Method for diagnosing whether an infection in a patient is bacterial or viral, as well as kit

Abstract

This invention relates to methods for diagnosing bacterial and viral infections. In particular, the invention relates to the use of biomarkers that can determine whether a patient with acute inflammation has a bacterial or viral infection.

Description

METHOD FOR DIAGNOSING WHETHER AN INFECTION IN A PATIENT IS BACTERIAL OR VIRAL, AS WELL AS KIT CROSS-REFERENCE

[001] This application claims the benefit of provisional application serial number US62 / 346,962, filed on June 7, 2016, the application for which is incorporated herein by reference. DECLARATION REGARDING RESEARCH OR DEVELOPMENT SPONSORED BY THE FEDERAL GOVERNMENT

[002] This invention was made with the support of the Government under contracts AI109662 and AI057229 granted by the National Institute of Health. The government has certain rights over the invention. TECHNICAL FIELD

[003] The present invention relates, in general, to methods for diagnosing bacterial and viral infections. In particular, the invention relates to the use of biomarkers that can distinguish whether a patient with acute inflammation has a bacterial or viral infection. BACKGROUND

[004] Early and accurate diagnosis of infection is crucial to improving patient outcomes and reducing antibiotic resistance. The mortality rate from bacterial sepsis increases by 8% for every hour that antibiotics are delayed1; however, administering antibiotics to patients without bacterial infections increases mortality rates and antimicrobial resistance. The rate of inappropriate antibiotic prescription in the hospital setting is estimated at 30–50%, and would be aided by improved diagnostics2,3. Surprisingly, nearly 95% of patients who received antibiotics for suspected enteric fever had negative cultures4. Currently, there is no gold standard reference point that can broadly determine the presence and type of infection. Thus, the White House established a National Action Plan to Combat Petition 870260069557, dated 07 / 14 / 2026, page 6 / 336 2 / 157 to Antibiotic-Resistant Bacteria, which required point-of-need diagnostic tests to quickly distinguish between bacterial and viral infections5.

[005] Although new molecular diagnostics based on PCR can perform pathogen profiling directly from a blood culture 6, such methods depend on the presence of a sufficient number of pathogens in the blood. Furthermore, they are limited to detecting a distinct range of pathogens. As a result, there is a growing interest in molecular diagnostics that determine the host gene response profile. These include diagnostics that can distinguish the presence of infection compared to inflamed but uninfected patients, such as the 11 'Sepsis Metascore'7 (SMS) gene (which has been validated through several cohorts8) among others9,10. Other groups have focused on gene sets that can distinguish between types of infections, such as viral infections, bacterial infections versus11-13. Tsalik et al.He described a model that distinguishes between all three classes (i.e., uninfected patients and those with bacterial or viral disease), although this model required the measurement of 122 probes14. A Meta-Virus Signature that describes a common response to viral infection has also been previously described, but it contained too many genes (396) for clinical application15. Overall, although great promise has been demonstrated in this field, no host gene expression infection diagnosis has become clinical practice.

[006] Data from these biomarker studies and dozens of other genome-wide expression studies in sepsis and acute infections have been published and deposited for further study in public databases such as the NIH Gene Expression Omnibus (GEO) and the EBI ArrayExpress. This data is a largely untapped resource that can be used for both confirmation and further study. Petition 870260069557, dated 07 / 14 / 2026, page 7 / 336 3 / 157 biomarker validation. It has also been previously shown that our integrated multicohort analysis of gene expression yields robust diagnostic tools for sepsis7, specific types of viral infections15 and active tuberculosis16. Furthermore, these data are also useful as a reference and validation tool for new host gene expression diagnostics17. However, such validation in public data has previously been limited only to those cohorts containing at least two classes of interest (i.e., where a direct comparison between classes is possible), since technical differences between studies make direct comparison of diagnostic scores between cohorts impossible.

[007] There remains a need for sensitive and specific diagnostic tests that can distinguish between bacterial and viral infections. SUMMARY

[008] The invention relates to the use of biomarkers that can determine whether a patient with acute inflammation has a bacterial or viral infection. These biomarkers can be used on their own or in combination with one or more additional biomarkers or relevant clinical parameters in the prognosis, diagnosis, or monitoring of the treatment of an infection.

[009] In one embodiment, the invention is set forth for a method of developing a classification used to diagnose an infection in a patient, wherein the method includes: (a) measuring the expression levels of at least two biomarkers in a biological sample from a patient; the at least two biomarkers selected from one or both of a first set of biomarkers in which a higher level of expression indicates a bacterial infection and a second set of biomarkers in which a higher level of expression indicates a viral infection; wherein the first set of biomarkers includes at least one of TSPO, EMR1, NINJ2, ACPP, Petition 870260069557, dated 07 / 14 / 2026, p. 8 / 336 4 / 157 TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR and LAPTM5; and wherein the second set of biomarkers includes at least one of the following: (a) OAS1, IFIT1, SAMD9, ISG15, HERC5, DDX60, HESX1, IFI6, MX1, OASL, LAX1, IFIT5, IFIT3, KCTD14, OAS2, RTP4, PARP12, LY6E, ADA, IFI44L, IFI27, RSAD2, IFI44, OAS3, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1, and GZMB; (b) use the expression levels of the biomarkers to develop a classification or generative algorithm that can determine the presence or likelihood of bacterial or viral infection in the patient; and (c) apply the algorithm to diagnose the patient as having or likely having a bacterial or viral infection.

[0010] In one embodiment, the invention is produced for a method for diagnosing an infection in a patient, the method including analyzing expression levels of at least two genes, wherein the at least two genes are predictive of a viral or bacterial infection; and wherein the expression levels of the at least two genes provide an area under a curve for predicting a viral or bacterial infection of at least 0.80; and diagnosing the patient as having a bacterial or viral infection.

[0011] In one embodiment, the invention is produced for a method for diagnosing and treating an infection in a patient, wherein the method includes (a) obtaining a biological sample from the patient; (b) measuring the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1 and CTSB in the biological sample; (c) analyzing the expression levels of each biomarker in conjunction with the respective reference value ranges for the biomarkers, wherein increased expression levels of the biomarkers IFI27, JUP, LAX1 Petition 870260069557, dated 07 / 14 / 2026, p. 9 / 336 5 / 157 compared to the reference ranges for the biomarkers for a control individual indicate that the patient has a viral infection, and increased expression levels of the biomarkers HK3, TNIP1, GPAA1, CTSB compared to the reference ranges for the biomarkers for a control individual indicate that the patient has a bacterial infection; and (d) administer an effective amount of an antiviral agent to the patient if the patient is diagnosed with a viral infection or administer an effective amount of an antibiotic to the patient if the patient is diagnosed with a bacterial infection.

[0012] In either modality, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0013] In either modality, biomarker levels may be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0014] In any modality, the method may include the calculation of a bacterial / viral metascore for the patient based on biomarker levels, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

[0015] In any modality, the method may include normalization data using COCONUT normalization.

[0016] In any modality, the patient can be a human being.

[0017] In any modality, measuring the level of biomarker plurality may include performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, or a serial gene expression analysis (SAGE). Petition 870260069557, dated 07 / 14 / 2026, page 10 / 336 6 / 157

[0018] In one embodiment, the invention is produced for a method of diagnosing and treating a patient with inflammation, the method including (a) obtaining a biological sample from the patient; (b) measuring the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in the sample;(c) first analyze the expression levels of each biomarker together with the respective reference ranges for the biomarkers, where increased expression levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF and C3AR1 and reduced expression levels of the biomarkers KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 compared to the reference ranges for the biomarkers for an uninfected control individual indicate that the patient has an infection and absence of differential expression of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 compared to the uninfected control individual indicate that the patient does not have an infection;(d) additionally analyze the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, and CTSB, if the patient is diagnosed with an infection, where increased expression levels of the biomarkers IFI27, JUP, LAX1 compared to the reference ranges for the biomarkers for a control individual indicate that the patient has a viral infection, and increased expression levels of the biomarkers HK3, TNIP1, GPAA1, CTSB compared to the reference ranges for the biomarkers for the control individual indicate that the patient has a bacterial infection; and (e) administer an effective amount of an antiviral agent to the patient if the patient is diagnosed with a viral infection, or administer an effective amount of an antibiotic to the patient if; Petition 870260069557, dated 07 / 14 / 2026, page 11 / 336 7 / 157 if the patient is diagnosed with a bacterial infection.

[0019] In any modality, the method may include calculating a sepsis metascore for the patient, where a sepsis metascore that is greater than the reference values ​​for an uninfected control individual indicates that the patient has an infection, and a sepsis metascore within the ranges of reference values ​​for an uninfected control individual indicates that the patient has a non-infectious inflammatory condition;

[0020] In any modality, the method may include calculating a bacterial / viral metascore for the patient if the patient is diagnosed with an infection, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

[0021] In any modality, biomarker levels can be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0022] In any modality, the non-infectious inflammatory condition can be selected from the group of systemic inflammatory response syndrome (SIRS), an autoimmune disorder, a traumatic injury, and surgery.

[0023] In any modality, the patient can be a human being.

[0024] In any modality, the measurement of biomarker levels may include performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, or a serial gene expression analysis (SAGE).

[0025] In one embodiment, the invention is produced for a kit that includes agents for measuring the levels of the biomarkers IFI27, JUP, Petition 870260069557, dated 07 / 14 / 2026, page 12 / 336 8 / 157 LAX1, HK3, TNIP1, GPAA1 and CTSB.

[0026] In any modality, the kit may include agents to measure the levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLADPB1.

[0027] In any form, the kit may include a microarray.

[0028] In any embodiment, the microarray may include an oligonucleotide that hybridizes with an IFI27 polynucleotide, an oligonucleotide that hybridizes with a JUP polynucleotide, an oligonucleotide that hybridizes with a LAX1 polynucleotide, an oligonucleotide that hybridizes with an HK3 polynucleotide, an oligonucleotide that hybridizes with a TNIP1 polynucleotide, an oligonucleotide that hybridizes with a GPAA1 polynucleotide, and an oligonucleotide that hybridizes with a CTSB polynucleotide.

[0029] In any embodiment, the microarray may include an oligonucleotide that hybridizes with a CEACAM1 polynucleotide, an oligonucleotide that hybridizes with a ZDHHC19 polynucleotide, an oligonucleotide that hybridizes with a C9orf95 polynucleotide, an oligonucleotide that hybridizes with a GNA15 polynucleotide, an oligonucleotide that hybridizes with a BATF polynucleotide, an oligonucleotide that hybridizes with a C3AR1 polynucleotide, an oligonucleotide that hybridizes with a KIAA1370 polynucleotide, an oligonucleotide that hybridizes with a TGFBI polynucleotide, an oligonucleotide that hybridizes with an MTCH1 polynucleotide, an oligonucleotide that hybridizes with an RPGRIP1 polynucleotide, and an oligonucleotide that... It hybridizes with the polynucleotide HLADPB1.

[0030] In any modality, the kit may include information, in electronic or paper format, with instructions for correlating the detected levels of each sepsis biomarker. Petition 870260069557, dated 07 / 14 / 2026, page 13 / 336 9 / 157

[0031] In one embodiment, the method is produced for a computer-implanted method to diagnose a patient suspected of having an infection, wherein the computer performs the steps of: (a) receiving entered patient data including values ​​for the levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, and CTSB in a biological sample from the patient; (b) analyzing the level of each of the biomarkers and comparing it with the respective reference ranges for the biomarkers; (c) calculating a bacterial / viral metascore for the patient based on the biomarker levels, wherein a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection; and (d) displaying information relating to the patient's diagnosis.

[0032] In any embodiment, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0033] In one embodiment, the invention is produced in a diagnostic system for performing the computer-implanted method, the diagnostic system including (a) a storage component for storing data, wherein the storage component has instructions for determining the patient's diagnosis stored therein; (b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component for displaying information relating to the patient's diagnosis.

[0034] In any modality, the storage component may include instructions for calculating the bacterial / viral metascore.

[0035] In one embodiment, the invention is produced for a Petition 870260069557, dated 07 / 14 / 2026, page 14 / 336 10 / 157 computer-implanted method for diagnosing a patient who has inflammation, in which the computer performs the following steps: a) receive entered patient data including values ​​for the levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in a biological sample from the patient; b) analyze the levels of each of the biomarkers and compare them with the respective reference ranges for the biomarkers; c) Calculate a sepsis metascore for the patient, where a sepsis metascore greater than the reference value ranges for an uninfected control individual indicates that the patient has an infection, and a sepsis metascore within the reference value ranges for uninfected control subjects indicates that the patient has a non-infectious inflammatory condition;d) Calculate a bacterial / viral metascore for the patient if the sepsis score indicates that the patient has an infection, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection; and ee) Display information about the patient's diagnosis.

[0036] In any embodiment, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0037] In one embodiment, the invention is produced in a diagnostic system for performing the computer-implanted method, the diagnostic system including a) a storage component for storing data, wherein the storage component has instructions for determining the patient's diagnosis stored therein; b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions. Petition 870260069557, dated 07 / 14 / 2026, page 15 / 336 11 / 157 stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component to display information relating to the patient's diagnosis.

[0038] In any modality, the storage component may include instructions for calculating the sepsis metascore and the bacterial / viral metascore.

[0039] In one embodiment, the invention is produced for a method for diagnosing and treating an infection in a patient, wherein the method includes: a) obtaining a biological sample from the patient; b) measuring the expression levels of a set of viral response genes and a set of bacterial response genes in the biological sample, wherein the set of viral response genes includes one or more genes selected from the OAS2, CUL1, ISG15, CHST12 group., IFIT1, SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58, STAT1, and the bacterial response gene set includes one or more genes selected from the group of SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, IMPA2, LTA4H, TALDO1, TKT, PYGL, CETP, PROS1, RTN3, CAT, CYBRD1; and c) analyze the expression levels of each biomarker in conjunction with the respective reference value ranges for an uninfected control individual, where the differential expression of viral response genes is compared to the reference value.

[0040] In any embodiment, the set of viral response genes and the set of bacterial response genes can be selected from the group of: a) a set of viral response genes Petition 870260069557, dated 07 / 14 / 2026, p. 16 / 336 12 / 157 including OAS2 and CUL1 and a set of bacterial response genes that includes SLC12A9, ACPP, STAT5B; b) a set of viral response genes that includes ISG15 and CHST12 and a set of bacterial response genes that includes EMR1 and FLII; c) a set of viral response genes that includes IFIT1, SIGLEC1 and ADA and a set of bacterial response genes that includes PTAFR, NRD1, PLP2; d) a set of viral response genes that includes MX1 and a set of bacterial response genes that includes DYSF, TWF2; e) a set of viral response genes that includes RSAD2 and a set of bacterial response genes that includes SORT1 and TSPO; f) a set of viral response genes that includes IFI44L, GZMB and KCTD14 and a set of bacterial response genes that includes TBXAS1, ACAA1 and S100A12; (g) a set of viral response genes that includes LY6E and a set of bacterial response genes that includes PGD and LAPTM5;h) a set of viral response genes that includes IFI44, HESX1, and OASL and a set of bacterial response genes that includes NINJ2, DOK3, SORL1, and RAB31; and i) a set of viral response genes that includes OAS1 and a set of bacterial response genes that includes IMPA2 and LTA4H.

[0041] In either modality, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0042] In either modality, biomarker levels may be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0043] In any modality, the method may include the calculation of a bacterial / viral metascore for the patient based on biomarker levels, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection. Petition 870260069557, dated 07 / 14 / 2026, p. 17 / 336 13 / 157

[0044] In any modality, the method may include measuring the expression levels of biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in the biological sample; and analyze the expression levels of each biomarker in conjunction with the respective reference ranges for the biomarkers, where increased expression levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, and C3AR1 and decreased expression levels of the biomarkers KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 compared to the reference ranges for the biomarkers for an uninfected control individual indicate that the patient has an infection, and the absence of differential expression of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 compared to the uninfected control individual indicates that the patient does not have an infection.

[0045] In one embodiment, the invention is produced for a kit that includes agents for measuring the expression levels of a set of viral response genes and a set of bacterial response genes selected from the following groups: (a) a set of viral response genes that includes OAS2 and CUL1 and a set of bacterial response genes that includes SLC12A9, ACPP, STAT5B; (b) a set of viral response genes that includes ISG15 and CHST12 and a set of bacterial response genes that includes EMR1 and FLII; (c) a set of viral response genes that includes IFIT1, SIGLEC1 and ADA and a set of bacterial response genes that includes PTAFR, NRD1, PLP2; (d) a set of viral response genes that includes MX1 and a set of bacterial response genes that includes DYSF, TWF2; (e) a set of viral response genes that includes RSAD2 and a set of bacterial response genes that includes SORT1 and TSPO; e) a set of Petition 870260069557, dated 07 / 14 / 2026, p. 18 / 336 14 / 157 viral response genes including IFI44L, GZMB and KCTD14 and a set of bacterial response genes including TBXAS1, ACAA1 and S100A12; f) a set of viral response genes including LY6E and a set of bacterial response genes including PGD and LAPTM5; g) a set of viral response genes including IFI44, HESX1 and OASL and a set of bacterial response genes including NINJ2, DOK3, SORL1 and RAB31; eh) a set of viral response genes including OAS1 and a set of bacterial response genes including IMPA2 and LTA4H.

[0046] In any form, the kit may include a microarray.

[0047] In one embodiment, the invention is produced for a computer-implanted method for diagnosing a patient suspected of having an infection, wherein the computer performs the steps of: a) receiving patient-entered data including values ​​for the expression levels in a biological sample of a set of viral response genes and a set of bacterial response genes in the biological sample, wherein the set of viral response genes includes one or more genes selected from the group of OAS2, CUL1, ISG15, CHST12, IFIT1, SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58, STAT1, and the bacterial response gene set includes one or more genes selected from the SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, and IMPA2 groups.a) analyze the expression levels of the viral response gene set and the bacterial response gene set and compare them with the respective reference ranges for an uninfected control individual; b) calculate, Petition 870260069557, dated 07 / 14 / 2026, p. 19 / 336 15 / 157 a bacterial / viral metascore for the patient based on the expression levels of the viral response gene set and the bacterial response gene set; and (d) display information relating to the patient's diagnosis.

[0048] In one embodiment, the invention is produced in a diagnostic system for performing the computer-implanted method, the diagnostic system including (a) a storage component for storing data, wherein the storage component has instructions for determining the patient's diagnosis stored therein; (b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component for displaying information relating to the patient's diagnosis.

[0049] In one embodiment, the invention includes a method for diagnosing an infection in a patient, which includes (a) measuring the expression levels of at least two biomarkers in a biological sample from a patient; the at least two biomarkers selected from one or both of a first set of biomarkers in which a higher level of expression indicates a bacterial infection and a second set of biomarkers in which a higher level of expression indicates a viral infection; wherein the first set of biomarkers includes at least one of the following: TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR, and LAPTM5; and wherein the second set of biomarkers includes at least one of the following: OAS1, IFIT1, SAMD9, Petition 870260069557, dated 07 / 14 / 2026, p. 20 / 336 16 / 157 (a) analyze the expression levels of each biomarker in conjunction with the respective biomarker reference ranges to determine a viral or bacterial infection. (b) analyze the expression levels of each biomarker in conjunction with the respective biomarker reference ranges to determine a viral or bacterial infection.

[0050] In either modality, the method may include administering an effective amount of an antiviral agent to the patient if the patient is diagnosed with a viral infection or administering an effective amount of an antibiotic to the patient if the patient is diagnosed with a bacterial infection.

[0051] In any modality, the expression levels of at least two biomarkers can provide an area under a curve of at least 0.80.

