Kit for identifying the class of infection of a sample to be tested by detecting the expression of genes in the peripheral blood of a host and method therefor

Abstract

This invention provides a kit and method for identifying the infection type of a test sample by detecting gene expression in host peripheral blood. The kit simultaneously detects the expression levels of two groups of genes in the test sample. The first group consists of genes for identifying bacterial infection, including the S100P, ANXA3, ITGAX, and ITGAM genes. The second group consists of genes for identifying viral infection, including the IFI44L, IFIT3, IFIH1, LY6E, IFIT1, and IFITM3 genes. Detection of these two groups of genes determines whether the test sample is infected with bacteria, virus, a combination of bacteria and virus, or neither bacteria nor virus. This invention screens out 10 genes expressed in peripheral blood, achieving a balance between the cost and accuracy of distinguishing between bacterial and viral infections, ultimately enabling clinical application.

Description

Technical Field

[0001] This invention relates to the field of bacterial and viral detection technology, and in particular to a kit and method for identifying the infection type of a test sample by detecting gene expression in host peripheral blood. Background Technology

[0002] Rapidly differentiating between viral and bacterial infections is crucial for preventing antibiotic overuse. Currently, some quick and simple methods exist in clinical practice to distinguish between viral and bacterial infections. These include analyzing changes in neutrophil and lymphocyte levels in peripheral blood or finger-prick blood, or detecting changes in C-reactive protein levels, to roughly differentiate between bacterial and viral infections. While these methods are simple, they lack specificity and sensitivity. Clinically, there is an urgent need for methods with better specificity and sensitivity to differentiate between bacterial and viral infections.

[0003] We have been trying to distinguish between bacterial and viral infections by the differences in gene expression in nucleated cells in the host’s blood. This is because after bacteria and viruses infect the body, there are differences in the mechanisms by which they activate the host’s immune system. Therefore, there must be genes in the host cells that are expressed differently for bacterial and viral infections. For example, in 2009, Zaas et al. found mRNA of 30 genes in peripheral blood gene expression to distinguish between viral and bacterial infections with an accuracy of 93% (1. Zaas AK, Chen M, Varkey J, Veldman T, Hero AO, 3rd, Lucas J, Huang Y, Turner R, Gilbert A, Lambkin-Williams R et al: Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 2009, 6(3):207-217). In 2013, the team used RT-PCR to detect the expression of six mRNAs in peripheral blood to identify viral infection, achieving 89% sensitivity and 94% specificity (Zaas AK, Burke T, Chen M, McClain M, Nicholson B, Veldman T, Tsalik EL, Fowler V, Rivers EP, Otero R et al: A host-based RT-PCR gene expression signature to identify acute respiratory viral infection. Sci Transl Med 2013, 5(203):203ra126). Xu et al. found that genes related to the interferon signaling pathway in peripheral blood were highly expressed during viral infection; while genes in the integrin signaling pathway were highly expressed during bacterial infection (Hu X, Yu J, Crosby SD, Storch GA: Gene expression profiles in febrile children with defined viral and bacterial infection. Proc Natl Acad Sci USA 2013, 110(31):12792-12797).Herberg et al. even identified two differentially expressed genes in peripheral blood, which can differentiate between viral and bacterial infections in febrile children. This method has relatively high sensitivity and specificity in smaller populations (Herberg JA, Kaforou M, Wright VJ, Shailes H, Eleftherohorinou H, Hoggart CJ, Cebey-Lopez M, Carter MJ, Janes VA, Gormley S et al: Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children. JAMA 2016, 316(8):835-845). However, with the expansion of case studies, the specificity dropped to 78.9%, and the sensitivity dropped to 48.8% (Myrsini Kaforou, Jethro A. Herberg, Victoria J. Wright, Lachlan JM Coin, Michael Levin, et al., Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger. JAMA, 2017;317:1577-1578). This indicates that when the number of genes is too small, the results of using mRNA expression changes to reflect pathogen infection are less stable across different populations. These findings, either due to the large number of genes making practical application difficult, or due to decreased accuracy in subsequent testing with more samples, have not yet entered clinical application.In 2025, the US FDA approved the clinical application diagnostic product IMX-BVN-1, developed by Inflammatix, which uses the expression of 29 genes mRNA in peripheral blood to differentiate between viral and bacterial infections in sepsis patients. (Halder A, Liesenfeld O, Whitfield N, Uhle F, Schenz J, Mehrabi A, Schmitt FCF, Weigand MA, Decker SO: A 29-mRNA host-response classifier identifies bacterial infections following liver transplantation - a pilot study. Langenbecks Arch Surg 2024, 409(1):185; Shojaei M, Chen UI, Midic U, Thair S, Teoh S, McLean A, Sweeney TE, Thompson M, Liesenfeld O, Khatri P et al: Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza. Eur J Clin Invest 2023, 53(5):e13957). However, the number of genes is still too large, making it difficult to widely promote in clinical practice. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a kit for identifying the infection type of a test sample by detecting gene expression in host peripheral blood. The kit simultaneously detects the expression levels of two groups of genes in the test sample. The first group consists of genes used to identify bacterial infection, including the S100P, ANXA3, ITGAX, and ITGAM genes. The second group consists of genes used to identify viral infection, including the IFI44L, IFIT3, IFIH1, LY6E, IFIT1, and IFITM3 genes. Detection of these two groups of genes determines whether the test sample is infected with bacteria, virus, both bacteria and virus, or neither bacteria nor virus.

[0005] In one embodiment, the primers and probes for detecting the host S100P gene are SEQ ID No. 37, SEQ ID No. 38, and SEQ ID No. 39; the primers and probes for detecting the host ANXA3 gene are SEQ ID No. 40, SEQ ID No. 41, and SEQ ID No. 42; the primers and probes for detecting the host ITGAX gene are SEQ ID No. 43, SEQ ID No. 44, and SEQ ID No. 45; and the primers and probes for detecting the host ITGAM gene are SEQ ID No. 46, SEQ ID No. 47, and SEQ ID No. 48.

[0006] In one embodiment, the primers and probes for detecting the host IFI44L gene are SEQ ID No. 52, SEQ ID No. 53, and SEQ ID No. 54; the primers and probes for detecting the host IFIT3 gene are SEQ ID No. 55, SEQ ID No. 56, and SEQ ID No. 57; the primers and probes for detecting the host IFIH1 gene are SEQ ID No. 58, SEQ ID No. 59, and SEQ ID No. 60; the primers and probes for detecting the host LY6E gene are SEQ ID No. 49, SEQ ID No. 50, and SEQ ID No. 51; the primers and probes for detecting the host IFIT1 gene are SEQ ID No. 58, SEQ ID No. 59, and SEQ ID No. 60; and the primers and probes for detecting the host IFITM3 gene are SEQ ID No. 64, SEQ ID No. 65, and SEQ ID No. 66.

[0007] In one embodiment, the kit is also used to detect the expression level of the internal reference gene HPRT1, and the primers and probes for detecting the host internal reference gene HPRT1 are SEQ ID No. 67, SEQ ID No. 68 and SEQ ID No. 69.

[0008] In one embodiment, the present invention also provides the application of the above-described kit in the preparation of products for identifying the infection category of a sample to be tested.

[0009] In one embodiment, the present invention provides primers, probes, or combinations thereof, which simultaneously detect the expression levels of two groups of genes in the sample to be tested. The first group consists of genes used to identify bacterial infection, namely the S100P gene, ANXA3 gene, ITGAX gene, and ITGAM gene. The second group consists of genes used to identify viral infection, namely the IFI44L gene, IFIT3 gene, IFIH1 gene, LY6E gene, IFIT1 gene, and IFITM3 gene. By detecting the above two groups of genes, it can be determined whether the sample to be tested is infected with bacteria, infected with a virus, infected with both bacteria and a virus, or infected with neither bacteria nor a virus.

