants that can distinguish between bacterial and viral infections. This allows for very detailed diagnosis of infections in a short time.

[0087] Thus, according to one aspect of the present invention there is provided a method of diagnosing an infectious disease in a subject, comprising: (a) measuring the expression level of at least one protein selected from the group consisting of tumor necrosis factor-inducible gene 14 protein (TSG-14), advanced glycation end-products-specific receptor (AGER), angiopoietin-2 (ANG-2), and interleukin-1 receptor-like 1 (ST2) in a sample from the subject; and (b) diagnosing said disease based on said expression level.

[0088] The term "diagnosing," as used herein, refers to determining the presence or absence of an infection, classifying the infection or its symptoms, determining the severity of an infection, monitoring the progression of an infection, predicting the outcome of an infection, and / or assessing the likelihood of recovery.

[0089] According to one embodiment of this aspect of the invention, diagnosing comprises determining or classifying the severity of the infection.

[0090] Information regarding particularly relevant protein markers that can be used for diagnosis (and in particular for determining severity) is set out in Table 1A below.

[0091] [Table 1A]

[0092] For example, the protein markers disclosed in Table 1A may be used to determine whether a severe infection is present or whether a non-severe infection is present, and each marker in Table 1A is increased in severe infections compared to non-severe infections, as further detailed herein.

[0093] Additionally or alternatively, the protein markers listed in Table 1A may be used to determine whether a severe infection or a non-severe infection is present.

[0094] In some embodiments, at least one of the protein markers listed in Table 1A may be used to determine whether or not a severe viral infection is present.

[0095] Additionally or alternatively, at least one of the protein markers listed in Table 1A may be used to determine a non-severe viral infection or to determine no non-severe viral infection.

[0096] In some embodiments, at least one of the protein markers listed in Table 1A may be used to determine whether or not a severe bacterial infection is present.

[0097] Additionally or alternatively, at least one of the proteins listed in Table 1A may be used to determine a non-severe bacterial infection or to determine that a bacterial infection is not a non-severe bacterial infection. If the level of any of the above-disclosed proteins is above a predetermined amount, a severe infection may be determined.

[0098] The predetermined level is the amount (i.e., level) of protein (or a function of that amount) in a control sample derived from one or more subjects without infection (i.e., healthy and / or non-infectious individuals) or one or more subjects without severe infection (i.e., subjects with a non-severe infection). In further embodiments, such subjects are monitored and / or periodically retested after such testing for a period relevant to the diagnosis to verify continued absence of infection ("longitudinal study"). Such a period can be 1 day, 2 days, 2-5 days, 5 days, 5-10 days, 10 days, or more than 10 days from the date of the initial test to determine the baseline value. Furthermore, retrospective measurements of protein levels in appropriately stored past subject samples can be used to identify these baseline values, thereby reducing the required study time.

[0099] Reference values can also include amounts of protein from subjects who have shown improvement upon treatment and / or therapy of the infection. Reference values can also include amounts of protein from subjects whose infection has been confirmed by known techniques.

[0100] According to a specific embodiment, if the expression level of TSG-14 is less than about 930 pg / ml, the disease is determined to be not severe. Other exemplary thresholds of TSG-14 that can be used to determine the disease is not severe include less than about 910 pg / ml, less than about 890 pg / ml, or less than about 870 pg / ml.

[0101] Other exemplary thresholds of TSG-14 that can be used to determine a non-severe infection include less than about 500 pg / ml, less than about 370 pg / ml, less than about 270 pg / ml, or less than about 170 pg / ml.

[0102] According to another embodiment, if the expression level of AGER is less than about 960 pg / ml, it is determined that the patient does not have a severe infectious disease. Other exemplary thresholds of AGER that can be used to determine that the patient does not have a severe infection include less than about 930 pg / ml, less than about 900 pg / ml, or less than about 880 pg / ml.

[0103] Other exemplary thresholds of AGER that can be used to determine a non-severe infection include less than about 710 pg / ml, less than about 610 pg / ml, less than about 590 pg / ml, or less than about 560 pg / ml.

[0104] According to another embodiment, a severe infectious disease is not determined if the expression level of ANG-2 is less than about 1800 pg / ml. Other exemplary thresholds of ANG-2 that can be used to determine a severe infection include less than about 1500 pg / ml, less than about 1300 pg / ml, or less than about 1100 pg / ml.

[0105] Other exemplary thresholds of ANG-2 that can be used to determine a non-severe infection include less than about 1200 pg / ml, less than about 1000 pg / ml, less than about 990 pg / ml, or less than about 920 pg / ml.

[0106] According to another embodiment, if the expression level of ST2 is less than about 28,000 pg / ml, the patient is determined not to have a severe infectious disease. Other exemplary thresholds of ST2 that can be used to determine not to have a severe infection include less than about 25,000 pg / ml, less than about 20,000 pg / ml, or less than about 15,000 pg / ml.

[0107] Other exemplary thresholds of ST2 that can be used to determine a non-severe infection include less than about 23,000 pg / ml, less than about 18,000 pg / ml, less than about 15,000 pg / ml, or less than about 13,000 pg / ml.

[0108] According to another embodiment, a severe infectious disease is not determined if the IL-10 expression level is less than about 0.17 pg / ml. Other exemplary thresholds of IL-10 that can be used to determine a severe infection include less than about 0.16 pg / ml, less than about 0.15 pg / ml, or less than about 0.14 pg / ml.

[0109] According to another embodiment, if the IL-6 expression level is less than about 9.8 pg / ml, the patient is determined not to have a severe infectious disease. Other exemplary thresholds of IL-6 that can be used to determine not to have a severe infection include less than about 9.6 pg / ml, less than about 9.4 pg / ml, or less than about 9.2 pg / ml.

[0110] Other exemplary thresholds of IL-6 that can be used to determine a non-severe infection include less than about 5.9 pg / ml, less than about 4.6 pg / ml, less than about 4.3 pg / ml, or less than about 3.5 pg / ml.

[0111] According to another embodiment, a TSG-14 expression level greater than about 6000 pg / ml is determined to indicate a severe infectious disease. Other exemplary thresholds of TSG-14 that can be used to determine a severe infection include greater than about 7000 pg / ml, greater than about 8000 pg / ml, or greater than about 10,000 pg / ml.

[0112] Other exemplary thresholds of TSG-14 that can be used to determine a severe infection include greater than about 7,100 pg / ml, greater than about 9,300 pg / ml, greater than about 15,000 pg / ml, greater than about 21,000 pg / ml, greater than about 30,000 pg / ml, or greater than about 39,000 pg / ml.

[0113] According to yet another embodiment, a severe infection may be determined if the expression level of TSG-14 increases by more than 2-fold or even more than 2.5-fold above the baseline value of TSG-14 (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is non-infectious).

[0114] According to another embodiment, a severe infectious disease is determined when the expression level of AGER is greater than about 3200 pg / ml. Other exemplary thresholds of AGER that can be used to determine a severe infection include greater than about 3300 pg / ml, greater than about 3500 pg / ml, or greater than about 4,000 pg / ml.

[0115] Other exemplary thresholds of AGER that can be used to determine a severe infection include greater than about 3,900 pg / ml, greater than about 5,500 pg / ml, greater than about 6,700 pg / ml, greater than about 10,000 pg / ml, greater than about 13,000 pg / ml, or greater than about 14,000 pg / ml.

[0116] According to yet another embodiment, a severe infection can be determined if the expression level of AGER increases by more than two-fold above the baseline level of AGER (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is non-infectious).

[0117] According to another embodiment, a severe infectious disease is determined when the expression level of ANG-2 is greater than about 5000 pg / ml. Other exemplary thresholds of ANG-2 that can be used to determine a severe infection include greater than about 6,000 pg / ml, greater than about 7,000 pg / ml, or greater than about 8,000 pg / ml.

[0118] Other exemplary thresholds of ANG-2 that can be used to determine a severe infection include greater than about 5,800 pg / ml, greater than about 7,000 pg / ml, greater than about 10,000 pg / ml, greater than about 14,000 pg / ml, greater than about 17,000 pg / ml, or greater than about 20,000 pg / ml.

[0119] According to yet another embodiment, a severe infection may be determined if the expression level of ANG-2 increases by more than two-fold above the baseline level of ANG-2 (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is non-infectious).

[0120] According to another embodiment, a severe infectious disease is determined when the expression level of ST2 is greater than about 140,000 pg / ml. Other exemplary thresholds of ST2 that can be used to determine a severe infection include greater than about 150,000 pg / ml, greater than about 170,000 pg / ml, or greater than about 200,000 pg / ml.

[0121] Other exemplary thresholds of ST2 that can be used to determine a severe infection include greater than about 180,000 pg / ml, greater than about 230,000 pg / ml, greater than about 390,000 pg / ml, greater than about 500,000 pg / ml, and greater than about 770,000 pg / ml.

[0122] According to yet another embodiment, a severe infection may be determined if the expression level of ST2 increases by more than three-fold above the baseline ST2 level (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is non-infectious).

[0123] According to another embodiment, an IL-10 expression level greater than about 68 pg / ml is determined to indicate a severe infectious disease. Other exemplary IL-10 thresholds that may be used to determine a severe infection include greater than about 70 pg / ml, greater than about 72 pg / ml, or greater than about 75 pg / ml.

[0124] Other exemplary thresholds of IL-10 that can be used to determine a severe infection include greater than about 88 pg / ml, greater than about 130 pg / ml, greater than about 210 pg / ml, greater than about 270 pg / ml, greater than about 350 pg / ml, and greater than about 1,900 pg / ml.

[0125] According to yet another embodiment, a severe infection may be determined if the expression level of IL-10 increases by more than three times above the baseline level of IL-10 (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is non-infectious).

[0126] According to another embodiment, a severe infectious disease is determined when the IL-6 expression level is greater than about 56 pg / ml. Other exemplary thresholds of IL-6 that can be used to determine a severe infection include greater than about 57 pg / ml, greater than about 60 pg / ml, or greater than about 65 pg / ml.

[0127] Other exemplary thresholds of IL-6 that can be used to determine a severe infection include greater than about 75 pg / ml, greater than about 130 pg / ml, greater than about 260 pg / ml, greater than about 410 pg / ml, greater than about 500 pg / ml, and greater than about 1,000 pg / ml.

[0128] According to yet another embodiment, a severe infection may be determined if the expression level of IL-6 increases by more than two-fold above the baseline level of IL-6 (e.g., when the subject has a non-severe infection, when the subject is healthy, or when the subject is uninfected).

[0129] The term "classifying severity" refers to assigning a severity of disease to an individual, which in one embodiment may be related to the probability of experiencing a particular adverse event (e.g., death, hospitalization, or admission to an ICU). Thus, classification may also be used to predict the outcome of infectious disease patients. Classification of disease severity may be done at a binary level (severe / not severe) or at a non-binary level (e.g., based on numerical severity categories such as 1, 2, 3, etc.).

[0130] In one embodiment, severity can be classified according to the WHO Disease Stratification Ordinal Scale, NEWS (National Early Warning Score), SOFA (Sequential Organ Failure Assessment) score, and qSOFA (Quick SOFA) score for sepsis.

[0131] In one embodiment, the term "severe" refers to an infection that has at least one of the following outcomes: requiring vasopressor therapy, requiring intubation with mechanical ventilation, requiring non-invasive ventilation, admission to an intensive care unit, and / or expected death within 14 days.

[0132] The term "non-severe" refers, in one embodiment, to infections that do not require vasopressor therapy, do not require intubation with mechanical ventilation, do not require non-invasive ventilation, are not admitted to an intensive care unit, and / or are not expected to result in death within 14 days.

[0133] Particular combinations of markers disclosed above that have shown very high accuracy in determining the severity of infection include ANG-2 and AGER; AGER and ST2; and ANG-2 and ST2.

[0134] The inventors have shown that the combination can be highly relevant to determining the severity of a specific patient subgroup. For example, the combination of AGER and ANG-2 can be used to determine the severity of a viral disease (e.g., to determine whether a viral disease is not severe or whether a viral disease is severe). An exemplary threshold for AGER is 1758 ng / ml, and for ANG-2 it is 999 ng / ml. The combination of ST2 and ANG-2 can be used to determine the severity of a bacterial disease (e.g., to determine whether a bacterial disease is not severe). An exemplary threshold for ST2 is 37,554 ng / ml, and an exemplary threshold for ANG-2 is 2,545 ng / ml. The combination of ST2 and AGER can be used to determine the severity of a bacterial disease (e.g., to determine whether a bacterial disease is severe). An exemplary threshold for AGER is 202,000 ng / ml, and an exemplary threshold for ANG-2 is 5,650 ng / ml.

[0135] Combining the determinants listed in Table 1A with IP-10 can provide a more accurate diagnosis of infection. For example, the following marker pairs are considered: TSG-14 and IP-10; AGER and IP-10; ANG-2 and IP-10; and ST2 and IP-10. Also considered for the diagnosis of infection are three-marker combinations: IP-10, ANG-2, and AGER; IP-10, AGER, and ST2; and IP-10, ANG-2, and ST2.

[0136] Provided below are additional protein combinations that can be used to diagnose infection, and more specifically, to determine the severity of infection.

[0137] MR-proADM and IL-6; MR-proADM and IL-10; MR-proADM and TSG-14; MR-proADM and AGER; MR-proADM and ANG-2; and MR-proADM and ST2.

[0138] IL-10 and IL-6; IL-10 and TSG-14; IL-10 and AGER; IL-10 and ANG-2; and IL-10 and ST2.

[0139] MR-proADM and IL-6; IL-6 and TSG-14; IL-6 and AGER; IL-6 and ANG-2; and MR-IL-6 and ST2.

[0140] The present inventors have found that the expression levels of the markers TRAIL, CRP, and IP-10 are particularly important in distinguishing between bacterial and viral infections. Therefore, in addition to these three markers, combinations of at least one of the markers listed in Table 1A are also contemplated. For example, combinations of TRAIL, CRP, IP-10, and TSG-14; combinations of TRAIL, CRP, IP-10 AGER, TRAIL, CRP, IP-10, and ANG-2; and combinations of TRAIL, CRP, IP-10, and ST2 are included.

[0141] TRAIL levels are increased in viral infections (compared to non-infectious diseases) and decreased in bacterial infections (compared to non-infectious diseases).

[0142] Thus, if the level of TRAIL exceeds a predetermined level, it indicates that the infection is a viral infection, and can be determined to be a viral infection (or not a bacterial infection).

[0143] If the level of TRAIL is below a predetermined level, it indicates that the infection is a bacterial infection, and may be determined to be a bacterial infection (or may not be determined to be a viral infection).

[0144] For example, if the measured TRAIL polypeptide concentration is higher than a predetermined first threshold, it can be determined that the infection is not bacterial.Optionally, the method further comprises determining whether the subject has a viral infection (i.e., determining that the infection is viral).If the TRAIL polypeptide concentration is higher than a predetermined second threshold, it is determined that the infection is viral.

[0145] In another specific embodiment, the present invention includes determining whether a subject does not have a viral infection (i.e., determining that the subject does not have a viral infection). If the determined TRAIL polypeptide concentration is lower than a predetermined first threshold, it is determined that the subject does not have a viral infection. Optionally, the method further includes determining whether the subject has a bacterial infection (i.e., determining that the subject has a bacterial infection). If the TRAIL polypeptide concentration is lower than a predetermined second threshold, it is determined that the subject has a bacterial infection.

[0146] More specifically, a TRAIL level of 100 to 1000 pg / ml typically indicates a viral infection, while a level of 0 to 85 pg / ml typically indicates a bacterial infection. Typically, a TRAIL level of less than 85 pg / ml, less than 70 pg / ml, less than 60 pg / ml, or more preferably less than 50 pg / ml, less than 40 pg / ml, less than 30 pg / ml, or less than 20 pg / ml is considered to be a bacterial infection, while a TRAIL level of more than 100 pg / ml, 120 pg / ml, 140 pg / ml, or preferably more than 150 pg / ml is considered to be a non-bacterial infection.

[0147] CRP levels are generally increased during infection (compared to non-infectious diseases) and tend to be higher in bacterial infections than in viral infections.

[0148] Thus, if the level of CRP exceeds a predetermined level, it indicates that the infection is a bacterial infection and may be determined to be a bacterial infection (or may not be determined to be a viral infection).

[0149] IP-10 levels are increased during infection (compared to non-infectious diseases) and tend to be higher in viral infections than bacterial infections.

[0150] Thus, if the level of IP-10 exceeds a predetermined level, it indicates that the infection is a viral infection, and may be determined to be a viral infection (or not a bacterial infection).

[0151] If the level of IP-10 is below a predetermined level, it indicates that the infection is a bacterial infection, which may be determined to be a bacterial infection (or may not be determined to be a viral infection).

[0152] IP-10 levels between 300 and 2000 pg / ml typically indicate a viral infection, while levels between 160 and 860 pg / ml typically indicate a bacterial infection.

[0153] Additional proteins that may be measured along with at least one, at least two, or at least three of the markers listed in Table 1A to determine the severity of infection include any of those listed in Table 6. Marker combinations that may be included to determine severity are listed as belonging to Group 3 or Group 4.

[0154] Additional proteins that can be measured to distinguish between bacterial and viral infections include any of those listed in Table 5. Marker combinations that can be included to distinguish between bacterial and viral infections are listed as belonging to Group 1 or Group 2.