[0052] In any modality, the first set of biomarkers may include at least one of HK3, TNIP1, GPAA1, and CTSB; and the second set of biomarkers may include at least one of IFI27, JUP, and LAX1.

[0053] In any modality, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0054] In any modality, biomarker levels can be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0055] In any modality, the method may include the calculation of a bacterial / viral metascore for the patient based on biomarker levels, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection. Petition 870260069557, dated 07 / 14 / 2026, p. 21 / 336 17 / 157

[0056] In any modality, the method may include data normalization using COCONUT normalization; COCONUT normalization, which includes the steps of (a) separating data from multiple cohorts into healthy and diseased components; (b) conormalizing the healthy components using ComBat conormalization without covariates; (c) obtaining estimated ComBat parameters for each dataset for the healthy component; and (d) applying the estimated ComBat parameters to the patient component.

[0057] In any modality, the patient can be a human being.

[0058] In any modality, measuring the level of biomarker plurality may include performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, or a serial gene expression analysis (SAGE).

[0059] In one embodiment, the invention may include a method for diagnosing and treating a patient with inflammation, wherein the method includes the steps of (a) measuring the expression levels of biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in a biological sample from the patient; (b) first analyze the expression levels of each biomarker together with the respective reference ranges for the biomarkers, where increased expression levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF and C3AR1 and reduced expression levels of the biomarkers KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 compared to the reference ranges for biomarkers for an uninfected control individual indicate that the patient has an infection and absence of differential expression of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, Petition 870260069557, dated 07 / 14 / 2026, p. 22 / 336 18 / 157 BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 compared to an uninfected control individual indicate that the patient does not have an infection; and; (c) additionally analyze the expression levels of at least two biomarkers in a biological sample from a patient; the at least two biomarkers selected from one or both of a first set of biomarkers in which a higher expression level indicates a bacterial infection and a second set of biomarkers in which a higher expression level indicates a viral infection; wherein the first set of biomarkers includes at least one of the following: TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR, and LAPTM5;and wherein the second set of biomarkers includes at least one of OAS1, IFIT1, SAMD9, ISG15, HERC5, DDX60, HESX1, IFI6, MX1, OASL, LAX1, IFIT5, IFIT3, KCTD14, OAS2, RTP4, PARP12, LY6E, ADA, IFI44L, IFI27, RSAD2, IFI44, OAS3, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1, and GZMB to determine a bacterial or viral infection.

[0060] In any modality, the method may include calculating a sepsis metascore for the patient, where a sepsis metascore that is greater than the reference values ​​for an uninfected control individual indicates that the patient has an infection, and a sepsis metascore within the ranges of reference values ​​for an uninfected control individual indicates that the patient has a non-infectious inflammatory condition;

[0061] In any modality, the method may include calculating a bacterial / viral metascore for the patient if the patient is diagnosed with an infection, where a bacterial / viral metascore Petition 870260069557, dated 07 / 14 / 2026, page 23 / 336 A positive 19 / 157 score for the patient indicates that the patient has a viral infection, and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

[0062] In any modality, biomarker levels can be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0063] In any modality, the non-infectious inflammatory condition can be selected from the group of systemic inflammatory response syndrome (SIRS), an autoimmune disorder, a traumatic injury, and surgery.

[0064] In any modality, the patient can be a human being.

[0065] In any modality, the measurement of biomarker levels may include performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, or a serial gene expression analysis (SAGE).

[0066] In one embodiment, the method is designed for a kit, and the kit includes agents to measure the levels of at least two biomarkers in a biological sample from a patient; wherein at least two biomarkers are selected from one or both of a first set of biomarkers in which a higher level of expression indicates a bacterial infection and a second set of biomarkers in which a higher level of expression indicates a viral infection, wherein the first set of biomarkers includes at least one of TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR, and LAPTM5; and wherein the second set of biomarkers includes at least Petition 870260069557, dated 07 / 14 / 2026, p. 24 / 336 20 / 157 minus one among OAS1, IFIT1, SAMD9, ISG15, HERC5, DDX60, HESX1, IFI6, MX1, OASL, LAX1, IFIT5, IFIT3, KCTD14, OAS2, RTP4, PARP12, LY6E, ADA, IFI44L, IFI27, RSAD2, IFI44, OAS3, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1 and GZMB.

[0067] In any modality, the kit may include agents to measure the levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLADPB1.

[0068] In any form, the kit may include a microarray.

[0069] In any embodiment, the microarray may include an oligonucleotide that hybridizes with an IFI27 polynucleotide, an oligonucleotide that hybridizes with a JUP polynucleotide, an oligonucleotide that hybridizes with a LAX1 polynucleotide, an oligonucleotide that hybridizes with an HK3 polynucleotide, an oligonucleotide that hybridizes with a TNIP1 polynucleotide, an oligonucleotide that hybridizes with a GPAA1 polynucleotide, and an oligonucleotide that hybridizes with a CTSB polynucleotide.

[0070] In any embodiment, the microarray may include an oligonucleotide that hybridizes with a CEACAM1 polynucleotide, an oligonucleotide that hybridizes with a ZDHHC19 polynucleotide, an oligonucleotide that hybridizes with a C9orf95 polynucleotide, an oligonucleotide that hybridizes with a GNA15 polynucleotide, an oligonucleotide that hybridizes with a BATF polynucleotide, an oligonucleotide that hybridizes with a C3AR1 polynucleotide, an oligonucleotide that hybridizes with a KIAA1370 polynucleotide, an oligonucleotide that hybridizes with a TGFBI polynucleotide, an oligonucleotide that hybridizes with an MTCH1 polynucleotide, an oligonucleotide that hybridizes with an RPGRIP1 polynucleotide, and an oligonucleotide that... hybridizes with an HLA polynucleotide. [Reference to document number 870260069557, dated 14 / 07 / 2026, page 25 / 336] 21 / 157 DPB1.

[0071] In any modality, the kit may include information, in electronic or paper format, with instructions for correlating the detected levels of each sepsis biomarker.

[0072] In one embodiment, the invention produces a computer-implanted method for diagnosing a patient suspected of having an infection, the computer performing steps of: (a) receiving patient-entered data including values ​​for the levels of at least two biomarkers in a biological sample from a patient; wherein the at least two biomarkers selected from one or both of a first set of biomarkers in which a higher level of expression indicates a bacterial infection and a second set of biomarkers in which a higher level of expression indicates a viral infection; wherein the first set of biomarkers includes at least one of the following: TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR, and LAPTM5;and wherein the second set of biomarkers includes at least one of the following: OAS1, IFIT1, SAMD9, ISG15, HERC5, DDX60, HESX1, IFI6, MX1, OASL, LAX1, IFIT5, IFIT3, KCTD14, OAS2, RTP4, PARP12, LY6E, ADA, IFI44L, IFI27, RSAD2, IFI44, OAS3, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNM1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1, and GZMB in the patient's biological sample; (b) analyze the level of each of the biomarkers and compare it with the respective reference ranges for the biomarkers; (c) calculate a bacterial / viral metascore for the patient based on biomarker levels, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient; Petition 870260069557, dated 07 / 14 / 2026, p. 26 / 336 22 / 157 indicates that the patient has a bacterial infection; and (d) display information relating to the patient's diagnosis.

[0073] In any embodiment, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0074] In one embodiment, the invention is developed in a computer-implanted diagnostic system that executes the method, which includes (a) a storage component for storing data, wherein the storage component has instructions for determining the patient's diagnosis stored therein; (b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component for displaying information relating to the patient's diagnosis.

[0075] In any modality, the storage component may include instructions for calculating the bacterial / viral metascore.

[0076] In one embodiment, the invention is produced for a computer-implanted method for diagnosing a patient having inflammation, the computer performing the steps of (a) receiving data from entered patients who have biomarker values ​​for the levels of IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in a biological sample from the patient; (b) analyzing the levels of each of the biomarkers and comparing them with the respective reference value ranges for the biomarkers; (c) calculating a sepsis metascore for the patient, wherein a sepsis metascore greater than the reference value ranges for an uninfected control individual indicates that the patient has a Petition 870260069557, dated 07 / 14 / 2026, page 27 / 336 23 / 157 infection and a sepsis metascore that is within the reference ranges for an uninfected control individual indicates that the patient has a non-infectious inflammatory condition; (d) calculate a bacterial / viral metascore for the patient if the sepsis score indicates that the patient has an infection, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection; and (e) display information relating to the patient's diagnosis.

[0077] In any embodiment, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0078] In one embodiment, the invention is developed in a computer-implanted diagnostic system that executes the method, which includes (a) a storage component for storing data, wherein the storage component has instructions for determining the patient's diagnosis stored therein; (b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component for displaying information relating to the patient's diagnosis.

[0079] In any modality, the storage component may include instructions for calculating the sepsis metascore and the bacterial / viral metascore.

[0080] In one embodiment, the invention is produced for a method for diagnosing and treating an infection in a patient, wherein the method includes (a) obtaining a biological sample from the patient; (b) measuring the expression levels of any set of at least two Petition 870260069557, dated 07 / 14 / 2026, page 28 / 336 24 / 157 biomarkers in a patient biological sample; at least two biomarkers selected from one or both of a first set of biomarkers in which a higher level of expression indicates a bacterial infection and a second set of biomarkers in which a higher level of expression indicates a viral infection; wherein the first set of biomarkers includes at least one of TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR and LAPTM5; and wherein the second set of biomarkers includes at least one of the following: OAS1, IFIT1, SAMD9, ISG15, HERC5, DDX60, HESX1, IFI6, MX1, OASL, LAX1, IFIT5, IFIT3, KCTD14, OAS2, RTP4, PARP12, LY6E, ADA, IFI44L, IFI27, RSAD2, IFI44, OAS3, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1, and GZMB;(c) analyze the expression levels of each biomarker together with the respective reference ranges for an uninfected control individual, where differential expression of viral response genes compared to the reference ranges for an uninfected control individual indicates that the patient has a viral infection and differential expression of bacterial response genes compared to the reference values ​​for an uninfected control individual indicates that the patient has a bacterial infection.

[0081] In any embodiment, the set of viral and bacterial response genes may be selected from the group of: (a) a set of viral response genes including OAS2 and CUL1 and a set of bacterial response genes including SLC12A9, ACPP, STAT5B; (b) a set of viral response genes including ISG15 and CHST12 and a set of bacterial response genes including EMR1 and FLII; (c) a Petition 870260069557, dated 07 / 14 / 2026, p. 29 / 336 25 / 157 viral response gene set including IFIT1, SIGLEC1 and ADA and a bacterial response gene set including PTAFR, NRD1, PLP2; (d) a viral response gene set including MX1 and a bacterial response gene set including DYSF, TWF2; (e) a viral response gene set including RSAD2 and a bacterial response gene set including SORT1 and TSPO; (f) a viral response gene set including IFI44L, GZMB and KCTD14 and a bacterial response gene set including TBXAS1, ACAA1 and S100A12; (g) a viral response gene set including LY6E and a bacterial response gene set including PGD and LAPTM5; (h) a set of viral response genes including IFI44, HESX1 and OASL and a set of bacterial response genes including NINJ2, DOK3, SORL1 and RAB31; and (i) a set of viral response genes including OAS1 and a set of bacterial response genes including IMPA2 and LTA4H.

[0082] In either modality, the biological sample may include whole blood or peripheral blood mononuclear cells (PBMCs).

[0083] In either modality, biomarker levels may be compared with corresponding time reference values ​​for infected or uninfected individuals.

[0084] In any modality, the method may include the calculation of a bacterial / viral metascore for the patient based on biomarker levels, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

[0085] In any modality, the method may include measuring the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 in the sample. Petition 870260069557, dated 07 / 14 / 2026, p. 30 / 336 26 / 157 biological; and analyze the expression levels of each biomarker in conjunction with the respective reference value ranges for the biomarkers, where increased expression levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, and C3AR1 and decreased expression levels of the biomarkers KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 compared to the reference value ranges for the biomarkers for an uninfected control individual indicate that the patient has an infection, and the absence of differential expression of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 compared to the uninfected control individual indicates that the patient does not have an infection.

[0086] In one embodiment, the method is produced for a kit, wherein the kit includes agents for measuring the expression levels of a set of viral response genes and a set of bacterial response genes selected from (a) a set of viral response genes that includes OAS2 and CUL1 and a set of bacterial response genes that includes SLC12A9, ACPP, STAT5B; (b) a set of viral response genes that includes ISG15 and CHST12 and a set of bacterial response genes that includes EMR1 and FLII; (c) a set of viral response genes that includes IFIT1, SIGLEC1 and ADA and a set of bacterial response genes that includes PTAFR, NRD1, PLP2; (d) a set of viral response genes that includes MX1 and a set of bacterial response genes that includes DYSF, TWF2; (e) a set of viral response genes that includes RSAD2 and a set of bacterial response genes that includes SORT1 and TSPO;(f) a set of viral response genes that includes IFI44L, GZMB and KCTD14 and a set of bacterial response genes that includes TBXAS1, ACAA1 and S100A12; (h) a set of viral response genes that includes IFI44, HESX1 and OASL and a set of bacterial response genes that includes NINJ2, DOK3; Petition 870260069557, dated 07 / 14 / 2026, p. 31 / 336 27 / 157 SORL1 and RAB31; and (i) a set of viral response genes including OAS1 and a set of bacterial response genes including IMPA2 and LTA4H.

[0087] In any form, the kit may include a microarray.

[0088] In one embodiment, the invention is designed for a computer-implanted method for diagnosing a patient suspected of having an infection, wherein the computer performs the steps of (a) receiving input patient data that includes values ​​for the expression levels of at least two biomarkers in a biological sample from a patient;at least two biomarkers selected from one or both of a first set of biomarkers where a higher level of expression indicates a bacterial infection and a second set of biomarkers where a higher level of expression indicates a viral infection, wherein the viral response gene set includes one or more genes selected from the group of OAS2, CUL1, ISG15, CHST12, IFIT1, SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58, STAT1 and the bacterial response gene set includes one or more genes selected from the group of SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, IMPA2, LTA4H, TALDO1, TKT, PYGL, CETP, PROS1, RTN3, CAT, CYBRD1;(b) analyze the expression levels of the viral response gene set and the bacterial response gene set and compare them with the respective reference value ranges for an uninfected control individual; (c) calculate a bacterial / viral metascore for the patient based on the expression levels of the viral response gene set and the bacterial response gene set; and (d) display; Petition 870260069557, dated 07 / 14 / 2026, p. 32 / 336 28 / 157 information regarding the patient's diagnosis.

[0089] In one embodiment, the invention is produced in a diagnostic system that executes the computer-implanted method, which includes in the diagnostic system (a) a storage component for storing data, wherein the storage component has instructions for determining the stored patient diagnosis; (b) a computer processor for data processing, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component to receive patient data and analyze patient data according to one or more algorithms; and (c) a display component for displaying information relating to the patient diagnosis.

[0090] These and other embodiments of the present invention will readily occur to those skilled in the art in view of the present disclosure. BRIEF DESCRIPTION OF THE FIGURES

[0091] Figures 1A and 1B show a summary of Receptor Operating Characteristic (ROC) curves for (Figure 1A) finding and (Figure 1B) direct validation datasets for bacterial / viral metascore. A summary ROC curve is shown in black, with 95% confidence intervals in dark gray.

[0092] Figure 2 shows bacterial / viral scores for COCONUT conormalized whole blood finding datasets. PBMC datasets are left out of Figure 2 because PBMC datasets are expected to have different genetic levels than whole blood. The overall AUC across all whole blood finding datasets is 0.92. Distribution of scores by dataset (dark gray = bacterial, light gray = viral), individual gene levels, and maintenance genes. Petition 870260069557, dated 07 / 14 / 2026, p. 33 / 336 29 / 157 (grayscale) are shown. The dashed line shows a possible global limit. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the white line in the middle shows the average score. The maintenance genes (POLG, ATP6V1B1, and PEG10) show the expected invariance between the post-COCONUT normalization datasets.

[0093] Figures 3A to 3C show an integrated antibiotic decision model (IADM) using conormalized public gene expression data from COCONUT that match the inclusion criteria. Figure 3A shows an IADM scheme. Figure 3B shows a distribution of scores and cutoffs for IADM in conormalized COCONUT data. Figure 3C shows a confusion matrix for diagnosis. Sensitivity to bacterial infection: 94.0%; Specificity to bacterial infection: 59.8%; Sensitivity to viral infection: 53.0%; Specificity to viral infection: 90.6%.

[0094] Figures 4A to 4E show gene expression data. NanoString targeted children with SIRS / sepsis from a GPSSSI cohort never tested with microarrays (total N = 96, of which SIRS = 36, bacterial sepsis = 49, viral sepsis = 11). Figure 4A shows the breakdown of infected patients by organism type. Figures 4B and 4C show ROC curves for SMS and bacterial / viral metascore. Figure 4D shows the distribution of scores and cutoffs for IADM. Figure 4E shows a confusion matrix for IADM; Sensitivity to bacterial infection: 89.7%; Specificity to bacterial infection: 70.0%; Sensitivity to viral infection: 54.5%; Specificity to viral infection: 96.5%.

[0095] Figures 5A and 5B show that the Sepsis Metascore (SMS) alone cannot determine the type of pathogen. The diagram in (Figure 5A) indicates how a decision model can be constructed. A Petition 870260069557, dated 07 / 14 / 2026, p. 34 / 336 30 / 157 Figure 5B shows the distribution of SMS in patients with bacterial versus viral infections. Of 11 datasets, there were only three for which the SMS distribution showed a significant difference between bacterial and viral infections.

[0096] Figure 6 shows a schematic of the workflow for multicohort analysis and determination of the bacterial-viral meta-signature.

[0097] Figure 7 shows forest plots of genes in the bacterial / viral metascore across the findings datasets. The geometric x-axes represent the standardized mean difference between bacterial and viral infection samples, calculated as Hedges' g, on a log2 scale. The size of the black rectangles is inversely proportional to the standard error of the mean in the study. Risk marks represent the 95% confidence interval. The light gray diamonds represent the pooled overall mean difference for a given gene. The width of the diamonds represents the 95% confidence interval of the pooled overall mean difference.

[0098] Figure 8 shows forest plots of the random effects meta-analysis of the alpha and beta summary ROC parameters for the finding datasets. Alpha roughly controls the distance from the identity line (greater alpha = greater AUC) and beta controls the slope of the actual ROC curve (beta = 0 means no slope).

[0099] Figure 9 shows forest plots of the random effects meta-analysis of the alpha and beta summary ROC parameters for the validation datasets. Alpha roughly controls the distance from the identity line (greater alpha = greater AUC) and beta controls the slope of the actual ROC curve (beta = 0 means no slope).

[00100] Figure 10 shows the ROC of bacterial / viral metascore in GSE53166, where monocyte-derived dendritic cells Petition 870260069557, dated 07 / 14 / 2026, page 35 / 336 31 / 157 stimulated in vitro with LPS or influenza virus, total N = 75 (39 LPS, 36 influenza virus).

[00101] Figure 11 shows a schematic representation of COCONUT conormalization. Light gray indicates healthy ('H'), medium gray means viral ('V'), and dark gray means bacterial ('B'). Different crosses mean different batch effects. See Methods for formal mathematical details.