[0010] In one embodiment, the present invention provides a method for identifying the infection category of a test sample by detecting gene expression in host peripheral blood for non-diagnostic purposes, the method comprising:

[0011] Step 1: Simultaneously detect the expression levels of two groups of genes in the sample to be tested. The first group consists of genes used to identify bacterial infection, namely S100P, ANXA3, ITGAX, and ITGAM genes. The second group consists of genes used to identify viral infection, namely IFI44L, IFIT3, IFIH1, LY6E, IFIT1, and IFITM3 genes.

[0012] Step 2: Based on the probability formula for bacterial infection of the sample, p = 1 - 1 / (1 + EXP(-score)), calculate the probability p value of bacterial infection, where Score = -6.171015 + 0.002756 *S100P + 0.001815 *ANXA3 + 0.000642 *ITGAX + 0.001021 *ITGAM. In the formula, each gene represents the expression value of each target gene relative to the internal reference gene HPRT1 = 2. （HPRT1 的Ct-靶基因的Ct） *100;

[0013] Based on the probability formula for viral infection of the sample, p = 1 - 1 / (1 + EXP(-score)), the probability p-value of viral infection of the sample is calculated, where Score = -6.015914 + 0.000499 *IFI44L + 0.000441 *IFIT3 + 0.001211 *IFIH1 + 0.000082 *LY6E + 0.001187 *IFIT1 + 0.000515 *IFITM3, where each gene in the formula represents the expression value of each target gene relative to the internal reference gene HPRT1 = 2. （HPRT1 的Ct-靶基因的Ct） *100;

[0014] Step 3: Compare the probability p-value of the sample to be infected with bacteria with the threshold for bacterial infection, and compare the probability p-value of the sample to be infected with virus with the threshold for viral infection.

[0015] If the probability p-value of the sample being infected with bacteria is greater than or equal to the threshold for bacterial infection and the probability p-value of the sample being infected with virus is greater than or equal to the threshold for viral infection, then the sample being tested is simultaneously infected with bacteria and virus.

[0016] If the probability p-value of the sample being infected with bacteria is less than the threshold for bacterial infection and the probability p-value of the sample being infected with virus is less than the threshold for viral infection, then the sample is neither bacterial nor viral infected.

[0017] If the probability p-value of the sample being infected with bacteria is greater than or equal to the bacterial infection threshold, and the probability p-value of the sample being infected with a virus is less than the viral-bacterial threshold, then the sample is infected with bacteria; and

[0018] If the probability p-value of the sample being infected with bacteria is less than the threshold for bacterial infection, and the probability p-value of the sample being infected with virus is greater than or equal to the threshold for viral infection, then the sample being tested is infected with virus.

[0019] In fields such as medical diagnosis and machine learning model evaluation, sensitivity, specificity, and AUC area are important indicators for measuring the performance of models or diagnostic methods. Sensitivity refers to the proportion of individuals who are correctly identified as having the disease (or being positive) among all actual infected (or positive) individuals. It reflects the model's ability to "not miss diagnoses"; higher sensitivity means it can identify more individuals who are actually infected. Specificity refers to the proportion of individuals who are correctly identified as not having the disease (or being negative) among all actual uninfected (or negative) individuals. It reflects the model's ability to "not misdiagnose"; higher specificity means it can exclude individuals who are not actually infected (e.g., in scenarios where overtreatment needs to be avoided, high specificity can reduce unnecessary intervention). AUC (Area Under the ROC Curve) is the area under the ROC curve (Receiver Operating Characteristic Curve), which is a curve plotted with the false positive rate (FPR) on the horizontal axis and the true positive rate (TPR, i.e., sensitivity) on the vertical axis. The AUC value ranges from 0 to 1, representing the model's overall ability to distinguish between "positive" and "negative" results. The closer the AUC is to 1, the stronger the model's ability to distinguish between positive and negative results. AUC is unaffected by the diagnostic threshold (the cutoff value for judging "positive / negative") and comprehensively reflects the model's overall performance at different thresholds, making it suitable for comparing the merits of different models. In summary, sensitivity and specificity are indicators for specific thresholds, focusing on "no missed diagnoses" and "no false diagnoses," respectively. AUC is a comprehensive indicator that reflects the model's overall discriminative ability across all possible thresholds.

[0020] When interpreting the sensitivity and specificity of bacterial infections based on p-values, only the combination of four genes—S100P, ANXA3, ITGAX, and ITGAM—achieved the optimal sensitivity, specificity, and AUC area. Similarly, when interpreting the sensitivity and specificity of viral infections based on p-values, only the combination of six genes—IFI44L, IFIT3, IFIH1, LY6E, IFIT1, and IFITM3—achieved the optimal sensitivity, specificity, and AUC area.

[0021] This invention screens out 10 genes expressed in peripheral blood, enabling a balance between the cost and accuracy of detecting bacterial and viral infections, ultimately leading to clinical applications.

[0022] This invention ultimately screened out four genes whose mRNA expression could diagnose bacterial infections and six genes whose mRNA expression could diagnose viral infections. An arithmetic model was constructed to calculate the distribution of mRNA expression in these four and six genes, resulting in a final scoring model to distinguish between bacterial and viral infections. Furthermore, by using a fully automated fluorescent PCR analysis system to detect the mRNA expression of these four and six genes in a single test, results can be obtained within <2 hours, meeting the clinical need for rapid differentiation between bacterial and viral infections. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this application, the present invention will be further described below with reference to embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0024] From published literature and public databases, genes that were significantly upregulated in peripheral blood nucleated cells during bacterial infections, as reported in multiple studies, include S100P, MMP9, FAM89A, ANXA3, ITGAX, ITGB5, ITGA2B, ITGAM, CXCR1, and CXCR2. Genes that were significantly upregulated in peripheral blood nucleated cells during viral infections, as reported in multiple studies, include OAS1, ARID1B, LY6E, IFI44L, IFIT1, IFI27, IFIT3, IFIH1, IFITM3, CEACAM6, JCHAIN, and TXNDC5. These were mostly detected using microarray methods, which lacked quantification and presented scattered results. Primers and probes were designed for the mRNA transcripts of each gene identified in the above literature and databases. It was required that at least one primer or probe contain a transgenomic intron to ensure that mRNA detection was not affected by residual genomic DNA in the extracted nucleic acid. Then, the mRNA expression levels of the above genes were detected using peripheral blood samples from patients with clinically confirmed bacterial and viral infections. Table 1 shows the primers and probes designed to detect the mRNA expression levels of 22 target genes and internal reference genes.

[0025] Table 1

[0026] .

[0027] The qPCR reaction system for the above primers and probes used a premixed qRT-PCR reaction system manufactured by Novizan. The specific reaction system is as follows:

[0028] qPCR reaction system composition 2×qPCR premix system 10μl HPRT1 gene upstream primer 0.20 μM (final concentration) HPRT1 gene downstream primer 0.20 μM (final concentration) HPRT1 gene probe 0.10 μM (final concentration) upstream primer of target gene 0.20 μM (final concentration) downstream primers of target gene 0.20 μM (final concentration) Target gene probe 0.10 μM (final concentration) Nucleic acid template 5μl

[0029] Hydrate up to 20ml

[0030] Reaction conditions:

[0031] .

[0032] Fluorescence was collected at 61℃. The Ct values ​​of the internal reference gene and the target gene were recorded.

[0033] The probes for each gene and the internal reference gene HPRT1 were combined for detection. The probes for the target genes were labeled with FAM fluorescent dye, and the probes for the HPRT1 gene were labeled with VIC fluorescent dye. Based on the Ct values ​​of the target genes and HPRT1 genes, the relative expression value of the target gene compared to HPRT1 was obtained. （HPRT1 的Ct-靶基因的Ct） *100, and then an expression score is obtained based on the relative abundance of each target gene.