[0155] Additional proteins that may be measured to distinguish between infectious and non-infectious disease include any of those listed in Table 7. Marker combinations that may be included to distinguish between infectious and non-infectious are listed as belonging to Group 5 or Group 6.

[0156] Additional factors that may be incorporated to diagnose infection, particularly into severity classification, include epidemiological information, symptom assessment, and conventional laboratory results, as summarized in Table 1B herein below.

[0157] [Table 1B]

[0158] suPAR (see, e.g., WO 2019 / 162334) and IP-10 (see, e.g., WO 2021 / 152595) are both markers known to correlate with disease severity.

[0159] We have shown that calculating a score based on the combination of these two markers significantly improves the accuracy of predicting severe outcomes in infectious diseases (see Example 3). We propose that these combined measurements will assist physicians in assessing their patients' risk profiles, enabling better-informed management decisions.

[0160] Thus, according to another aspect of the present invention, there is provided a method for diagnosing an infectious disease in a subject, comprising measuring the amount of soluble urokinase plasminogen activator receptor (suPAR) and the amount of at least one determinant selected from the group consisting of interferon gamma-inducible protein 10 (IP-10) and interleukin-6 (IL-6) in a sample from the subject, wherein the total amount of suPAR and the determinant indicates the severity of the infection.

[0161] The protein suPAR (UniProt ID: Q03405, NCBI accession number AAK31795, and receptor isoforms NP_002650, 003405, NP_002650, NP_001005376) is the soluble portion released by cleavage of the GPI anchor of the membrane-bound urokinase-type plasminogen activator receptor (uPAR). SuPAR belongs to a family of glycosylated proteins and consists of full-length suPAR (277 amino acids (1-277)) and suPAR fragments D1 (1-83) and D2D3 (84-277), which are generated by urokinase cleavage or human airway trypsin-like proteases; D1 (1-87) and D2D3 (88-277), which are generated by MMP cleavage; D1 (1-89) and D2D3 (90-277), which are also generated by urokinase cleavage or human airway trypsin-like proteases; and D1 (1-91) and D2D3 (92-277), which are generated by plasmin cleavage. In one embodiment, the severity is assessed by generating a score based on the amounts of both suPAR and IP-10 (i.e., the combination of suPAR and IP-10). This combination refers to any mathematical combination of suPAR and IP-10.

[0162] In one embodiment, the score is a function of increasing amounts of suPAR and increasing amounts of IP-10, where a score above a predetermined level indicates severe infection, where the predetermined level is based on the amounts of both suPAR and IP-10 in subjects without severe infection.

[0163] The score is a monotonically increasing function of the amount of suPAR, and may be a monotonically increasing function of the amount of IP- 10. In one embodiment, the function is linear.

[0164] In another embodiment, the score can be a function of the amount of suPAR decreasing and the amount of IP-10 decreasing, where a score below a predetermined level indicates severe infection, where the predetermined level is based on the amounts of both suPAR and IP-10 in subjects without severe infection.

[0165] The score may be a monotonically decreasing function of the amount of suPAR and the amount of IP-10. In one embodiment, the function is linear.

[0166] In one embodiment, the score is based on the ratio of suPAR to IP-10.

[0167] In yet another embodiment, the score is based on the ratio of IP-10 to suPAR.

[0168] In one embodiment, the severity assessment is performed by generating a score based on the amount of both suPAR and IL-6 (i.e., the combination of suPAR and IL-6), which refers to any mathematical combination of suPAR and IL-6.

[0169] In one embodiment, the score is a function of increasing amounts of suPAR and increasing amounts of IL-6, where a score above a predetermined level indicates severe infection, where the predetermined level is based on the amounts of both suPAR and IL-6 in subjects without severe infection.

[0170] The score is a monotonically increasing function of the amount of suPAR and may be a monotonically increasing function of the amount of IL-6. In one embodiment, the function is linear.

[0171] In another embodiment, the score can be a function of the amount of suPAR decreasing and the amount of IL-6 decreasing, where a score below a predetermined level indicates severe infection, where the predetermined level is based on the amounts of both suPAR and IL-6 in subjects without severe infection.

[0172] The score may be a monotonically decreasing function of the amount of suPAR and the amount of IL-6. In one embodiment, the function is linear.

[0173] In one embodiment, the score is based on the ratio of suPAR to IL-6.

[0174] In yet another embodiment, the score is based on the ratio of IL-6 to suPAR.

[0175] The predetermined level in any embodiment of the invention may be, but is not limited to, a reference value obtained from a population study including subjects with known infection, subjects in the same or similar age range, subjects belonging to the same or similar ethnic group, or a reference value for an initial sample of subjects undergoing treatment for the infection. Such reference values may be derived from statistical analysis of populations and / or risk prediction data obtained from mathematical algorithms and calculated infection indices. Reference determinant indices may also be constructed and used using statistical and structural classification algorithms and other methods.

[0176] In one embodiment of the present invention, the predetermined level is the amount (i.e., level) of IP-10 (and / or IL-6) and suPAR (or a function of the amount) in a control sample derived from one or more subjects who do not have an infection (i.e., healthy and / or non-infectious individuals) or one or more subjects who do not have a severe infection (i.e., subjects who have a non-severe infection).

[0177] Creation of the score (i.e., construction of the clinical algorithm) can be performed using methods known in the art and are described in detail below.

[0178] According to certain embodiments, the levels of the proteins disclosed above (alone as single markers or in combination) are used to provide a risk assessment for a subject.

[0179] The term "risk assessment" refers to assigning to an individual a probability of experiencing a particular adverse event (e.g., death, hospitalization, or ICU admission), which preferably allows the individual to be classified into a particular risk category, such as high risk and low risk, or numerically based risk categories such as risk categories 1, 2, 3, etc.

[0180] The risk assessment may be performed in a hospital, for example, in an emergency department of a hospital, and may be part of a patient triage process, which may determine which patients to treat first based on the expression level of at least one of the proteins disclosed above.

[0181] Emergency departments (EDs) are increasingly overwhelmed by patients with both urgent and non-urgent issues. This leads to longer wait times, adverse outcomes, and an increase in dissatisfied patients, resulting in overcrowded ED waiting rooms. As a result, patients needing urgent care may not receive treatment in a timely manner, while patients with non-urgent issues may receive unnecessary, expensive, and non-essential treatment. Time to effective treatment is one of the key predictors of outcomes in various medical conditions. For these reasons, we propose utilizing the protein expression analysis of the present disclosure in a risk stratification system in EDs for the initial triage of medical patients.

[0182] Thus, for example, the proteins described herein (e.g., at least one of TSG-14, AGER, ANG-2, or ST2) can be used in conjunction with patient and resource allocation triage systems such as the Emergency Severity Index (ESI) or the Canadian Triage Acuity Scale (CTAS).

[0183] In another embodiment, the risk assessment is performed in the intensive care unit of a hospital.

[0184] Risk measures may be used to determine the course of patient management: they may assist in the selection of treatment priorities and decisions about the location of care delivery (i.e., outpatient versus inpatient management), as well as the early identification and adjustment of post-acute care needs.

[0185] If a patient is assessed as high risk, the management process is usually more aggressive than if the patient had not been assessed as high risk. Thus, treatment options such as mechanical ventilation, life support, catheterization, hemofiltration, invasive monitoring, sedation, intensive care admission, surgical intervention, last resort medications, and hospitalization may be selected that would otherwise not have been recommended if the patient had not been assessed as high risk.

[0186] The risk analysis can be performed with at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least nine parameters of the subject's clinical indices to provide a risk score based on the clinical indices.

[0187] In one embodiment, the risk analysis is performed with all parameters of the subject's clinical index.

[0188] Exemplary clinical indices include, but are not limited to, the Acute Physiology and Chronic Health Evaluation (APACHE II) to assess the likelihood of survival from the intensive care unit; the Simplified Acute Physiology (SAP) score; the Glasgow Coma Scale (GCS) as an assessment of the level of consciousness; the Sequential Organ Failure Assessment (SOFA) score as an assessment of organ function or organ failure rate; and the qSOFA (Quick Response Assessment) score in sepsis. These include the State of Origin of Flavor (SOFA) score (which identifies patients outside the ICU who are suspected of having an infection and are at high risk of in-hospital death); the CURB-65 score for pneumonia severity (which estimates mortality in community-acquired pneumonia and helps decide between inpatient and outpatient treatment); the National Early Warning Score (NEWS) (which determines the severity of a patient's illness and prompts critical care intervention); the Modified Early Warning Score for Clinical Deterioration (MEWS) (which determines the severity of a patient's illness); the National Early Warning Score (NEWS)2 (which determines the severity of a patient's illness and prompts critical care intervention); the Apgar assessment as a vital assessment for neonates; the Pain Perception Profile; the Visual Analogue Scale (VAS); quality of life assessment measures such as the EDLQ and SF36; depression scales such as the CES-D; the Impact of Events Scale (IES); or thrombosis risk assessment or propensity, or a combination of these.

[0189] According to one embodiment, the clinical indices are NEWS, NEWS 2, and MEWS.

[0190] According to certain embodiments, the clinical index is the Acute Physiology and Chronic Health Evaluation II (APACHE II). This system is an example of a disease severity classification system that uses a point score based on the initial values of 12 routine physiological measurements, including temperature, mean arterial pressure, pH, heart rate, respiratory rate, AaDO2 or PaO2, sodium, potassium, creatinine, hematocrit, white blood cell count, and the Glasgow Coma Scale. These parameters are measured during the first 24 hours after admission and are utilized in addition to information about previous health conditions (recent surgery, history of severe organ failure, immunosuppressive status), and baseline demographics such as age. An integer score ranging from 0 to 71 is calculated, with higher scores corresponding to more severe disease and a higher risk of death.

[0191] Many other predictive models have been developed for various purposes contemplated by the present invention. Such predictive models are used to determine population-based outcome risk. By way of example, and not limitation, a partial list of predictive models includes SAPS II expansion and predicted mortality, SAPS II and predicted mortality, APACHE I-IV and predicted mortality, SOFA (Sequential Organ Failure Assessment), MODS (Multiple Organ Dysfunction Score), ODIN (Organ Dysfunction and / or Infection), MPM (Probability of Mortality Model), MPM II LODS (Logistic Organ Dysfunction System), TRIOS (3-day Recalibrated ICU Outcome Score), EUROSCORE (Cardiac Surgery), ONTARIO (Cardiac Surgery), Parsonnet Score (Cardiac Surgery), System 97 Score (Cardiac Surgery), QMMI Score (Coronary Artery Surgery), Early Mortality Risk in Coronary Revascularization Surgery, MPM in Cancer Patients, POSSUM (Physiological and Operative Severity Score for Mortality and Morbidity Enumeration) (Surgery, Any), Portsmouth POSSUM (Surgery, Any), I RISS score: graft failure after lung transplantation, Glasgow Coma Scale, ISS (Injury Severity Score), RTS (Modified Trauma Score), TRISS (Trauma Severity Score), ASCOT (Trauma Severity Characterization), 24h-ICU Trauma Score, TISS (Therapeutic Intervention Scoring System), TISS-28 (Simplified TISS), PRISM (Pediatric RISk of Mortality), P-MODS (Pediatric Multiple Organ Dysfunction Score), DORA (Dynamic Objective Risk Assessment), PELOD (Pediatric Logistic Organ Dysfunction), PIM These include the Pediatric Mortality Index II (PMS), Pediatric Mortality Index (PIM), CRIB II (Clinical Risk Index for Infants), CRIB (Clinical Risk Index for Infants), SNAP (Neonatal Acute Physiology Score), SNAP-PE (SNAP Perinatal Extension), SNAP II and SNAPPE II, MSSS (Meningococcal Septic Shock Score), GMSPS (Glasgow Meningococcal Sepsis Prognostic Score), Rotterdam Score (Meningococcal Septic Shock), Raimondi Coma Scale for Children, Simpson & Reilly Coma Scale for Children, and Pediatric Trauma Score, Rochester Criteria, Philadelphia Criteria, and Milwaukee Criteria, the last three of which are specific for neonatal fever / sepsis.Of course, the above list of metrics for quality of care relative to patient health risk is not limiting and various other scores and ratings known in the medical field can be used.

[0192] Classification of subjects into subgroups (e.g., severe / not severe; high risk, low risk, etc.) as performed in embodiments of the present invention is preferably performed with an acceptable level of clinical or diagnostic accuracy. An "acceptable level of diagnostic accuracy" is defined herein as a test or assay (such as the test used in some embodiments of the present invention) having an AUC (area under the ROC curve of a test or assay) of 0.60 or greater, desirably 0.65 or greater, more desirably 0.70 or greater, preferably 0.75 or greater, more preferably 0.80 or greater, and most preferably 0.85 or greater.

[0193] By "very high diagnostic accuracy" is meant a test or assay having an AUC (area under the ROC curve of the test or assay) of 0.75 or greater, 0.80 or greater, desirably 0.85 or greater, more desirably 0.875 or greater, preferably 0.90 or greater, more preferably 0.925 or greater, and most preferably 0.95 or greater.

[0194] Alternatively, the method may be used to determine whether a condition is severe or not severe with an overall accuracy of 75% or greater, and more preferably with an overall accuracy of 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater.

[0195] Alternatively, the method predicts correct management or treatment if the MCC is greater than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0.

[0196] Clinical decisions can be made based on the classification of the infection.

[0197] According to some embodiments of the invention, the method further comprises informing the subject of the results of the classification.

[0198] As used herein, the phrase "informing a subject" refers to advising a subject that they should seek an appropriate treatment regimen based on the diagnosis.

[0199] Once a diagnosis is determined, the result can be recorded in the subject's medical file, which may aid in selecting a treatment regimen and / or determining the subject's prognosis.

[0200] Examples of clinical decisions that may be made in light of the severity classification include oxygen therapy, noninvasive ventilation, mechanical ventilation, invasive monitoring, drugs of last resort, sedation, intensive care admission, admission to a step-down unit, surgical intervention, hospitalization, antiviral agents, antibiotic treatment, antiviral regimens, antifungal agents, immunoglobulin treatment, glucocorticoid therapy, extracorporeal membrane oxygenation, and renal replacement therapy.

[0201] An example of a clinical decision that may be made based on a non-severe classification is isolation.

[0202] The antiviral agent may be selected from the group consisting of remdesivir, ribavirin, adefovir, tenofovir, acyclovir, brivudine, cidofovir, fomivirsen, foscarnet, ganciclovir, penciclovir, amantadine, rimantadine, zanamivir, molnupiravir, paxlovid, oseltamivir phosphate, ivermectin, interferon beta, interferon alpha, interferon lambda, nitazoxanide, hydroxychloroquine, peramivir, boroxavir marbocil, entecavir, lamivudine, and telbivudine.

[0203] Treatment with plasma from surviving infected people and / or the anti-HIV drugs lopinavir and ritonavir, as well as chloroquine, are also being considered.

[0204] Specific examples of drugs routinely used to treat COVID-19 include, but are not limited to, lopinavir / ritonavir, nucleoside analogues, neuraminidase inhibitors, remdesivir, polypeptides (EK1), abidol, RNA synthesis inhibitors (TDF, 3TC, etc.), anti-inflammatory drugs (hormones and other molecules, etc.), monoclonal antibodies (Ixagevimab and silgavimab (Evusheld), adrecizumab, procizumab, tixagevimab and silgavimab (Evusheld)), and traditional Chinese medicines such as ShuFengJieDu Capsules and Lianhuaqingwen Capsules, which may be options for drug treatment of COVID-19.

[0205] If a bacterial infection is determined, the subject may be treated with an antibiotic or other antibacterial agent.

[0206] As used herein, the term "antibiotic" refers to a group of chemicals isolated from natural sources or derived from antibiotic preparations isolated from natural sources that have the ability to inhibit the growth of or destroy bacteria. Examples of antibiotic preparations include, but are not limited to, amikacin; amoxicillin; ampicillin; azithromycin; azlocillin; aztreonam; carbenicillin; cefaclor; cefepime; cefetamet; cefinetazole; cefixime; cefonicid; cefoperazone; cefotaxime; cefotetan; cefoxitin; cefpodoxime; cefprozil; cefsulodin; ceftazidime; ceftizometrium; ceftizometrium. Oximethicone; Ceftriaxone; Cefuroxime; Cephalexin; Cephalothin; Cethromycin; Chloramphenicol; Cinoxacin; Ciprofloxacin; Clarithromycin; Clindamycin; Cloxacillin; Amoxicillin clavulanate (Co-amoxiclavuanate); Dalbavancin; Daptomycin; Dicloxacillin; Doxycycline; Enoxacin; Erythromycin estolate; Erythromycin Ethylsuccinate; Erythromycin glucoheptonate; Erythromycin lactobionate; Erythromycin stearate; Erythromycin; Fidaxomicin; Fleroxacin; Gentamicin; Imipenem; Kanamycin; Lomefloxacin; Loracarbef; Methicillin; Metronidazole; Mezlocillin; Minocycline; Mupirocin; Nafcillin; Nalidixic acid; Netilmicin; Nitrofurantoin; Norfloxacin These include fluoxacin; ofloxacin; oxacillin; penicillin G; piperacillin; retapamulin; rifaxamine, rifampin; roxithromycin; streptomycin; sulfamethoxazole; teicoplanin; tetracycline; ticarcillin; tigecycline; tobramycin; trimethoprim; vancomycin; a combination of piperacillin and tazobactam; and various salts, acid, base, and other derivatives thereof.Antibacterial antibiotic agents include, but are not limited to, aminoglycosides, carbacephems, carbapenems, cephalosporins, cephamycins, fluoroquinolones, glycopeptides, lincosamides, macrolides, monobactams, penicillins, quinolones, sulfonamides, and tetracyclines.