[00102] Figures 12A and 12B show data from whole blood finding datasets. PBMC datasets are left out of Figures 12A and 12B because PBMC datasets are expected to have different genetic levels than whole blood. Figure 12A shows raw data and Figure 12B shows COCONUT conormalized data. COCONUT conormalization redefines each gene to be at the same location and scale for control patients. The distribution of a gene within a dataset is unchanged (the median difference in the T-statistic for healthy versus diseased across datasets is 0, range (-1e-13, 1e-13), across all genes and all datasets). The ATP6V1B1 maintenance gene exhibits the expected invariance with respect to disease and is invariant across datasets after normalization.A gene that is expected to be induced by disease, for example, CEACAM1, exhibits invariance in healthy controls but may vary in disease states among datasets. Upper colored bars indicate datasets; the lower colored bar indicates the disease class.

[00103] Figure 13 shows the bacterial / viral score in the overall ROC conormalization COCONUT of whole blood validation datasets. The overall AUC across all whole blood validation datasets is 0.93. The score distribution by dataset (dark gray violins = bacterial, light gray violins = 0.93) Petition 870260069557, dated 07 / 14 / 2026, p. 36 / 336 32 / 157 = viral) and maintenance genes (grayscale) are shown. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the white line in the middle shows the average score. The dashed line shows a possible global limit. The maintenance genes (POLG, ATP6V1B1, and PEG10) show the expected invariance between the post-COCONUT normalization datasets.

[00104] Figure 14 shows the overall bacterial / viral ROC score of non-connormalized whole blood finding datasets. PBMC datasets are left out of Figure 14 because PBMC datasets are expected to have different genetic levels than whole blood. The overall AUC across all whole blood finding datasets is 0.93. The score distribution by dataset (dark gray violins = bacterial, light gray violin = viral) and maintenance genes (grayscale) are shown. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the middle white dash shows the average score. Note the highly variable locations and scales of the POLG, ATP6V1B1, and PEG10 maintenance genes.

[00105] Figure 15 shows the bacterial / viral score in the overall ROC of non-connormalized whole blood validation datasets. PBMC datasets are left out of Figure 15 due to the fact that PBMC datasets are expected to have different genetic levels than whole blood. The score distribution by dataset (dark gray violins = bacterial, light gray violin = viral) and maintenance genes (grayscale) are shown. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin is Petition 870260069557, dated 07 / 14 / 2026, p. 37 / 336 33 / 157 extends from the 25th to the 75th, and the white line in the middle shows the average score. Note the highly variable locations and scales of the POLG, ATP6V1B1, and PEG10 maintenance genes.

[00106] Figure 16 shows the bacterial / viral score in global ROC of COCONUT conormalization of PBMC validation datasets. The PBMC datasets are examined separately due to the fact that PBMC datasets are expected to have different gene levels than whole blood. The overall AUC across all PBMC validation datasets is 0.92. The score distribution by dataset (dark gray violins = bacterial, light gray violin = viral) and maintenance genes (grayscale) are shown. The dashed line shows a possible global limit. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the middle white dash shows the average score. The maintenance genes (POLG, ATP6V1B1) show the expected invariance between post-COCONUT normalization datasets.

[00107] Figure 17 shows the overall bacterial / viral ROC score of non-conormalized PBMC validation datasets. PBMC datasets are examined separately due to the fact that PBMC datasets are expected to have different gene levels than whole blood. The score distribution across the dataset (dark gray violins = bacterial, light gray violins = viral), individual gene levels, and maintenance genes (grayscale) are shown. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the middle white line shows the average score. The highly variable locations and scales of the POLG and ATP6V1B1 maintenance genes are noted. Petition 870260069557, dated 07 / 14 / 2026, p. 38 / 336 34 / 157

[00108] Figure 18 shows the distribution of mean AUCs across all finding datasets for 10,000 randomly selected pairs of 2 genes.

[00109] Figures 19A to 19D show the effects of age on the Sepsis Metascore in conormalized COCONUT data. Figure 19A shows age versus SMS by pathogen type, to assess whether pathogen type is driving age differences in SMS. Figure 19B shows log10(age) vs. SMS by pathogen type, which shows that at age extremes, SMS may have a different attainable maximum. Figure 19C shows log10(age) versus SMS by dataset, which demonstrates that the relationship between age and SMS is independent of dataset. Figures 19A to 19C include only samples from infected patients; Figure 19D shows samples from healthy and uninfected SIRS, in addition to showing the baseline across ages. In all cases, the age data for GSE25504 are randomly distributed according to the mean age determined in their manuscript, approximately 2 weeks, + / - 1 week, to show the data density.All ages = 0 were reset to age = 1 / 365.

[00110] Figures 20A and 20B show the Sepsis Metascore in all whole blood data (both at finding and validation) before (Figure 20B) and after COCONUT conormalization (Figure 20A). The overall AUC is 0.86 (95% CI 0.84 to 0.89) after COCONUT conormalization. The score distribution by dataset (light gray violin = non-infectious inflammation, dark gray violin = infection / sepsis) and maintenance genes (grayscale) are shown. The dashed line shows a possible overall threshold. The width of each violin corresponds to the distribution of scores in the given dataset. The vertical bar within each violin extends from the 25th to the 75th, and the white line in the middle shows the mean score. Note Petition 870260069557, dated 07 / 14 / 2026, page 39 / 336 35 / 157 shows that the invariance of the maintenance genes POLG, ATP6V1B1, and PEG10 in the datasets of Figure 20A post-COCONUT normalization, with highly variable locations and scales of the maintenance genes before normalization in Figure 20B.

[00111] Figures 21A and 21B show IADM using conormalized public gene expression data from COCONUT that includes healthy controls. The included datasets (and cutoffs used) are the same as those in Figures 3A to 3C. Figure 21A shows the distribution of scores for IADM in conormalized COCONUT data. Figure 21B shows a confusion matrix for diagnosis. Sensitivity to bacterial infection: 94.2%; Specificity to bacterial infection: 68.5%; Sensitivity to viral infection: 53.0%; Specificity to viral infection: 94.1%. SIRS refers to non-infectious inflammation.

[00112] Figure 22 shows NPV and PPV versus prevalence for a diagnostic test with a sensitivity of 94.0% and a specificity of 59.8%. The red lines show an NPV of 98.3% with a prevalence of 15%, as a rough estimate of actual infection case rates.

[00113] Figures 23A to 23D show results for the GSE63990 dataset (adults with acute respiratory infections). Figures 23A and 23B show ROC curves for the Sepsis Metascore and the bacterial / viral metascore. Figure 23C shows the distribution of scores and cutoffs for the IADM. Figure 23D shows a confusion matrix for the IADM; Sensitivity to bacterial infection: 94.3%; Specificity to bacterial infection: 52.2%; Sensitivity to viral infection: 52.2%; Specificity to viral infection: 94.3%. DETAILED DESCRIPTION

[00114] Unless otherwise indicated, the practice of the present invention will employ conventional methods of pharmacology, chemistry, biochemistry, Petition 870260069557, dated 07 / 14 / 2026, p. 40 / 336 36 / 157 recombinant DNA and immunology techniques, in the skill of the technique. Such techniques are fully explained in the literature. See, for example, JE Bennett, R. Dolin and MJ Blaser Mandell, Douglas, and Bennett's Principles and Practice of Infectious Diseases (Saunders, 8th edition, 2014); JR Brown Sepsis: Symptoms, Diagnosis, and Treatment (Public Health in the 21st Century Series, Nova Science Publishers, Inc., 2013); Sepsis and Non-Infectious Systemic Inflammation: From Biology to Critical Care (J. Cavaillon, C. Adrie editors, Wiley-Blackwell, 2008); Sepsis: Diagnosis, Management, and Health Outcomes (Allergies and Infectious Diseases, N. Khardori editors, Nova Science Pub Inc., 2014); Handbook of Experimental Immunology, Volumes I-IV (DM Weir and CC Blackwell editors, Blackwell Scientific Publications); AL Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al.Molecular Cloning: A Laboratory Manual (3rd edition, 2001); Methods In Enzymology (S. Colowick and N. Kaplan editors, Academic Press, Inc.).

[00115] All publications, patents and patent applications cited in this document, whether above or below, are incorporated herein by reference in their entirety. I. DEFINITIONS

[00116] In the description of the present invention, the following terms shall be used, and shall be defined as indicated below.

[00117] It should be noted that, as used in this descriptive report and the accompanying claims, the singular forms a, an, and others include plural referents, unless the content clearly dictates otherwise. Thus, for example, reference to a biomarker includes a mixture of two or more biomarkers, and the like.

[00118] The term about, particularly in reference to a specific quantity, is intended to cover deviations of plus or minus five percent. Petition 870260069557, dated 07 / 14 / 2026, p. 41 / 336 37 / 157

[00119] The term Area Under the Curve (AUC) as used in this document shall be understood as referring to the area under a Receiving Operating Characteristic Curve (ROC Curve).

[00120] A biomarker in the context of the present invention refers to a biological compound, such as a polynucleotide, that is differentially expressed in a sample taken from patients who have an infection, compared to a comparable sample taken from control individuals (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual). The biomarker may be a nucleic acid, a fragment of a nucleic acid, a polynucleotide, or an oligonucleotide that can be detected and / or quantified.Biomarkers include polynucleotides comprising nucleotide sequences of genes or RNA gene transcripts, including, but not limited to, IFI27, JUP, LAX1, OAS2, CUL1, ISG15, CHST12, IFIT1, SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58, STAT1, HK3, TNIP1, GPAA1, CTSB, SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, IMPA2, LTA4H, TALDO1, TKT, PYGL, CETP, PROS1, RTN3, CAT, CYBRD1, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1.

[00121] Viral response genes refer to genes that are differentially expressed in a sample taken from patients who have a viral infection, compared to a comparable sample taken from control subjects (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual). Viral response genes include, but are not limited to, IFI27, JUP, Petition 870260069557, dated 07 / 14 / 2026, p. 42 / 336 38 / 157 LAX1, OAS2, CUL1, ISG15, CHST12, IFIT1, SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58 and STAT1.

[00122] Bacterial response genes refer to genes that are differentially expressed in a sample of patients with a bacterial infection compared to a comparable sample from a control group (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual). Bacterial response genes include, but are not limited to, HK3, TNIP1, GPAA1, CTSB, SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, IMPA2, LTA4H, TALDO1, TKT, PYGL, CETP, PROS1, RTN3, CAT, and CYBRD1.

[00123] Sepsis response genes refer to genes that are differentially expressed in a sample taken from patients who have sepsis or an infection, compared to a comparable sample taken from control subjects (e.g., a person with a negative diagnosis, normal or healthy or uninfected individuals). Sepsis response genes include, but are not limited to, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1.

[00124] The terms polypeptide and protein refer to a polymer of amino acid residues and are not limited to a minimum length. Thus, peptides, oligopeptides, dimers, multimers, and the like are included in the definition. Both whole proteins and their fragments are covered by the definition. The terms also include post-expression modifications of the polypeptide, for example, glycosylation, acetylation, phosphorylation, hydroxylation, oxidation, and the like. Petition 870260069557, dated 07 / 14 / 2026, p. 43 / 336 39 / 157

[00125] The terms polynucleotide, oligonucleotide, nucleic acid, and nucleic acid molecule are used in this document to include a polymeric form of nucleotides of any length, ribonucleotides, or deoxyribonucleotides. This term refers only to the primary structure of the molecule. Thus, the term includes triple-stranded, double-stranded, and single-stranded DNA, as well as triple-stranded, double-stranded, and single-stranded RNA. It also includes modifications, such as by methylation and / or capping, and unmodified forms of the polynucleotide. More particularly, the terms polynucleotide, oligonucleotide, nucleic acid, and nucleic acid molecule include polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose), and any other type of polynucleotide that is a N- or C-glycoside of a purine or pyrimidine base.There is no intended distinction in terms of length between the terms polynucleotide, oligonucleotide, nucleic acid, and nucleic acid molecule, and these terms are used interchangeably.

[00126] The term differentially expressed refers to differences in the amount and / or frequency of a biomarker present in a sample from patients who have, for example, an infection (e.g., viral infection or bacterial infection) compared to a control individual or an uninfected individual. For example, a biomarker might be a polynucleotide that is present at a high level or a low level in samples from patients with an infection (e.g., viral infection or bacterial infection) compared to samples from control individuals. Alternatively, a biomarker might be a polynucleotide that is detected with a higher frequency or a lower frequency in samples from patients with an infection (e.g., viral infection or bacterial infection) compared to samples from control individuals. A biomarker can be differentially expressed in terms of Petition 870260069557, dated 07 / 14 / 2026, page 44 / 336 40 / 157 quantity, frequency, or both.

[00127] A polynucleotide is differentially expressed between two samples if the amount of the polynucleotide in one sample is statistically different, by a method significant, from the amount of the polynucleotide in the other sample. For example, a polynucleotide is differentially expressed in two samples if it is present at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1,000% greater than it is present in the other sample, or if it is detectable in one sample and not detectable in the other.

[00128] Alternatively or additionally, a polynucleotide is differentially expressed in two sets of samples if the frequency of detection of the polynucleotide in samples from patients suffering from sepsis is statistically higher or lower, by a significant method, than in control samples. For example, a polynucleotide is differentially expressed in two sets of samples if detected at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1,000% more frequently or less frequently observed in one set of samples than the other set of samples.

[00129] A similarity value is a number that represents the degree of similarity between two things being compared. For example, a similarity value might be a number that indicates the overall similarity between a patient's expression profile using specific phenotype-related biomarkers and reference value ranges for the biomarkers in one or more control samples or Petition 870260069557, dated 07 / 14 / 2026, page 45 / 336 41 / 157 a reference expression profile (e.g., similarity to a viral infection expression profile or a bacterial infection expression profile). The similarity value can be expressed as a similarity metric, such as a correlation coefficient, or it can simply be expressed as the expression level difference or the aggregate of expression level differences between biomarker levels in a patient sample and a control sample or reference expression profile.

[00130] The terms individual, subject, and patient are used interchangeably herein and refer to any mammalian individual for whom diagnosis, prognosis, treatment, or therapy is desired, particularly humans. Other individuals may include cattle, dogs, cats, guinea pigs, rabbits, rats, mice, horses, and so forth. In some cases, the methods of the invention will be used on experimental animals, in veterinary applications, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters; and primates.

[00131] As used in this document, a biological sample refers to a sample of tissue, cells or fluid isolated from an individual, including but not limited to, for example, blood, buffy coat, plasma, serum, blood cells (e.g., peripheral blood mononuclear cells (PBMCs)), fecal matter, urine, bone marrow, bile, spinal fluid, lymphatic fluid, skin samples, external skin secretions, respiratory, intestinal and genitourinary tract secretions, tears, saliva, milk, organs, biopsies and also samples of constituents of in vitro cell culture, which includes, but is not limited to, conditioned media resulting from the growth of cells and tissues in culture medium, for example, recombinant cells and cellular components. Petition 870260069557, dated 07 / 14 / 2026, page 46 / 336 42 / 157

[00132] A test quantity of a biomarker refers to the amount of a biomarker present in a sample that is tested. A test quantity can be an absolute quantity (e.g., pg / ml) or a relative quantity (e.g., relative signal intensity).

[00133] A diagnostic quantity of a biomarker refers to an amount of a biomarker in an individual's sample that is consistent with the diagnosis of an infection (e.g., viral or bacterial infection). A diagnostic quantity can be an absolute quantity (e.g., pg / ml) or a relative quantity (e.g., relative signal intensity).

[00134] A control quantity of a biomarker can be any quantity or a range of quantities that is to be compared with a test quantity of a biomarker. For example, a control quantity of a biomarker could be the quantity of a biomarker in a person without an infection (e.g., viral infection or bacterial infection). A control quantity can be in absolute quantity (e.g., μg / ml) or in relative quantity (e.g., relative signal intensity).

[00135] The term antibody encompasses preparations of polyclonal and monoclonal antibodies, as well as preparations including hybrid antibodies, altered antibodies, chimeric antibodies and humanized antibodies, as well as: hybrid (chimeric) antibody molecules (see, for example, Winter et al. (1991) Nature 349: 293 to 299; and US Patent No. 4,816,567); F(ab')2 and F(ab) fragments; Fv molecules (non-covalent heterodimers, see, for example, Inbar et al. (1972) Proc Natl Acad Sci USA 69: 2,659 to 2,662; and Ehrlich et al. (1980) Biochem 19: 4,091 to 4,096); single-chain Fv (sFv) molecules (see, for example, Huston et al. (1988) Proc Natl Acad Sci USA 85: 5879–5883); dimeric antibody fragment constructs and Petition 870260069557, dated 07 / 14 / 2026, p. 47 / 336 43 / 157 trimeric; minibodies (see, for example, Pack et al. (1992) Biochem 31: 1579 to 1584; Cumber et al. (1992) J Immunology 149B: 120 to 126); humanized antibody molecules (see, for example, Riechmann et al. (1988) Nature 332: 323 to 327; Verhoeyan et al. (1988) Science 239: 1534 to 1536; and UK Patent Publication in GB 2,276,169, published on 21 September 1994); and any functional fragments obtained from these molecules, wherein these fragments retain specific binding properties of the parental antibody molecule.

[00136] Detectable portions or detectable markers contemplated for use in the invention include, but are not limited to, radioisotopes, fluorescent dyes such as fluorescein, phycoerythrin, Cy-3, Cy-5, allophycin, DAPI, Texas Red, rhodamine, Oregon Green, Lucifer Yellow and the like, green fluorescent protein (GFP), red fluorescent protein (DsRed), cyan fluorescent protein (CFP), yellow fluorescent protein (YFP), Cerianthus orange fluorescent protein (cOFP), alkaline phosphatase (AP), beta-lactamase, chloramphenicol acetyltransferase (CAT), adenosine deaminase (ADA), aminoglycoside phosphotransferase (neor, G418r), dihydrofolate reductase (DHFR), hygromycin-B-phosphotransferase (HPH), thymidine kinase (TK), lacZ (encoding β-alactosidase) and xanthine guanine phosphoribosyltransferase (XGPRT), beta-glucuronidase (gus), placental alkaline phosphatase (PLAP), secreted embryonic alkaline phosphatase (SEAP), or firefly or bacterial luciferase (LUC).Enzyme labels are used with their cognate substrate. The terms also include color-coded microspheres of known fluorescent light intensities (see, for example, microspheres with xMAP technology produced by Luminex (Austin, TX, USA)); microspheres containing quantum dot nanocrystals, for example, containing different proportions and color combinations of Qdot quantum dot nanocrystals. Petition 870260069557, dated 07 / 14 / 2026, page 48 / 336 44 / 157 produced by Life Technologies (Carlsbad, CA, USA); glass-coated metal nanoparticles (see, for example, SERS nano-tags produced by Nanoplex Technologies, Inc. (Mountain View, CA, USA)); barcode materials (see, for example, submicron-sized striped metal rods as nano-barcodes produced by Nanoplex Technologies, Inc.); microparticles encoded with colored barcodes (see, for example, CellCard produced by Vitra Bioscience, vitrabio.com); and glass microparticles with digital holographic code images (see, for example, CyVera microspheres produced by Illumina (San Diego, CA, USA)). As with many of the standard procedures associated with the practice of the invention, those skilled in the art will be aware that additional markers may be used.

[00137] Developing a classifier refers to using input variables to generate an algorithm or classifier with the ability to distinguish between two or more states.

[00138] Diagnosis as used in this document generally includes determining the probability of an individual being affected by a given disease, disorder, or dysfunction. Those skilled in the art often make a diagnosis based on one or more diagnostic indicators, that is, a biomarker, the presence, absence, or quantity of which is indicative of the presence or absence of the disease, disorder, or dysfunction.