[0034] Nucleic acid extracted from peripheral blood samples (including 89 patients with viral infections, 78 patients with bacterial infections, and 108 patients without infection) was used as training data. The expression abundance of 22 target genes was detected using reverse transcription quantitative PCR, and the relative expression value of each gene was obtained by comparing it with the internal reference gene HPRT1. This training data was then input into a logistic regression model to combine different genes and assign an interpretation parameter to each gene to distinguish between viral and bacterial infections in the training data with maximum accuracy, and to determine the thresholds for bacterial and viral infection. From the 10 genes with high expression in peripheral blood caused by bacterial infection, a score was calculated based on the combination of gene expression abundance to obtain the probability of bacterial infection p = 1 - 1 / (1 + EXP(-score)). Bacterial infection was defined as a probability p >= the bacterial infection threshold. From the 12 genes that are highly expressed in peripheral blood due to viral infection, the probability of viral infection in a sample is obtained by scoring the combination of gene expression abundance, p = 1 - 1 / (1 + EXP(-score)). Viral infection is defined as a p-value >= the threshold of viral infection. If the p-values ​​of bacterial and viral infections are both less than their respective thresholds, the sample is not infected with bacteria or virus. If the p-values ​​of bacterial and viral infections are both greater than their respective thresholds, it is a mixed infection of bacteria and virus.

[0035] Then, using the gene combinations and interpretation parameters obtained from the training data, a new set of test specimens (74 viral infections, 25 bacterial infections, 18 bacterial + viral co-infections, and 73 no infection, totaling 190 samples) were interpreted. The sensitivity and specificity of the top 5 gene combinations with the highest accuracy in the test specimens are listed below. Here, for both bacterial and viral identification, the Score scoring formulas for the top 5 gene combinations with the highest accuracy are listed. The numbers preceding each target gene in the formula, as well as the thresholds for bacterial and viral infections, are calculated by the logistic regression model based on known clinical specimen groupings. The p-value is then calculated using the Score, and the sensitivity and specificity for distinguishing between bacterial and viral infections are then determined based on the p-value.

[0036] Combination 1: S100P+FAM89A+ITGB5+CXCR1+CXCR2 combination;

[0037] Score=-3.961128+0.003242*S100P+0.005618*FAM89A-0.001245*ITGB5 +0.028943*CXCR1+0.062554*CXCR2.

[0038] Combination 2: MMP9 + FAM89A + ITGAX + ITGAX2B combination:

[0039] Score=-2.110296-0.002465*MMP9+0.001778*FAM89A+0.012554*ITGAX-0.003361*ITGA2B.

[0040] Combination 3: S100P + ANXA3 + ITGAX + ITGAM

[0041] Score=-6.171015 + 0.002756 *S100P + 0.001815 *ANXA3 + 0.000642 *ITGAX+ 0.001021 *ITGAM.

[0042] Combination 4: FAM89A + ITGAX + ITGB5 + ITGAM + CXCR1 combination

[0043] Score=-4.119604-0.011443*FAM89A+0.002485*ITGAX+0.001108*ITGB5-0.010223*ITGAM-0.004443*CXCR1.

[0044] Combination 5: S100P + ITGAX + ITGAM + CXCR1 + CXCR2

[0045] Score=-1.064433-0.005644*S100P+0.051104*ITGAX-0.001323*ITGAM +0.003114*CXCR1+0.000288*CXCR2.

[0046] The sensitivity and specificity of combination 1 were calculated based on the corresponding score and p-value, as shown in Table 2 below. Similarly, for combinations 2, 3, 4, and 5, the score and p-value were calculated to obtain the corresponding sensitivity and specificity data for detecting bacterial infections. Table 2: Sensitivity, specificity, and AUC area obtained from different gene combinations in 43 clinically confirmed cases of bacterial infection (25 cases of bacterial infection and 18 cases of bacterial + viral co-infection) and 147 cases of non-viral infection.

[0047] Table 2

[0048] .

[0049] As shown above, the sensitivity and specificity of assessing bacterial infection based on different gene expression in peripheral blood were as follows. Combination 3 (S100P+ANXA3+ITGAX+ITGAM combination) achieved the highest sensitivity, specificity and accuracy.

[0050] Table 3 shows the specific data for identifying bacterial infections using combination 3, including the expression value of each gene relative to the internal reference gene, z-score, p-value, and interpretation results.