[0207] Antibacterial agents also include antibacterial peptides, including, but not limited to, abaecins, andropins, apidaecins, bombinins, brevinins, buforin II, CAP18, cecropins, ceratotoxins, defensins, dermaseptins, dermcidins, drosomycins, esculentin, indolicidin, LL37, magainins, maximum H5, melittin, moricins, prophenins, protegrins, and / or tachyplesins.

[0208] Once a classification is made, additional tests may be performed to confirm the results or to further classify the infectious agent.

[0209] Examples of such tests include PCR analysis, sequencing analysis, viral culture, antibody testing, or antigen testing.

[0210] A "subject" in the context of the present invention can be a mammal (e.g., a human, dog, cat, horse, cow, sheep, pig, or goat). According to another embodiment, the subject is a bird (e.g., a chicken, turkey, duck, or goose). According to particular embodiments, the subject is a human. The subject can be male or female. The subject can be an adult (e.g., over 18, over 21, or over 22 years of age) or a child (e.g., under 18, under 21, or under 22 years of age). In another embodiment, the subject is an adolescent (12-21 years of age), an infant (29 days to under 2 years of age), or a neonatal (first 28 days of life). In yet another embodiment, the subject is over 60, over 70, or even over 80 years of age.

[0211] The subject of this aspect of the invention may have symptoms of an infection.

[0212] Exemplary symptoms include, but are not limited to, fever, headache, cough, runny nose, chills, muscle aches, loss of taste, and / or loss of smell.

[0213] According to certain embodiments, measurements of the determinants (i.e., proteins) described herein are made within 24 hours of the onset of symptoms, within 36 hours of the onset of symptoms, within 48 hours of the onset of symptoms, within 72 hours of the onset of symptoms, within 96 hours of the onset of symptoms, within 1 week of the onset of symptoms, or within 2 weeks of the onset of symptoms.

[0214] According to another embodiment, the subject is asymptomatic.

[0215] It will be understood that a subject may or may not be infectious, whether symptomatic or asymptomatic.

[0216] In one embodiment, the subject does not have a chronic non-infectious disease, such as cancer, a chronic immune disorder, or a chronic inflammatory disorder.

[0217] In another embodiment, the subject does not have coronary artery disease.

[0218] According to one embodiment, the subject is suspected of suffering from (or confirmed to suffer from) SIRS without infection, sepsis, severe sepsis, or septic shock.

[0219] In one embodiment, the subject is hospitalized.

[0220] In another embodiment, the subject is not hospitalized.

[0221] In any of the aspects disclosed herein, the terms "measuring" or "measurement" or alternatively "detecting" or "detection" refer to assessing the presence, absence, quantity, or amount (which may be an effective amount) of a determinant in a clinical sample or a sample derived from a subject, including the derivation of a qualitative or quantitative concentration level of such a determinant.

[0222] Methods for measuring levels of protein determinants are well known in the art and include, for example, immunoassays based on antibodies, aptamers, or molecular imprints against the protein.

[0223] Protein determinants can be detected in any suitable manner, but are typically detected by contacting a sample from a subject with an antibody that binds to the protein 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, and detecting the reaction product may be carried out by any suitable immunoassay.

[0224] In one embodiment, an antibody that specifically binds to a determinant is conjugated (directly or indirectly) to a signal-generating label, including, but not limited to, a radioactive label, an enzyme label, a hapten, a reporter dye, or a fluorescent label.

[0225] Immunoassays performed according to some embodiments of the present invention can be homogeneous or heterogeneous. In homogeneous assays, the immunological reaction typically involves a specific antibody (e.g., an anti-determinant antibody), a labeled analyte, and the sample of interest. The signal resulting from the label is altered, directly or indirectly, by the antibody binding to the labeled analyte. Both the immunological reaction and the detection of its extent can be carried out in a homogeneous solution. Possible immunochemical labels include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

[0226] In a heterogeneous assay approach, the reagents are typically the sample, the antibody, and a means for generating a detectable signal. The sample may be as described above. The antibody may be immobilized on a support such as beads (such as protein A and protein G agarose beads), a plate, a pipette tip, or a slide, and contacted with a specimen suspected of containing the antigen in a liquid phase.

[0227] The support is then separated from the liquid phase, and either the support phase or the liquid phase is examined for a detectable signal using a means for generating such a signal. The signal is related to the presence of the analyte in the sample. Means for generating a detectable signal include the use of radioactive, fluorescent, or enzymatic labels. For example, if the antigen to be detected contains a second binding site, an antibody that 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 oligonucleotide, immunoblotting, immunofluorescence, immunoprecipitation, chemiluminescence, electrochemiluminescence (ECL), or enzyme-linked immunoassays.

[0228] Those of skill in the art will be familiar with the many specific immunoassay formats and variations thereof that may be useful in carrying out the methods disclosed herein. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); also Skold et al., “Methods for Modulating Ligand-Receptor Interactions and their Applications” (US Pat. No. 4,727,022), Forrest et al., “Immunoassay of Antigens” (US Pat. No. 4,659,678), David et al., “Immunometric Assays Using Monoclonal Antibodies” (US Pat. No. 4,376,110), Litman et al., “Macromolecular Environment Control in Specific Receptor Assays” (US Pat. No. 4,275,149), Maggio et al., “Reagents and Method Employing Channeling” (US Pat. No. See, e.g., U.S. Pat. No. 4,233,402, and Boguslaski et al., "Heterogenous Specific Binding Assay Employing a Coenzyme as Label" (U.S. Pat. No. 4,230,767). Determinants can also be detected by antibodies using flow cytometry. Those skilled in the art are familiar with flow cytometry techniques (Shapiro 2005) that can be useful in carrying out the methods disclosed herein. These include, but are not limited to, Cytokine Bead Array (Becton Dickinson) and Luminex technology.

[0229] The antibodies can be coupled to a solid support suitable for diagnostic assays (e.g., beads (magnetic beads, protein A agarose or protein G agarose, microspheres), latex or polystyrene plates, slides, pipette tips, or wells) according to known techniques, such as passive coupling. The antibodies described herein can also be coupled to a radiolabel (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).

[0230] In certain embodiments, the antibodies of the present invention comprise monoclonal antibodies.

[0231] In another embodiment, the antibodies of the present invention comprise polyclonal antibodies.

[0232] Suitable sources of antibodies for detecting determinants include, 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, Immunometrics, Immunostar, Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory, KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, Lee Laboratories, Lifescreen, Maine Biotechnology Services, Mediclone, and 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.Commercially available sources include, but are not limited to, Promega Corporation, Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D Systems, Repligen, Research Diagnostics, Roboscreen, Santa Cruz Biotechnology, Seikagaku America, Serological Corporation, Serotec, Sigma-Aldrich, 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, one of ordinary skill in the art can routinely generate antibodies to any of the polypeptide determinants described herein.

[0233] The presence of the label can be detected visually, or the detection reagent label can be detected using a detector that monitors a specific probe or probe combination. Typical detectors include spectrophotometers, phototubes, and photodiodes, microscopes, scintillation counters, cameras, film, and the like, as well as combinations thereof. Those skilled in the art are familiar with many suitable detectors that are widely available from various commercial sources and may be useful in carrying out the methods disclosed herein. Generally, optical images of the substrate containing the bound label moieties are digitized for subsequent computer analysis. For more information, see The Immunoassay Handbook [The Immunoassay Handbook. Third Edition. 2005].

[0234] Suitable antibodies for the specific detection of ST2 include recombinant rabbit anti-human monoclonal antibody against ST2 (ab259721) (Abcam), mouse anti-human monoclonal antibody against ST2 / IL-33R antibody, clone number 97203, (MAB523) (biotechne® R&D Systems), IL-33R (ST2) mouse anti-human monoclonal antibody against ST2 (IL-33R), clone hIL33Rcap, eBioscience™ catalog number 17-9338-42 (Invitrogen).

[0235] Suitable antibodies for the specific detection of ANG-2 include mouse anti-human monoclonal antibody to angiopoietin-2, clone number 85816, (MAB098) (biotechne® R&D Systems), recombinant rabbit anti-human monoclonal antibody to angiopoietin-2 / ANG-2 (ab285368) (Abcam), and rabbit anti-human polyclonal antibody to angiopoietin-2, catalog number PA5-27297 (Invitrogen).

[0236] Suitable antibodies for the specific detection of AGER include recombinant rabbit anti-human monoclonal antibody against AGER / RAGE (ab289826) (Abcam); mouse anti-human monoclonal antibody against AGER / RAGE, clone number 176902 (MAB11451) (Biotechne® R&D Systems); and rabbit anti-human polyclonal antibody against AGER / RAGE (TA346145) (OriGene).

[0237] Antibodies suitable for the specific detection of TSG-14 include recombinant rabbit anti-human monoclonal antibody against TSG-14 / pentraxin 3 / PTX3 antibody (ab242624) (abcam); mouse anti-human monoclonal antibody against TSG-14 / pentraxin 3, clone number 247911, (MAB1826) (biotechne® R&D Systems); and rabbit anti-human polyclonal antibody against TSG-14 / PTX3 (SAB4502545) (Sigma-Aldrich®).

[0238] Antibodies suitable for specific detection of MR-proADM include a mouse anti-human monoclonal antibody against MR-Pro ADM, SAB4200700 (Sigma-Aldrich®), and a rabbit anti-human polyclonal antibody against pro-adrenomedullin (45-92), (TA364336) (OriGene). Measurement of MR-proADM can be an alternative to measuring adrenomedullin (ADM). MR-proADM is a 48-amino acid fragment cleaved from the pro-ADM molecule at a 1:1 ratio with adrenomedullin.

[0239] Antibodies suitable for specific detection of IL-6 include, but are not limited to, mouse anti-human monoclonal antibody to IL-6 (MAB2063) (biotechne® R&D Systems), mouse anti-human monoclonal antibody to IL-6, clone 5IL6, catalog number M620 (Invitrogen), and mouse anti-human monoclonal antibody to IL-6, clone OTI3G9 (TA500067) (OriGene).

[0240] Suitable antibodies for detecting IL-10 include recombinant rabbit anti-human monoclonal antibody to IL-10 (ab244835) (abcam); mouse anti-human monoclonal antibody to IL-10, clone number 127107, (MAB2172) (biotechne® R&D Systems); rat anti-human monoclonal antibody to IL-10, clone JES3-9D7, eBioscience™, catalog number 14-7108-81 (Invitrogen).

[0241] Antibodies suitable for measuring TRAIL include, but are not limited to, mouse monoclonal (55B709-3) IgG (Thermo Fisher Scientific); mouse monoclonal (2E5) IgG1 (Enzo Lifesciences); mouse monoclonal (2E05) IgG1; mouse monoclonal (M912292) IgG1 kappa (My BioSource); mouse monoclonal (IIIF6) IgG2b; mouse monoclonal (2E1-1B9) IgG1 (EpiGentek); mouse monoclonal (RIK-2) IgG1, kappa (BioLegend); mouse monoclonal M181 IgG1 (Immunex Corporation); mouse monoclonal VI10E IgG2b (Novus Biologicals); mouse monoclonal MAB375 IgG1 (R&D Systems); mouse monoclonal MAB687 IgG1 (R&D Systems); mouse monoclonal HS501 IgG1 (Enzo Lifesciences); mouse, monoclonal clone 75411.11 mouse IgG1 (Abcam); mouse, monoclonal T8175-50 IgG (X-Zell Biotech Co); mouse, monoclonal 2B2.108 IgG1; mouse, monoclonal B-T24 IgG1 (Cell Sciences); mouse, monoclonal 55B709.3 IgG1 (Thermo Fisher Scientific); mouse, monoclonal D3 IgG1 (Thermo Fisher Scientific); goat, polyclonal C19 IgG; rabbit, polyclonal H257 IgG (Santa Cruz Biotechnology); mouse, monoclonal 500-M49 IgG; mouse, monoclonal 05-607 IgG; mouse, monoclonal B-T24 IgG1 (Thermo Fisher Scientific); rat, monoclonal (N2B2), IgG2a, kappa (Thermo Fisher Scientific); mouse monoclonal (1A7-2B7), IgG1 (Genxbio); mouse monoclonal (55B709).3), IgG (Thermo Fisher Scientific); mouse, monoclonal B-S23*IgG1 (Cell Sciences), human TRAIL / TNFSF10 MAb (clone 75411), mouse IgG1 (R&D Systems); human TRAIL / TNFSF10 MAb (clone 124723), mouse IgG1 (R&D Systems), and human TRAIL / TNFSF10 MAb (clone 75402), mouse IgG1 (R&D Systems).

[0242] Suitable antibodies for measuring IP-10 include, but are not limited to, mouse anti-human CXCL10 (IP-10) monoclonal antibody (catalog number 524401) (BioLegend), rabbit anti-human CXCL10 (IP-10) polyclonal antibody (ab9807) (Abcam), mouse anti-human CXCL10 (IP-10) monoclonal antibody (4D5) (MCA1693) (Bio-Rad), goat anti-human CXCL10 (IP-10) monoclonal antibody (PA5-46999) (Invitrogen), and mouse anti-human CXCL10 (IP-10) monoclonal antibody (MA5-23819) (Invitrogen).

[0243] Suitable antibodies for measuring CRP include, but are not limited to, rabbit anti-human C-reactive protein / CRP polyclonal antibody (ab31156) (Abcam), 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), and mouse anti-human C-reactive protein / CRP monoclonal antibody (C1688) (MilliporeSigma).

[0244] Continuous and discontinuous epitopes present in the protein suPAR and its cleavage products can be used to monitor their presence and concentration in biological fluids by immunodetection using monoclonal or polyclonal antibodies. Antibodies targeting accessible epitopes common to suPAR and its cleavage products (e.g., D2D3) can be used to detect both suPAR and its cleavage products in biological fluids. Because there is a one-to-one relationship between suPAR and its cleavage products, antibodies targeting epitopes common to both full-length suPAR and D2D3 cleavage products will simultaneously measure suPAR levels directly and indirectly. That is, a measured value of, for example, 3 ng / ml in an assay is considered to indicate a suPAR level of 3 ng / ml, even if some of the detected protein is D2D3 cleavage product. Therefore, in the context of an assay, "suPAR" refers to full-length suPAR and its cleavage product, D2D3. The term D2D3 refers to any suPAR-derived fragment that corresponds to the 84-277 region of suPAR, has an N-terminus in the 84-92 amino acid region of suPAR, and a C-terminus corresponding to the C-terminus of suPAR (amino acid 277), e.g., 84-277, 88-277, 90-277, and 92-277.

[0245] SuPAR levels can be measured in body fluids by the methods described in WO 2008 / 077958, the contents of which are incorporated herein by reference.

[0246] More specifically, suPAR levels can be measured by the following ELISA assay: Nunc Maxisorp ELISA plates (Nunc, Roskilde, Denmark) are coated with monoclonal rat anti-suPAR antibody (VG-1, ViroGates A / S, Copenhagen, Denmark, 3 pg / ml, 100 μg / well) overnight at 4° C. The plates are blocked with PBS buffer (1% BSA, 0.1% Tween 20) at room temperature for 1 hour and washed three times with PBS buffer (0.1% Tween 20). 85 μL of dilution buffer (100 mM phosphate, 97.5 mM NaCl, 10 g / L bovine serum albumin (BSA, fraction V, Roche Diagnostics GmbH Penzberg, Germany), 50 U / mL heparin sodium salt (Sigma Chemical Co., St. Louis, MO), 0.1% (v / v) Tween 20, pH 7.4) containing 1.5 pg / mL HRP-conjugated mouse anti-suPAR antibody (VG-2-HRP, ViroGates) and 15 μL of plasma (or serum or urine) sample was added to an ELISA plate in duplicate. After incubation at 37°C for 1 hour, the plate was washed 10 times with PBS buffer (0.1% Tween 20), and 100 μL of HRP substrate (Substrate Reagent Pack, R&D Systems, Minneapolis, MN) was added to each well. After 30 minutes, the color reaction is stopped using 50 μL of 1M H 2 SO 4 per well and measured at 450 nm.

[0247] Additionally, suPAR can be measured in biological fluids using commercially available CE / IVD-approved assays (e.g., the suPARnostic® product line) according to the manufacturer's instructions. In the TRIAGE III study, suPAR was quantified using the suPARnostic Quick Triage lateral flow assay.

[0248] SuPAR levels can be assayed, for example, using the suPARnostic® Autoflex ELISA test sold by ViroGates A / S (Banevaenget 13, DK-3460 Birkerpd, Denmark). Alternatively, suPAR levels can be measured using proteomic analytical techniques (such as Western blot, Luminex, MALDI-TOF, HPLC, or Genspeed devices) and automated immunoanalyzer platforms (such as Bayer Centaur, Abbott Architect, Abbott AxSym, Roche CO BAS, and Axis Shield Afinion), or using turbidimetric assays (such as Roche's suPARnostic® Turbilatex, Cobas cl11, Cobas c501 / 2+c701 / 2, or Siemens' ADVIA XPT or Centaur, or Abbott Architect).