[00139] Prognosis, as used in this document, generally refers to a prediction of the likely course and outcome of a clinical condition or disease. A patient's prognosis is generally made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease. It is understood that the term prognosis does not necessarily refer to the ability to predict the course or outcome. Petition 870260069557, dated 07 / 14 / 2026, page 49 / 336 45 / 157 of a condition with 100% accuracy. Instead, those versed in the technique will understand that the term prognosis refers to an increased probability that a given course or outcome will occur; that is, that a course or outcome is more likely to occur in a patient exhibiting a given condition, when compared to the same individuals who do not exhibit the condition.

[00140] Substantially purified refers to nucleic acid or protease molecules that are removed from their natural environment and are isolated or separated and are at least 60% free, preferably about 75% free, and more preferably about 90% free from other components with which they are naturally associated. II. METHODS OF CARRYING OUT THE INVENTION

[00141] Before describing the present invention in detail, it should be understood that this invention is not limited to particular formulations or process parameters, and thus may, of course, vary. It should also be understood that the terminology used herein serves the purpose of describing only particular embodiments of the invention, and is not intended to be limiting.

[00142] Although various methods and materials similar or equivalent to those described in this document may be used in the practice of the present invention, the preferred materials and methods are described in this document.

[00143] The invention is based on the discovery of biomarkers that can be used for the diagnosis of an infection (see Example 1). In particular, the invention relates to the use of biomarkers that can be used to determine whether a patient with acute inflammation has a bacterial or viral infection that would benefit from treatment with an antibiotic or antiviral agent. In order to further understand the invention, a more detailed discussion is provided below on the identified biomarkers and the methods for Petition 870260069557, dated 07 / 14 / 2026, p. 50 / 336 46 / 157 use the same in the diagnosis and treatment of infections. A. BIOMARKERS

[00144] Biomarkers that can be used in the practice of the present invention include polynucleotides comprising nucleotide sequences from genes or RNA transcripts of genes, which include viral response genes that are differentially expressed in patients who have a viral infection compared to control subjects (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual who does not have a viral infection), such as IFI27, JUP, LAX1, OAS2, CUL1, ISG15, CHST12, IFIT1, etc. SIGLEC1, ADA, MX1, RSAD2, IFI44L, GZMB, KCTD14, LY6E, IFI44, HESX1, OASL, OAS1, OAS3, EIF2AK2, DDX60, DNMT1, HERC5, IFIH1, SAMD9, IFI6, IFIT3, IFIT5, XAF1, ISG20, PARP12, IFIT2, DHX58 and STAT1;Bacterial response genes that are differentially expressed in patients with a bacterial infection compared to control individuals (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual without bacterial infection), such as, but not limited to, HK3, TNIP1, GPAA1, CTSB, SLC12A9, ACPP, STAT5B, EMR1, FLII, PTAFR, NRD1, PLP2, DYSF, TWF2, SORT1, TSPO, TBXAS1, ACAA1, S100A12, PGD, LAPTM5, NINJ2, DOK3, SORL1, RAB31, IMPA2, LTA4H, TALDO1, TKT, PYGL, CETP, PROS1, RTN3, CAT, and CYBRD1; and sepsis response genes that are differentially expressed in patients with sepsis or an infection compared to control subjects (e.g., a person with a negative diagnosis, a normal or healthy individual, or an uninfected individual not having sepsis), such as, but not limited to, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1.

[00145] In one aspect, the invention includes a diagnostic method. Petition 870260069557, dated 07 / 14 / 2026, p. 51 / 336 47 / 157 of an infection in a patient. The method comprises a) obtaining a biological sample from the patient; b) measuring the expression levels in the biological sample of a set of viral response genes showing differential expression associated with a viral infection and a set of bacterial response genes showing differential expression associated with a bacterial infection; and c) analyzing the expression levels of the viral response genes and the bacterial response genes together with their respective reference ranges.

[00146] When analyzing biomarker levels in a biological sample, reference value ranges may represent the levels of one or more biomarkers found in one or more samples from one or more individuals without an infection (e.g., healthy or uninfected individual). Alternatively, reference values ​​may represent the levels of one or more biomarkers found in one or more samples from one or more individuals with a viral or bacterial infection. In certain embodiments, biomarker levels are compared to corresponding time reference value ranges for uninfected or infected individuals.

[00147] In certain embodiments, the viral response gene set and the bacterial response gene set are selected from the group consisting of: a) a viral response gene set comprising IFI27, JUP, and LAX1 and a bacterial response gene set comprising HK3, TNIP1, GPAA1, and CTSB; b) a viral response gene set comprising OAS2 and CUL1 and a bacterial response gene set comprising SLC12A9, ACPP, and STAT5B; c) a viral response gene set comprising ISG15 and CHST12 and a bacterial response gene set comprising EMR1 and FLII; d) a viral response gene set comprising IFIT1, SIGLEC1, and ADA and a bacterial response gene set comprising IFIT1, SIGLEC1, and ADA; e) a bacterial response gene set comprising IFIT1, SIGLEC1, and ADA; and a bacterial response gene set comprising IFIT1, SIGLEC1, and ADA. Petition 870260069557, dated 07 / 14 / 2026, p. 52 / 336 48 / 157 of bacterial response genes comprising PTAFR, NRD1, PLP2; e) a set of viral response genes comprising MX1 and a set of bacterial response genes comprising DYSF, TWF2; f) a set of viral response genes comprising RSAD2 and a set of bacterial response genes comprising SORT1 and TSPO; g) a set of viral response genes comprising IFI44L, GZMB, and KCTD14 and a set of bacterial response genes comprising TBXAS1, ACAA1, and S100A12; h) a set of viral response genes comprising LY6E and a set of bacterial response genes comprising PGD and LAPTM5; i) a set of viral response genes comprising IFI44, HESX1, and OASL and a set of bacterial response genes comprising NINJ2, DOK3, SORL1, and RAB31; (e.g., e) a set of viral response genes comprising OAS1 and a set of bacterial response genes comprising IMPA2 and LTA4H.

[00148] The biological sample obtained from the patient to be diagnosed is typically whole blood or blood cells (e.g., PBMCs), but it can be any sample of body fluids, tissue, or cells containing the expressed biomarkers. A control sample, as used herein, refers to a biological sample, such as body fluid, tissue, or cells that are not diseased. That is, a control sample is obtained from a normal or uninfected individual (e.g., an individual known not to have a viral infection, bacterial infection, sepsis, or inflammation). A biological sample can be obtained from a patient by conventional techniques. For example, blood can be obtained by venipuncture, and solid tissue samples can be obtained by surgical techniques according to methods well known in the art.

[00149] In certain modalities, a panel of biomarkers Petition 870260069557, dated 07 / 14 / 2026, p. 53 / 336 49 / 157 is used for the diagnosis of an infection. Biomarker panels of any size can be used in the practice of the invention. Biomarker panels for diagnosing an infection typically comprise at least 3 biomarkers and up to 30 biomarkers, including any number of intermediate biomarkers, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 biomarkers. In certain embodiments, the invention includes a biomarker panel comprising at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11 or more biomarkers. While smaller biomarker panels are generally more economical, larger biomarker panels (i.e., more than 30 biomarkers) have the advantage of providing more detailed information and can also be used in the practice of the invention.

[00150] In certain embodiments, the invention includes a biomarker panel for diagnosing an infection comprising one or more polynucleotides comprising a nucleotide sequence of a gene or an RNA transcript of a gene selected from the group consisting of IFI27, JUP, LAX1, HK3, TNIP1, GPAA1 and CTSB. In another embodiment, the biomarker panel further comprises one or more polynucleotides comprising a nucleotide sequence of a gene or an RNA transcript of a gene selected from the group consisting of CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLADPB1.

[00151] In certain modalities, biomarkers to distinguish viral and bacterial infections, as described in this document, are combined with additional biomarkers that have the ability to distinguish the possibility of inflammation in an individual. Petition 870260069557, dated 07 / 14 / 2026, p. 54 / 336 50 / 157 can be caused by an infection or a non-infectious source of inflammation (e.g., traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS)). A first diagnostic test is used to determine whether the acute inflammation is caused by an infectious or non-infectious source, and whether the source of the inflammation is an infection. A second diagnostic test is used to determine whether the infection is a viral infection or a bacterial infection that will benefit from treatment with antiviral agents or antibiotics, respectively.

[00152] In one embodiment, the invention includes a method for diagnosing and treating a patient having inflammation, comprising the method: a) obtaining a biological sample from the patient; b) measuring the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in the biological sample;(ec) first analyze the expression levels of each biomarker together with the respective reference value ranges for the biomarkers, where increased expression levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, and C3AR1 and reduced expression levels of the biomarkers KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1, compared to the reference value ranges for the biomarkers for an uninfected control individual, indicate that the patient has an infection, and the absence of differential expression of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1, compared to the uninfected control individual, indicates that the patient does not have an infection; d) analyze for a second time the expression levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1 and CTSB, if the patient is; Petition 870260069557, dated 07 / 14 / 2026, p. 55 / 336 51 / 157 diagnosed as having an infection, where increased expression levels of the biomarkers IFI27, JUP, LAX1 compared to the reference ranges for the biomarkers for a control individual indicate that the patient has a viral infection and increased expression levels of the biomarkers HK3, TNIP1, GPAA1, CTSB compared to the reference ranges for the biomarkers for the control individual indicate that the patient has a bacterial infection; and e) administer an effective amount of an antiviral agent to the patient if the patient is diagnosed with a viral infection, or administer an effective amount of an antibiotic to the patient if the patient is diagnosed with a bacterial infection.

[00153] In another embodiment, the method further comprises calculating a sepsis metascore for the patient, wherein a sepsis metascore greater than the reference ranges for an uninfected control individual indicates that the patient has an infection and a sepsis metascore that is within the reference ranges for an uninfected control individual indicates that the patient has a non-infectious inflammatory condition.

[00154] In another embodiment, the method further comprises calculating a bacterial / viral metascore for the patient if the patient is diagnosed with an infection, wherein a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

[00155] In another embodiment, the invention includes a method for treating a patient suspected of having an infection, comprising the method: a) receiving information relating to the patient's diagnosis in accordance with a method described herein; and b) administering a therapeutically effective amount of an agent. Petition 870260069557, dated 07 / 14 / 2026, page 56 / 336 52 / 157 antiviral if the patient is diagnosed with a viral infection or administer an effective amount of an antibiotic if the patient is diagnosed with a bacterial infection.

[00156] In certain embodiments, a patient diagnosed with a viral infection by a method described herein is administered a therapeutically effective dose of an antiviral agent, such as a broad-spectrum antiviral agent, an antiviral vaccine, a neuraminidase inhibitor (e.g., zanamivir (Relenza) and oseltamivir (Tamiflu)), a nucleoside analogue (e.g., acyclovir, zidovudine (AZT), and lamivudine), an antisense antiviral agent (e.g., phosphorothioate antisense antiviral agents (e.g., fomivirsen (Vitravene) for cytomegalovirus retinitis), morpholino antimessenger antiviral agents, viral clearance inhibitors (e.g., amantadine and rimantadine for influenza, pleconaril for rhinovirus), a viral entry inhibitor (e.g., fuzeon for HIV), a viral assembly inhibitor (e.g., rifampicin), or an agent antiviral that stimulates the immune system (e.g., interferons).Examples of antiviral agents include Abacavir, Acyclovir, Adefovir, Amantadine, Amprenavir, Ampligen, Arbidol, Atazanavir, Atripla (fixed-dose drug), Balavir, Cidofovir, Combivir (fixed-dose drug), Dolutegravir, Darunavir, Delavirdine, Didanosine, Docosanol, Edoxuridine, Efavirenz, Emtricitabine, Enfuvirtide, Entecavir, Ecoliever, Famciclovir, Fixed-dose combination (antiretroviral), Fomivirsen, Fosamprenavir, Foscarnet, Fosfonet, Fusion inhibitor, Ganciclovir, Ibacitabine, Immunovir, Idoxuridine, Imiquimod, Indinavir, Inosine, Integrase inhibitor, Interferon type III, Interferon type II, Interferon type I, Interferon, Lamivudine, Lopinavir, Loviride, Maraviroc, Moroxidine, Methiazolinone, Nelfinavir, Nevirapine, Nexavir, Nitazoxanide, Nucleoside analogs, Novir, Oseltamivir (Tamiflu), Peginterferon alfa-2a, Penciclovir, Peramivir, Pleconaril, Podophyllotoxin, Protease inhibitor, Raltegravir, Inhibitor. Petition 870260069557, dated 07 / 14 / 2026, page 57 / 336 53 / 157 reverse transcriptase inhibitors, Ribavirin, Rimantadine, Ritonavir, Pyramidine, Saquinavir, Sofosbuvir, Stavudine, Synergistic enhancer (antiretrovirals), Telaprevir, Tenofovir, Tenofovir disoproxil, Tipranavir, Trifluridine, Trizivir, Tromantadine, Truvada, Valacyclovir (Valtrex), Valganciclovir, Vicriviroc, Vidarabine, Viramidine, Zalcitabine, Zanamivir (Relenza), and Zidovudine.

[00157] In certain modalities, a patient diagnosed with a bacterial infection by a method described in this document is administered a therapeutically effective dose of an antibiotic. Antibiotics may include broad-spectrum, bactericidal, or bacteriostatic antibiotics. Exemplary antibiotics include aminoglycosides, such as Amikacin, Amycin, Gentamicin, Garamycin, Kanamycin, Cantrex, Neomycin, Neo-Fradina, Netilmicin, Netromycin, Tobramycin, Nebina, Paromomycin, Humatin, Streptomycin, Spectinomycin (Bs), and Trobicin; ansamycins, such as Geldanamycin, Herbimycin, Rifaximin, and Xifaxan; carbacefems, such as Loracarbef and Lorabid; carbapenems, such as Erapenem, Invanz, Doripenem, Doribax, Imipenem / Cilastatin, Primaxin, Meropenem, and Merrem;cephalosporins, such as Cefadroxil, Duricef, Cefazolina, Ancef, Cefalotina ou Cefalotina, Keflin, Cefalexina, Keflex, Cefaclor, Distaclor, Cefamandola, Mandol, Cefoxitina, Mefoxina, Cefprozil, Cefzil, Cefuroxime, Ceftina, Zinnat, Cefixime, Cefdinir, Cefditoren, Cefoperazona, Cefotaxime, Cefpodime, Ceftazidime, Ceftibuteno, Ceftizoxime, Ceftriaxona, Cefepime, Maxipima, Ceftaroline fosamil, Teflaro, Ceftobiprole and Zeftera; glycopéptides, such as Teicoplanin, Targocida, Vancomycin, Vancocin, Telavancin, Vibativ, Dalbavancin, Dalvance, Oritavancin and Orbactiv; lincosamidas, also known as Clindamycin, Cleocin, Lincomycin and Lincocina; lipopeptides, such as daptomycin and cubicin; macrolidos, such as Azithromycin, Zithromax, Sumamed, Xitrona, Clarithromycin, Biaxina, Dirithromycin, Dynabac, Erythromycin, Eritocina, Eritrope,; Petition 870260069557, 14 / 07 / 2026, pág. 58 / 336 54 / 157 Roxithromycin, Troleandomycin, Tao, Telithromycin, Ketek, Spiramycin and Rovamycin; monobactams, such as Aztreonam and Azactam; nitrofurans, such as Furazolidone, Furoxone, Nitrofurantoin, Macrodantin and Macrobida; oxazolidinones, such as Linezolid, Zyvox, VRSA, Posizolid, Radezolid and Torezolid; penicillins, such as Penicillin V, Veetids (Pen-Vee-K), Piperacillin, Pipracil, Penicillin G, Pfizerpen, Temocillin, Negaban, Ticarcillin and Ticar; penicillin combinations, such as Amoxicillin / clavulanate, Augmentin, Ampicillin / sulbactam, Unasyn, Piperacillin / tazobactam, Zosyn, Ticarcillin / clavulanate and Timentin; polypeptides such as Bacitracin, Colistin, Colia-Mycin-S and Polymyxin B;quinolones / fluoroquinolones such as Ciprofloxacin, Cipro, Ciproxina, Ciprobay, Enoxacin, Penetrex, Gatifloxacin, Tequina, Gemifloxacin, Factive, Levofloxacin, Levaquina, Lomefloxacin, Maxaquina, Moxifloxacin, Avelox, Nalidixic Acid, Neggram, Norfloxacin, Noroxina, Ofloxacin, Floxina, Ocuflox, Trovafloxacin, Trovana, Grepafloxacin, Raxar, Sparfloxacin, Zagam, Temafloxacin and Omniflox;Sulfonamides, such as Amoxicillin, Novamox, Amoxil, Ampicillin, Principin, Azlocillin, Carbenicillin, Geocillin, Cloxacillin, Tegopene, Dicloxacillin, Dynapen, Flucloxacillin, Floxapen, Mezlocillin, Mezlin, Methicillin, Stafilin, Nafcillin, Unipen, Oxacillin, Prostaflin, Penicillin G, Pentides, Mafenide, Sulfamylon, Sulfacetamide, Sulamyd, Bleph-10, Sulfadiazine, Micro-Sulfon, silver sulfadiazine, Silvadene, Sulfadimethoxine Di-Methoxy, Albon, Sulfamethizole, Tiosulfil Forte, Sulfamethoxazole, Gantanol, Sulfanilimide, Sulfasalazine, Azulfidine, Sulfisoxazole, Gantrisine Trimethoprim-Sulfamethoxazole (Co-trimoxazole) (TMP-SMX), Bactrim, Septra, Sulfonamidocrisoidine and Prontosil; tetracyclines, such as Demeclocycline, Declomycin, Doxycycline, Vibramycin, Minocycline, Minocine, Oxytetracycline, Terramycin, Tetracycline and Sumicin, Acromycin V and Steclin; antimycobacterial drugs, such as Clofazimine, Lamprene, Dapsone, Avlosulfone, Capreomycin, Capastat, Cycloserine; Petition 870260069557, dated 07 / 14 / 2026, page 59 / 336 55 / 157 Seromycin, Ethambutol, Myambutol, Ethionamide, Trecator, Isoniazid, INH, Pyrazinamide, Aldinamide, Rifampicin, Rifadin, Rimactane, Rifabutin, Mycobutin, Rifapentine, Priftin and streptomycin; other antibiotics, such as Arsphenamine, Salvarsan, Chloramfenicol, Chloromycetin, Fosfomycin, Monurol, Monuril, Fusic acid, Fucidin, Metronidazole, Flagyl, Mupirocin, Bactroban, Platensimycin, Quinupristin / Dalfopristin, Synercid, Tianfenicol, Tigecycline, Tigacil, Tinidazole, Tindamax Fasigyn, Trimethoprim, Proloprim and Trimpex. B. DETECTION AND MEASUREMENT OF BIOMARKERS

[00158] It is understood that biomarkers in a sample can be measured by any suitable method known in the art. The measurement of the expression level of a biomarker can be direct or indirect. For example, the abundance levels of RNAs or proteins can be directly quantified. Alternatively, the amount of a biomarker can be determined indirectly by measuring the abundance levels of cDNAs, amplified RNAs or DNAs, or by measuring the amounts or activities of RNAs, proteins or other molecules (e.g., metabolites) that are indicative of the biomarker's expression level. Methods for measuring biomarkers in a sample have many applications.For example, one or more biomarkers may be measured to aid in the diagnosis of an infection, to determine the appropriate treatment for an individual, to monitor an individual's responses to treatment, or to identify therapeutic compounds that modulate the expression of biomarkers in vivo or in vitro. DETECTION OF BIOMARKER POLINUCLEOTIDES

[00159] In one embodiment, the expression levels of biomarkers are determined by measuring the polynucleotide levels of the biomarkers. The transcript levels of specific biomarker genes can be determined from the amount of mRNA, or Petition 870260069557, dated 07 / 14 / 2026, p. 60 / 336 56 / 157 derived polynucleotides, present in a biological sample. Polynucleotides can be detected and quantified by a variety of methods including, but not limited to, microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, serial gene expression analysis), RNA switches, and solid-state nanopore detection. See, for example, Draghici Data Analysis Tools for DNA Microarrays, Chapman and Hall / CRC, 2003; Simon et al. Design and Analysis of DNA Microarray Investigations, Springer, 2004; PRealTime PCR: Current Technology and Applications, Logan, Edwards and Saunders, editors, Caister Academic Press, 2009; Bustin AZ of Quantitative PCR (IUL Biotechnology, no. 5), International University Line, 2004; Velculescu et al. (1995) Science 270: 484 to 487; Matsumura et al. (2005) Cell. Microbiol.7: 11 to 18; Serial Gene Expression Analysis (SAGE): Methods and Protocols (Methods in Molecular Biology), Humana Press, 2008; incorporated herein by reference in their entirety.