[0051] Table 3

[0052] Sample number Clinical determination of infection type S100P expression value ANXA3 expression value ITGAX expression value ITGAM expression value Z-score P (bacteria) A value >=0.5 indicates a bacterial infection. Does the interpretation match the actual situation? Path-094 bacteria 1493 762 2747 2343 3.48208 0.97017 bacteria Consistent Path-099 bacteria 1300 865 1247 650 0.44460 0.60935 bacteria Consistent Path-102 bacteria 155 243 2767 1204 -2.29816 0.09128 normal Inconsistent Path-106 bacteria 531 27 1006 594 -3.40474 0.03215 normal Inconsistent Path-108 bacteria 168 348 3600 4987 2.32720 0.91110 bacteria Consistent Path-127 bacteria 542 624 3336 1503 0.13157 0.53285 bacteria Consistent Path-129 bacteria 2825 285 4222 1611 6.48641 0.99848 bacteria Consistent Path-130 bacteria 2053 246 2053 1374 2.65617 0.93439 bacteria Consistent Path-132 bacteria 1668 329 3625 2599 4.00496 0.98210 bacteria Consistent Path-134 bacteria 1943 204 3650 1514 3.44279 0.96902 bacteria Consistent Path-135 bacteria 1556 158 2216 1556 1.41582 0.80468 bacteria Consistent Path-136 bacteria 1273 756 1680 1171 0.98301 0.72771 bacteria Consistent Path-137 bacteria 1355 74 2186 1056 0.17755 0.54427 bacteria Consistent Path-142 bacteria 1255 53 2617 1056 0.14268 0.53561 bacteria Consistent Path-156 bacteria 1825 154 822 1997 1.70555 0.84626 bacteria Consistent Path-161 bacteria 2581 281 1291 3478 5.83190 0.99708 bacteria Consistent Path-166 bacteria 2563 121 1179 2247 4.16412 0.98469 bacteria Consistent Path-170 bacteria 2617 99 673 1611 3.29808 0.96436 bacteria Consistent Path-176 bacteria 1929 363 1147 2654 3.25101 0.96271 bacteria Consistent Path-181 bacteria 2392 279 1093 1813 3.47924 0.97009 bacteria Consistent Path-183 bacteria 2747 283 1034 2635 5.26879 0.99488 bacteria Consistent Path-186 bacteria 1171 513 1163 1589 0.35784 0.58852 bacteria Consistent Path-187 bacteria 1645 346 1163 2068 1.84816 0.86391 bacteria Consistent Path-189 bacteria 2294 420 1188 1108 2.80806 0.94311 bacteria Consistent Path-190 bacteria 1221 216 1188 2728 1.13402 0.75658 bacteria Consistent Path-073 bacteria + virus 1916 277 3701 1634 3.65641 0.97483 bacteria Consistent Path-096 bacteria + virus 358 574 2156 1264 -1.46880 0.18713 normal Inconsistent Path-098 bacteria + virus 757 76 711 602 -2.87634 0.05334 normal Inconsistent Path-101 bacteria + virus 157 578 3156 1600 -1.03054 0.26298 normal Inconsistent Path-105 bacteria + virus 3006 778 3912 2442 8.53221 0.99980 bacteria Consistent Path-107 bacteria + virus 822 817 4432 1364 1.81671 0.86017 bacteria Consistent Path-111 bacteria + virus 2068 1013 6400 1124 6.62157 0.99867 bacteria Consistent Path-115 bacteria + virus 73 2111 4432 1535 2.27521 0.90680 bacteria Consistent Path-126 bacteria + virus 92 550 2805 3832 0.79453 0.68880 bacteria Consistent Path-131 bacteria + virus 1403 1997 9114 5382 12.66551 1.00000 bacteria Consistent Path-133 bacteria + virus 1273 67 2201 1383 0.28451 0.57065 bacteria Consistent Path-139 bacteria + virus 1462 131 3134 1622 1.76498 0.85383 bacteria Consistent Path-148 bacteria + virus 3575 570 971 2082 7.46591 0.99943 bacteria Consistent Path-149 bacteria + virus 2141 155 2691 2617 4.40938 0.98798 bacteria Consistent Path-151 bacteria + virus 2392 558 5382 1668 6.59130 0.99863 bacteria Consistent Path-172 bacteria + virus 811 817 2126 2247 1.20620 0.76963 bacteria Consistent Path-180 bacteria + virus 2476 531 2025 2359 5.32630 0.99516 bacteria Consistent Path-185 bacteria + virus 363 1048 2654 1877 0.35182 0.58706 bacteria Consistent Path-003 Virus 305 746 2476 1535 -0.81826 0.30613 normal Consistent Path-004 Virus 281 400 2786 1230 -1.62711 0.16423 normal Consistent Path-005 Virus 75 129 1452 469 -4.31927 0.01313 normal Consistent Path-010 Virus 62 123 971 444 -4.69997 0.00901 normal Consistent Path-011 Virus 142 119 1556 894 -3.65099 0.02531 normal Consistent Path-013 Virus 142 341 2359 1196 -2.42417 0.08135 normal Consistent Path-015 Virus 47 104 1188 677 -4.39867 0.01214 normal Consistent Path-016 Virus 116 189 1703 906 -3.48793 0.02966 normal Consistent Path-018 Virus 1155 356 2864 1691 1.22334 0.77265 bacteria Inconsistent Path-019 Virus 334 132 1300 645 -3.51771 0.02881 normal Consistent Path-020 Virus 888 243 1838 1213 -0.86590 0.29611 normal Consistent Path-021 Virus 325 111 1020 711 -3.69358 0.02428 normal Consistent Path-022 Virus 56 114 900 472 -4.74986 0.00858 normal Consistent Path-025 Virus 70 88 1634 654 -4.10287 0.01626 normal Consistent Path-027 Virus 110 120 1171 992 -3.88544 0.02013 normal Consistent Path-029 Virus 44 132 1056 726 -4.39008 0.01225 normal Consistent Path-030 Virus 103 255 1514 1116 -3.31398 0.03509 normal Consistent Path-032 Virus 206 57 1056 641 -4.16950 0.01522 normal Consistent Path-033 Virus 33 45 736 528 -4.98807 0.00677 normal Consistent Path-035 Virus 78 75 971 762 -4.41740 0.01192 normal Consistent Path-036 Virus 70 103 863 778 -4.44212 0.01163 normal Consistent Path-037 Virus 392 231 1116 778 -3.16062 0.04067 normal Consistent Path-038 Virus 246 341 1514 1131 -2.74648 0.06029 normal Consistent Path-040 Virus 696 60 971 834 -2.66710 0.06494 normal Consistent Path-042 Virus 394 168 1622 447 -3.28071 0.03624 normal Consistent Path-043 Virus 60 140 1877 1221 -3.29971 0.03558 normal Consistent Path-044 Virus 444 132 822 543 -3.62626 0.02593 normal Consistent Path-045 Virus 513 74 1139 682 -3.19455 0.03937 normal Consistent Path-046 Virus 114 414 2459 1273 -2.22675 0.09737 normal Consistent Path-047 Virus 49 59 767 677 -4.74438 0.00863 normal Consistent Path-049 Virus 1230 327 3134 1085 0.93148 0.71738 bacteria Inconsistent Path-050 Virus 79 233 2247 1070 -2.99502 0.04765 normal Consistent Path-051 Virus 204 264 2141 1462 -2.26210 0.09431 normal Consistent Path-052 Virus 668 353 2581 1230 -0.77649 0.31508 normal Consistent Path-054 Virus 55 83 1412 687 -4.26066 0.01392 normal Consistent Path-055 Virus 701 74 1355 546 -2.67682 0.06436 normal Consistent Path-056 Virus 752 316 1442 1139 -1.43706 0.19200 normal Consistent Path-058 Virus 1556 216 1336 757 0.14031 0.53502 bacteria Inconsistent Path-059 Virus 179 111 1336 900 -3.69954 0.02414 normal Consistent Path-063 Virus 356 187 1645 741 -3.03955 0.04567 normal Consistent Path-065 Virus 281 168 1703 1188 -2.78576 0.05810 normal Consistent Path-068 Virus 429 197 1916 1364 -2.00857 0.11831 normal Consistent Path-069 Virus 444 80 721 757 -3.56781 0.02744 normal Consistent Path-070 Virus 169 107 828 696 -4.26698 0.01383 normal Consistent Path-071 Virus 366 62 1041 411 -3.96285 0.01865 normal Consistent Path-074 Virus 84 228 2924 1213 -2.40975 0.08243 normal Consistent Path-075 Virus 554 109 1273 711 -2.90365 0.05197 normal Consistent Path-077 Virus 223 223 1800 925 -3.04915 0.04525 normal Consistent Path-078 Virus 731 503 1545 1383 -0.83909 0.30173 normal Consistent Path-079 Virus 115 539 3091 1916 -0.93572 0.28177 normal Consistent Path-081 Virus 191 210 1139 894 -3.62088 0.02606 normal Consistent Path-082 Virus 40 61 1204 731 -4.43168 0.01175 normal Consistent Path-084 Virus 108 201 2171 1255 -2.83285 0.05557 normal Consistent Path-085 Virus 757 351 1943 1108 -1.07015 0.25537 normal Consistent Path-086 Virus 469 138 1622 857 -2.71146 0.06230 normal Consistent Path-088 Virus 245 139 863 619 -4.05763 0.01700 normal Consistent Path-089 Virus 506 119 875 706 -3.27678 0.03638 normal Consistent Path-090 Virus 145 116 2171 1027 -3.11709 0.04241 normal Consistent Path-091 Virus 1567 211 2025 1093 0.94750 0.72061 bacteria Inconsistent Path-093 Virus 83 107 1775 900 -3.68923 0.02438 normal Consistent Path-103 Virus 400 106 432 611 -3.97630 0.01841 normal Consistent Path-109 Virus 35 543 4621 673 -1.43543 0.19225 normal Consistent Path-112 Virus 314 303 1048 1374 -2.68031 0.06415 normal Consistent Path-114 Virus 57 325 2247 611 -3.35815 0.03363 normal Consistent Path-124 Virus 101 456 1903 1155 -2.66435 0.06511 normal Consistent Path-138 Virus 95 71 2247 958 -3.36030 0.03356 normal Consistent Path-146 Virus 1188 115 696 636 -1.59254 0.16903 normal Consistent Path-159 Virus 180 55 251 659 -4.73944 0.00867 normal Consistent Path-163 Virus 164 93 361 650 -4.65595 0.00942 normal Consistent Path-167 Virus 773 341 811 2805 -0.03749 0.49063 normal Consistent Path-168 Virus 938 143 623 1751 -1.13707 0.24286 normal Consistent Path-171 Virus 496 479 985 3200 -0.03548 0.49113 normal Consistent Path-177 Virus 767 453 913 851 -1.77828 0.14452 normal Consistent Path-178 Virus 366 757 1524 2097 -0.67072 0.33833 normal Consistent Path-001 No infection 90 56 1238 602 -4.41338 0.01197 normal Consistent Path-002 No infection 57 25 992 531 -4.78800 0.00826 normal Consistent Path-006 No infection 172 86 1600 726 -3.77240 0.02248 normal Consistent Path-007 No infection 811 64 741 528 -2.80499 0.05706 normal Consistent Path-008 No infection 450 251 2617 1255 -1.51246 0.18057 normal Consistent Path-009 No infection 75 65 925 513 -4.72870 0.00876 normal Consistent Path-012 No infection 99 106 1680 840 -3.77035 0.02252 normal Consistent Path-014 No infection 32 38 673 510 -5.06261 0.00629 normal Consistent Path-017 No infection 42 29 999 486 -4.86600 0.00765 normal Consistent Path-023 No infection 285 52 1131 636 -3.91468 0.01956 normal Consistent Path-024 No infection 570 75 1300 673 -2.94429 0.05001 normal Consistent Path-026 No infection 48 45 598 623 -4.93709 0.00712 normal Consistent Path-028 No infection 67 51 1412 869 -4.09899 0.01632 normal Consistent Path-031 No infection 539 77 1196 811 -2.95040 0.04972 normal Consistent Path-034 No infection 602 113 938 828 -2.85804 0.05427 normal Consistent Path-039 No infection 606 109 1196 757 -2.76235 0.05939 normal Consistent Path-041 No infection 1163 115 1984 773 -0.69440 0.33305 normal Consistent Path-048 No infection 696 35 517 752 -3.08862 0.04358 normal Consistent Path-053 No infection 64 55 938 746 -4.53038 0.01066 normal Consistent Path-057 No infection 343 68 925 570 -3.92576 0.01935 normal Consistent Path-060 No infection 57 63 978 789 -4.46624 0.01136 normal Consistent Path-061 No infection 71 34 1355 623 -4.40881 0.01202 normal Consistent Path-062 No infection 726 121 1147 789 -2.40923 0.08247 normal Consistent Path-064 No infection 107 57 1282 913 -4.01748 0.01768 normal Consistent Path-066 No infection 682 41 1545 828 -2.37909 0.08478 normal Consistent Path-067 No infection 49 45 1247 602 -4.54009 0.01056 normal Consistent Path-072 No infection 62 42 1442 757 -4.22365 0.01443 normal Consistent Path-076 No infection 77 45 721 510 -4.89460 0.00743 normal Consistent Path-080 No infection 60 46 945 668 -4.63326 0.00963 normal Consistent Path-083 No infection 96 76 789 789 -4.45605 0.01147 normal Consistent Path-087 No infection 103 54 1578 611 -4.15266 0.01548 normal Consistent Path-092 No infection 971 44 1188 650 -1.98795 0.12047 normal Consistent Path-095 No infection 510 20 582 368 -3.98115 0.01832 normal Consistent Path-097 No infection 42 20 857 506 -4.95063 0.00703 normal Consistent Path-100 No infection 323 38 800 586 -4.10146 0.01628 normal Consistent Path-104 No infection 346 23 513 339 -4.50166 0.01097 normal Consistent Path-110 No infection 214 376 650 767 -3.69746 0.02419 normal Consistent Path-113 No infection 389 358 2635 951 -1.78564 0.14361 normal Consistent Path-116 No infection 57 130 1739 938 -3.70444 0.02402 normal Consistent Path-117 No infection 36 40 919 220 -5.18435 0.00557 normal Consistent Path-118 No infection 52 50 736 2082 -3.33650 0.03434 normal Consistent Path-119 No infection 65 417 2425 900 -2.76021 0.05951 normal Consistent Path-120 No infection 919 86 1503 586 -1.91952 0.12791 normal Consistent Path-121 No infection 114 566 5808 822 -0.26132 0.43504 normal Consistent Path-122 No infection 353 179 4281 1048 -1.05406 0.25845 normal Consistent Path-123 No infection 510 66 1462 1013 -2.67280 0.06460 normal Consistent Path-125 No infection 64 70 663 546 -4.88288 0.00752 normal Consistent Path-128 No infection 29 13 998 329 -5.09087 0.00612 normal Consistent Path-140 No infection 85 42 2375 1238 -3.07023 0.04435 normal Consistent Path-141 No infection 339 165 2459 2263 -1.04966 0.25929 normal Consistent Path-143 No infection 204 83 1545 1535 -2.89845 0.05223 normal Consistent Path-144 No infection 432 368 2201 2111 -0.74471 0.32197 normal Consistent Path-145 No infection 115 210 2617 800 -2.97629 0.04851 normal Consistent Path-147 No infection 134 168 2654 1567 -2.19330 0.10035 normal Consistent Path-150 No infection 174 297 4371 2141 -0.16025 0.46002 normal Consistent Path-152 No infection 397 51 985 875 -3.45808 0.03053 normal Consistent Path-153 No infection 1318 112 1656 999 -0.25346 0.43697 normal Consistent Path-154 No infection 158 49 741 1345 -3.79769 0.02193 normal Consistent Path-155 No infection 42 13 217 376 -5.50792 0.00404 normal Consistent Path-157 No infection 767 43 332 757 -2.99241 0.04777 normal Consistent Path-158 No infection 489 143 817 2581 -1.40306 0.19733 normal Consistent Path-160 No infection 211 116 535 971 -4.04311 0.01724 normal Consistent Path-162 No infection 762 127 482 1589 -1.90734 0.12928 normal Consistent Path-164 No infection 61 20 208 645 -5.17397 0.00563 normal Consistent Path-165 No infection 1247 115 524 767 -1.40669 0.19676 normal Consistent Path-169 No infection 180 397 1600 2654 -1.21651 0.22855 normal Consistent Path-173 No infection 166 69 641 1204 -3.94815 0.01893 normal Consistent Path-174 No infection 528 92 258 794 -3.57228 0.02732 normal Consistent Path-175 No infection 10 2 16 23 -6.10667 0.00222 normal Consistent Path-179 No infection 1383 44 840 1247 -0.46611 0.38554 normal Consistent Path-182 No infection 682 127 358 932 -2.88019 0.05314 normal Consistent Path-184 No infection 1345 196 623 513 -1.18314 0.23449 normal Consistent Path-188 No infection 299 54 389 269 -4.72423 0.00880 normal Consistent .