[0249] The suPAR level in blood can be measured directly from a blood sample, serum, plasma, or urine. Anticoagulated plasma (e.g., EDTA or citrate plasma) is preferred. When the biological sample is urine, the measurement can be based on the subject's urinary suPAR / creatinine value. This value is known to be highly correlated with the suPAR concentration in the plasma sample of the same subject. Therefore, a urine sample can be used to measure suPAR, and in this case, the measured level in urine is normalized to the protein content (e.g., creatinine). These normalized values can be used as markers for the purposes of the present invention.

[0250] In the context of the present invention, a "sample" refers to a biological sample taken from a subject, and examples may include, but are not limited to, whole blood, serum, plasma, saliva, mucus, exhaled breath, urine, CSF, sputum, sweat, stool, hair, semen, biopsy, nasal discharge, tissue biopsy, cytological sample, platelets, reticulocytes, white blood cells, epithelial cells, or whole blood cells.

[0251] In certain embodiments, the sample is a blood sample, e.g., serum, plasma, or whole blood. The sample can be a venous sample, a peripheral blood mononuclear cell sample, or a peripheral blood sample. In one embodiment, the sample contains white blood cells, including, for example, granulocytes, lymphocytes, and / or monocytes. In one embodiment, red blood cells are removed from the sample.

[0252] The subject typically suffers from a bacterial or viral infection.

[0253] The bacterial or viral infection may be an acute or chronic infection.

[0254] Chronic infections are infections that develop slowly and persist for a long time. Viruses that can cause chronic infections include hepatitis C and HIV. In acute infections, the immune system often produces IgM+ antibodies against the infectious agent, whereas the chronic phase of chronic infections is usually characterized by IgM- / IgG+ antibodies. Furthermore, acute infections often cause immune-mediated necrotic processes, whereas chronic infections often cause inflammation-mediated fibrotic processes and scar formation (e.g., hepatitis C in the liver). Therefore, acute and chronic infections may trigger different underlying immunological mechanisms.

[0255] According to certain embodiments, the infection diagnosed is an acute infection.

[0256] Exemplary viral diseases that can be diagnosed by the methods described herein are summarized in Table 2.

[0257] [Table 2]

[0258] According to a specific embodiment, the viral disease is COVID-19.

[0259] Exemplary virus families are summarized in Table 3 below.

[0260] [Table 3]

[0261] According to another specific embodiment, the virus is human metapneumovirus, bocavirus, or enterovirus.

[0262] According to another specific embodiment, the virus is RSV, Flu A, Flu B, HCoV, or SARS-Cov-2.

[0263] Examples of coronaviruses include human coronavirus 229E, human coronavirus OC43, SARS-CoV, HCoV NL63, HKU1, MERS-CoV, and SARS-CoV-2.

[0264] According to a particular embodiment, the coronavirus is SARS-CoV-2.

[0265] Bacterial infections that may be determined according to embodiments of the present invention may be caused by gram-positive bacteria, gram-negative bacteria, or atypical bacteria.

[0266] The term "Gram-positive bacteria" refers to bacteria that stain dark blue with the Gram stain. Gram-positive bacteria are able to retain crystal violet staining due to the high amount of peptidoglycan in their cell walls.

[0267] The term "Gram-negative bacteria" refers to bacteria that do not retain crystal violet dye in a Gram staining protocol.

[0268] The term "atypical bacteria" refers to bacteria that do not fall into one of the classical "gram" groups. They are usually, but not always, intracellular bacterial pathogens. They include, but are not limited to, Mycoplasma species, Legionella species, Rickettsia species, and Chlamydia species.

[0269] The inventors have further discovered a unique set of immune proteins that are specific markers of viral or bacterial infection, markers of infection in general, and / or markers of severity.

[0270] Thus, according to another aspect of the present invention there is provided a method of distinguishing between a viral and a bacterial infection in a subject, comprising the steps of: (a) measuring the expression level of a combination of proteins described in Group 1 or Group 2; and (b) determining whether the infection is bacterial or viral based on said expression level. A method is provided, comprising:

[0271] According to yet another aspect of the present invention, there is provided a method of diagnosing an infectious disease in a subject, comprising: (a) measuring the expression level of a combination of proteins, wherein said combination is described in Group 5 or Group 6; and (b) determining whether the disease is infectious or non-infectious based on said expression level. A method is provided, comprising:

[0272] According to another aspect of the present invention, there is provided a method for determining the severity of an infectious disease in a subject, comprising: (a) measuring the expression level of a combination of proteins, wherein said combination is described in Group 3 or Group 4; and (b) determining the severity based on the expression level; A method is provided, comprising:

[0273] The concentrations of each of the above-identified polypeptides may be combined (e.g., by a predetermined mathematical function) to calculate a score, and the score may be compared to a predetermined reference value, as further described herein below.

[0274] Markers in each combination shown in Groups 1 or 2 are up- or down-regulated in bacterial disease (compared to healthy or virally infected patients). The direction of marker variation is summarized in Table 5.

[0275] Markers in each combination listed in Group 3 or Group 4 are up- or down-regulated in severe disease (compared to non-severely infected patients). The direction of marker variation is listed in Table 6.

[0276] The markers in each combination listed in Groups 5 and 6 are up- or down-regulated in infectious disease (compared to healthy patients). The direction of marker variation is shown in Table 7.

[0277] For viral disease classification, in one embodiment at least one protein in Table 5 is measured and at least one protein in Table 6 is measured.

[0278] In one embodiment, the classification is performed by generating a score based on the amount of proteins listed in the combinations set forth in Group 1, Group 2, Group 3, Group 4, Group 5, or Group 6.

[0279] Specific contemplated combinations described in Group 1 include PLA2G2A and TRAIL and IP-10 and CRP; RNASE3 and TRAIL and IP-10 and CRP; TGFA and TRAIL and IP-10 and CRP; AZU1 and TRAIL and IP-10 and CRP; CD177 and TRAIL and IP-10 and CRP; CLEC4D and TRAIL and IP-10 and CRP; CEACAM8 and TRAIL and IP-10 and CRP; HGF and TRAIL and IP-10 and CRP; VWA1 and TRAIL and IP-10 and CRP. P; PRTN3 and TRAIL and IP-10 and CRP; MMP9 and TRAIL and IP-10 and CRP; GH2 and TRAIL and IP-10 and CRP; LCN2 and TRAIL and IP-10 and CRP; CST7 and TRAIL and IP-10 and CRP; EPO and TRAIL and IP-10 and CRP; DEFA1_DEFA1B and TRAIL and IP-10 and CRP; LBP and TRAIL and IP-10 and CRP; OLR1 and TRAIL and IP-10 and CRP; LRIG1 and TRAIL and IP-10 and CRP; V STM1 and TRAIL and IP-10 and CRP; TNFRSFlOC and TRAIL and IP-10 and CRP; JCHAIN and TRAIL and IP-10 and CRP; C4BPB and TRAIL and IP-10 and CRP; MPO and TRAIL and IP-10 and CRP; TNFSF14 and TRAIL and IP-10 and CRP; QPCT and TRAIL and IP-10 and CRP; IL1B and TRAIL and IP-10 and CRP; ST6GAL1 and TRAIL and IP-10 and CRP; PGLYRP1 and TRAIL and IP-10 and CRP; PIGR and TRAIL and IP-10 and CRP; RARRES2 and TRAIL and IP-10 and CRP; LIF and TRAIL and IP-10 and CRP; CCL23 and TRAIL and IP-10 and CRP; SPARC and TRAIL and IP-10 and CRP; FCAR and TRAIL and IP-10 and CRP; VEGFA and TRAIL and IP-10 and CRP; VNN2 and TRAIL and IP-10 and CRP; CCL18 and TRAIL and IP-10 and CRP; CA4 and TRAIL and IP-10 and CRP;TIE1 and TRAIL and IP-10 and CRP; MCFD2 and TRAIL and IP-10 and CRP; TGFB1 and TRAIL and IP-10 and CRP; C2 and TRAIL and IP-10 and CRP; PADI2 and TRAIL and IP-10 and CRP; NID1 and TRAIL and IP-10 and CRP; ERP44 and TRAIL and IP-10 and CRP; CD34 and TRAIL and IP-10 and CRP; NAAA and TRAIL and IP-10 and CRP; PRTG and TRAIL and IP-10 and CRP; TPPP3 and and TRAIL and IP-10 and CRP; SEZ6L and TRAIL and IP-10 and CRP; CPM and TRAIL and IP-10 and CRP; MEGF10 and TRAIL and IP-10 and CRP; GDF2 and TRAIL and IP-10 and CRP; MCAM and TRAIL and IP-10 and CRP; ICOSLG and TRAIL and IP-10 and CRP; AOC3 and TRAIL and IP-10 and CRP; NCAN and TRAIL and IP-10 and CRP; CCL25 and TRAIL and IP-10 and CRP; IL22RA1 and TRAIL and IP-10 and CRP; HSD11B1 and TRAIL and IP-10 and CRP; APLP1 and TRAIL and IP-10 and CRP; CTSV and TRAIL and IP-10 and CRP; LAG3 and TRAIL and IP-10 and CRP; DKK3 and TRAIL and IP-10 and CRP; ITM2A and TRAIL and IP-10 and CRP; SCGB1A1 and TRAIL and IP-10 and CRP; LGALS4 and TRAIL and IP-10 and CRP; EPCAM and TRAIL and IP-10 and CRP; TNF SF11 and TRAIL and IP-10 and CRP; RBP2 and TRAIL and IP-10 and CRP; GPA33 and TRAIL and IP-10 and CRP; FABP1 and TRAIL and IP-10 and CRP; FGF23 and TRAIL and IP-10 and CRP; REG1B and TRAIL and IP-10 and CRP; REG1A and TRAIL and IP-10 and CRP; MMP12 and TRAIL and IP-10 and CRP; CHI3L1 and TRAIL and IP-10 and CRP; ULBP2 and TRAIL and IP-10 and CRP;PRL and TRAIL and IP-10 and CRP; DSC2 and TRAIL and IP-10 and CRP; CD300LG and TRAIL and IP-10 and CRP; TNFRSF9 and TRAIL and IP-10 and CRP; ROR1 and TRAIL and IP-10 and CRP; CCL4 and TRAIL and IP-10 and CRP; CD300LF and TRAIL and IP-10 and CRP; IL4R and TRAIL and IP-10 and CRP; IL17A and TRAIL and IP-10 and CRP; TNFRSF1A and TRAIL and IP-10 and CRP RP; CDON and TRAIL and IP-10 and CRP; CCN4 and TRAIL and IP-10 and CRP; TNFRSF4 and TRAIL and IP-10 and CRP; MMP13 and TRAIL and IP-10 and CRP; AKT1S1 and TRAIL and IP-10 and CRP; SCARF2 and TRAIL and IP-10 and CRP; VTA1 and TRAIL and IP-10 and CRP; TRAF2 and TRAIL and IP-10 and CRP; USP8 and TRAIL and IP-10 and CRP; HSPG2 and TRAIL and IP-10 and C RP; SIT1 and TRAIL and IP-10 and CRP; TSHB and TRAIL and IP-10 and CRP; ANGPT2 and TRAIL and IP-10 and CRP; CD163 and TRAIL and IP-10 and CRP; FLRT2 and TRAIL and IP-10 and CRP; CDH6 and TRAIL and IP-10 and CRP; TIMD4 and TRAIL and IP-10 and CRP; CD93 and TRAIL and IP-10 and CRP; CLEC10A and TRAIL and IP-10 and CRP; PRKRA and TRAIL and IP-10 and CRP P; CCL14 and TRAIL and IP-10 and CRP; COMP and TRAIL and IP-10 and CRP; THBS4 and TRAIL and IP-10 and CRP; NRP1 and TRAIL and IP-10 and CRP; TIMP1 and TRAIL and IP-10 and CRP; MAP4K5 and TRAIL and IP-10 and CRP; ITGB2 and TRAIL and IP-10 and CRP; AMIGO2 and TRAIL and IP-10 and CRP; ADAM15 and TRAIL and IP-10 and CRP; AXL and TRAIL and IP-10 and CRP;ESAM and TRAIL and IP-10 and CRP; CD6 and TRAIL and IP-10 and CRP; CD55 and TRAIL and IP-10 and CRP; TNXB and TRAIL and IP-10 and CRP; GPNMB and TRAIL and IP-10 and CRP; SELE and TRAIL and IP-10 and CRP; IL10RB and TRAIL and IP-10 and CRP; ADAM8 and TRAIL and IP-10 and CRP; CDH5 and TRAIL and IP-10 and CRP; VASN and TRAIL and IP-10 and CRP; COL1A 1 and TRAIL and IP-10 and CRP; ROBO1 and TRAIL and IP-10 and CRP; SCARF1 and TRAIL and IP-10 and CRP; ICAM1 and TRAIL and IP-10 and CRP; SELP and TRAIL and IP-10 and CRP; FCGR3B and TRAIL and IP-10 and CRP; OSCAR and TRAIL and IP-10 and CRP; BOC and TRAIL and IP-10 and CRP; PTPRM and TRAIL and IP-10 and CRP; GFRA2 and TRAIL and IP-10 and CRP; EDI L3 and TRAIL and IP-10 and CRP; APOH and TRAIL and IP-10 and CRP; ALCAM and TRAIL and IP-10 and CRP; DDR1 and TRAIL and IP-10 and CRP; HYOU1 and TRAIL and IP-10 and CRP; SIGLEC7 and TRAIL and IP-10 and CRP; ARID4B and TRAIL and IP-10 and CRP; CNTN3 and TRAIL and IP-10 and CRP; ENTPD6 and TRAIL and IP-10 and CRP; ARSA and TRAIL and IP-10 and CRP; CDH2 and TRAIL and IP-10 and CRP; B4GAT1 and TRAIL and IP-10 and CRP; IFNLR1 and TRAIL and IP-10 and CRP; PLXNB2 and TRAIL and IP-10 and CRP; ERBB3 and TRAIL and IP-10 and CRP; GLB1 and TRAIL and IP-10 and CRP; IRAG2 and TRAIL and IP-10 and CRP; PCSK9 and TRAIL and IP-10 and CRP; CASP1 and TRAIL and IP-10 and CRP; SSB and TRAIL and IP-10 and CRP;FOSB and TRAIL and IP-10 and CRP; GRN and TRAIL and IP-10 and CRP; ACP5 and TRAIL and IP-10 and CRP; UXS1 and TRAIL and IP-10 and CRP; SLC27A4 and TRAIL and IP-10 and CRP; INHBC and TRAIL and IP-10 and CRP; BID and TRAIL and IP-10 and CRP; ANGPTL1 and TRAIL and IP-10 and CRP; TPP1 and TRAIL and IP-10 and CRP; DNAJB8 and TRAIL and IP-10 and CRP; CB LN4 and TRAIL and IP-10 and CRP; ADAMTS13 and TRAIL and IP-10 and CRP; TACC3 and TRAIL and IP-10 and CRP; TXNRD1 and TRAIL and IP-10 and CRP; SMPDL3A and TRAIL and IP-10 and CRP; SMPD1 and TRAIL and IP-10 and CRP; GOLM2 and TRAIL and IP-10 and CRP; CDKN1A and TRAIL and IP-10 and CRP; C2CD2L and TRAIL and IP-10 and CRP; ST3GAL1 and TRAIL and IP- 10 and CRP; GGH and TRAIL and IP-10 and CRP; JUN and TRAIL and IP-10 and CRP; AIF1 and TRAIL and IP-10 and CRP; WARS and TRAIL and IP-10 and CRP; GBP4 and TRAIL and IP-10 and CRP; TINAGL1 and TRAIL and IP-10 and CRP; LGALS9 and TRAIL and IP-10 and CRP; EZR and TRAIL and IP-10 and CRP; TCN2 and TRAIL and IP-10 and CRP; SCRN1 and TRAIL and IP-10 and CRP ;PRDX1 and TRAIL and IP-10 and CRP;CTSC and TRAIL and IP-10 and CRP;LAMP3 and TRAIL and IP-10 and CRP;MSTN and TRAIL and IP-10 and CRP;TCL1A and TRAIL and IP-10 and CRP;SFTPA2 and TRAIL and IP-10 and CRP;CCL8 and TRAIL and IP-10 and CRP;SAMD9L and TRAIL and IP-10 and CRP;TRIM21 and TRAIL and IP-10 and CRP;AGER and TRAIL and IP-10 and CRP;NADK and TRAIL and IP-10 and CRP; TYMP and TRAIL and IP-10 and CRP; LAP3 and TRAIL and IP-10 and CRP; AGR2 and TRAIL and IP-10 and CRP; CCL7 and TRAIL and IP-10 and CRP; RRM2 and TRAIL; and IP-10 and CRP; BRK1 and TRAIL and IP-10 and CRP; DDX58 and TRAIL and IP-10 and CRP; CXCL11 and TRAIL and IP-10 and CRP; KRT19 and TRAIL and IP-10 and CRP.