[00160] In one embodiment, microarrays are used to measure biomarker levels. An advantage of microarray analysis is that the expression of each of the biomarkers can be measured simultaneously, and microarrays can be specifically designed to provide a diagnostic expression profile for a particular disease or condition (e.g., sepsis).

[00161] Microarrays are prepared by selecting probes comprising a polynucleotide sequence and then immobilizing these probes on a support or solid surface. For example, the probes may comprise DNA sequences, RNA sequences, or DNA-RNA copolymer sequences. The polynucleotide sequences of the probes may also comprise DNA and / or RNA analogs, or combinations thereof. For example, the Petition 870260069557, dated 07 / 14 / 2026, p. 61 / 336 57 / 157 Polynucleotide sequences of probes can be complete or partial fragments of genomic DNA. The polynucleotide sequences of the probes can also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

[00162] The probes used in the methods of the invention are preferably immobilized on a solid support that may be porous or non-porous. For example, the probes can be polynucleotide sequences that are covalently attached to a nitrocellulose or nylon membrane or filter at the 3' or 5' end of the polynucleotide. Such hybridization probes are well known in the art (see, for example, Sambrook, et al, Molecular Cloning: A Laboratory Manual (3rd edition, 2001).Alternatively, the support or solid surface may be a glass, silicon, or plastic surface. In one embodiment, hybridization levels are measured in probe microarrays consisting of a solid phase on the surface on which a population of polynucleotides is immobilized, such as a population of DNA or DNA mimic, or alternatively, a population of RNA or RNA mimic. The solid phase may be a non-porous material or, optionally, a porous material, such as a gel, or a porous wafer, such as a TipChip (Axela, Ontario, Canada).

[00163] In one embodiment, the microarray comprises a support or surface with an ordered arrangement of binding sites (e.g., hybridization) or probes, each representing one of the biomarkers described herein. Preferably, the microarrays are addressable arrays and, more preferably, positionally addressable arrays. More specifically, each probe in the array is preferably located at a known predetermined position on the solid support, such that the identity (i.e., Petition 870260069557, dated 07 / 14 / 2026, p. 62 / 336 58 / 157 the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). Each probe is preferably covalently affixed to the solid support at a single location.

[00164] Microarrays can be made in several ways, several of which are described below. Regardless of how they are produced, microarrays share certain characteristics. Arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared to each other. Preferably, microarrays are made of materials that are stable under binding conditions (e.g., nucleic acid hybridization). Microarrays are, as a general method, small, e.g., between 0.1 cm2 and 25 cm2; however, larger arrays can also be used, e.g., in screening arrays.Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (i.e., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA or a specific derived cDNA). However, in general, other related or similar sequences will cross-hybridge to a given binding site.

[00165] As noted above, the probe to which a particular polynucleotide molecule specifically hybridizes contains a complementary polynucleotide sequence. Microarray probes typically consist of nucleotide sequences no longer than 1,000 nucleotides. In some embodiments, the array probes consist of nucleotide sequences from 10 to 1,000 nucleotides. In one embodiment, the nucleotide sequences of the probes are in the range of 10 to 200 nucleotides in length and are genomic sequences of a species of organism, so that a plurality of different probes are present, with complementary sequences and thus have the ability to hybridize to the genome of that species. Petition 870260069557, dated 07 / 14 / 2026, page 63 / 336 59 / 157 of organism, sequentially coated in all or part of the genome. In other embodiments, the probes are in the range of 10 to 30 nucleotides in length, in the range of 10 to 40 nucleotides in length, in the range of 20 to 50 nucleotides in length, in the range of 40 to 80 nucleotides in length, in the range of 50 to 150 nucleotides in length, in the range of 80 to 120 nucleotides in length, or are 60 nucleotides in length.

[00166] Probes may comprise DNA or DNA mimics (e.g., derivatives and analogs) corresponding to a portion of an organism's genome. In another embodiment, the microarray probes are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits with the capacity for Watson-Crick-like specific hybridization with DNA, or for specific hybridization with RNA. Nucleic acids may be modified in the base chemical portion, the sugar chemical portion, or the phosphate structure (e.g., phosphorothioates).

[00167] DNA can be obtained, for example, by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known genome sequence that will result in the amplification of specific genomic DNA fragments. Computer programs that are well-known in the art are useful in designing primers with the required specificity and ideal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically, each probe in the microarray will be between 10 and 50,000 bases, generally between 20 and 200 bases in length. PCR methods are well-known in the art and are described, for example, in Innis et al., Editions, PCR Protocols: A Guide To Methods And Applications, Academic Press Inc., San Diego, CA, USA (1990); incorporated herein by reference. Petition 870260069557, dated 07 / 14 / 2026, page 64 / 336 60 / 157 in its entirety. It will be evident to a person versed in the technique that controlled robotic systems are useful for isolating and amplifying nucleic acids.

[00168] A preferred alternative means of generating polynucleotide probes is by synthesis of synthetic polynucleotides or oligonucleotides, for example, using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14: 5399 to 5407 (1986); McBride et al., Tetrahedron Lett. 24: 246 to 248 (1983)). The synthetic sequences typically have between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, the synthetic nucleic acids include non-natural bases, such as, but not limited to, inosine. As noted above, nucleic acid analogs can be used as binding sites for hybridization. An example of a suitable nucleic acid analog is peptide nucleic acid (see, for example, Egholm et al., Nature 363: 566 to 568 (1993); Patent no. US5,539,083).

[00169] Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure. See Friend et al., International Patent Publication WO 01 / 05935, published January 25, 2001; Hughes et al., Nat. Biotech. 19: 342-347 (2001).

[00170] A person skilled in the art will also observe that positive control probes, for example, probes known to be complementary and hybridizable in sequences on the target polynucleotide molecules, and negative control probes, for example, probes known to be non-complementary and non-hybridizable in sequences on the target polynucleotide molecules, should be included in the array. Petition 870260069557, dated 07 / 14 / 2026, p. 65 / 336 61 / 157 In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal bands across the array. In yet another embodiment, the inverse complement for each probe is synthesized near the probe position to serve as a negative control. In yet another embodiment, sequences from other species of organisms are used as negative controls or as peak controls.

[00171] The probes are affixed to a support or solid surface, which may be made, for example, of glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, silicon, or other porous or non-porous material. One method for affixing nucleic acids to a surface is to print on glass plates, as described, in general method, by Schena et al., Science 270: 467 to 470 (1995). This method is especially useful for preparing cDNA microarrays (See also, DeRisi et al., Nature Genetics 14: 457 to 460 (1996); Shalon et al., Genome Res. 6: 639 to 645 (1996); and Schena et al., Proc. Natl. Acad. Sci. EUA 93: 10,539 to 11,286 (1995); incorporated into this document by reference in its entirety).

[00172] A second method for producing microarrays produces high-density oligonucleotide arrays. The techniques are known to produce arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for in situ synthesis (see, Fodor et al., 1991, Science 251: 767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. USA 91: 5022-5026; Lockhart et al., 1996, Nature Biotechnology 14: 1675; US Patents no. 5,578,832; US5,556,752 and US5,510,270, incorporated herein by reference in their entirety) or other methods for the synthesis and rapid deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11: 687 to 690, incorporated herein). Petition 870260069557, dated 07 / 14 / 2026, p. 66 / 336 62 / 157 document by reference in its entirety). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly onto a surface, such as a derivatized glass slide. Typically, the array produced is redundant, with several oligonucleotide molecules per RNA.

[00173] Other methods for making microarrays, for example, by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20: 1679 to 1684; incorporated herein by reference in its entirety), can also be used. In principle, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook, et al., Molecular Cloning: A Laboratory Manual, 3rd edition, 2001) could be used. However, as those versed in the art will recognize, very small sets will often be preferred due to the fact that the hybridization volumes will be smaller.

[00174] Microarrays can also be fabricated using an inkjet printing device for oligonucleotide synthesis, for example, using the methods and systems described by Blanchard in US Patent No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11: 687 to 690; Blanchard, 1998, in Synthetic DNA Arrays in Genetic Engineering, volume 20, J.K. Setlow, Edition, Plenum Press, New York, USA, pages 111 to 123; incorporated herein by reference in its entirety. Specifically, the oligonucleotide probes in such microarrays are synthesized in arrays, for example, on a glass slide, which serially deposits the individual nucleotide bases in microdroplets of a high surface tension solvent, such as propylene carbonate.Microdroplets have small volumes (e.g., 100 µl or less, more preferably 50 µl or less) and are separated from each other in the microarray (e.g., by hydrophobic domains). Petition 870260069557, dated 07 / 14 / 2026, page 67 / 336 63 / 157 form circular surface tension cavities that define the locations of the array elements (i.e., the different probes). Microarrays fabricated by this inkjet method are typically high-density, preferably with a density of at least about 2,500 different probes per 1 cm2. The polynucleotide probes are covalently attached to the support at the 3' or 5' end of the polynucleotide.

[00175] Biomarker polynucleotides that can be measured by microarray analysis can be expressed RNA or a nucleic acid derived from it (e.g., cDNA or amplified cDNA-derived RNA incorporating an RNA polymerase promoter), which includes naturally occurring nucleic acid molecules as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules comprise RNA, including, but not limited to, total cellular RNA, poly(A)+ messenger RNA (mRNA) or a weak form thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA: see, for example, Linsley & Schelter, patent application serial no. US 09 / 411,074, filed October 4, 1999, or US Patent nos. 5,545,522, US 5,891,636, or US 5,716,785). The methods for preparing poly(A)+ and total RNA are well known in the art and are described, in general terms, for example, in Sambrook, et al.RNA can be extracted from a cell of interest using lysis with guanidine thiocyanate followed by centrifugation with CsCl (Chirgwin et al., 1979, Biochemistry 18: 5294 to 5299), a silica gel-based column (e.g., RNeasy (Qiagen, Valencia, California, USA)) or StrataPrep (Stratagene, La Jolla, California, USA)), or using phenol and chloroform, as described in Ausubel et al., eds., 1989, Current Protocols in Molecular Biology, volume III, Publishing Associates Green, Inc., John Wiley & Sons, Inc., New York, USA. Petition 870260069557, dated 07 / 14 / 2026, page 68 / 336 64 / 157 pages 13.12.1 to 13.12.5). Poly(A)+ RNA can be selected, for example, by selection with cellulose oligo-dT or, alternatively, by reverse transcription initiated with oligo-dT of total cellular RNA. RNA can be fragmented by methods known in the art, for example, by incubation with ZnCl2, to generate RNA fragments.

[00176] In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom are isolated from a sample taken from a patient who has an infection or inflammation. Biomarker polynucleotides that are poorly expressed in particular cells can be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6: 791 to 806).

[00177] As described above, biomarker polynucleotides can be detectably labeled at one or more nucleotides. Any method known in the art can be used to label the target polynucleotides. Preferably, this labeling incorporates the marker uniformly along the length of the RNA, and preferably, the labeling is performed with a high degree of efficiency. For example, polynucleotides can be labeled by oligo-dT-initiated reverse transcription. Random primers (e.g., 9-mers) can be used in reverse transcription to uniformly incorporate labeled nucleotides along the entire length of the polynucleotides. Alternatively, random primers can be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods to amplify polynucleotides.

[00178] The detectable marker may be a luminescent marker. For example, fluorescent markers, bioluminescent markers, chemiluminescent markers, and colorimetric markers may be used in the practice of the invention. Fluorescent markers that may be used include, but are not limited to, fluorescence, a Petition 870260069557, dated 07 / 14 / 2026, page 69 / 336 65 / 157 phosphorus, a rhodamine, or a polymethine dye derivative. Chemiluminescent markers that can be used include, but are not limited to, luminol. Additionally, commercially available fluorescent markers include, but are not limited to, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, NJ), Fluoredite (Miilipore, Bedford, Massachusetts, USA), FAM (ABI, Foster City, California, USA), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, NJ, USA). Alternatively, the detectable marker can be a radiolabeled nucleotide.

[00179] In one embodiment, biomarker polynucleotide molecules from a patient sample are differentially labeled from the corresponding polynucleotide molecules from a reference sample. The reference may comprise polynucleotide molecules from a normal biological sample (i.e., control sample, e.g., blood or PBMCs from an individual without infection or inflammation) or from a reference biological sample (e.g., blood or PBMCs from an individual with a viral infection or bacterial infection).

[00180] Hybridization and nucleic acid washing conditions are chosen so that the target polynucleotide molecules specifically bind to or hybridize to the complementary polynucleotide sequences of the array, preferably at a specific location on the array where their complementary DNA is located. Arrays containing double-stranded probe DNA are preferably subjected to denaturation conditions to generate single-stranded DNA before contacting the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured before contacting the target polynucleotide molecules, for example, to remove Petition 870260069557, dated 07 / 14 / 2026, p. 70 / 336 66 / 157 staples or dimers that form due to self-complementary sequences.

[00181] Ideal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA or DNA) of probe and target nucleic acids. A person skilled in the technique will observe that as oligonucleotides become shorter, it may be necessary to adjust their lengths to obtain a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) nucleic acid hybridization conditions are described in Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd edition, 2001), and in Ausubel et al., Current Protocols in Molecular Biology, volume 2, Current Protocols Publishing, New York, USA (1994). Typical hybridization conditions for cDNA microarrays from Schena et al. are 5-fold hybridization.SSC plus 0.2% SDS at 65°C for four hours, followed by washes at 25°C in low-stringency washing buffer (1x SSC plus 0.2% SDS), followed by 10 minutes at 25°C in higher-stringency washing buffer (0.1x SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. USA 93: 10.614 (1993)). Useful hybridization conditions are also provided in, for example, Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers BV; and Kricka, 1992, Nonnotopic DNA Probe Techniques, Academic Press, San Diego, California, USA. Particularly preferred hybridization conditions include hybridization at a temperature at or near the average melting temperature of the probes (e.g., within 51°C, more preferably within 21°C) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine, and 30% formamide.

[00182] When fluorescently labeled gene products are used, the fluorescence emissions at each site of one. Petition 870260069557, dated 07 / 14 / 2026, p. 71 / 336 67 / 157 microarrays can preferably be detected by confocal laser microscopy scanning. In one embodiment, a separate scan is performed, using the appropriate excitation line, for each of the two fluorophores used. Alternatively, a laser can be used that allows simultaneous illumination of specimens at wavelengths specific to the two fluorophores, and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization, Genome Research 6: 639 to 645, which is incorporated herein by reference in its entirety for all purposes). Array scans can be performed with a laser fluorescent scanner with a computer-controlled XY stage and a microscope objective.Sequential excitation of the two fluorophores is achieved with a multiline mixed-gas laser, and the emitted light is divided by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6: 639-645 (1996) and in other references cited in this document. Alternatively, the optical fiber array described by Ferguson et al., Nature Biotech. 14: 1681-1684 (1996), can be used to monitor mRNA abundance levels at a large number of sites simultaneously. Alternatively, probes can be labeled with fluorophores and targets measured with quenchers, so that amplification is tracked by measuring the decreasing signal intensity.

[00183] In certain embodiments, the invention includes a microarray comprising a plurality of probes for detecting gene expression of a set of viral response genes and a set of bacterial response genes and / or a set of sepsis response genes. Petition 870260069557, dated 07 / 14 / 2026, p. 72 / 336 68 / 157

[00184] In one embodiment, the microarray comprises an oligonucleotide that hybridizes with an IFI27 polynucleotide, an oligonucleotide that hybridizes with a JUP polynucleotide, an oligonucleotide that hybridizes with a LAX1 polynucleotide, an oligonucleotide that hybridizes with an HK3 polynucleotide, an oligonucleotide that hybridizes with a TNIP1 polynucleotide, an oligonucleotide that hybridizes with a GPAA1 polynucleotide, and an oligonucleotide that hybridizes with a CTSB polynucleotide.

[00185] In another embodiment, the microarray further comprises an oligonucleotide that hybridizes with a CEACAM1 polynucleotide, an oligonucleotide that hybridizes with a ZDHHC19 polynucleotide, an oligonucleotide that hybridizes with a C9orf95 polynucleotide, an oligonucleotide that hybridizes with a GNA15 polynucleotide, an oligonucleotide that hybridizes with a BATF polynucleotide, an oligonucleotide that hybridizes with a C3AR1 polynucleotide, an oligonucleotide that hybridizes with a KIAA1370 polynucleotide, an oligonucleotide that hybridizes with a TGFBI polynucleotide, an oligonucleotide that hybridizes with an MTCH1 polynucleotide, an oligonucleotide that hybridizes with an RPGRIP1 polynucleotide, and a an oligonucleotide that hybridizes with an HLA-DPB1 polynucleotide.

[00186] Polynucleotides can also be analyzed by other methods including, but not limited to, Northern blotting, nuclease protection assays, RNA detection, polymerase chain reaction, ligase chain reaction, Qbeta replicase, isothermal amplification method, chain displacement amplification, transcription-based amplification systems, nuclease protection (S1 or RNase nuclease protection assays), SAGE, as well as methods disclosed in International Publications WO 88 / 10315 and WO 89 / 06700, and International Applications PCT / US87 / 00880 and Petition 870260069557, dated 07 / 14 / 2026, page 73 / 336 69 / 157 PCT / US89 / 01025; incorporated herein by reference in its entirety.

[00187] A standard Northern blot assay can be used to determine RNA transcript size, identify alternatively split RNA transcripts, and the relative amounts of mRNA in a sample, according to conventional Northern hybridization techniques known to people of ordinary skill in the art. In Northern blots, RNA samples are first separated by size by agarose gel electrophoresis under denaturing conditions. The RNA is then transferred to a membrane, cross-linked, and hybridized with a labeled probe. Radiolabeled probes of high specific activity or non-isotopic can be used, including randomly generated, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides. In addition, sequences with only partial homology (e.g., cDNA from a different species or genomic DNA fragments that may contain an exon) can be used as probes.The labeled probe, for example, a radiolabeled cDNA containing full-length single-stranded DNA or a fragment of that DNA sequence, may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides long. The probe may be labeled by any of the many different methods known to those skilled in this technique. The most commonly employed markers for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others. A number of fluorescent materials are known and can be used as markers. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA Blue, and Lucifer Yellow. A particular detection material is anti-rabbit antibody prepared in goats and conjugated with fluorescein via... Petition 870260069557, dated 07 / 14 / 2026, page 74 / 336 70 / 157 isothiocyanate. Proteins can also be labeled with a radioactive element or enzyme. The radioactive label can be detected by any of the currently available counting procedures. Isotopes that can be used include, but are not limited to, 3H, 14C, 32P, 35S, 36Cl, 35Cr, 57Co, 58Co, 59Fe, 90Y, 1251, 1311 and 186Re. Enzymatic labels are equally useful and can be detected by any of the currently used colorimetric, spectrophotometric, fluorophotometric, amperometric or gasometric techniques. The enzyme is conjugated to the selected particle by reaction with binding molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to a person skilled in the art can be used. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase, and alkaline phosphatase. Patents in US3,654,090, US3.752 and US4,016,043 are referred to by way of example for their disclosure of alternative material and marking methods.