[0053] II. Identification of Viral Infections

[0054] Combination 1: OAS1+LY6E+IFI27+CEACAM6+TXNDC5 combination;

[0055] Score=-3.446005+0.001516*OAS1+0.002186*LY6E-0.001244*IFI27+0.001223*CEACAM6-0.006222*TXNDC5.

[0056] Combination 2: IFI44L+IFIT3+IFIH1+LY6E+IFIT1+IFITM3 combination:

[0057] Score = -6.015914 + 0.000499 *IFI44L + 0.000441 *IFIT3 + 0.001211 *IFIH1 + 0.000082 *LY6E + 0.001187 *IFIT1 + 0.000515 *IFITM3.

[0058] Combination 3: ARID1B+IFI44L+IFIT1+CEACAM6 combination;

[0059] Score=-4.326145+0.002236*ARID1B-0.018187*IFI44L-0.007124*IFIT1 +0.000821*CEACAM6.

[0060] Combination 4: IFI44L+IFIT1+IFI27+JCHAIN+TXNDC5 combination;

[0061] Score=-1.565428+0.010527*IFI44L+0.008156*IFIT1-0.003454*IFI27+0.008332*JCHAIN-0.003343*TXNDC5.

[0062] Combination 5: OAS1+ARID1B+IFIT1+IFI27+IFITM3+JCHAIN ​​combination;

[0063] Score=-7.323235+0.008417*OAS1+0.003156*ARID1B-0.081334*IFIT1+0.013423*IFI27+0.001223*JCHAIN.

[0064] Table 4 shows the sensitivity, specificity, and AUC area of ​​92 clinically confirmed cases with bacterial infection (74 of which were viral infections and 18 were combined bacterial and viral infections) and 98 cases without viral infection, obtained from different gene combinations.