[0280] Specific combinations listed in Group 2 include: PLA2G2A and FGF23; PLA2G2A and CCL20; PLA2G2A and EPO; PLA2G2A and REG1B; PLA2G2A and REG1A; PLA2G2A and CTSB; PLA2G2A and MMP12; PLA2G2A and CHI3L1; PLA2G2A and ULBP2; PLA2G2A and PRL; CSF3 and FGF23; CSF3 and CCL20; CSF3 and EPO; CSF3 and REG1B; CSF3 and REG1A; CSF3 and CTSB; CSF3 and MMP12; CSF3 and CHI3L1; CSF3 and ULBP2; CSF3 and PRL; MMP8 and FGF23; MMP8 and CCL20; MMP8 and EPO; MMP8 and REG1B; MMP8 and REG1A; MMP8 and CTSB; MMP8 and MMP12; MMP8 and CHI3L1; MMP8 and ULBP2; MMP8 and PRL; OSM and EPO; OSM and REG1B; OSM and REG1A; OSM and CTSB; OSM and MMP12; OSM and CHI3L1; OSM and ULBP2; OSM and PRL; RNASE3 and FGF23 ;RNASE3 and CCL20;RNASE3 and EPO;RNASE3 and REG1B;RNASE3 and REG1A;RNASE3 and CTSB;RNASE3 and MMP12;RNASE3 and CHI3L1;RNASE3 and ULBP2;RNASE3 and PRL;TGFA and FGF23;TGFA and CCL20;TGFA and EPO;TGFA and REG1B;TGFA and REG1A;TGFA and CTSB;TGFA and MMP12;TGFA and CHI3L1;TGFA and ULBP2;TGFA and PRL;IL-6 and FGF 23;IL-6 and CCL20;IL-6 and EPO;IL-6 and REG1B;IL-6 and REG1A;IL-6 and CTSB;IL-6 and MMP12;IL-6 and CHI3L1;IL-6 and ULBP2;IL-6 and PRL;AZU1 and FGF23;AZU1 and CCL20;AZU1 and EPO;AZU1 and REG1B;AZU1 and REG1A;AZU1 and CTSB;AZU1 and MMP12;AZU1 and CHI3L1;AZU1 and ULBP2;AZU1 and PRL;CD177 and FGF23;CD177 and CCL20;These include CD177 and EPO; CD177 and REG1B; CD177 and REG1A; CD177 and CTSB; CD177 and MMP12; CD177 and CHI3L1; CD177 and ULBP2; CD177 and PRL; CLEC4D and FGF23; CLEC4D and CCL20; CLEC4D and EPO; CLEC4D and REG1B; CLEC4D and REG1A; CLEC4D and CTSB; CLEC4D and MMP12; CLEC4D and CHI3L1; CLEC4D and ULBP2; CLEC4D and PRL.

[0281] Specific combinations of proteins in Group 3 include ADAM15 and TRAIL and IP-10 and CRP; AGER and TRAIL and IP-10 and CRP; AGR2 and TRAIL and IP-10 and CRP; AREG and TRAIL and IP-10 and CRP; ASAH2 and TRAIL and IP-10 and CRP; CBLN4 and TRAIL and IP-10 and CRP; CCL17 and TRAIL and IP-10 and CRP; CCL24 and TRAIL and IP-10 and CRP; CCL8 and TRAIL and IP-10 and CRP; CD1 C and TRAIL and IP-10 and CRP; CDH5 and TRAIL and IP-10 and CRP; CDON and TRAIL and IP-10 and CRP; CRTAC1 and TRAIL and IP-10 and CRP; CTSL and TRAIL and IP-10 and CRP; DDX58 and TRAIL and IP-10 and CRP; DSC2 and TRAIL and IP-10 and CRP; EZR and TRAIL and IP-10 and CRP; FBP1 and TRAIL and IP-10 and CRP; FCGR3B and TRAIL and IP-10 and CRP; GRPEL1 and and TRAIL and IP-10 and CRP; IL-10 and TRAIL and IP-10 and CRP; KRT18 and TRAIL and IP-10 and CRP; MATN3 and TRAIL and IP-10 and CRP; MPHOSPH8 and TRAIL and IP-10 and CRP; NADK and TRAIL and IP-10 and CRP; P4HB and TRAIL and IP-10 and CRP; PLA2GA2 and TRAIL and IP-10 and CRP; POLR2F and TRAIL and IP-10 and CRP; PQBP1 and TRAIL and IP-10 and CRP; PTS and TRAIL and IP-10 and CRP; QPCT and TRAIL and IP-10 and CRP; REG1A and TRAIL and IP-10 and CRP; REG1B and TRAIL and IP-10 and CRP; RRM2 and TRAIL and IP-10 and CRP; SFTPA1 and TRAIL and IP-10 and CRP; SIGLEC6 and TRAIL and IP-10 and CRP; SIT1 and TRAIL and IP-10 and CRP; TNXB and TRAIL and IP-10 and CRP; TRIAP1 and TRAIL and IP-10 and CRP;TRIM21 and TRAIL and IP-10 and CRP; UMOD and TRAIL and IP-10 and CRP;

[0282] Specific combinations in Group 4 include: FGF23 and PLA2G2A; FGF23 and PTS; FGF23 and SFTPA1; FGF23 and EZR; FGF23 and SPP1; FGF23 and SCRN1; FGF23 and DDAH1; FGF23 and SFTPA2; FGF23 and POLR2F; IL-10 and PLA2G2A; IL-10 and PTS; IL-10 and SFTPA1; IL-10 and EZR; IL-10 and SPP1; IL-10 and SCRN1; IL-10 and DDAH1; IL-10 and SFTPA2; IL-10 and POLR2F; CCL20 and PLA2G2A; CCL20 and PTS; CCL20 and SFTPA1; CCL20 and EZR; CCL20 and SPP1; CCL20 and SCRN1; CCL20 and DDAH1; CCL20 and SFTPA2; CCL20 and POLR2F; CALCA and PLA2G2A; CALCA and PTS; CALCA and SFTPA1; CALCA and EZR; CALCA and PRDX1; CALCA and SCRN1; CALCA and DDAH1; CALCA and SFTPA2; CALCA and POLR2F; IL-6 and PLA2G2A; I IL-6 and PTS; IL-6 and SFTPA1; IL-6 and EZR; IL-6 and PRDX1; IL-6 and SCRN1; IL-6 and DDAH1; IL-6 and SFTPA2; IL-6 and POLR2F; CXCL8 and PLA2G2A; CXCL8 and PTS; CXCL8 and SFTPA1; CXCL8 and EZR; CXCL8 and SPP1; CXCL8 and SCRN1; CXCL8 and DDAH1; CXCL8 and SFTPA2; CXCL8 and POLR2F; IL1RL1 and PLA2G2A; IL1RL1 and PTS; IL1RL1 and SFT PA1;IL1RL1 and EZR;IL1RL1 and PRDX1;IL1RL1 and SPP1;IL1RL1 and SCRN1;IL1RL1 and DDAH1;IL1RL1 and SFTPA2;IL1RL1 and POLR2F;IL1RN and PLA2G2A;IL1RN and PTS;IL1RN and SFTPA1;IL1RN and EZR;IL1RN and PRDX1;IL1RN and SPP1;IL1RN and SCRN1;IL1RN and DDAH1;IL1RN and SFTPA2;IL1RN and POLR2F;TNFRSF10B and PLA2G2A;These include TNFRSF10B and PTS; TNFRSF10B and SFTPA1; TNFRSF10B and EZR; TNFRSF10B and PRDX1; TNFRSF10B and SPP1; TNFRSF10B and SCRN1; TNFRSF10B and DDAH1; TNFRSF10B and SFTPA2; TNFRSF10B and POLR2F; STC1 and PLA2G2A; STC1 and PTS; STC1 and SFTPA1; STC1 and EZR; STC1 and SPP1; STC1 and SCRN1; STC1 and DDAH1; STC1 and SFTPA2; STC1 and POLR2F.

[0283] Additional combinations contemplated by the inventors include FGF23 and KRT19; FGF23 and CCL7; FGF23 and FBP1; FGF23 and AGR2; FGF23 and RRM2; FGF23 and GRPEL1; FGF23 and TRIM21; FGF23 and DDX58; FGF23 and KRT18; FGF23 and AGER; IL-10 and KRT19; IL-10 and CCL7; IL-10 and FBP1; IL-10 and AGR2; IL-10 and RRM2; IL-10 and GRPEL1; IL-10 and TRIM21; IL-1 0 and DDX58; IL-10 and KRT18; IL-10 and AGER; CCL20 and KRT19; CCL20 and CCL7; CCL20 and FBP1; CCL20 and AGR2; CCL20 and RRM2; CCL20 and GRPEL1; CCL20 and TRIM21; CCL20 and DDX58; CCL20 and KRT18; CCL20 and AGER; CALCA and KRT19; CALCA and CCL7; CALCA and FBP1; CALCA and AGR2; CALCA and RRM2; CALCA and GRPEL1; CALCA and TRIM2 1;CALCA and DDX58;CALCA and KRT18;CALCA and AGER;IL-6 and KRT19;IL-6 and CCL7;IL-6 and FBP1;IL-6 and AGR2;IL-6 and RRM2;IL-6 and GRPEL1;IL-6 and TRIM21;IL-6 and DDX58;IL-6 and KRT18;IL-6 and AGER;CXCL8 and KRT19;CXCL8 and CCL7;CXCL8 and FBP1;CXCL8 and AGR2;CXCL8 and RRM2;CXCL8 and GRPEL1;CXCL8 and TRIM21;CX CL8 and DDX58; CXCL8 and KRT18; CXCL8 and AGER; IL1RL1 and KRT19; IL1RL1 and CCL7; IL1RL1 and FBP1; IL1RL1 and AGR2; IL1RL1 and RRM2; IL1RL1 and GRPEL1; IL1RL1 and TRIM21; IL1RL1 and DDX58; IL1RL1 and KRT18; IL1RL1 and AGER; IL1RN and KRT19; IL1RN and CCL7; IL1RN and FBP1; IL1RN and AGR2; IL1RN and RRM2; IL1RN and GRPEL1;IL1RN and TRIM21; IL1RN and DDX58; IL1RN and KRT18; IL1RN and AGER; TNFRSFlOB and KRT19; TNFRSFlOB and CCL7; TNFRSFlOB and FBP1; TNFRSFlOB and AGR2; TNFRSFlOB and RRM2; TNFRSFlOB and GRPEL1; TNFRSFlOB and TRIM21; TNFRSFlOB and DDX58; TNFRSFlOB and KRT18; TNFRSFlOB and AGER; STC1 and KRT19; STC1 and CCL7; STC1 and FBP1; STC1 and AGR2; STC1 and RRM2; STC1 and GRPEL1; STC1 and TRIM21; STC1 and DDX58; STC1 and KRT18; STC1 and AGER.

[0284] Additional combinations contemplated by the inventors include FGF23 and SIT1; FGF23 and CRTAC1; FGF23 and CDON; FGF23 and CCL17; FGF23 and TNFRSF10C; FGF23 and CD1C; FGF23 and DSC2; FGF23 and FCGR3B; FGF23 and QPCT; FGF23 and TNXB; IL-10 and SIT1; IL-10 and CRTAC1; IL-10 and CDON; IL-10 and CCL17; IL-10 and TNFRSF10C; IL-10 and CD1C; IL-10 and DSC2 ;IL-10 and FCGR3B;IL-10 and QPCT;IL-10 and TNXB;CCL20 and SIT1;CCL20 and CRTAC1;CCL20 and CDON;CCL20 and CCL17;CCL20 and TNFRSF10C;CCL20 and CD1C;CCL20 and DSC2;CCL20 and FCGR3B;CCL20 and QPCT;CCL20 and TNXB;CALCA and SIT1;CALCA and CRTAC1;CALCA and CDON;CALCA and CCL17;CALCA and CD1C;CALCA and DSC2;CALC A and FCGR3B; CALCA and QPCT; CALCA and TNXB; IL-6 and SIT1; IL-6 and CRTAC1; IL-6 and CDON; IL-6 and CCL17; IL-6 and CD1C; IL-6 and DSC2; IL-6 and FCGR3B; IL-6 and QPCT; IL-6 and TNXB; CXCL8 and SIT1; CXCL8 and CRTAC1; CXCL8 and CDON; CXCL8 and CCL17; CXCL8 and TNFRSF10C; CXCL8 and CD1C; CXCL8 and DSC2; CXCL8 and FCGR3B; CXCL 8 and QPCT; CXCL8 and TNXB; IL1RL1 and SIT1; IL1RL1 and CRTAC1; IL1RL1 and CDON; IL1RL1 and CCL17; IL1RL1 and TNFRSF10C; IL1RL1 and CD1C; IL1RL1 and DSC2; IL1RL1 and FCGR3B; IL1RL1 and QPCT; IL1RL1 and TNXB; IL1RN and SIT1; IL1RN and CRTAC1; IL1RN and CDON; IL1RN and CCL17; IL1RN and CD1C; IL1RN and DSC2; IL1RN and FCGR3B;IL1RN and QPCT; IL1RN and TNXB; TNFRSFlOB and SIT1; TNFRSFlOB and CRTAC1; TNFRSFlOB and CDON; TNFRSFlOB and CCL17; TNFRSFlOB and TNFRSFlOC; TNFRSFlOB and CD1C; TNFRSFlOB and DSC2; TNFRSFlOB and FCGR3B; TNFRSFlOB and QPCT; TNFRSFlOB and TNXB; STC1 and SIT1; STC1 and CRTAC1; STC1 and CDON; STC1 and CCL17; STC1 and TNFRSFlOC; STC1 and CD1C; STC1 and DSC2; STC1 and FCGR3B; STC1 and QPCT; STC1 and TNXB.

[0285] Specific protein combinations in Group 5 include IL-6 and PM20D1; IL-6 and IFNG; IL-6 and IL-10; IL-6 and DDX58; IL-6 and CXCL11; IL-6 and SIGLEC5; IL-6 and NADK; IL-6 and CCL8; IL-6 and PPP1R9B; IL-6 and SIGLEC1; PLA2G2A and PM20D1; PLA2G2A and IFNG; PLA2G2A and IL-10; PLA2G2A and DDX58; PLA2G2A and CXCL11; PLA2G2A and SIGLEC5; PLA2G2A and NADK; PLA2G2A and CCL8; PLA2G2A and PPP1R9B; PLA2G2A and SIGLEC1; CSF3 and PM20D1; CSF3 and IFNG; CSF3 and IL-10; CSF3 and DDX58; CSF3 and CXCL11; CSF3 and SIGLEC5; CSF3 and NADK; CSF3 and CCL8; CSF3 and PPP1R9B; CSF3 and SIGLEC1; PRTN3 and PM20D1; PRTN3 and IFNG; PRTN3 and IL-10; PRTN3 and DDX58; PRTN3 and CXCL11; PRTN3 and SIGLEC5; PRTN3 and NADK; PRTN3 and CCL8; PRTN3 and PPP1R9B; PRTN3 and SIGLEC1; MMP8 and PM20D1; MMP8 and IFNG; MMP8 and IL-10; MMP8 and DDX58; MMP8 and CXCL11; MMP8 and SIGLEC5; MMP8 and NADK; MMP8 and CCL8; MMP8 and PPP1R9B; MMP8 and SIGLEC1; LBP and PM20D1; LBP and IFNG; LBP and IL-10; LBP and DDX58; LBP and CXCL11; LBP and and SIGLEC5; LBP and NADK; LBP and CCL8; LBP and PPP1R9B; LBP and SIGLEC1; VWA1 and PM20D1; VWA1 and IFNG; VWA1 and IL-10; VWA1 and DDX58; VWA1 and CXCL11; VWA1 and SIGLEC5; VWA1 and NADK; VWA1 and CCL8; VWA1 and PPP1R9B; VWA1 and SIGLEC1; OSM and PM20D1; OSM and IFNG; OSM and IL-10; OSM and DDX58; OSM and CXCL11; OSM and SIGLEC5;Examples include OSM and NADK; OSM and CCL8; OSM and PPP1R9B; OSM and SIGLEC1; GPR37 and PM20D1; GPR37 and IFNG; GPR37 and IL-10; GPR37 and DDX58; GPR37 and CXCL11; GPR37 and SIGLEC5; GPR37 and NADK; GPR37 and CCL8; GPR37 and PPP1R9B; GPR37 and SIGLEC1; IL1RN and PM20D1; IL1RN and IFNG; IL1RN and IL-10; IL1RN and DDX58; IL1RN and SIGLEC5; IL1RN and NADK; IL1RN and CCL8; IL1RN and PPP1R9B; IL1RN and SIGLEC1.