[00188] Nuclease protection assays (which include both ribonuclease protection assays and S1 nuclease assays) can be used to detect and quantify specific mRNAs. In nuclease protection assays, an antisense probe (labeled with, for example, radiolabeled or non-isotopic) hybridizes in solution with an RNA sample. After hybridization, the unhybridized single-stranded probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more effective than membrane-based hybridization, and it can accommodate up to 100 pg of sample RNA, compared to the 20 to 30 pg maximum of blot hybridizations.

[00189] The ribonuclease protection assay, which is the most Petition 870260069557, dated 07 / 14 / 2026, page 75 / 336 71 / 157 common nuclease protection assay requires the use of RNA probes. Oligonucleotides and other single-stranded DNA probes can only be used in assays containing S1 nuclease. The single-stranded antisense probe must typically be fully homologous to the target RNA to avoid probe:target hybrid cleavage by nuclease.

[00190] Serial Gene Expression Analysis (SAGE) can also be used to determine RNA abundances in a cell sample. See, for example, Velculescu et al., 1995, Science 270: 484-487; Carulli, et al. 1998, Journal of Cellular Biochemistry Supplements 30 / 31: 286-296; incorporated herein by reference in its entirety. SAGE analysis does not require a special detection device and is one of the preferred analytical methods for simultaneously detecting the expression of a large number of transcription products. First, poly A+ RNA is extracted from the cells. Then, the RNA is converted to cDNA using a biotinylated oligo(dT) primer and treated with a four-base recognition restriction enzyme (Docking Enzyme: AE) resulting in AE-treated fragments containing a biotin group at their 3' end. Next, the fragments treated with AE are incubated with streptavidin for binding.The ligated cDNA is split into two fractions, and each fraction is then ligated to a different double-stranded oligonucleotide adapter (linker) A or B. These linkers are composed of: (1) a projecting single-stranded portion that has a sequence complementary to the projecting portion sequence formed by the action of the anchoring enzyme, (2) a 5' nucleotide recognition sequence of type IIS restriction enzyme (cleaves at a predetermined site no more than 20 bp from the recognition site) that serves as a labeling enzyme (TE), and (3) an additional sequence of sufficient length to construct a primer. Petition 870260069557, dated 07 / 14 / 2026, p. 76 / 336 72 / 157 specific to PCR. The ligand-bound cDNA is cleaved using a labeling enzyme, and only the ligand-bound portion of the cDNA sequence remains, which is present as a short-strand sequence marker. Then, sets of short-strand sequence markers from the two different types of ligands are ligated together, followed by PCR amplification using primers specific for ligands A and B. As a result, the amplification product is obtained as a mixture comprising a myriad of sequences from two adjacent sequence markers (ditags) bound to ligands A and B. The amplification product is treated with an anchoring enzyme, and the free ditag portions are ligated into strands in a standard ligation reaction. The amplification product is then cloned. The nucleotide sequence determination of the clone can be used to obtain a reading of consecutive ditags of constant length.The presence of mRNA corresponding to each marker can then be identified from the nucleotide sequence of the clone and the information about the sequence markers.

[00191] Quantitative reverse transcriptase PCR (qRT-PCR) can also be used to determine biomarker expression profiles (see, for example, Patent Application Publication No. US2005 / 0048542A1; incorporated herein by reference in its entirety). The first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT). The reverse transcription step is typically initiated using specific primers, random hexamers, or oligo-dT primers. Petition 870260069557, dated 07 / 14 / 2026, page 77 / 336 73 / 157 depending on the circumstances and the objective of the expression profile. For example, the extracted RNA can be reverse transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, California, USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

[00192] Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs Taq DNA polymerase, which has 5'-3' nuclease activity but lacks 3'-5' proofreading endonuclease activity. Thus, TAQMAN PCR typically utilizes the 5' nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplification product, but any enzyme with equivalent 5' nuclease activity can be used. Two oligonucleotide primers are used to generate a typical amplicon of a PCR reaction. A third oligonucleotide, or probe, is designed to detect the nucleotide sequence located between the two PCR primers. The probe is not extensible by the Taq DNA polymerase enzyme and is labeled with a fluorescent reporter dye and a fluorescent quenching dye.Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close to each other, as they are in the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragments dissociate in solution, and the signal from the released reporter dye is free from the abrupt cooling effect of the second fluorophore. One molecule of reporter dye is released for each new molecule synthesized, and the detection of the non-abruptly cooled reporter dye provides the basis for the quantitative interpretation of the data.

[00193] TAQMAN RT-PCR can be performed using the Petition 870260069557, dated 07 / 14 / 2026, page 78 / 336 74 / 157 commercially available equipment, such as, for example, the ABI PRISM 7700 sequence detection system (Perkin-Elmer-Applied Biosystems, Foster City, California, USA), or the Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). Alternatives include, but are not limited to, point-of-use sample-to-response devices, such as the cobas Liat system (Roche Molecular Diagnostics, Pleasanton, California, USA) or the GeneXpert system (Cepheid, Sunnyvale, California, USA). A person of ordinary skill in the art will observe that the invention is not limited to the devices listed, and that other devices may be used for TAQMAN-PCR. In a preferred embodiment, the 5' nuclease procedure is performed on a real-time quantitative PCR device, such as the ABI PRISM 7700 sequence detection system. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera, and computer.The system includes software to run the instrument and analyze the data. Data from the 5' nuclease assay are initially expressed as Ct, or the cycle threshold. Fluorescence values ​​are recorded during each cycle and represent the amount of product amplified up to that point in the amplification reaction. The point at which the fluorescent signal is first recorded as statistically significant is the cycle threshold (Ct). Alternatives to the standard thermal cycle include, but are not limited to, continuous thermal gradient amplification, or isothermal amplification with endpoint detection, and other devices known to those of common skill. To minimize errors and the effect of sample-to-sample variation, RT-PCR is often performed using an internal standard. The ideal internal standard is expressed at a constant level across different tissues and is unaffected by experimental treatment.RNAs most frequently used to normalize gene expression patterns are mRNAs for the glyceraldehyde-3-phosphate maintenance genes. Petition 870260069557, dated 07 / 14 / 2026, page 79 / 336. 75 / 157 dehydrogenase (GAPDH) and beta-actin.

[00194] A more recent variation of the RT-PCR technique is quantitative real-time PCR, which measures the accumulation of PCR products through a dual fluorogenic probe (i.e., TAQMAN probe). Real-time PCR is compatible with both competitive quantitative PCR, in which the internal competitor for each target sequence is used for normalization, and comparative quantitative PCR, in which a normalization gene contained in the sample, or a maintenance gene for RT-PCR, is used. For more details see, for example, Held et al., Genome Research 6: 986 to 994 (1996).

[00195] One alternative is the detection of PCR products using digital counting methods. These include, but are not limited to, digital droplet PCR and solid-state nanopore detection of PCR products. In these methods, the counts of the products of interest can be normalized to the counts of maintenance genes. Other PCR detection methods known to those of common skill in the art may be used, and the invention is not limited to the methods listed. BIOMARKER DATA ANALYSIS

[00196] Biomarker data can be analyzed by a variety of methods to identify biomarkers and determine the statistical significance of differences in observed biomarker levels between test profiles and reference expression, in order to assess whether a patient has inflammation originating from a non-infectious source, such as traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS), or an infection, and if the patient is diagnosed with an infection, to diagnose the type of infection, including determining whether a patient has a viral or bacterial infection. In certain modalities, patient data are analyzed by one or more methods including, but not limited to, Petition 870260069557, dated 07 / 14 / 2026, page 80 / 336 76 / 157 limited to multivariate linear discriminant analysis (LDA), receptor operating characteristic (ROC) analysis, principal component analysis (PCA), ensemble mining methods, microarray significance analysis (SAM), cell-specific microarray significance analysis (csSAM), density-normalized event extension tree progression analysis (SPADE), and multidimensional protein identification technology (MUDPIT) analysis. (See, for example, Hilbe (2009) Logistic Regression Models, Chapman & Hall / CRC Press; McLachlan (2004) Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience; Zweig et al. (1993) Clin. Chem. 39: 561 to 577; Pepe (2003) The statistical evaluation of medical tests for classification and prediction, New York, NY: Oxford; Sing et al. (2005) Bioinformatics 21:3940–3941;121; Oza (2006) Ensemble Data Mining, NASA Ames Research Center, Moffett Field, CA, USA; English et al. (2009) J. Biomed. Inform. 42 (2): 287 to 295; Zhang (2007) Bioinformatics 8: 230; Shen-Orr et al. (2010) Journal of Immunology 184: 144 to 130; Qiu et al. (2011) Nat. Biotechnol. 29 (10): 886 to 891; Ru et al. (2006) J. Chromatogr. A. 1111 (2): 166 to 174, Jolliffe Principal Component Analysis (Springer Series in Statistics, 2nd edition, Springer, New York, USA, 2002), Koren et al. (2004) IEEE Trans Vis Comput Graph 10: 459 to 470; incorporated herein by reference in its entirety.) C. KITS.

[00197] In yet another aspect, the invention provides kits for diagnosing an infection in an individual, wherein the kits can be used to detect the biomarkers of the present invention. For example, the kits can be used to detect any one or more of the biomarkers described herein, which are differentially expressed in samples from a patient who has an infection. Petition 870260069557, dated 07 / 14 / 2026, p. 81 / 336 77 / 157 viral or bacterial and healthy or uninfected individuals. The kit may include one or more agents to measure the expression levels of a set of viral response genes and a set of bacterial response genes, a container to hold a biological sample isolated from a human individual suspected of having an infection; and printed instructions for reacting agents with the biological sample or a pore of the biological sample to measure the expression levels of a set of viral response genes and a set of bacterial response genes in the biological sample. The agents may be packaged in separate containers. The kit may additionally comprise one or more control reference samples and reagents to perform an immunoassay, PCR, or microarray analysis.

[00198] In one embodiment, the kit comprises agents to measure the levels of IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, and CTSB biomarkers to distinguish viral infections from bacterial infections.

[00199] In another embodiment, the kit additionally comprises agents for measuring the levels of the biomarkers CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1, and HLA-DPB1 to distinguish the likelihood of inflammation being caused by an infectious agent or a non-infectious source.

[00200] In certain embodiments, the kit additionally comprises a microarray for analysis of a plurality of biomarker polynucleotides. In one embodiment, the microarray comprises an oligonucleotide that hybridizes with a polynucleotide. IFI27, an oligonucleotide that hybridizes with a polynucleotide. JUP, an oligonucleotide that hybridizes with a polynucleotide. LAX1, an oligonucleotide that hybridizes with an HK3 polynucleotide, an oligonucleotide that hybridizes with a polynucleotide TNIP1, an oligonucleotide that hybridizes with a polynucleotide GPAA1, and an oligonucleotide that hybridizes with a polynucleotide Petition 870260069557, dated 07 / 14 / 2026, page 82 / 336 78 / 157 CTSB.

[00201] In another embodiment, the kit further comprises a microarray comprising an oligonucleotide that hybridizes with a CEACAM1 polynucleotide, an oligonucleotide that hybridizes with a ZDHHC19 polynucleotide, an oligonucleotide that hybridizes with a C9orf95 polynucleotide, an oligonucleotide that hybridizes with a GNA15 polynucleotide, an oligonucleotide that hybridizes with a BATF polynucleotide, an oligonucleotide that hybridizes with a C3AR1 polynucleotide, an oligonucleotide that hybridizes with a KIAA1370 polynucleotide, an oligonucleotide that hybridizes with a TGFBI polynucleotide, an oligonucleotide that hybridizes with an MTCH1 polynucleotide, an oligonucleotide that hybridizes with an RPGRIP1 polynucleotide, and an oligonucleotide that hybridizes with an HLA-DPB1 polynucleotide.

[00202] The kit may comprise one or more containers for compositions contained in the kit. The compositions may be in liquid form or may be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers may be made from a variety of materials, including glass or plastic. The kit may also include a package insert containing written instructions for infection diagnosis methods.

[00203] The kits of the invention have several applications. For example, the kits can be used to determine if an individual has an infection or some other inflammatory condition resulting from a non-infectious source, such as traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS). If a patient is diagnosed with an infection, the kits can be used to further determine the type of infection (i.e., viral or bacterial infection). In another example, kits can be used to Petition 870260069557, dated 07 / 14 / 2026, p. 83 / 336 79 / 157 determine the possibility of treating a patient with acute inflammation, for example, with broad-spectrum antibiotics or antiviral agents. In another example, the kits can be used to monitor the effectiveness of treatment for a patient with an infection. In yet another example, the kits can be used to identify compounds that modulate the expression of one or more biomarkers in in vitro or in vivo animal models to determine the effects of treatment. D. DIAGNOSTIC SYSTEM AND COMPUTERIZED METHODS FOR DIAGNOSING AN INFECTION

[00204] In another aspect, the invention includes a computer-implanted method for diagnosing a patient suspected of having an infection. The computer performs steps comprising: receiving entered patient data comprising values ​​for the expression levels of one or both of a set of viral response genes and a set of bacterial response genes in a biological sample from the patient; analyzing the expression levels of the gene set; calculating a bacterial / viral metascore for the patient based on the gene set expression levels, where the bacterial / viral metascore value indicates the likelihood of the patient having a viral infection or a bacterial infection; and displaying information about the patient's diagnosis.

[00205] In certain embodiments, the entered patient data comprise values ​​for the expression levels of a set of viral response genes and a set of bacterial response genes selected from the group consisting of: a) a set of viral response genes comprising IFI27, JUP, and LAX1 and a set of bacterial response genes comprising HK3, TNIP1, GPAA1, and CTSB; b) a set of viral response genes comprising OAS2 and CUL1 and a set of bacterial response genes comprising SLC12A9, ACPP, and STAT5B; c) a set of Petition 870260069557, dated 07 / 14 / 2026, p. 84 / 336 80 / 157 viral response genes comprising ISG15 and CHST12 and a set of bacterial response genes comprising EMR1 and FLII; d) a set of viral response genes comprising IFIT1, SIGLEC1, and ADA and a set of bacterial response genes comprising PTAFR, NRD1, PLP2; e) a set of viral response genes comprising MX1 and a set of bacterial response genes comprising DYSF, TWF2; f) a set of viral response genes comprising RSAD2 and a set of bacterial response genes comprising SORT1 and TSPO; g) a set of viral response genes comprising IFI44L, GZMB, and KCTD14 and a set of bacterial response genes comprising TBXAS1, ACAA1, and S100A12; h) a set of viral response genes comprising LY6E and a set of bacterial response genes comprising PGD and LAPTM5;i) a set of viral response genes comprising IFI44, HESX1, and OASL and a set of bacterial response genes comprising NINJ2, DOK3, SORL1, and RAB31; j) a set of viral response genes comprising OAS1 and a set of bacterial response genes comprising IMPA2 and LTA4H.

[00206] In another embodiment, the invention includes a computer-implanted method for diagnosing a patient suspected of having an infection, wherein the computer performing the steps comprises: a) receiving entered patient data comprising values ​​for the levels in a biological sample from the patient of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1 and CTSB; b) analyzing the level of each of the biomarkers and comparing it with the respective reference ranges for the biomarkers; c) calculating a bacterial / viral metascore for the patient based on the expression levels of the biomarkers, wherein a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a Petition 870260069557, dated 07 / 14 / 2026, page 85 / 336 A negative bacterial / viral metascore of 81 / 157 for the patient indicates that the patient has a bacterial infection; ed) display information about the patient's diagnosis.

[00207] In certain embodiments, the patient data entered additionally comprise values ​​for the expression levels of a set of sepsis response genes comprising CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1, wherein the computer-implanted method additionally comprises calculating a sepsis metascore for the patient, wherein a sepsis metascore that is greater than the reference ranges for an uninfected control individual indicates that the patient has an infection, and a sepsis metascore that is within the reference ranges for an uninfected control individual indicates that the patient has a non-infectious inflammatory condition.

[00208] In another embodiment, the invention includes a computer-implanted method for diagnosing a patient who has inflammation, wherein the computer performing the steps comprises: a) receiving entered patient data including values ​​for the levels of the biomarkers IFI27, JUP, LAX1, HK3, TNIP1, GPAA1, CTSB, CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1 in a biological sample from the patient; b) analyzing the levels of each of the biomarkers and comparing them with the respective reference value ranges for the biomarkers; c) Calculate a sepsis metascore for the patient, where a sepsis metascore greater than the reference value ranges for an uninfected control individual indicates that the patient has an infection, and a sepsis metascore within the reference value ranges for uninfected control subjects indicates that the patient has a non-infectious inflammatory condition; d) Calculate a metascore Petition 870260069557, dated 07 / 14 / 2026, page 86 / 336 82 / 157 bacterial / viral for the patient if the sepsis score indicates that the patient has an infection, where a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection; and ee) display information about the patient's diagnosis.

[00209] In another aspect, the invention includes a diagnostic system for performing the computer-implanted method as described. A diagnostic system includes a computer containing a processor, a storage component (i.e., memory), a display component, and other components normally present in general-purpose computers. The storage component stores information accessible by the processor, which includes instructions that can be executed by the processor and data that can be retrieved, manipulated, or stored by the processor.

[00210] The storage component includes instructions for determining the patient's diagnosis. For example, the storage component includes instructions for calculating a viral / bacterial metascore and / or sepsis metascore, as described in this document (see Example 1). In addition, the storage component may further comprise instructions for performing multivariate linear discriminant analysis (LDA), ROC (receptor operating characteristic) analysis, principal component analysis (PCA), dataset mining methods, microarray cell-specific significance analysis (csSAM), or multidimensional protein identification technology (MUDPIT) analysis. The computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive patient data and Petition 870260069557, dated 07 / 14 / 2026, page 87 / 336 83 / 157 Analyze patient data according to one or more algorithms. The display component shows information about the patient's diagnosis.

[00211] The storage component can be of any type that has the capacity to store information accessible by the processor, such as a hard disk, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write-capable and read-only memories. The processor can be any known processor, such as processors from Intel Corporation. Alternatively, the processor can be a dedicated controller, such as an ASIC.

[00212] Instructions can be any set of instructions to be executed directly (as machine code) or indirectly (as scripts) by the processor. In this sense, the terms instructions, steps, and programs can be used interchangeably in this document. Instructions can be stored in object code form for direct processing by the processor, or in any other computer language, including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

[00213] Data can be retrieved, stored, or modified by the processor according to instructions. For example, although the diagnostic system is not limited by any particular data structure, data can be stored in computer records, in a relational database as a table with a plurality of different fields and records, XML documents, or simple files. Data can also be formatted in any computer-readable format, such as, but not limited to, binary, ASCII, or Unicode values. Furthermore, data can include any information sufficient to identify the information. Petition 870260069557, dated 07 / 14 / 2026, page 88 / 336 84 / 157 relevant, such as numbers, descriptive text, proprietary codes, indicators, references to data stored in other memories (including other network locations) or information that is used by a function to calculate the relevant data.

[00214] In certain embodiments, the processor and storage component may comprise multiple processors and storage components that may or may not be housed within the same physical housing. For example, some of the instructions and data may be stored on a removable CD-ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a physically remote location, but still accessible by the processor. Similarly, the processor may, in fact, include a collection of processors that may or may not operate in parallel.