[0065] Table 4

[0066] .

[0067] The sensitivity and specificity of assessing viral infection based on the expression of different genes in peripheral blood are as described above. Only combination 2 (IFI44L+IFIT3+IFIH1+LY6E+IFIT1+IFITM3 combination) achieved the best sensitivity, specificity, and accuracy. Table 5 shows the specific data for identifying viral infection using combination 2, including the expression value of each gene relative to the internal reference gene, z-score, p-value, and interpretation results.

[0068] Table 5

[0069] Sample number Clinical determination of infection type IFI44L expression value IFIT3 expression value IFIH1 expression level LY6E expression value IFIT1 expression value IFITM3 expression value Z-score P (virus) A value >=0.5 indicates a viral infection. Is the judgment consistent with reality? Path-003 Virus 3779 21527 10763 22132 18871 11299 48.43092 1.00000 Virus Consistent Path-004 Virus 2326 11860 4718 13624 7301 5235 18.56755 1.00000 Virus Consistent Path-005 Virus 1093 5768 3650 4432 5091 3650 9.78075 0.99994 Virus Consistent Path-010 Virus 1171 3832 1838 6907 2786 2563 3.67739 0.97533 Virus Consistent Path-011 Virus 1556 4589 2459 7934 4222 3267 7.10735 0.99918 Virus Consistent Path-013 Virus 1890 9114 5235 7506 6626 5308 16.49894 1.00000 Virus Consistent Path-015 Virus 1788 4784 2728 6580 2528 2945 5.34671 0.99526 Virus Consistent Path-016 Virus 1800 8504 5163 10689 7352 6055 17.60536 1.00000 Virus Consistent Path-018 Virus 3359 14104 7403 15222 9768 7101 27.34512 1.00000 Virus Consistent Path-019 Virus 1691 5271 3222 7052 3832 3027 7.74055 0.99957 Virus Consistent Path-020 Virus 1483 3526 2068 6626 2864 2904 4.22151 0.98554 Virus Consistent Path-021 Virus 938 6765 4463 9836 4136 3967 10.59898 0.99998 Virus Consistent Path-022 Virus 992 4952 2528 6445 3701 3222 6.30595 0.99818 Virus Consistent Path-025 Virus 1462 4193 2672 5533 6356 2375 9.02033 0.99988 Virus Consistent Path-027 Virus 932 4252 1139 5495 3313 73 2.12407 0.89322 Virus Consistent Path-029 Virus 1545 3454 1503 6055 5235 846 5.24414 0.99475 Virus Consistent Path-030 Virus 1956 14909 7301 8504 20938 7403 39.73980 1.00000 Virus Consistent Path-032 Virus 1493 4987 3178 5495 6489 2710 10.32542 0.99997 Virus Consistent Path-033 Virus 906 3805 1825 4107 3290 1327 3.25020 0.96268 Virus Consistent Path-035 Virus 1556 5808 2216 6055 5849 2392 8.67627 0.99983 Virus Consistent Path-036 Virus 1775 6097 2864 7825 7825 3070 12.53776 1.00000 Virus Consistent Path-037 Virus 1929 7934 3995 8329 11536 3832 19.63291 1.00000 Virus Consistent Path-038 Virus 1545 7101 3048 10183 6955 4164 12.81392 1.00000 Virus Consistent Path-040 Virus 978 4817 4022 6718 2805 3753 7.28097 0.99931 Virus Consistent Path-042 Virus 1813 6626 3112 12624 7352 2654 12.70804 1.00000 Virus Consistent Path-043 Virus 2039 9177 4107 8803 10113 2141 17.85033 1.00000 Virus Consistent Path-044 Virus 1300 5572 2599 5271 5689 1851 8.37496 0.99977 Virus Consistent Path-045 Virus 1155 4952 2216 6097 4952 2186 6.93205 0.99902 Virus Consistent Path-046 Virus 2011 13530 7151 9051 19536 6097 36.68521 1.00000 Virus Consistent Path-047 Virus 992 3502 1634 4525 3336 1788 3.25299 0.96278 Virus Consistent Path-049 Virus 510 3091 2599 1100 5610 1727 6.38818 0.99832 Virus Consistent Path-050 Virus 2171 7454 5808 9567 9904 3885 19.93021 1.00000 Virus Consistent Path-051 Virus 2945 10183 3995 11143 16090 4341 27.02959 1.00000 Virus Consistent Path-052 Virus 2392 10914 5889 9435 14202 4463 27.05294 1.00000 Virus Consistent Path-054 Virus 1108 3727 2068 4402 4557 1984 5.47633 0.99583 Virus Consistent Path-055 Virus 1300 2635 1545 5382 4494 2247 4.59965 0.99004 Virus Consistent Path-056 Virus 1524 7934 3575 8387 10914 4164 18.36023 1.00000 Virus Consistent Path-058 Virus 1196 4557 1864 5849 6955 2053 8.64007 0.99982 Virus Consistent Path-059 Virus 1567 7200 3551 8101 9973 3406 16.49789 1.00000 Virus Consistent Path-063 Virus 1864 9051 3600 7506 12364 5235 21.25267 1.00000 Virus Consistent Path-065 Virus 882 6097 4718 2654 9973 2581 16.21105 1.00000 Virus Consistent Path-068 Virus 2442 11143 5728 12026 14401 3779 27.07963 1.00000 Virus Consistent Path-069 Virus 1355 4222 2082 5728 4371 869 5.14991 0.99423 Virus Consistent Path-070 Virus 1374 4494 2359 6097 5572 1691 7.49239 0.99944 Virus Consistent Path-071 Virus 711 4107 1984 4222 4281 1984 5.00188 0.99332 Virus Consistent Path-074 Virus 2728 10183 5889 9904 12279 6055 25.47308 1.00000 Virus Consistent Path-075 Virus 2310 10542 7052 7200 12979 4589 26.68541 1.00000 Virus Consistent Path-077 Virus 2476 11778 5768 4750 16090 4432 29.16975 1.00000 Virus Consistent Path-078 Virus 1851 8682 5572 8214 12889 6013 24.55307 1.00000 Virus Consistent Path-079 Virus 2599 11143 6097 11456 11066 6534 25.01861 1.00000 Virus Consistent Path-081 Virus 636 4817 1715 4918 3091 2232 3.72409 0.97643 Virus Consistent Path-082 Virus 1483 5021 3600 6812 6268 4136 11.42695 0.99999 Virus Consistent Path-084 Virus 1838 8329 5198 13814 10397 8157 22.54414 1.00000 Virus Consistent Path-085 Virus 2528 13344 8214 7771 16090 6097 33.95286 1.00000 Virus Consistent Path-086 Virus 1634 6182 3178 6765 8045 3006 13.02629 1.00000 Virus Consistent Path-088 Virus 1567 7825 5457 6955 11616 5163 21.84259 1.00000 Virus Consistent Path-089 Virus 1364 8803 3727 5308 8743 2528 15.17548 1.00000 Virus Consistent Path-090 Virus 1656 6907 4164 7250 9904 3995 17.30794 1.00000 Virus Consistent Path-091 Virus 2563 9177 4685 10254 12110 5056 22.80286 1.00000 Virus Consistent Path-093 Virus 1309 4918 3526 6580 5056 3222 9.27684 0.99991 Virus Consistent Path-103 Virus 195 985 822 1751 378 811 -3.47795 0.02995 normal Inconsistent Path-109 Virus 521 5271 1472 3885 1715 1943 1.70591 0.84630 Virus Consistent Path-112 Virus 420 6445 1727 2186 435 1462 0.57496 0.63991 Virus Consistent Path-114 Virus 789 4222 1739 2476 1070 5127 2.45940 0.92125 Virus Consistent Path-124 Virus 1085 840 363 4952 2493 5308 1.43468 0.80763 Virus Consistent Path-138 Virus 650 2201 1851 5849 1452 2232 0.87253 0.70527 Virus Consistent Path-146 Virus 673 2528 1345 21677 1680 9501 5.72810 0.99676 Virus Consistent Path-159 Virus 1013 2343 1155 6534 4784 1634 3.97649 0.98159 Virus Consistent Path-163 Virus 900 3290 1611 4817 5271 1403 5.20912 0.99456 Virus Consistent Path-167 Virus 1078 8445 2053 9370 8387 4525 13.78662 1.00000 Virus Consistent Path-168 Virus 846 2747 1432 6055 3859 2945 3.94496 0.98102 Virus Consistent Path-171 Virus 1452 12026 7352 7934 18611 6312 34.90724 1.00000 Virus Consistent Path-177 Virus 1622 5649 2747 6013 7301 3859 11.75847 0.99999 Virus Consistent Path-178 Virus 2216 12889 5127 10615 22286 6765 37.79061 1.00000 Virus Consistent Path-073 bacteria + virus 731 2563 1600 5056 3245 1838 2.62952 0.93274 Virus Consistent Path-096 bacteria + virus 195 888 696 682 925 784 -3.12615 0.04204 normal Inconsistent Path-098 bacteria + virus 531 1282 932 2278 1364 696 -1.89225 0.13099 normal Inconsistent Path-101 bacteria + virus 1085 5198 3156 2986 6400 3112 10.08456 0.99996 Virus Consistent Path-105 bacteria + virus 339 1514 1300 1063 2747 1970 0.75725 0.68076 Virus Consistent Path-107 bacteria + virus 1503 2904 811 7151 3245 6765 4.91892 0.99275 Virus Consistent Path-111 bacteria + virus 3091 12537 2691 8865 12537 4136 22.05139 1.00000 Virus Consistent Path-115 bacteria + virus 1255 7771 1291 8214 4022 6489 8.39058 0.99977 Virus Consistent Path-126 bacteria + virus 2945 61737 24388 12712 15542 9567 76.63028 1.00000 Virus Consistent Path-131 bacteria + virus 245 2528 3912 1668 106 10113 5.43005 0.99564 Virus Consistent Path-133 bacteria + virus 682 1703 1163 6139 1041 2011 -0.74096 0.32279 normal Inconsistent Path-139 bacteria + virus 2581 8926 5163 14401 4252 3727 13.60767 1.00000 Virus Consistent Path-148 bacteria + virus 88 38802 3995 351668 510 314752 207.51677 1.00000 Virus Consistent Path-149 bacteria + virus 86 5382 3478 21527 341 27820 17.10914 1.00000 Virus Consistent Path-151 bacteria + virus 673 2786 2359 4653 2392 2111 2.71265 0.93777 Virus Consistent Path-172 bacteria + virus 2097 