[0286] Specific protein combinations in Group 6 include PM20D1 and IP-10 and CRP; IL-6 and IP-10 and CRP; PLA2G2A and IP-10 and CRP; IFNG and IP-10 and CRP; PRTN3 and IP-10 and CRP; CXCL10 (IP-10) and IP-10 and CRP; LBP and IP-10 and CRP; VWA1 and IP-10 and CRP; OSM and IP-10 and CRP; IL-10 and IP-10 and CRP; GPR37 and IP-10 and CRP; AGXT and IP-10 and CRP; C4BPB and and IP-10 and CRP; AZU1 and IP-10 and CRP; DEFA1 / DEFA1B and IP-10 and CRP; SERPINB8 and IP-10 and CRP; RRM2 and IP-10 and CRP; NADK and IP-10 and CRP; RNASE3 and IP-10 and CRP; PIK3AP1 and IP-10 and CRP; HCLS1 and IP-10 and CRP; LCN2 and IP-10 and CRP; SLAMF7 and IP-10 and CRP; CD14 and IP-10 and CRP; SHMT1 and IP-10 and CRP; SERPINB1 and I P-10 and CRP; CLEC6A and IP-10 and CRP; IL1B and IP-10 and CRP; CLEC4D and IP-10 and CRP; AHCY and IP-10 and CRP; CEACAM8 and IP-10 and CRP; LIF and IP-10 and CRP; FKBP5 and IP-10 and CRP; EGLN1 and IP-10 and CRP; CASP10 and IP-10 and CRP; B4GALT1 and IP-10 and CRP; CCL23 and IP-10 and CRP; PXN and IP-10 and CRP; IPCEF1 and IP-10 and CRP; IL 10RA and IP-10 and CRP; STC1 and IP-10 and CRP; GZMB and IP-10 and CRP; TYMP and IP-10 and CRP; TXLNA and IP-10 and CRP; IL15 and IP-10 and CRP; LRIG1 and IP-10 and CRP; CXCL13 and IP-10 and CRP; RETN and IP-10 and CRP; SIRPB1 and IP-10 and CRP; SAMD9L and IP-10 and CRP; FYB1 and IP-10 and CRP; CD300E and IP-10 and CRP; SELE and IP-10 and CRP;FCN2 and IP-10 and CRP; CCL7 and IP-10 and CRP; LILRA5 and IP-10 and CRP; CXCL3 and IP-10 and CRP; TNFRSF8 and IP-10 and CRP; CSF1 and IP-10 and CRP; NOS3 and IP-10 and CRP; MPO and IP-10 and CRP; ICAM2 and IP-10 and CRP; ST6GAL1 and IP-10 and CRP; PAG1 and IP-10 and CRP; MCFD2 and IP-10 and CRP; BCL2L11 and IP-10 and CRP; SLC39A14 and IP-1 0 and CRP; PGLYRP1 and IP-10 and CRP; SORD and IP-10 and CRP; FCAR and IP-10 and CRP; EFNA1 and IP-10 and CRP; PTPN6 and IP-10 and CRP; MILR1 and IP-10 and CRP; SNAP29 and IP-10 and CRP; CCL18 and IP-10 and CRP; GNLY and IP-10 and CRP; USP8 and IP-10 and CRP; SKAP2 and IP-10 and CRP; NUDC and IP-10 and CRP; FLT4 and IP-10 and CRP; IKBKG and IP-1 0 and CRP; ICAM1 and IP-10 and CRP; BACH1 and IP-10 and CRP; CLEC4G and IP-10 and CRP; SEMA3F and IP-10 and CRP; LAT2 and IP-10 and CRP; TPP1 and IP-10 and CRP; CD300LF and IP-10 and CRP; TNFRSF1A and IP-10 and CRP; TNF and IP-10 and CRP; TARBP2 and IP-10 and CRP; IL2RA and IP-10 and CRP; TIMD4 and IP-10 and CRP; DDX58 and IP-10 and CRP; NBN and and IP-10 and CRP; TNFSF13B and IP-10 and CRP; RARRES2 and IP-10 and CRP; PTPN1 and IP-10 and CRP; GBP4 and IP-10 and CRP; ANGPTL2 and IP-10 and CRP; GOLM2 and IP-10 and CRP; GRN and IP-10 and CRP; SIGLEC1 and IP-10 and CRP; PTK7 and IP-10 and CRP; C1QA and IP-10 and CRP; IL18BP and IP-10 and CRP; FOLR2 and IP-10 and CRP; GGH and IP-10 and CRP;SOD2 and IP-10 and CRP; LILRB1 and IP-10 and CRP; LYN and IP-10 and CRP; TXNDC15 and IP-10 and CRP; DECR1 and IP-10 and CRP; F9 and IP-10 and CRP; TIE1 and IP-10 and CRP; YES1 and IP-10 and CRP; C2 and IP-10 and CRP; FCGR3B and IP-10 and CRP; IMPA1 and IP-10 and CRP; SEMA4D and IP-10 and CRP; ADAM8 and IP-10 and CRP; SIGLEC9 and IP-10 and CRP; C A4 and IP-10 and CRP; VCAN and IP-10 and CRP; PLAU and IP-10 and CRP; IL13RA1 and IP-10 and CRP; TIA1 and IP-10 and CRP; ROBO2 and IP-10 and CRP; HYAL1 and IP-10 and CRP; BOC and IP-10 and CRP; MCAM and IP-10 and CRP; PRTG and IP-10 and CRP; GUCA2A and IP-10 and CRP; DDC and IP-10 and CRP; CDON and IP-10 and CRP; IL22RA1 and IP-10 and CRP; BMP4 and and IP-10 and CRP; CES3 and IP-10 and CRP; HSD11B1 and IP-10 and CRP; GDF2 and IP-10 and CRP; DCBLD2 and IP-10 and CRP; EPCAM and IP-10 and CRP; CCL25 and IP-10 and CRP; CCN1 and IP-10 and CRP; CPM and IP-10 and CRP; ISM1 and IP-10 and CRP; NPTX1 and IP-10 and CRP; SERPINA12 and IP-10 and CRP; LGALS4 and IP-10 and CRP; TCL1A and IP-10 and CRP; E PHA1 and IP-10 and CRP; CTSV and IP-10 and CRP; CRH and IP-10 and CRP; CTSF and IP-10 and CRP; TNFSF11 and IP-10 and CRP; SIGLEC5 and IP-10 and CRP; CCL8 and IP-10 and CRP; PPP1R9B and IP-10 and CRP; TRIM21 and IP-10 and CRP; ITM2A and IP-10 and CRP; BANK1 and IP-10 and CRP; LAMP3 and IP-10 and CRP; NUB1 and IP-10 and CRP; BCR and IP-10 and CRP;GZMH and IP-10 and CRP; FEN1 and IP-10 and CRP; APBB1IP and IP-10 and CRP; CNST and IP-10 and CRP; IL12B and IP-10 and CRP; LAG3 and IP-10 and CRP; PPP1R12A and IP-10 and CRP; LAP3 and IP-10 and CRP; AIF1 and IP-10 and CRP; ARHGAP25 and IP-10 and CRP; INPPL1 and IP-10 and CRP; TDRKH and IP-10 and CRP; MMP1 3 and IP-10 and CRP; LGALS9 and IP-10 and CRP; PRKAR1A and IP-10 and CRP; AXIN1 and IP-10 and CRP; CASP3 and IP-10 and CRP; CERT and IP-10 and CRP; CPPED1 and IP-10 and CRP; RHOC and IP-10 and CRP; PPP1R2 and IP-10 and CRP; COMT and IP-10 and CRP; KIFBP and IP-10 and CRP; IL17RA and IP-10 and CRP; CLUL1 and IP -10 and CRP; S100A11 and IP-10 and CRP; FOXO1 and IP-10 and CRP; ILKAP and IP-10 and CRP; BST2 and IP-10 and CRP; TIGAR and IP-10 and CRP; NFKBIE and IP-10 and CRP; ADA2 and IP-10 and CRP; GRAP2 and IP-10 and CRP; TBL1X and IP-10 and CRP; FASLG and IP-10 and CRP; AXL and IP-10 and CRP; MANSC1 and IP-10 and CRP P; DPP4 and IP-10 and CRP; CD34 and IP-10 and CRP; ENTPD5 and IP-10 and CRP; CD244 and IP-10 and CRP; SLITRK6 and IP-10 and CRP; TSPAN1 and IP-10 and CRP; VNN2 and IP-10 and CRP; CCL16 and IP-10 and CRP; MMP9 and IP-10 and CRP; ASAH2 and IP-10 and CRP; HBEGF and IP-10 and CRP; KLK12 and IP-10 and CRP.

[0287] The threshold levels provided above may be used to diagnose an infection (e.g., to determine severity and / or distinguish between bacterial and viral infections using two or more protein determinants), or a score may be generated based on the abundance of these proteins, taking into account the weight of each protein, as further explained herein below.

[0288] Preferably, the combination tested to classify an infectious disease does not exceed 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, or 2 markers. In another embodiment, 40 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 30 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 20 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 10 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 9 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 8 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 7 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, 6 or fewer protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, no more than five protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, no more than four protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, no more than three protein markers are analyzed in a single test / analysis for classification purposes. In another embodiment, no more than two protein markers are analyzed in a single test / analysis for classification purposes.

[0289] Performance and Accuracy Measurements of the Invention The performance and absolute and relative clinical utility of the present invention can be evaluated in multiple ways, as described above. Among various evaluations of performance, some aspects of the present invention are intended to provide clinical diagnostic and prognostic accuracy. The accuracy of a diagnostic or prognostic test, assay, or method relates to the ability of the test, assay, or method to distinguish subjects with an infection and is based on whether the subjects exhibit a "significant change" (e.g., clinically significant, diagnostically significant) in the level of a determinant. An "effective amount" refers to measuring an appropriate number of determinants (which may be one or more) that results in a "significant change" (e.g., a level of expression or activity of a determinant) that differs from a predetermined cutoff value (or threshold) for that determinant, thereby indicating that the subject has the infection indicated by that determinant. The difference in determinant levels is preferably statistically significant. As described below, and not limiting of the present invention, achieving statistical significance, and thus favorable analytical, diagnostic, and clinical accuracy, may require the use of a panel of several determinant combinations combined with a mathematical algorithm to achieve a statistically significant determinant index.

[0290] In categorical diagnosis of disease states, changing the cutoff value or threshold of a test (or assay) typically changes sensitivity and specificity, although qualitatively they are inversely related. Therefore, when evaluating the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject's condition, both sensitivity and specificity should always be considered, and attention should be paid to the cutoff values at which sensitivity and specificity are reported, as sensitivity and specificity can vary significantly across a range of cutoff values. One way to achieve this is to use the Matthews correlation coefficient (MCC) metric, which relies on both sensitivity and specificity. The use of statistical methods, such as the area under the receiver operating characteristic curve (ROC) curve (AUC), which encompasses all potential cutoff values, is preferred for most categorical risk measurements when using some embodiments of the present invention. On the other hand, for continuous risk measurements, statistical evaluation of goodness of fit and calibration against observed outcomes or other gold standards are preferred.

[0291] A predetermined level of predictive accuracy means that the method provides an acceptable level of clinical or diagnostic accuracy. Using such statistics, "acceptable diagnostic accuracy" is defined herein as a test or assay (such as a test that, in some embodiments of the invention, is used to provide clinically significant detection of a determinant, thereby indicating the presence and / or severity of an infection) having an AUC (area under the ROC curve of the test or assay) of 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.

[0292] By "very high diagnostic accuracy" is meant a test or assay having an AUC (area under the ROC curve of the test or assay) of 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.

[0293] Alternatively, the method predicts the presence or severity of infection with at least 75% overall accuracy, and more preferably with an overall accuracy of 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater.

[0294] Alternatively, the method predicts the presence of, response to treatment, or severity of a bacterial infection with at least 75% sensitivity, and more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater sensitivity.

[0295] Alternatively, the method predicts the presence of a viral infection, response to treatment, or severity of a viral infection with at least 75% specificity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater specificity. Alternatively, the method predicts the presence or absence of an infection or response to treatment based on an MCC greater than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0.

[0296] Alternative methods for assessing diagnostic accuracy are generally used for continuous measurements when disease categories have not yet been clearly defined in the relevant medical community and clinical practice, when thresholds for therapeutic use have not yet been identified, or when no existing gold standard exists for diagnosing pre-symptomatic disease. For continuous measures of risk, measures of the diagnostic accuracy of calculated indices are typically based on curve fitting and calibration between predicted continuous values and actual observed values (or past index calculations), utilizing measures such as R-squared, Hosmer-Lemeshow P-statistics, and confidence intervals. Even in the case of a test for the risk of future breast cancer recurrence commercially available from Genomic Health, Inc. (Redwood City, California), it is not uncommon for predicted values using such algorithms to be reported, including confidence intervals (usually 90% or 95% CI) based on predictions from historical observational cohorts.

[0297] Generally, by determining the degree of diagnostic accuracy, i.e., defining the cutoff value on the ROC curve, defining acceptable AUC values, and determining the acceptable range of relative concentrations of components that constitute an effective amount of a determinant of the present invention, one skilled in the art will be able to use the determinant to identify, diagnose, or predict subjects with a predetermined level of predictive accuracy and predictive performance.

[0298] Furthermore, other undescribed biomarkers correlate very strongly with the determinants (for the purposes of this application, a coefficient of determination (R) of any two variables greater than or equal to 0.5). 2) are considered "very strongly correlated." Some embodiments of the present invention encompass such functional and statistical equivalents to the above-mentioned determinants. Furthermore, the statistical utility of such additional determinants is highly dependent on the cross-correlation between multiple biomarkers, often necessitating the incorporation of any new biomarkers within a panel to reveal the underlying biological meaning.

[0299] Determinant panel construction Groups of determinants can be included in a "panel," also referred to as a "determinant-signature," "determinant signature," or "multi-determinant signature." A "panel," in the context of the present invention, refers to a collection of biomarkers that includes one or more determinants (whether determinants, clinical parameters, or traditional laboratory risk factors). A panel can also include additional biomarkers (e.g., clinical parameters, traditional laboratory risk factors) that are known to be present or known to be associated with infection in combination with a selected group of determinants listed herein.

[0300] As noted above, when used alone and not as members of a biomarker panel containing multiple determinants, many of the listed individual determinants, clinical parameters, and traditional laboratory risk factors have little or no clinical utility in reliably distinguishing between normal individual subjects, subjects at risk for infection (e.g., bacterial infection, viral infection, or co-infection), or the severity of infection, and therefore cannot be reliably used alone to classify individual subjects between these conditions. Even when there is a statistically significant difference in the mean measurements in each of these populations, as is commonly found in well-powered studies, the applicability of such biomarkers in individual subjects remains limited and may contribute little to the diagnosis or prognosis of that subject. A common measure of statistical significance is the p-value, which indicates the probability that an observed event occurred by chance alone. Preferably, such a p-value is 0.05 or less, indicating that the probability that the observed event occurred by chance is 5% or less. Such a p-value depends largely on the power of the study performed.

[0301] Despite this performance of individual determinants, and the general performance of formulas that combine only traditional clinical parameters with a few traditional laboratory risk factors, the inventors have noted that certain specific combinations of two or more determinants can also be used as multi-biomarker panels, including combinations of determinants known to be involved in one or more physiological or biological pathways, and that such information can be combined and made clinically useful through the use of various formulas, including statistical classification algorithms, that combine and often extend the performance characteristics of the combination beyond those of the individual determinants. These particular combinations demonstrate acceptable levels of diagnostic accuracy, and when sufficient information from multiple determinants is combined in a trained formula, they often reliably achieve high levels of diagnostic accuracy that are applicable from one population to another.

[0302] The general concept of how two low-specificity or low-performance determinants can be combined into a novel and more useful combination for the intended indication is an important aspect of some embodiments of the present invention. Multiple biomarkers, when used with appropriate mathematical and clinical algorithms, can provide significant improvements in performance compared to their individual components. This is often evident in both sensitivity and specificity, resulting in a larger AUC or MCC. A significant improvement in performance can mean an increase of 1%, 2%, 3%, 4%, 5%, 8%, 10%, or more than 10% in different measures of accuracy, such as overall accuracy, AUC, MCC, sensitivity, specificity, PPV, or NPV. Second, existing biomarkers often contain novel information that was previously unrecognized, which was necessary to improve the level of sensitivity or specificity through a new mathematical formula. This hidden information can also apply to biomarkers that are generally considered to have suboptimal clinical performance on their own. Indeed, suboptimal performance in terms of a high false positive rate of a single biomarker measured alone may be an indicator that some important additional information is contained within the results of that biomarker, information that would not be revealed without the combination of a second biomarker and a mathematical formula.

[0303] On the other hand, limiting the number of diagnostic determinants (e.g., protein markers) measured is often useful because it allows for significant cost savings and reduces the required sample volume and assay complexity. Therefore, even if two signatures have similar diagnostic performance (e.g., similar AUC or sensitivity), a signature incorporating fewer proteins may have significant utility and potential in terms of enhancing feasibility. For example, a signature including five proteins compared to ten proteins and having similar performance would be desirable because it would have many advantages in a real-world clinical environment. Therefore, the ability to reduce the number of proteins incorporated into a signature while maintaining a similar level of accuracy is valuable and an inventive feature. In this context, a similar level of accuracy may mean a range of ±1%, 2%, 3%, 4%, 5%, 8%, or 10% in different accuracy measures, such as overall accuracy, AUC, MCC, sensitivity, specificity, PPV, or NPV. Additionally, a significant reduction in the number of proteins in a signature includes reducing the number of proteins by 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10.

[0304] Several statistical and modeling algorithms known in the art can be used to assist in the selection of determinants and to optimize these combined selection algorithms. Statistical tools such as factor and cross-biomarker correlation / covariance analysis allow for a more rational approach to panel construction. Mathematical clustering and classification trees showing the standardized Euclidean distance between determinants can be advantageously used. Seeding statistical classification methods with pathway information can also be employed, so that a rational approach based on the selection of individual determinants based on their involvement in specific pathways or physiological functions can be employed.