[00215] In one aspect, a computer is a server that communicates with one or more client computers. Each client computer may be configured similarly to the server, with a processor, storage component, and instructions. Each client computer may be a personal computer, intended for use by one person, which has all the internal components normally found in a personal computer, such as a central processing unit (CPU), display (e.g., a monitor that displays information processed by the processor), CD-ROM, hard disk, user input device (e.g., mouse, keyboard, touch screen, or microphone), speakers, modem and / or network interface device (telephone, cable, or other), and all components used to connect these elements to each other and allow them to communicate (directly or indirectly) with each other.Furthermore, computers, according to the systems and methods described in this document, may comprise any device with a... Petition 870260069557, dated 07 / 14 / 2026, page 89 / 336 85 / 157 capacity to process instructions and transmit data to and from humans and other computers, including network computers without local storage capability.

[00216] Although client computers can comprise a full-size personal computer, many aspects of the system and method are particularly advantageous when used in connection with mobile devices capable of wirelessly exchanging data with a server on a network such as the Internet. For example, the client computer could be a wireless-enabled PDA, such as a Blackberry, Apple iPhone, Android, or other mobile phone with Internet access capability. In this sense, the user can input information using a small keyboard, a keyboard, a touchscreen, or any other means of user input. The computer may have an antenna to receive a wireless signal.

[00217] The server and client computers have the capability for direct and indirect communication, as in a network. Although only a few computers may be used, it should be noted that a typical system may include a large number of connected computers, where each different computer is on a different node of the network. The network, and intermediate nodes, may comprise various combinations of devices and communication protocols, including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local area networks, cellular networks, private networks using proprietary communication protocols from one or more companies, Ethernet, WiFi, and HTTP. This communication can be facilitated by any device with the capability to transmit data to and from other computers, such as modems (e.g., dial-up or cable), wireless networks, and interfaces. The server may be a web server.

[00218] Although certain advantages are gained when the Petition 870260069557, dated 07 / 14 / 2026, page 90 / 336 86 / 157 information is transmitted or received, as mentioned above, other aspects of the system and method are not limited to any particular way of transmitting information. For example, in some aspects, information may be sent via a disk, tape, flash drive, DVD, or CD-ROM. In other aspects, information may be transmitted in a non-electronic format and entered manually into the system. Furthermore, although some functions are indicated as occurring on a server and others on a client, several aspects of the system and method may be implemented by a single computer with a single processor. III. EXPERIMENT

[00219] Below are examples of specific embodiments for carrying out the present invention. The examples are offered for illustrative purposes only and are not intended to limit the scope of the present invention in any way.

[00220] Efforts have been made to ensure accuracy with regard to the numbers used (e.g., quantities, temperatures, etc.), but some experimental errors and deviations must, of course, be allowed for. EXAMPLE 1 Robust classification of bacterial and viral infections through integrated host gene expression diagnostics. INTRODUCTION

[00221] Here, the aim is to improve the diagnostic power of the Sepsis Metascore (SMS) by adding the ability to discriminate between bacterial and viral infections. Thus, in order to derive a new biomarker to discriminate infection types, a multi-cohort analysis framework was applied to clinical microarray cohorts that compared the host response to bacterial and viral infections. Petition 870260069557, dated 07 / 14 / 2026, page 91 / 336 87 / 157 Furthermore, a new method was developed to conormalize gene expression data across multiple cohorts, allowing for direct comparison of a diagnostic score across multiple cohorts. Finally, the Sepsis MetaScore and the new bacterial / viral diagnosis were combined into an integrated antibiotic decision model (IADM) that can determine the likelihood of a patient with acute inflammation from any source having an underlying bacterial infection. RESULTS DERIVATION OF THE 7-GENE BACTERIAL / VIRAL METASCORE

[00222] The previously published 11-gene SMS cannot reliably differentiate between bacterial and viral infections, showing mainly non-significant differences in score distribution between patients with bacterial and viral infections (Figures 5A and 5B). Given that a conserved host genetic response to viral infections has been previously shown15, it is presumed that a classifier for bacterial vs. viral infections would allow for an improved diagnostic model. Thus, a systematic search was conducted for gene expression microarray cohorts that studied patients with viral and / or bacterial infections. Eight cohorts11,18-26 (both whole blood and PBMC) were identified, which included N>5 patients with both viral and bacterial infections (Table 1A).The 8 cohorts are composed of 426 patient samples (142 viral and 284 bacterial infections), including children and adults, medical and surgical patients, and those with multiple infection sites. A multi-cohort analysis was performed on the 8 cohorts, as previously described (Figure 6)7,15,16,27. Significance thresholds of an effect size >2 times and an FDR <1% were established in a round-robin analysis of a single dataset output. However, in order to ensure that none of the tissue types were influencing the results, only genes that also had a size of [missing value] were selected. Petition 870260069557, dated 07 / 14 / 2026, page 92 / 336 88 / 157 effect >1.5 times in separate analyses of PBMCs and whole blood cohorts. This process resulted in 72 genes being expressed significantly differently (Supplementary Table 1). A greedy breakthrough search7 was then used to find a set of genes optimized for diagnosis, resulting in 7 genes (higher in viral infections: IFI27, JUP, LAX1, higher in bacterial infections: HK3, TNIP1, GPAA1, CTSB; Figure 7). As expected, a bacterial / viral metascore based on these 7 genes robustly distinguished viral from bacterial infections in all 8 finding cohorts (summary ROC AUC = 0.97, 95% CI = 0.89 to 0.99, Figure 1A, Figure 8).

[00223] The set of 7 genes was then tested in the remaining 6 independent clinical cohorts13,14,28-30 that directly compared bacterial and viral infections (total of 341 samples, 138 bacterial and 203 viral), and a ROC summary and AUC of 0.91 (95% CI = 0.82 to 0.96) was found (Table 1B, Figure 1B, Figure 9). As a signature generalization test, the possibility that cells stimulated in vitro with LPS or influenza virus could be separated with bacterial / viral metascore (GSE5316631, N = 75, AUC = 0.99) Figure 10) was also tested. GLOBAL VALIDATION THROUGH COCONUT STANDARDIZATION

[00224] There are dozens of microarray cohorts in the public domain that have studied bacterial or viral infections, but not both, thus preventing a direct (within-dataset) estimate of the diagnostic power to distinguish between bacterial and viral diseases. In order to apply and compare a genetic score across these cohorts, a new method was needed that could remove batch effects between datasets while remaining unbiased regarding the diagnosis of patients with disease. Here, Petition 870260069557, dated 07 / 14 / 2026, page 93 / 336 89 / 157 A new type of array normalization was conceived and implemented that uses empirical Bayes normalization methods from ComBat32 in healthy controls to obtain bias-free corrections of disease samples (a method we call Combat CONormalization with Use of Controls, or COCONUT, Methods section below and Figure 11). Essentially, maintenance genes are invariant across both disease and cohorts after COCONUT conormalization, while each gene still maintains the same distribution between disease and controls within each dataset (Figures 12A and 12B). Since the method assumes that all healthy samples are derived from the same distribution, whole blood and PBMC samples were split, as different immune cell types have significantly different baseline gene expression distributions.Using COCONUT conormalization, it was possible to show that the bacterial / viral metascore has an overall AUC of 0.92 (95% CI 0.89 to 0.96) in the finding cohorts (Figure 2, prenormalized data in Figure 14). This method was then applied to test the bacterial / viral metascore in all publicly available microarray cohorts that met the inclusion criteria and used whole blood (including the four direct validation cohorts that included control patients and 20 cohorts that measured bacterial or viral infections, but not both33-49, N = 143 + 897 = 1,040), and exhibited an overall ROC AUC = 0.93 (95% CI 0.91 to 0.94) in all these data (Table 2, Figure 13, prenormalized data in Figure 15). The wide clinical variety of the data, which includes a broad range of infection types (Gram-positive, Gram-negative, atypical bacterial, common respiratory viruses, and dengue) and severities (mild infections to septic shock), is particularly noteworthy.Thus, a single cutoff point can be established across all cohorts (shown as horizontal dashed lines). Finally, the following was carried out... Petition 870260069557, dated 07 / 14 / 2026, p. 94 / 336 90 / 157 separately, the same process in the available PBMC validation cohorts (6 cohorts50-54, N = 259, overall AUC = 0.92 (95% CI 0.87 to 0.97, Figure 16, pre-normalized data in Figure 17). Notably, all three overall ROC AUCs using COCONUT conormalization (finding whole blood = 0.92, validation whole blood = 0.93, validation PBMC = 0.92) roughly matched the summary AUC of the direct validation cohorts (0.91), providing high confidence at this level of diagnostic power.

[00225] Supplementary Table 4 shows bacterial / viral metascores for all combinations of two (2) genes selected from the set of 71 genes obtained by iterating the greedy advancement algorithm on finding datasets. All combinations of 2 genes in the set of 71 genes show a mean AUC obtained greater than or equal to 0.80 (>0.80). In comparison, Figure 18 shows the distribution of mean AUCs in finding datasets for ten thousand (10,000) randomly chosen pairs of 2 genes, where they show that an AUC greater than or equal to 0.80 is not obtainable by chance alone. As illustrated in Figure 18, the randomly chosen pairs of 2 genes result in a normally limited mean AUC distribution greater than 0.2 (>0.20) and less than 0.80 (<0.80).The combinations of 2 genes provided in Supplementary Table 4 with an AUC equal to or greater than 0.80 (>0.80) are a clinically useful determination of the likelihood of an infection being viral or bacterial. Integrated antibiotic decision model

[00226] A key clinical need is to diagnose the possibility that a patient with signs and symptoms of inflammation may have an underlying bacterial infection, since the rapid and decisive administration of antibiotics is fundamental to improving patient outcomes. Neither SMS nor the bacterial / viral metascore alone Petition 870260069557, dated 07 / 14 / 2026, page 95 / 336 91 / 157 can robustly distinguish between all three classes of (1) non-infectious inflammation, (2) bacterial disease, and (3) viral disease. Thus, to increase clinical relevance, an integrated antibiotic decision model (IADM) was tested, whereby the previously described SMS7 was first applied to test for the presence of an infection, and then samples that tested positive for infection were tested with the bacterial / viral metascore (Figure 3A). As above, the only way to establish test characteristics for IADM simultaneously across cohorts is to use COCONUT conormalization. However, SMS in COCONUT conormalized data was found to be strongly influenced by age, which may be due to differences between healthy patients or infected patients, or both (Figures 19A and 19B). Thus, cohorts focused on infants (children <1 year of age) were excluded from IADM, resulting in a total of 20 cohorts (N = 1,057).The resulting global AUC for SMS from the available data was 0.86 (95% CI 0.84 to 0.89) (Supplementary Table 2, Figures 20A and 20B). Global thresholds were established for an SMS sensitivity for infection of 95% and a bacterial / viral metabolite sensitivity for bacterial infection of 95%. This yielded a global sensitivity and specificity for bacterial infections of 94.0% and 59.8%, respectively, and for viral infections of 53.0% and 90.6%, respectively (Figures 3A to 3C). These were virtually unchanged if healthy patients were included in the uninfected class (Figures 21A and 21B). The overall positive and negative odds ratios for bacterial infection in the IADM are therefore 2.34 (LR+) and 0.10 (LR-); where a recent meta-analysis of procalcitonin showed a negative LR of 0.29 (95% CI 0.22 to 0.38)55. Plotted NPV and PPV vs.prevalence for these test characteristics; where the NPV and PPV for bacterial infection at a prevalence of 15% are 98.3% and 29.2% (Figure 22). Petition 870260069557, dated 07 / 14 / 2026, p. 96 / 336 92 / 157

[00227] There was only one dataset (GSE6399014) that included patients with uninfected SIRS and patients with bacterial and viral disease, but did not include healthy controls, preventing their addition to the overall calculations. Therefore, the IADM was tested with locally derived test thresholds. Overall bacterial infection sensitivity and specificity of 94.3% and 52.2%, respectively, were found (Figures 21A and 21B). Nanostring Validation

[00228] Finally, NanoString nCounter56 gene expression assays were used to validate these results in independent whole blood samples from children with sepsis from the Genomic of Pediatric SIRS and Septic Shock Investigators (GPSSSI) cohort (total N = 96, with 36 SIRS, 49 patients with bacterial sepsis, and 11 patients with viral sepsis, Figures 4A to 4E). The GPSSSI cohort was also used by the GSE66099 dataset, but the profiled children were never described by microarray and therefore are not part of the findings datasets. In the NanoString validation cohort, the SMS AUC was 0.81 (AUC of 0.80 in GSE66099). Similarly, the bacterial / viral metascore AUC was 0.84 (AUC of 0.83 in GSE66099). The AUCs of the microarrays are thus preserved when tested with a targeted gene expression assay in new patients.Applying the same IADM, the sensitivity and specificity for bacterial infections were 89.7% and 70.0%, and for viral infections were 54.5% and 96.5%, respectively. DISCUSSION.

[00229] Improved diagnostics for acute infections are needed in both hospital and outpatient settings. In low-acuity outpatient settings, a simple diagnosis that can distinguish between bacterial and viral infections may be sufficient to assist in the appropriate use of antibiotics. In higher-acuity settings, the Petition 870260069557, dated 07 / 14 / 2026, page 97 / 336 93 / 157 causes of non-infectious inflammation become more important to rule out, and therefore, a decision model for antibiotic prescription should include a non-infected (non-healthy) case. Thus, a reliable diagnosis needs to distinguish all three cases (non-infected inflammation, bacterial infection, and viral infection). Here, using 426 samples from 8 cohorts, a set of only 7 genes was derived that can accurately discriminate bacterial from viral infections across a wide range of clinical conditions in independent cohorts (a total of 30 cohorts composed of 1,299 patients). It is further demonstrated that by coupling the previous Sepsis Metascore (to distinguish the presence or absence of infection) to this new bacterial / viral metascore (to determine the type of infection) in a single integrated antibiotic decision model, it is possible to determine with high precision which patients would benefit from antibiotics.Finally, the diagnostic power of the 7-gene set and IADM in independent samples was confirmed using a targeted NanoString assay, showing that the signatures retain diagnostic power when they are not microarray-dependent.

[00230] The IADM has a low negative likelihood ratio (0.10) and a high estimated NPV, meaning it would potentially be effective as a screening test. Notably, a meta-analysis of procalcitonin that included 3,244 patients from 30 studies resulted in an overall estimated negative likelihood ratio of 0.29 (95% CI 0.22 to 0.38)55. Thus, the negative likelihood ratio of IADM is significantly lower than the estimate for procalcitonin. Furthermore, these test characteristics assume no knowledge of the patient and are therefore only estimates of the actual clinical utility of such a test. History and physical, vital, and laboratory signs would also aid in diagnosis. Even with these caveats, a recent economic decision model for patient screening in Petition 870260069557, dated 07 / 14 / 2026, page 98 / 336 94 / 157 Studies in the ICU for hospital-acquired infections suggested that a test like the IADM, which can accurately diagnose bacterial and viral infections, could be cost-effective.57 Ultimately, only interventional tests will have the capacity to establish the economic and clinical utility of a new diagnostic.

[00231] The diagnosis in pediatric sepsis patients from the GPSSSI cohort was validated using a NanoString assay. NanoString is highly accurate and a useful tool for measuring the expression levels of multiple genes at once; however, it is also likely too slow for clinical application (4–6 hours per assay). Thus, while the assay confirms that the gene pool is robust in targeted measurements, further work is needed to improve turnaround time. Several possibilities exist for an eventual commercial product based on rapid multiplexed qPCR. However, this technical hurdle is something that all gene expression infection diagnostics must overcome to achieve clinical relevance.

[00232] Several groups have published models for diagnosing infections based on host gene expression; none have yet done so in clinical practice. Most previous classifiers were not tested on multiple independent cohorts, had too many genes to allow the rapid profiling needed for useful diagnosis, or both. For example, Suarez et al. created a K-classifier of 10 closest genes, but did not test it outside the published dataset (GSE60244)13. Tsalik et al. created a 122-probe (120-gene) classifier based on multiple regression models, but when testing it on external GEO cohorts, they retrained their regression coefficients on each new dataset14. Such model retraining resulted in a strong upward skewing for these validation numbers (assuming a final model would not be Petition 870260069557, dated 07 / 14 / 2026, page 99 / 336 95 / 157 locally retrained), or suggests that each new clinical site would have to assemble a large prospective cohort to train the model before deployment. Other groups have made gene expression classifiers for sepsis, but have not included models to discriminate viral infections7,9,10. The new IADM is robust across a wide range of disease types and severity, but has relatively lower sensitivity to viral infections. Non-genetic expression biomarkers have also been used for infection diagnosis. Procalcitonin has been extensively studied in the diagnostic context of sepsis, but fails to distinguish between uninfected individuals and those with viral infections58. Protein panel assays have been shown to discriminate bacterial from viral infections, but cannot discriminate patients with non-infectious inflammation59,60.Thus, all these classifiers have certain strengths and weaknesses that will become more apparent with further prospective testing and direct comparison.

[00233] Although the aim of this study was to identify new biomarkers and not necessarily new biology, it is still important that a set of biomarkers has biological plausibility. Of the seven genes in the bacterial / viral metascore, six have been previously associated with infections or leukocyte activation. Both IFI27 and JUP have been shown in single-cohort genome-wide expression studies to be induced in response to viral infection52,61, while TNIP1 and CTSB have been shown to be important in modulating NF-kB and necrotic responses to bacterial infection62,63. Finally, LAX1 (upregulated in viral infections) is involved in the activation of T cells and B cells64, while HK3 is fundamental in the neutrophil differentiation pathway65. Thus, the role of these transcripts as biomarkers for the type of infection is new, but not without precedent. Petition 870260069557, dated 07 / 14 / 2026, page 100 / 336 96 / 157

[00234] A new method, COCONUT, was relied upon to directly compare the model across a huge cluster of single-class cohorts that, by another method, would not be usable for comparison of a new diagnosis. COCONUT assumes that all controls result from the same distribution; that is, the genes of each control group are redefined to have the same mean and variance, with batch parameters learned empirically from gene clusters. This method corrects for microarray and batch processing differences between cohorts and thus allows the creation of a global ROC curve with a single threshold. This is a more realistic measure of diagnostic power than simply reporting multiple validation ROC curves, since no single cohort could obtain the same test characteristics across different cohorts16.The most important aspects of the COCONUT conormalized data are that both the bacterial / viral metascore and the IADM retain diagnostic power across a wide range of infection types and severities, with overall AUCs that are similar to the summary AUCs of close comparisons within cohorts.

[00235] Overall, proven multicohort analysis pipelines were leveraged to obtain a highly robust model for improving infection diagnosis. Using a novel method, this was validated in dozens of independent microarray cohorts. The use of a targeted NanoString assay in pediatric patients with sepsis was also validated. While IADM still needs to be optimized for rapid turnaround as well as prospective interventional testing, it seems evident that host genome molecular profiling will become part of the clinical toolkit in the future.