9567 3832 12279 12364 7611 23.49236 1.00000 Virus Consistent Path-180 bacteria + virus 999 1775 1611 6225 3454 2375 3.04945 0.95476 Virus Consistent Path-185 bacteria + virus 778 13910 5271 22441 3676 14202 20.40760 1.00000 Virus Consistent Path-001 No infection 223 773 489 2392 366 611 -4.02696 0.01752 normal Consistent Path-002 No infection 137 570 444 1578 204 550 -4.50393 0.01094 normal Consistent Path-006 No infection 381 1877 925 3091 958 767 -2.09181 0.10990 normal Consistent Path-007 No infection 650 2710 1336 4621 1056 1890 -0.27364 0.43201 normal Consistent Path-008 No infection 192 913 1147 2011 245 1493 -2.90451 0.05193 normal Consistent Path-009 No infection 706 1524 767 3290 711 1336 -2.26009 0.09448 normal Consistent Path-012 No infection 371 2082 869 3478 958 1124 -1.85900 0.13482 normal Consistent Path-014 No infection 444 1063 806 3048 371 1163 -3.06119 0.04474 normal Consistent Path-017 No infection 184 778 570 2216 444 594 -3.87675 0.02030 normal Consistent Path-023 No infection 264 1230 716 2247 459 985 -3.23802 0.03776 normal Consistent Path-024 No infection 456 1567 822 3245 590 1085 -2.57613 0.07069 normal Consistent Path-026 No infection 356 1751 846 2786 906 1027 -2.20934 0.09892 normal Consistent Path-028 No infection 318 1452 736 2786 1063 303 -2.67903 0.06422 normal Consistent Path-031 No infection 582 1800 1247 3222 1656 1309 -0.51780 0.37337 normal Consistent Path-034 No infection 632 2216 1393 3313 1247 1116 -0.71034 0.32952 normal Consistent Path-039 No infection 543 1775 2097 3995 1063 840 -0.40144 0.40097 normal Consistent Path-041 No infection 602 2156 2672 3676 1163 971 0.65363 0.65783 Virus Inconsistent Path-048 No infection 378 1041 659 2171 687 673 -3.23034 0.03804 normal Consistent Path-053 No infection 37 283 381 456 225 200 -5.00395 0.00667 normal Consistent Path-057 No infection 426 1727 1006 3006 1345 1264 -1.32961 0.20922 normal Consistent Path-060 No infection 677 2232 1124 3478 1247 1230 -0.93499 0.28191 normal Consistent Path-061 No infection 101 550 546 1556 654 312 -3.99651 0.01805 normal Consistent Path-062 No infection 343 1238 731 2493 1100 1048 -2.36269 0.08606 normal Consistent Path-064 No infection 441 1472 811 3006 1645 659 -1.62592 0.16439 normal Consistent Path-066 No infection 334 1300 875 2310 992 628 -2.52603 0.07405 normal Consistent Path-067 No infection 392 1452 757 2326 978 794 -2.50260 0.07568 normal Consistent Path-072 No infection 285 1196 894 2053 817 1188 -2.51442 0.07485 normal Consistent Path-076 No infection 400 2844 1056 2635 2186 857 -0.03158 0.49211 normal Consistent Path-080 No infection 327 1063 682 2141 746 800 -3.08432 0.04376 normal Consistent Path-083 No infection 598 1739 1147 3336 1327 1567 -0.90590 0.28784 normal Consistent Path-087 No infection 246 938 741 2186 1013 794 -2.79125 0.05780 normal Consistent Path-092 No infection 283 985 701 1970 906 834 -2.92436 0.05096 normal Consistent Path-095 No infection 20 45 371 356 26 40 -5.45692 0.00425 normal Consistent Path-097 No infection 30 155 406 403 109 60 -5.24828 0.00523 normal Consistent Path-100 No infection 66 281 429 513 318 287 -4.77221 0.00839 normal Consistent Path-104 No infection 21 122 187 499 124 52 -5.51066 0.00403 normal Consistent Path-110 No infection 394 778 141 2126 632 1078 -3.82504 0.02135 normal Consistent Path-113 No infection 489 992 985 3267 1100 3575 -0.72636 0.32599 normal Consistent Path-116 No infection 12 128 126 314 59 220 -5.59239 0.00371 normal Consistent Path-117 No infection 636 2171 389 2476 641 925 -2.82967 0.05574 normal Consistent Path-118 No infection 64 574 594 521 168 574 -4.47413 0.01127 normal Consistent Path-119 No infection 386 1336 376 1524 767 3526 -1.92693 0.12709 normal Consistent Path-120 No infection 411 4107 999 2528 752 1291 -1.02600 0.26386 normal Consistent Path-121 No infection 92 834 450 539 238 1645 -3.88356 0.02016 normal Consistent Path-122 No infection 57 235 293 489 160 570 -5.00558 0.00666 normal Consistent Path-123 No infection 45 590 381 574 156 594 -4.73393 0.00872 normal Consistent Path-125 No infection 466 4589 531 1956 574 888 -1.81783 0.13970 normal Consistent Path-128 No infection 66 281 440 509 83 60 -5.15506 0.00574 normal Consistent Path-140 No infection 136 932 840 2408 336 570 -3.63024 0.02583 normal Consistent Path-141 No infection 20 223 408 320 52 323 -5.15877 0.00572 normal Consistent Path-143 No infection 152 668 716 1300 441 673 -3.80236 0.02183 normal Consistent Path-144 No infection 96 706 817 1483 196 1204 -3.69323 0.02429 normal Consistent Path-145 No infection 554 2097 1336 4022 1432 1567 -0.36005 0.41095 normal Consistent Path-147 No infection 76 741 1139 1763 233 1600 -3.02624 0.04625 normal Consistent Path-150 No infection 24 275 543 346 70 784 -4.71042 0.00892 normal Consistent Path-152 No infection 121 598 752 978 204 373 -4.26704 0.01383 normal Consistent Path-153 No infection 29 356 524 985 66 499 -4.79326 0.00822 normal Consistent Path-154 No infection 35 188 310 373 174 307 -5.14538 0.00579 normal Consistent Path-155 No infection 35 145 206 668 121 138 -5.41564 0.00443 normal Consistent Path-157 No infection 80 206 353 673 239 109 -5.06227 0.00629 normal Consistent Path-158 No infection 7 99 285 99 30 204 -5.47508 0.00417 normal Consistent Path-160 No infection 126 450 496 965 578 517 -4.12331 0.01593 normal Consistent Path-162 No infection 44 361 438 611 219 506 -4.73454 0.00871 normal Consistent Path-164 No infection 716 1196 692 3382 1336 806 -2.01545 0.11759 normal Consistent Path-165 No infection 4 35 97 102 25 86 -5.80003 0.00302 normal Consistent Path-169 No infection 51 472 539 361 283 447 -4.53406 0.01062 normal Consistent Path-173 No infection 26 36 192 196 29 155 -5.62484 0.00359 normal Consistent Path-174 No infection 4 26 116 70 11 62 -5.80996 0.00299 normal Consistent Path-175 No infection 6 16 65 75 4 11 -5.91052 0.00270 normal Consistent Path-179 No infection 11 90 238 253 34 466 -5.38109 0.00458 normal Consistent Path-182 No infection 6 32 101 91 23 88 -5.79746 0.00303 normal Consistent Path-184 No infection 7 34 44 81 21 54 -5.88457 0.00277 normal Consistent Path-188 No infection 236 894 535 1556 517 510 -3.85200 0.02080 normal Consistent Path-094 bacteria 8 114 241 160 26 654 -5.28848 0.00502 normal Consistent Path-099 bacteria 17 139 323 303 109 129 -5.33444 0.00480 normal Consistent Path-102 bacteria 29 260 323 386 171 291 -5.11211 0.00599 normal Consistent Path-106 bacteria 23 109 353 432 101 61 -5.34264 0.00476 normal Consistent Path-108 bacteria 223 1309 794 1472 800 3200 -1.64686 0.16153 normal Consistent Path-127 bacteria 45 650 640 400 102 249 -4.64966 0.00947 normal Consistent Path-129 bacteria 18 403 463 746 61 999 -4.62134 0.00974 normal Consistent Path-130 bacteria 8 285 373 543 17 447 -5.13916 0.00583 normal Consistent Path-132 bacteria 8 245 432 400 33 356 -5.12648 0.00590 normal Consistent Path-134 bacteria 48 479 654 1147 203 615 -4.33698 0.01291 normal Consistent Path-135 bacteria 10 228 566 531 36 951 -4.64953 0.00948 normal Consistent Path-136 bacteria 19 373 594 668 72 351 -4.80144 0.00815 normal Consistent Path-137 bacteria 22 361 463 435 62 231 -5.05683 0.00633 normal Consistent Path-142 bacteria 32 506 752 711 151 389 -4.42929 0.01178 normal Consistent Path-156 bacteria 16 269 875 336 86 281 -4.55396 0.01042 normal Consistent Path-161 bacteria 92 951 696 992 778 645 -3.36976 0.03325 normal Consistent Path-166 bacteria 4 49 492 159 16 116 -5.30410 0.00495 normal Consistent Path-170 bacteria 5 39 188 95 29 142 -5.65321 0.00349 normal Consistent Path-176 bacteria 4 37 191 141 26 197 -5.62315 0.00360 normal Consistent Path-181 bacteria 19 85 188 207 83 320 -5.46074 0.00423 normal Consistent Path-183 bacteria 4 26 112 69 8 346 -5.67430 0.00342 normal Consistent Path-186 bacteria 3 19 72 86 5 158 -5.82426 0.00295 normal Consistent Path-187 bacteria 2 15 138 107 2 182 -5.73699 0.00321 normal Consistent Path-189 bacteria 9 25 48 89 23 173 -5.81824 0.00296 normal Consistent Path-190 bacteria 2 18 131 82 4 299 -5.68333 0.00339 normal Consistent .