[0305] Ultimately, mathematical formulas such as statistical classification algorithms can be directly used to create and train optimal formulas required for selecting determinants and integrating the results of multiple determinants into a single index. Often, selection methods such as forward (starting with zero potential explanatory parameters) and backward (starting with all available potential explanatory parameters) are used, and information criteria such as AIC or BIC are used to quantify the trade-off between the performance and diagnostic accuracy of the panel and the number of determinants used. The position of individual determinants within a forward or backward selected panel can be closely related to providing incremental information content for the algorithm, so the order of contribution depends heavily on the other constituent determinants within the panel.

[0306] Building clinical algorithms Any mathematical formula can be used to combine the results of the determinants to calculate an index useful in the practice of the present invention. As indicated above, but without limitation, such an index can indicate, among other indicators, probability, likelihood, absolute or relative risk, the time or rate of conversion from one disease state to another, or can be used to predict future biomarker measurements of infection. This can be for a specific time period or prediction period, or for remaining lifetime risk, or can simply be provided as an index relative to another reference population.

[0307] Although various preferred formulas are described herein, several other types of models and formulas beyond those mentioned herein and in the above definitions are well known to those skilled in the art. The actual model type or formula itself used may be selected from a field of potential models based on the performance and diagnostic accuracy characteristics of the results in a training population. The details of the formula itself may generally be derived from the results of the determinants in the relevant training population. In particular, such a formula may be intended to map a feature space derived from the input of one or more determinants to a set of subject classes (e.g., useful for predicting a subject's class membership as "normal" or "infected"), and derive an estimate of the probability function of risk using a Bayesian approach, or to estimate class conditional probabilities, and then use Bayes' rule to calculate the class probability function as in the previous case.

[0308] Preferred formulations include the use of a broad class of statistical classification algorithms, particularly discriminant analysis. The goal of discriminant analysis is to predict class membership from a set of previously identified features. In linear discriminant analysis (LDA), a linear combination of features is identified that maximizes separation between groups by some criterion. LDA features can be identified using stepping algorithms based on eigengene-based methods (ELDA) or multivariate analysis of variance (MANOVA) with different thresholds. Forward, backward, and stepwise algorithms can be implemented to minimize the probability of no separation based on the Hotelling-Lowry statistic.

[0309] Eigengene-based linear discriminant analysis (ELDA) is a feature selection method developed by Shen et al. (2006). This method selects features (e.g., biomarkers) within a multivariate framework using modified eigenanalysis to identify features associated with the most significant eigenvectors. "Important" is defined as the eigenvector that explains the most variance in the differences between the samples being classified for a given threshold.

[0310] Support vector machines (SVMs) are classification methods that aim to find a hyperplane that separates two classes. This hyperplane contains support vectors, i.e., data points exactly a margin distance away from the hyperplane. If the separating hyperplane is unlikely to exist within the current dimensionality of the data, the dimensionality can be significantly expanded by projecting the data into a higher dimension using a nonlinear function of the original variables (Venables and Ripley, 2002). While not required, feature filtering for SVMs often improves predictive accuracy. Features (e.g., biomarkers) may be identified for support vector machines using the nonparametric Kruskal-Wallis (KW) test to select the best univariate features. Random forests (RF, Breiman, 2001) or recursive partitioning (RPART, Breiman et al., 1984) can also be used, individually or in combination, to identify the most important combinations of biomarkers. Both KW and RF require the selection of a fixed number of features from the entire population. RPART creates a single classification tree using a subset of available biomarkers.

[0311] Other mathematical formulas may be used to preprocess the results of individual determinant measurements into more valuable forms of information before submission to the prediction formula. In particular, normalization of biomarker results using any of the common mathematical transformations, such as logarithmic or logistic functions, as a normal or other distribution location, based on population means, etc., are all well known to those skilled in the art. Of particular interest are normalization sets based on clinical determinants, such as time since symptom onset, sex, race, or gender, where a particular formula is used only for subjects within a class, or combines clinical determinants continuously as inputs. In other cases, analyte-based biomarkers can be integrated into calculated variables before being submitted to the formula.

[0312] In addition to the possibility of normalizing individual parameter values for a single subject, the overall prediction formula itself may be recalibrated or otherwise adjusted for all subjects or any known class of subjects based on the expected prevalence of the population and adjustments to the mean biomarker parameter values, according to the methods outlined in D'Agostino et al., (2001) JAMA 286:180-187, or other similar normalization and recalibration methods. Such epidemiological adjustment statistics may be continually captured, confirmed, refined, and updated through a registry of historical data presented to the model, provided machine-readable or otherwise, or sometimes through retrospective querying of archived samples or by reference to previous studies of those parameters and statistics. Further examples that may be subject to formula recalibration or other adjustments include the statistics used in the studies of Pepe, MS et al., 2004, regarding constraints on odds ratios; and Cook, NR, 2007, regarding receiver operating characteristic curves. Finally, the numerical results of the classifier formula itself can be post-processed and transformed by reference to actual clinical populations and study results, as well as observed endpoints, to calibrate to absolute risk and thus provide confidence intervals for the variability of the numerical results of the classifier formula or risk formula.

[0313] Some determinants may exhibit trends that depend on the patient's age (e.g., a population baseline may rise or fall as a function of age). To adjust for age-related differences, an "age-dependent normalization or stratification" scheme can be used. Age-dependent normalization, stratification, or the application of separate mathematical formulas can be used to improve the accuracy of determinants in distinguishing between different types of infection. For example, one skilled in the art can create a function that fits the population average level of each determinant as a function of age and use it to normalize the determinant levels of individual subjects across different ages. Another example is to stratify subjects according to age and independently determine age-specific thresholds or index values for each age group.

[0314] In the context of the present invention, the following statistical terms may be used:

[0315] "TP" is true positive, meaning a positive test result that accurately reflects the activity tested for. For example, in the context of the present invention, a TP is, for example, but not limited to, the true classification of a bacterial infection as such.

[0316] "TN" means true negative, a negative test result that accurately reflects the activity tested for. For example, in the context of the present invention, TN refers to, but is not limited to, truly classifying a viral infection as such.

[0317] "FN" refers to a false negative, a result that appears negative but does not reflect the actual situation. For example, in the context of the present invention, FN refers to, but is not limited to, the incorrect classification of a bacterial infection as a viral infection.

[0318] "FP" refers to a false positive, a test result that is incorrectly classified into a positive category. For example, in the context of the present invention, an FP is, for example, but not limited to, the incorrect classification of a viral infection as a bacterial infection.

[0319] "Sensitivity" is calculated by TP / (TP+FN) or the true positive proportion of diseased subjects.

[0320] "Specificity" is calculated by TN / (TN+FP) or the true negative rate in non-diseased or normal subjects.

[0321] "Overall accuracy" is calculated as (TN+TP) / (TN+FP+TP+FN).

[0322] "Positive predictive value" or "PPV" is calculated by TP / (TP+FP), or the true positive fraction of all positive test results. It is essentially influenced by the prevalence of the disease and the pre-test probability of the population intended to be tested.

[0323] "Negative predictive value" or "NPV" is calculated by TN / (TN+FN), or the true negative proportion of all negative test results. It is also inherently affected by the prevalence of the disease and the pre-test probability of the population intended to be tested. For a discussion of the specificity, sensitivity, and positive and negative predictive values of tests, e.g., clinical diagnostic tests, 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.

[0324] The "MCC" (Mathews correlation coefficient) is calculated as follows: MCC = (TP * TN - FP * FN) / {(TP + FN) * (TP + FP) * (TN + FP) * (TN + FN)}^0.5, where TP, FP, TN, and FN are true positives, false positives, true negatives, and false negatives, respectively. Note that MCC values range from -1 to +1, indicating completely misclassified and perfect classification, respectively. An MCC of 0 indicates random classification. MCC has been shown to be 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 imbalanced class sizes (Baldi, Brunak et al. 2000).

[0325] In binary disease state classification approaches that often use continuous diagnostic test measurements, sensitivity and specificity are summarized by the receiver operating characteristic (ROC) 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. They are also summarized by the area under the curve (AUC) or c-statistic, which is a measure that allows a single value to express the sensitivity and specificity of a test, assay, or method across the entire range of test (or assay) cutoff values. See, for example, Shultz, "Clinical Interpretation Of Laboratory Procedures," chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4 th See also 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. Alternative approaches using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness of fit), and reclassification measures are summarized according to Cook, "Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction," Circulation 2007, 115:928-935.

[0326] "Accuracy" refers to the degree to which a measured or calculated quantity (test-reported value) agrees with its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN)) and misclassified outcomes (false positives (FP) or false negatives (FN)), and may be described, along with other measures, as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), Matthews correlation coefficient (MCC), or likelihood, odds ratio, receiver operating characteristic (ROC) curve, area under the curve (AUC).

[0327] A "mathematical formula," "algorithm," or "model" refers to any mathematical equation, algorithmic process, analytical process, or programmatic process, or statistical method that accepts one or more continuous or categorical inputs (referred to herein as "parameters") and calculates an output value, which may also be referred to as an "index" or "index value." Non-limiting examples of "mathematical formulas" include sums, ratios, and regression operators (coefficients or exponents), transformations and normalizations of biomarker values (including, but not limited to, normalization schemes based on clinical determinants such as sex, 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 nonlinear equations and statistical classification analyses to determine the relationship between the level of a determinant detected in a subject sample and the probability of the subject having an infection or a particular type of infection. Of particular interest in panel and combination construction are structural and syntactic statistical classification algorithms that utilize pattern recognition features, including established methods such as cross-correlation, principal component analysis (PCA), factor rotation, logistic regression (LogReg), linear discriminant analysis (LDA), eigengene linear discriminant analysis (ELDA), support vector machines (SVM), random forests (RF), recursive partitioning trees (RPART), and other related decision tree classification methods, shrunken centroids (SC), StepAIC, Kth-nearest neighbor, boosting, decision trees, neural networks, Bayesian networks, and hidden Markov models, as well as methods of index construction. Other methods, including Cox, Weibull, Kaplan-Meier, and Greenwood models well known to those skilled in the art, can be used in survival and time-to-event hazard analyses. Many of these approaches are useful in combination with determinant selection approaches such as forward, backward, or stepwise selection, exhaustive enumeration of all potential panels of a given size, genetic algorithms, or they themselves can incorporate biomarker selection methodologies into their own approaches.These can be combined with information criteria such as Akaike's information criterion (AIC) or Bayesian information criterion (BIC) to quantify the trade-off between additional biomarkers and model improvement and minimize overfitting. The resulting predictive models may be validated in other studies or cross-validated in the study on which they were originally trained using techniques such as bootstrap cross-validation, leave-one-out (LOO) cross-validation, and 10-fold cross-validation (10-fold CV). At various steps, false discovery rates may be estimated by value substitution, according to techniques known in the art. A "health economic utility function" is a mathematical formula derived from the combination of expected probabilities of a set of clinical outcomes in an idealized, applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into standard care. It encapsulates estimates of the accuracy, effectiveness, and performance characteristics of such interventions, as well as measures of the costs and / or value (utility) associated with each outcome, and may be derived from actual health system costs of treatment (such as services, supplies, equipment, and drugs) and / or as estimated allowances per quality-adjusted life-year (QALY) gained resulting from each outcome. The sum of the products of the predicted population size for each outcome and the predicted utility for each outcome, across all predicted outcomes, is the total health economic utility of a given standard of care. The difference between (i) the calculated total health economic utility for the standard of care with the intervention and (ii) the calculated total health economic utility for the standard of care without the intervention provides an overall measure of the health economic cost or value of the intervention. This can be divided among the entire patient population under analysis (or just the intervention group) to calculate the cost per unit of intervention and guide decisions such as market positioning, pricing, and health system acceptance assumptions. Such health economic utility functions are commonly used to compare the cost-effectiveness of interventions, but they can also be transformed to estimate the acceptable value per QALY that health systems are willing to pay or the acceptable cost-effective clinical performance characteristics required for a new intervention.

[0328] Because each outcome (which in a disease classification diagnostic test may be TP, FP, TN, or FN) in a diagnostic (or prognostic) intervention of the present invention carries a different cost, the health economic utility function may prioritize sensitivity over specificity or PPV over NPV based on the clinical context and the cost and value of each outcome. This provides another measure of health economic performance and value that may differ from more direct clinical or analytical performance measures. These different measures and relative tradeoffs generally favor all performance measures over imperfect ones, but to different degrees, and converge only for a perfect test with a zero error rate (i.e., zero misclassification of the outcome of interest, i.e., FP and FN).

[0329] "Analytical precision" refers to the reproducibility and predictability of the measurement process itself and can be summarized in measures such as coefficient of variation (CV), Pearson correlation, and tests of agreement and calibration when measuring the same sample or control with different times, users, instruments, and / or reagents. These and other considerations when evaluating novel biomarkers are also summarized in Vasan, 2006.

[0330] "Performance" is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test and includes, among other things, clinical and analytical accuracy, other analytical and process characteristics (e.g., use characteristics (e.g., stability, ease of use)), health economic value, and the relative cost of the test's components. Any of these factors can contribute to good performance and thus enhance the usefulness of a test, and can be measured by relevant and appropriate "performance indicators," such as AUC and MCC, time to result, shelf life, etc.

[0331] "Statistical significance" means that the change exceeds the range that would be expected to occur simply by chance (potentially a "false positive"). Statistical significance can be determined by any method known in the art. A commonly used measure of significance is the p-value, which represents the probability of obtaining a result that is at least as extreme as that data point, assuming that the data point is the result of mere chance. A result is often considered to have high statistical significance if the p-value is 0.05 or less.

[0332] kit Some embodiments of the present invention also include determinant detection reagents, such as antibodies, packaged together in kit form. The kit may contain, in separate containers, the antibody (already bound to a solid matrix or separately packaged with reagents for binding to the matrix), control preparations (positive and / or negative), and / or a detectable label, such as fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, or a radioactive label. The detectable label may be attached to a secondary antibody that binds to the Fc portion of the antibody that recognizes the determinant. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit.

[0333] Kits according to this aspect of the invention may include additional components to aid in the detection of determinants such as enzymes, salts, buffers, etc. required to carry out the detection reaction.

[0334] For example, a determinant detection reagent (e.g., an antibody) can be immobilized on a solid support, such as a porous strip or array, to form at least one determinant detection site. The measurement or detection region of the porous strip can include multiple sites. A test strip can also include sites for negative and / or positive controls. Alternatively, the control sites can be located on a strip separate from the test strip. Optionally, different detection sites can contain different amounts of immobilized detection reagent, e.g., a higher amount in the first detection site and a lower amount in subsequent sites. Upon addition of test sample, the number of sites exhibiting a detectable signal provides a quantitative indication of the amount of determinant present in the sample. The detection sites can be configured in any suitably detectable shape, typically in the shape of a bar or dot spanning the width of the test strip.

[0335] Polyclonal antibodies for measuring determinants include, but are not limited to, antibodies produced from serum obtained by active immunization of one or more of the following animals: rabbit, goat, sheep, chicken, duck, guinea pig, mouse, donkey, camel, rat, and horse.

[0336] Examples of detection agents include, but are not limited to, scFv, dsFv, Fab, sVH, F(ab')2, cyclic peptides, haptamer, single domain antibody, Fab fragment, single chain variable fragment, affibody molecule, affilin, nanophytin, anticalin, avimer, DARPins, Kunitz domain, phynomers, and monobodies.

[0337] In certain embodiments, the kit does not include several antibodies that specifically recognize more than 50, 20, 15, 10, 9, 8, 7, 6, 5, or 4 polypeptides.

[0338] In other embodiments, the arrays of the invention do not comprise several antibodies that specifically recognize more than 50, 20, 15, 10, 9, 8, 7, 6, 5, or 4 polypeptides.

[0339] In one embodiment, the kit comprises 10 or less, 9 or less, 8 or less, 7 or less, 6 or less, 6 or less, 5 or less, 4 or less, 3 or less, or 2 or less antibodies.

[0340] A machine-readable storage medium can include data storage material encoded with machine-readable data or data arrays that can be used for a variety of purposes when used with a machine programmed with instructions for using the data. Determining effective amounts of the biomarkers of the present invention and / or assessing risk derived from those biomarkers can be implemented by a computer program running on a programmable computer that includes, among other things, 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. The program code can be applied to the 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 can be, for example, a personal computer, microcomputer, or workstation of conventional design.

[0341] Each program may be implemented in a high-level procedural or object-oriented programming language to enable communication with a computer system. However, the programs may also be implemented in assembly or machine language, if desired. The language may be a compiled or interpreted language. Each such computer program may be stored on a storage medium or device (e.g., a ROM or magnetic diskette, or as otherwise defined elsewhere herein) readable by a general-purpose or special-purpose programmable computer. When the storage medium or device is read by a computer, it configures and operates the computer to perform the procedures described herein. It is contemplated that the health-related data management system used in some aspects of the present invention may also be implemented as a computer-readable storage medium configured with a computer program. Such a configured storage medium causes the computer to operate in a specific, predetermined manner to perform the various functions described herein.

[0342] In some embodiments, the polypeptide determinants of the present invention can be used to generate a "reference determinant profile" for subjects without an infection. The determinants disclosed herein can also be used to generate a "subject determinant profile" obtained from a subject with an infection. Comparing a subject determinant profile to a reference determinant profile can diagnose or identify a subject with an infection. Comparing a subject determinant profile with a different type of infection can diagnose or identify the type of infection. In some embodiments, the reference determinant profiles and subject determinant profiles of the present invention can be contained on a machine-readable medium, such as, but not limited to, an analog tape readable by a VCR, CD-ROM, DVD-ROM, or USB flash media. Such machine-readable media can also include additional test results, such as, but not limited to, clinical parameters and measurements of traditional laboratory risk factors. Alternatively or additionally, the machine-readable medium can also include subject information, such as medical history and any relevant family history. The machine-readable medium can also include information regarding other disease risk algorithms and calculated indices, including those described herein.

[0343] As used herein, the term "about" refers to ±10%.

[0344] The words "comprises," "comprising," "includes," "including," "having," and their conjugations mean "including but not limited to."

[0345] The term "consisting of" means "including and limited to."

[0346] The term "consisting essentially of" means that a composition, method, or structure may include additional components, steps, and / or moieties, but only if those additional components, steps, and / or moieties do not materially alter the basic and novel characteristics of the claimed composition, method, or structure.

[0347] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" can include a plurality of compounds, including mixtures thereof.

[0348] Throughout this application, various embodiments of the 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 a strict limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values within that range. For example, description of a range such as 1 to 6 should be considered to have specifically disclosed subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as individual numerical values within that range (e.g., 1, 2, 3, 4, 5, and 6). This applies regardless of the breadth of the range.

[0349] When a range of values is given herein, it is meant to include any number (fractional or integer) within the given range. The phrases "ranging / ranges between" a first and a second indicated value and "ranging / ranges from" a first indicated value to a second indicated value are used interchangeably herein and are meant to include the first and second indicated values and all fractional and integer values therebetween.

[0350] As used herein, the term "method" refers to ways, means, techniques, and procedures for accomplishing a given task, and includes, but is not limited to, modalities, methods, techniques, and procedures that are known to those of skill in the art of, for example, chemistry, pharmacology, biology, biochemistry, and medicine, or that are readily developed from known ways, means, techniques, and procedures.

[0351] As used herein, the term "treating" includes arresting, substantially inhibiting, slowing, or reversing the progression of a condition, substantially ameliorating the clinical or cosmetic symptoms of a condition, or substantially preventing the appearance of clinical or cosmetic symptoms of a condition.

[0352] It will be understood that certain features of the invention that 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 that are, for brevity, described in the context of a single embodiment, may also be provided separately, in any suitable subcombination, or as appropriate with any other described embodiment of the invention. Particular features described in the context of various embodiments should not be considered essential features of those embodiments, unless the embodiment is non-functional without that element.

[0353] While the present invention has been described in conjunction with specific embodiments, 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.

[0354] It is the intention of the applicants that all publications, patents, and patent applications mentioned herein be incorporated by reference in their entireties to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated by reference. Furthermore, 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. Section headings, if used, should not be construed as necessarily limiting. Additionally, the priority documents of this application are incorporated herein by reference in their entireties.

[0355] Various embodiments and aspects of the present invention as delineated above and as claimed in the claims section below find experimental support in the following examples.

[0356] Example Reference is now made to the following examples, which together with the above descriptions, illustrate some embodiments of the invention in a non-limiting manner.

[0357] Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular biology, biochemistry, microbiology, and recombinant DNA techniques. These techniques are fully explained in the literature. See, for example, "Molecular Cloning: A Laboratory Manual" by Sambrook et al. (1989); "Current Protocols in Molecular Biology" Volumes I-III, Ausubel, R.M., ed. (1994); Ausubel et al., "Current Protocols in Molecular Biology", John Wiley & 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); 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-III Cellis, JE, ed. (1994); "Culture of Animal Cells - A Manual of Basic Technique" by Freshney, Wiley-Liss, NY (1994), Third Edition; "Current Protocols in Immunology" Volumes I-III Coligan, JE, 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", WHFreeman and Co., New York (1980); available immunoassays are widely described in the patent and scientific literature, e.g., U.S. Patent 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, MJ, ed.(1984);“Nucleic Acid Hybridization” Hames, BD, and Higgins SJ, eds.(1985);“Transcription and Translation” Hames, BD, and Higgins SJ, eds.(1984);“Animal Cell Culture” Freshney, RI, ed.(1986);“Immobilized Cells and Enzymes” IRL Press, (1986);"A Practical Guide to Molecular Cloning" Perbal, B., (1984), "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 herein by reference in their entirety. Other general references are provided throughout this specification. These procedures are believed to be well known in the art and are provided for the convenience of the reader, and all information contained therein is incorporated herein by reference.

[0358] Example 1 The study cohort is shown in Table 4.

[0359] [Table 4]

[0360] Some patients were included in more than one group.

[0361] Currently, COVID-19 is the main cause of viral infection, so in some of the above groups, COVID-19 patients are included in the viral infection patients.

[0362] The etiology of the disease was determined by applying a rigorous expert panel adjudication process.

[0363] All patients had blood drawn during their hospital stay or emergency department visit. Comparisons were made between patient groups to identify differential markers in serum.

[0364] In addition to this cohort, we used two publicly available datasets to compare severe and non-severe COVID-19.

[0365] Severity endpoint definitions: Certain biomarkers and / or signatures can be used to predict the following severe endpoints: SIRS without infection, sepsis, severe sepsis, septic shock, maximum NEWS score, maximum SOFA score, lowest SaO2 / FiO2 ratio, lowest PaO2 / FiO2 ratio. Specifically, the following severe outcomes can be predicted: requirement for vasopressors, invasive mechanical ventilation (IMV), intensive care unit (ICU) / step-down unit / emergency department (ED) monitoring, emergency department length of stay (ED-LOS), hospital length of stay (Hospital LOS), ICU length of stay (ICU LOS), renal replacement therapy, and death (24-hour mortality, 3-day mortality, 7-day mortality, 14-day mortality, 28-day mortality, in-hospital mortality).

[0366] Materials and Methods Protein screening was performed using Olink Proteomics' PEA technology (Olink® Explore 1536). In total, 1472 proteins were measured across four different panels (cardiometabolic, inflammatory, neurological, and oncological). The protein measurements obtained allowed for relative quantification, and results are expressed in arbitrary units of normalized protein expression (NPX) on a log2 scale.

[0367] Biomarker performance evaluation: Performance indicators for distinguishing between the two patient groups include: 1. Area under the receiver operating characteristic curve (AUC) 2. Difference (Delta) between group medians in NPX.

[0368] Since NPX units are on a log2 scale, the ratio of group medians (also known as fold change) can be calculated by exponentially calculating NPX delta, yielding a ratio of 2 デルタ It can be found that:

[0369] For comparisons between patient groups, markers with an AUC > 0.8 were included in the list of high-performing markers, and this list was prioritized based on NPX delta. The public COVID dataset was used to expand the list of high-performing biomarkers. Markers with an AUC < 0.8 in the primary cohort but > 0.75 in the public cohort were added to the list.

[0370] result The following proteins were found to have high AUC and exhibit differential expression between bacterial and viral infections:

[0371] [Table 5]

[0372] The proteins that showed the largest delta values (log2 of fold change) were REG1B (delta value 2.760628), FGF23 (delta value 2.352782), and CCL20 (delta value 2.256179).

[0373] The proteins listed in Table 6 showed high AUC and were found to be differentially expressed between severe and non-severe infections.

[0374] [Table 6-1] [Table 6-2] [Table 6-3]

[0375] Table 7 lists proteins that showed high AUC and differential expression between infectious and non-infectious etiologies.

[0376] [Table 7]

[0377] Example 2 A second study was carried out to identify specific markers useful in determining the severity of infectious disease.

[0378] Test Overview Inclusion Criteria: Patients suspected of having an acute infection 18 years or older -Patients with clinical suspicion of acute infection as defined by the attending physician based on clinical symptoms Healthy subjects 18 years or older -Those without clinical suspicion of acute infection

[0379] Exclusion criteria: In patients with suspected acute infection: Patients who met the following criteria were not eligible to participate in this study: HIV, HBV, active HCV, or active tuberculosis infection (self-reported or based on medical records) Pregnancy (self-reported or medically confirmed) Healthy subjects: Those who met the following criteria were ineligible to participate in this study: History of infection in the past two weeks Major trauma and / or burns and / or surgery in the past two weeks HIV, HBV, active HCV, or active tuberculosis infection (self-reported or based on medical records) Those who are planning to undergo planned surgery Pregnancy (self-reported or medically confirmed)

[0380] Protein screening was performed using two multiplex immunoassays: the Human Magnetic Luminex® assay and the RayBiotech Custom Quantibody® human assay, as well as four single ELISAs. A total of 54 proteins were measured, and absolute protein concentrations were obtained. The study cohort included 247 patients, of which 87 were severely ill and 160 were non-severely ill (see Table 8). Additionally, MR-proADM was measured in a subset of the cohort (44 severely ill and 75 non-severely ill) using the B·R·A·H·M·S MR-proADM KRYPTOR assay.

[0381] [Table 8]

[0382] The etiology of the disease was determined by applying a rigorous expert panel adjudication process.

[0383] All patients had blood drawn during their hospital stay or emergency department visit. Comparisons were made between patient groups to identify differential markers in serum.

[0384] The National Early Warning Score (NEWS) was calculated for a subset of the cohort (62 severely affected and 121 non-severely affected patients).

[0385] Severe cases were defined as those who died within 14 days of blood collection or those who met any of the following outcomes within 3 days of blood collection: Vasopressor therapy Intubation with mechanical ventilation Non-invasive ventilation Admission to the intensive care unit (ICU)

[0386] Patients who did not have any of the above outcomes were defined as non-severe.

[0387] Measurement of biomarker performance: Performance measures for distinguishing between severe and non-severe groups included sensitivity (detection of severe patients) and specificity at two cutoffs: Exclusion cutoff: Determined based on a required sensitivity of 90% Confirmatory cutoff: Determined based on a required specificity of 80% Performance of multiple marker combinations is based on probability from a logistic regression model.

[0388] result Table 9 summarizes the results of the associated proteins with respect to their ability to confirm or rule out severe infection using specific cutoffs.

[0389] [Table 9]

[0390] Table 10 summarizes the results of protein pairs regarding their ability to confirm or rule out severe infection based on probability from a logistic regression model.

[0391] The pair AGER+ANG-2 showed improved performance compared to the single markers.

[0392] The pairs AGER+ST2 and ST2+ANG-2 showed improved performance in "definite".

[0393] [Table 10]

[0394] Table 11 summarizes the results when AGER and ANG-2 are used as single markers or as a pair of markers to determine severity in subgroups of subjects, or when different definitions of severity are applied.

[0395] [Table 11]

[0396] Table 12 summarizes the results when ST2 and ANG-2 are used as single markers or as a pair of markers to determine severity in subgroups of subjects, or when different definitions of severity are applied.

[0397] [Table 12]

[0398] Table 13 summarizes the results when AGER and ST2 were used as single markers and as a pair of markers to determine severity in subgroups of subjects, or when different definitions of severity were applied.

[0399] [Table 13]

[0400] The ability to predict the severity of infection using three pairs (AGER and ST2; ANG and ST2; and ANG and AGER) was compared with an existing clinical index, the National Early Warning Score (NEWS). The results are summarized in Table 14.

[0401] [Table 14]

[0402] The ability of the marker MR-proADM to predict the severity of infection was also analyzed. As shown in Table 15, in combination with additional determinants, this marker showed utility in determining the severity of infection.

[0403] [Table 15]

[0404] As summarized in Table 16, IP10 improved the ability of certain markers to determine the severity of infectious disease.

[0405] [Table 16]

[0406] As summarized in Table 17, IP10 improved the ability of certain pairs to determine the severity of infectious disease.

[0407] [Table 17]

[0408] Example 3 Materials and Methods The study cohort consisted of 261 COVID-19 patients prospectively enrolled at 37 study sites (29 in Greece and 8 in Italy) as part of a double-blind, randomized trial. Of the 261 patients, 167 (64.0%) were men, and 188 (73.2%) had severe pneumonia diagnosed according to the WHO classification. The mean age was 55.5 years, and the mean BMI was 25.7. All patients in this cohort were treated according to standard treatment guidelines. Of note, 206 patients (78.9%) received dexamethasone treatment during the study.

[0409] Sample measurements: suPAR levels were measured using an ELISA assay. IP-10 levels were measured using the MeMed key™ platform.

[0410] Severe outcome was defined as severe respiratory failure (SRF) or death within 14 days of blood sampling. SRF was defined as a respiratory ratio (partial pressure of oxygen (PaO2) / fraction of inspired oxygen (FiO2)) of less than 150 mmHg requiring noninvasive ventilation (NIV) or mechanical ventilation (MV). Fifteen percent of patients in the cohort had a severe outcome.

[0411] result The combination of IP-10 and suPAR was shown to accurately discriminate between severe and non-severe outcomes, as summarized in Table 18.

[0412] Table 18. Accuracy of IP-10 and suPAR alone / in combination to distinguish between severe and non-severe outcomes [Table 18]

[0413] The combination of IL-6 and suPAR was shown to accurately discriminate between severe and non-severe outcomes, as summarized in Table 19.

[0414] Table 19. Accuracy of IL-6 and suPAR alone / in combination to distinguish between severe and non-severe outcomes [Table 19]

[0415] Additionally, the priority document of this application is incorporated herein by reference in its entirety.

Claims

1. A method for determining infectious diseases in a subject, This includes measuring the expression level of at least one protein selected from the group consisting of tumor necrosis factor-inducible gene 14 protein (TSG-14), advanced glycation end product-specific receptor (AGER), angiopoietin-2 (ANG-2), and interleukin-1 receptor-like 1 (ST2) in the target sample. The aforementioned expression level indicates the presence of an infectious disease. method.

2. (i) If the expression level of TSG-14 is more than twice as high as that in the control sample, it is determined to be a severe infectious disease; (ii) If the expression level of AGER is more than twice as high as the level in the control sample, it is determined to be a severe infectious disease; (iii) If the expression level of ANG-2 is more than twice as high as the level in the control sample, it is determined to be a severe infectious disease; and / or (iv) If the expression level of ST2 is three times or more higher than the level in the control sample, it is determined to be a severe infectious disease. The method according to claim 1.

3. (i) If the expression level of TSG-14 is less than approximately 930 pg / ml, it is determined that the patient does not have a severe infectious disease; (ii) If the AGE expression level is less than approximately 960 pg / ml, it is determined that the patient does not have a severe infectious disease; (iii) If the expression level of ANG-2 is less than approximately 1800 pg / ml, it is determined that the patient is not suffering from a severe infectious disease; and / or (iv) If the expression level of ST2 is less than approximately 28,000 pg / ml, it is determined that it is not a severe infectious disease. The method according to claim 1.

4. (i) If the expression level of TSG-14 exceeds approximately 6000 pg / ml, it is determined to be a severe infectious disease; (ii) If the AGE expression level exceeds approximately 3200 pg / ml, it is determined to be a severe infectious disease; (iii) If the expression level of ANG-2 exceeds approximately 5000 pg / ml, it is determined to be a severe infectious disease; and / or (iv) If the expression level of ST2 exceeds approximately 140,000 pg / ml, it is determined to be a severe infectious disease. The method according to claim 1.

5. The method according to claim 1, wherein the at least one protein comprises at least two proteins.

6. The method according to claim 5, wherein the at least two proteins comprise ANG-2 and AGER; AGER and ST2; or ANG-2 and ST2.

7. The method according to claim 6, further comprising measuring the expression level of IP-10 and determining the infectious disease based on the expression level of IP-10 in combination with the expression levels of the at least two proteins.

8. The method according to claim 1, further comprising measuring the expression levels of TRAIL and / or CRP.

9. The method according to claim 1, further comprising measuring all components of a clinical index selected from the group consisting of NEWS, NEWS 2, MEWS APACHE I, APACHE II, APACHE III, CURB-65, SMART-COP, SAPS II, SAPS III, PIM2, CMM, SOFA, qSOFA, MPM, RIFLE, CP, MODS, LODS, Rochester Criteria, Philadelphia Criteria, Milwaukee Criteria, and Lanson Score.

10. The method according to claim 1, wherein the infectious disease is a viral infection.

11. The method according to claim 1, wherein the infectious disease is a bacterial infection.

12. The method according to claim 1, wherein the subject exhibits symptoms of an infectious disease.

13. The method according to claim 1, wherein the sample is whole blood or a fraction thereof.

14. The method according to claim 1, wherein six or fewer proteins are measured to determine the infectious disease.

15. The method according to claim 1, wherein determining the infectious disease includes determining the severity of the infectious disease.

16. A kit for diagnosing infection, comprising a detection reagent that specifically detects at least two determinants selected from the group consisting of TSG-14, AGER, ANG-2, and ST2.

17. The kit according to claim 16, further comprising a detection reagent for specifically detecting IP-10.

18. The kit according to claim 16, further comprising a detection reagent for specifically detecting TRAIL and / or CRP.

19. The kit according to claim 16, wherein the detection reagent is an antibody.

20. The kit according to claim 16, comprising a detection reagent for specifically detecting six or fewer protein markers.