[0070] The disclosed invention is not limited to the specific methods, schemes, and substances described, as these are all subject to variation. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention, which is limited only by the appended claims.

[0071] Those skilled in the art will also recognize, or be able to identify, many equivalents of the specific embodiments of the invention described herein using no more than conventional experiments. These equivalents are also included in the appended claims.

Claims

1. A kit for identifying the infection type of a test sample by detecting gene expression in host peripheral blood, characterized in that, The kit contains a detection reagent for simultaneously detecting the expression levels of two groups of genes in the test sample. The first group of genes is used to identify bacterial infection, and this group includes the S100P gene, ANXA3 gene, ITGAX gene, and ITGAM gene. The second group of genes is used to identify viral infection, and this group includes the IFI44L gene, IFIT3 gene, IFIH1 gene, LY6E gene, IFIT1 gene, and IFITM3 gene. The detection of these two groups of genes determines whether the test sample is infected with bacteria, virus, both bacteria and virus, or neither bacteria nor virus. The test sample is peripheral blood.

2. The reagent kit according to claim 1, characterized in that, The primers and probes for detecting the host S100P gene are SEQ ID No. 37, SEQ ID No. 38, and SEQ ID No. 39; the primers and probes for detecting the host ANXA3 gene are SEQ ID No. 40, SEQ ID No. 41, and SEQ ID No. 42; the primers and probes for detecting the host ITGAX gene are SEQ ID No. 43, SEQ ID No. 44, and SEQ ID No. 45; and the primers and probes for detecting the host ITGAM gene are SEQ ID No. 46, SEQ ID No. 47, and SEQ ID No.

48.

3. The reagent kit according to claim 1, characterized in that, The primers and probes for detecting the host IFI44L gene are SEQ ID No. 52, SEQ ID No. 53, and SEQ ID No. 54; the primers and probes for detecting the host IFIH1 gene are SEQ ID No. 58, SEQ ID No. 59, and SEQ ID No. 60; the primers and probes for detecting the host LY6E gene are SEQ ID No. 49, SEQ ID No. 50, and SEQ ID No. 51; the primers and probes for detecting the host IFIT1 gene are SEQ ID No. 61, SEQ ID No. 62, and SEQ ID No. 63; and the primers and probes for detecting the host IFITM3 gene are SEQ ID No. 64, SEQ ID No. 65, and SEQ ID No.

66.

4. The reagent kit according to claim 1, characterized in that, The kit also includes detection reagents for detecting the expression level of the host internal reference gene HPRT1, and the primers and probes for detecting the host internal reference gene HPRT1 are SEQ ID No. 67, SEQ ID No. 68 and SEQ ID No.

69.

5. The use of the kit according to any one of claims 1-4 in the preparation of products for identifying the infection category of a test sample.