[00236] A person versed in the technique will understand that alternative methods to bacterial / viral meta-scoring can be used to develop a classifier with the ability to distinguish between Petition 870260069557, dated 07 / 14 / 2026, p. 101 / 336 97 / 157 bacterial and viral infections. Any machine learning method known in the art can be used to develop the classifier. The method of developing a classifier can include clustered algorithms that are made up of a multitude of algorithms, such as logistic regression, support vector machines, and decision trees, such as random forests and gradient-boosting decision trees. Classification can be developed using neural networks, which include a large number of nodes arranged in layers, where the output of a node in the first layer is used as input for a node in the next layer. Alternatively, classification can be developed using a support vector machine model, which is a representation of the examples as points in space, mapped in such a way that the examples of the separate categories are divided by the widest possible empty gap.New examples are then mapped onto the same space and predicted to belong to a category based on which side of the gap the new examples fall on. A person versed in the technique will understand that any number of machine learning algorithms can be used to develop a classification with the ability to distinguish between a bacterial and viral infection. METHODS SYSTEMATIC RESEARCH AND MULTICORT ANALYSIS

[00237] A systematic search was conducted in NIH GEO and EBI ArrayExpress for genome-wide public expression studies of the human microarray using the search terms: bact[wildcard], vir[wildcard], infection, sepsis, SIRS, ICU, nosocomial, fever, pneumonia. Abstracts were screened to remove all studies that were (1) nonclinical, (2) performed using tissues other than whole blood or PBMCs, or (3) patients compared who were not matched by clinical time. Petition 870260069557, dated 07 / 14 / 2026, page 102 / 336 98 / 157

[00238] All microarray data were renormalized from raw data (when available) using standardized methods. Affymetrix arrays were renormalized using gcRMA (in arrays with perfect-matching probes) or RMA. Illumina, Agilent, GE, and other commercial arrays were renormalized using normal exponential background correction followed by quantile normalization. Custom arrays were not renormalized. Data were log2 transformed, and a fixed-effects model was used to summarize probes to genes within each study. Within each study, cohorts tested with different microarray types were treated as independent.

[00239] Multicohort meta-analysis was performed, as previously described7,15,16,27. Briefly, genes were summarized using Hedges' g, and the DerSimonian-Laird random-effects model was used for meta-analysis, followed by Benjamini-Hochberg multiple hypothesis correction66. Patients with bacterial infections were compared to patients with viral infections in studies, such that a positive effect size indicates that a gene was more expressed in virus-infected patients, and a negative effect size indicates that a gene was more expressed in bacteria-infected patients.

[00240] In order to identify a set of genes highly conserved in differential expression between bacterial and viral infections, all cohorts that directly compared patients with bacterial and viral infections were selected. Patients with documented co-infections (i.e., bacterial and viral) were removed. Cohorts were required to have > 5 patients in each group to be included in the meta-analysis. Both PBMCs and whole blood cohorts were included. Significant genes were those that had an effect of Petition 870260069557, dated 07 / 14 / 2026, page 103 / 336 99 / 157 size >2 times and an FDR <1% in a round-robin analysis of the output dataset. However, in order to ensure that both tissue types were represented in the final gene set, separate meta-analyses of the PBMCs and whole blood cohorts were also performed, and all genes that had an effect size <1.5 times in either tissue type were removed separately. The remaining genes were considered significant. Derivation of the set of 7 genes

[00241] To find a set of highly diagnostic genes, significant genes from the meta-analysis were run through direct and greedy searching, as described previously7. Briefly, this algorithm starts with zero genes and in each cycle adds one gene that best improves the AUC for diagnosis in the finding cohorts, until a new gene cannot improve the finding AUC by more than a threshold. The resulting genes are used to calculate a single bacterial / viral metascore, calculated as the geometric mean of the viral response genes minus the geometric mean of the bacterial response genes, times the ratio of the number of genes in each set. The resulting continuous score can then be tested for diagnostic power using ROC curves. DERIVATION OF ADDITIONAL SETS OF GENES

[00242] In order to identify additional sets of diagnostic genes, a recursive greedy advance search was implemented, in which, at the conclusion of the algorithm, the resulting set of diagnostic genes was removed from the possible set of significant genes, and the algorithm was run again. The first set of genes was taken for further validation, but it was observed that the other sets of genes were run similarly in the finding cohorts (Supplementary Table 3). Petition 870260069557, dated 07 / 14 / 2026, p. 104 / 336 100 / 157 DIRECT VALIDATION OF THE SET OF 7 GENES

[00243] The resulting gene pool was first validated in the remaining public gene expression cohorts, which directly compared bacterial to viral infections, but were too small to be used in the meta-analysis. Two cohorts (GSE6024413 and GSE6399014) were released after the completion of our meta-analysis, and thus were used for validation. To demonstrate generalizability, a large in vitro dataset comparing LPS with influenza exposure in monocyte-derived dendritic cells was also examined, but this was not included in the abbreviated AUC as it is not expected to result from the same distribution as the clinical studies. SUMMARY ROC CURVES

[00244] For both verification and validation cohorts, summary ROC curves were constructed according to the method of Kester and Buntinx67, and previously described16. Briefly, linear exponential models are made of each ROC curve, and the parameters of these individual curves are summarized using a random effects model to estimate the overall parameters of the summary ROC curve. The alpha parameter controls the AUC (in particular, the distance of the line from the identity line) and the beta parameter controls the skewness of the ROC curve. The confidence intervals of the summary AUC are estimated from the standard error of alpha and beta in the meta-analysis. COCONUT STANDARDIZATION

[00245] There are dozens of public microarray cohorts that feature profiles of bacterial or viral infections, but not both. It would be advantageous to be able to compare a genetic score between these cohorts, but this has not been possible previously due to the fact that each different microarray has widely different background measurements for Petition 870260069557, dated 07 / 14 / 2026, page 105 / 336 101 / 157 each gene and, among studies using the same types of microarrays, there are large batch effects. In order to use these data, it is necessary to conormalize these cohorts so that: (1) no bias is introduced that could influence the final classification (i.e., the normalization protocol must be blind to the diagnosis); (2) there should be no change in the distribution of a gene within a study; and (3) a gene should show the same distributions across studies after normalization. A method with these characteristics would allow the genetic score to be calculated and compared across multiple studies and thus allow for broad testing of its generalizability.

[00246] The Bayes empirical normalization method32 is popular for cross-platform normalization, but crucially falls short of the desired criteria due to the fact that it assumes an equal distribution across disease states.Thus, a modified version of the ComBat method was developed that conormalizes control samples from different cohorts to allow direct comparison of disease samples from the same cohorts. This method is called CONormalization with the Use of Controls, or COCONUT. COCONUT makes a strong assumption, which is that it forces healthy / control patients from different cohorts to represent the same distribution. In short, all cohorts are divided into healthy and diseased components. The healthy components undergo ComBat conormalization without covariates. The estimated ComBat parameters α, β, σ, δ* and y* are obtained for each dataset for the healthy component and then applied to the diseased component (Figure 10).This forces the diseased components of all cohorts to be from the same background distribution, but maintains their relative distance from the healthy component (the T-statistics in the datasets are only different after COCONUT due to floating-point mathematics). Essentially, this one also requires none. Petition 870260069557, dated 07 / 14 / 2026, page 106 / 336 102 / 157 prior knowledge of disease classification (i.e., bacterial or viral infection), thus meeting pre-specified criteria. This method has the notable requirement that healthy / control patients need to be present in a dataset in order to be grouped with other available data. Furthermore, once healthy / control patients are established to be in the same distribution, they should only be used when this assumption is reasonable (i.e., within the same tissue type, among the same species, etc.). The Combat Model and the Coconut Method

[00247] As described by Johnson et al., the ComBat model corrects for the location and scale of each gene by first solving a common least squares model for gene expression, and then decreasing the resulting parameters using an iteratively solved empirical Bayes estimator32. Formally, it is assumed that each gene expression level Yijg (for gene g for sample j in batch i) is composed of overall gene expression ag, sample condition design matrix X with regression coefficients β9, additive and multiplicative batch effects y / ge õig, and an error term £ijg: =ffç + + Yig 5

[00248] Estimating parameters using common least squares regression standardizes Yijg to a new term Zijg (where ogé is the standard deviation of ε^): 7_ Yijg ~

[00249] The standardized data are now distributed according to:

[00250] ν(υ^> whereYlffΝ(Χι>τι) and gamajn_ verse(A,6 / ) Petition 870260069557, dated 07 / 14 / 2026, page 107 / 336 103 / 157

[00251] The inverse gamma is assumed to be a non-informative prior pattern. The remaining hyperparameters are estimated empirically, with the derivation and solution found in the original reference32. The estimated batch effects can then be used to fit the standardized data to an empirical Bayes-adjusted batch final output. *\q

[00252] In this modified version of this method (COCONUT), all the above items are executed according to the original method without modification. However, it is applied only to healthy / control patients in each dataset (i.e., Y is a matrix of only samples of healthy patients). The estimated parameters aÇ / 3' aÇ δ* ey* are all taken and applied directly to a matrix D consisting only of samples of patients with disease (which must be ordered in the same way as Y): "_ ~ &q ~ --q±^qf * \ DifW “ ~ Xtdj) + %

[00253] In this way, a batch-corrected version of samples with disease D* can be obtained, which corrects for differences between healthy controls, but does not alter each submatrix Di in relation to each Y. GLOBAL ROCS

[00254] COCONUT conormalization was used to test (1) all finding cohorts and (2) all validation cohorts, even those containing only bacterial or only viral disease. This was done separately for PBMC and whole blood data. Petition 870260069557, dated 07 / 14 / 2026, p. 108 / 336 104 / 157 for the reasons described above. After conormalization, the distributions for the individual cohorts were plotted together to allow direct comparison. For each plot, we showed (1) the distribution of scores for each dataset, (2) the normalized gene expression levels for each gene within the diagnostic test, and (3) maintenance genes that show no difference between classes based on meta-analysis. Healthy patients were removed from these plots. However, to show that gene distributions between healthy and diseased patients within cohorts do not change after COCONUT conormalization, we also showed plots with both types of patients with both target genes and maintenance genes (Figure 11). Genes with minimum effect size and minimum variance in the meta-analysis were selected as maintenance genes.

[00255] For each comparison, a single global ROC AUC was calculated and a single threshold was set to allow an estimate of the actual diagnostic performance of the tests. The thresholds for the cutoffs for bacterial versus viral infection were adjusted to approximate a sensitivity for bacterial infection of 90%, since a false negative bacterial infection (i.e., the recommendation not to give antibiotics when antibiotics are needed) can be devastating. INTEGRATED ANTIBIOTIC DECISION MODEL

[00256] The SMS can discriminate patients with severe acute infections from those with inflammation from other sources; however, it cannot distinguish between infection types (Figures 5A and 5B). Therefore, an integrated antibiotic decision model (IADM) was tested, in which the 11-gene SMS is applied, followed by the 7-gene bacterial / viral metascore. This model identifies (1) the likelihood of a patient having an infection, and (2) if so, what type of infection is present (bacterial or viral). It was not possible Petition 870260069557, dated 07 / 14 / 2026, page 109 / 336 105 / 157 identify sufficient validation cohorts with patients with non-infectious inflammation that also included healthy controls, so that, in constructing the global ROCs, the finding and validation cohorts were used. Using COCONUT conormalization, global thresholds were established in all included cohorts, and these were applied to each individual dataset to test the IADM's ability to correctly distinguish patients with non-infectious inflammation, bacterial infection, and viral infection. Healthy patients were not included as a diagnostic class, since they were used in the conormalization procedure. The IADM was also applied separately to all cohorts that did not have healthy controls but included (1) patients with non-infectious SIRS and (2) patients with bacterial and viral infections.

[00257] Since the positive and negative predictive values ​​(PPV and NPV) depend on prevalence, and the prevalence of the data used here does not correspond to the prevalence of infections in a hospital setting, the PPV and NPV curves were calculated based on the sensitivity and specificity for bacterial infections obtained with the integrated antibiotic decision model. Formally, NPV = specificity x (prevalence 1) / ((sensitivity 1) x prevalence + specificity x (prevalence 1)); PPV = sensitivity x prevalence / (sensitivity x prevalence + (specificity 1) x (prevalence 1)). Nanostring Validation

[00258] Finally, 96 independent patient samples (i.e., those never profiled via microarray) from the Genomics of Pediatric SIRS and Septic Shiock Investigators tests18-22 were tested using a targeted digital multiplexed gene quantification assay, NanoString56. The 18 genes were not normalized back to any maintenance genes. The bacterial / viral SMS and metascore genes were tested, and the Petition 870260069557, dated 07 / 14 / 2026, p. 110 / 336 106 / 157 diagnostic performance of the IADM was calculated.

[00259] All analyses were performed in the R statistical computing language (version 3.1.1). The code to recreate the multicohort meta-analysis was previously deposited and is available at khatrilab.stanford.edu / sepsis. Table 1. Data sets used in the direct verification and validation of the bacterial / viral metascore. CAP: community-acquired pneumonia. PICU: pediatric intensive care unit. RSV: respiratory syncytial virus. CMV: cytomegalovirus. MPV: metapneumovirus. Accession Number Author Tissue Platform Demographic Data Bacteria Viruses Bacterial Number Viral Number A. Data sets of findings GSE6269 Ramilo PBMC GPL96 Children admitted with E. coli, S. aureus, S. pneumococcal Influenza infection 16 8 GPL570 S. aureus, S. pneumococcal Influenza infection 12 10 GPL2507 S. aureus, S. pneumococcal Influenza infection 73 18 GSE20346 Parnell Whole Blood GPL6947 Adults with CAP Unknown bacterial pneumonia Influenza infection 12 8 GSE40012 Parnell Whole Blood GPL6947 Adults with CAP Unknown bacterial pneumonia Influenza infection 36 11 GSE40396 Hu Whole Blood GPL1055 8 Febrile children in the emergency department Multiple Adenovirus, enterovirus, rhinovirus, HHV6 8 35 Petition 870260069557, dated 07 / 14 / 2026, p. 111 / 336 107 / 157 GSE42026 Herbeg Whole Blood GPL6947 Children admitted with Streptococcus and Staphylococcus spp. Influenza, RSV 18 41 GSE66099 Wong Whole Blood GPL570 Septic children in PICU Multiple Influenza, HSV, CMV, BK, Adenovirus 109 11 B. Validation datasets GSE15297 Pop-per Whole Blood GPL8328 Febrile children Scarlet fever (Streptococcus) Adenovirus 5 8 GSE25504 Smith Whole Blood GPL1366 7 Septic newborns Multiple Rhinovirus, CMV 11 3 GPL6947 Multiple CMV 26 1 GSE60244 Suarez Whole Blood GPL1055 8 Hospitalized adults with LTRI Gram-positive and atypical Influenza, RSV, MPV 22 71 GSE63990 Tsalik Whole Blood GPL571 Adults with Multiple ARI Multiple 70 115 E-MEXP- 3589 Almans a Whole Blood GPL1033 2 Adults with COPD with Gram-positive, Gram-negative, atypical infection Influenza, RSV, MPV 4 5 Table 2. Validation datasets that meet the inclusion criteria and have a single known pathogen type (viral or bacterial). PICU: Pediatric Intensive Care Unit. RSV: Respiratory Syncytial Virus. LTI: Lower Respiratory Tract Infection. DHF: Dengue Hemorrhagic Fever. DSS: Dengue Shock Syndrome. Petition 870260069557, dated 07 / 14 / 2026, page 112 / 336 108 / 157 Accession Number Author Tissue Platform Demographic Data Specific Pathogens Bacterial Number Viral Number E-MEXP-3567 Irwin Whole Blood GPL96 Malawian children with meningitis or bacterial pneumonia S. pneumoniae, N. meningitidis or H. influenzae 12 0 GSE11755 Emonts Whole Blood GPL570 Children in PICU with meningococcal sepsis N. meningitidis 6 0 GSE13015 Pankla Whole Blood GPL6106 Adults with bacterial sepsis B. pseudomallei ie others 45 0 GPL6947 15 0 GSE22098 Berry Whole Blood GPL6947 Children with Gram-positive infections Staphylococcus and Streptococcus 52 0 GSE28750 Sutherland Whole Blood GPL570 Adults with community-acquired bacterial sepsis Multiple bacteria 10 0 GSE29161 Thuny Whole Blood GPL6480 Adults with native valve infected endocarditis Staphylococcus and Streptococcus 5 0 GSE33341 Ahn Whole Blood GPl571 Adults with septic bloodstream infections S. aureus or E.coli 51 0 GSE40586 Lill Whole Blood GPL6244 Bacterial meningitis Multiple bacteria 21 0 GSE42834 Bloom Whole Blood GPL10558 Bacterial pneumonia 19 0 GSE57065 Cazalis Whole Blood GPL570 Adults with bacterial septic shock Multiple bacteria 82 0 GSE69528 Conejero Whole Blood GPL10558 Adults with bacterial sepsis B. pseudomallei and others 83 0. Petition 870260069557, dated 07 / 14 / 2026, page 113 / 336 109 / 157 E-MTAB- 3162 van de Weg Whole Blood GPL570 Indonesian patients >14 years with uncomplicated and severe dengue Dengue 0 30 GSE17156 Zaas Whole Blood GPL571 Volunteers with peak viral challenge symptoms Influenza, RSV, rhinovirus 0 27 GSE21802 BermejoMartin Whole Blood GPL6102 Adults with septic influenza Influenza (H1N1) 0 12 GSE27131 Berdal Whole Blood GPL6244 Adults with septic influenza on mechanical ventilation Influenza (H1N1) 0 7 GSE38900 Mejias Whole Blood GPL10558 Children with acute LRTI RSV 0 28 GPL6884 Influenza, RSV, rhinovirus 0 153 GSE51808 Kwissa Whole Blood GPL13158 Children and adults with uncomplicated dengue and DHF Dengue 0 28 GSE68310 Zhai Whole Blood GPL10558 Adults with acute respiratory infections Mainly influenza and rhinovirus 0 211 GSE16129 Ardura PBMC GPL6106 Children with invasive Staph infections S. aureus 9 0 GPL96 46 0 GSE23140 Liu PBMC GPL6254 Children with acute otitis media S.pneumoniae 4 0 GSE34205 Ioannidis PBMC GPL570 Infants and children with acute respiratory infections Influenza, RSV 0 79 GSE38246 Popper PBMC GPL15615 Nicaraguan children with uncomplicated dengue, DHF and DSS Dengue 0 95. Petition 870260069557, dated 07 / 14 / 2026, p. 114 / 336 110 / 157 GSE69606 Brand PBMC GPL570 Children with mild to severe RSV RSV 0 26 Supplementary Table 1. List of all genes considered significant (q <0.01, ES>2 times overall and ES>1.5 times in PBMCs and whole blood separately) in analysis of several cohorts. summarized effect size summarized effect size std.err. tauA2 heterogeneity p-value Q df overall p-value Overall FDR (q-value) Weighted AUC of average finding OAS1 1.184 0.146 0.105 0.003 21.322 7 4.56E-16 5.43E-12 0.808 IFIT1 1.422 0.203 0.192 0.007 19.389 7 2.47E-12 4.42E-09 0.826 TSPO - 1.233 0.177 0.141 0.009 18.858 7 3.42E-12 5.79E-09 0.781 SAMD 9 1.063 0.155 0.072 0.121 11.416 7 7.30E- 12 9.66E-09 0.752 EMR1 - 1.074 0.158 0.054 0.206 9.705 7 9.39E- 12 1.12E-08 0.768 ISG15 1.625 0.242 0.278 0.008 19.227 7 1.79E- 11 1.93E-08 0.829 HERC 5 1.361 0.207 0.178 0.032 15.336 7 4.58E- 11 3.89E-08 0.794 NINJ2 - 1.008 0.154 0.048 0.223 9.434 7 5.75E- 11 4.67E-08 0.741 DDX6 0 1.303 0.200 0.159 0.042 14.565 7 6.91E- 11 5.25E-08 0.797 HESX 1 1.107 0.172 0.091 0.116 11.549 7 1.28E- 10 8.69E-08 0.749 IFI6 1.292 0.204 0.199 0.005 20.207 7 2.28E- 10 1.33E-07 0.794 Petition 870260069557, dated 07 / 14 / 2026, p. 115 / 336 111 / 157 MX1 1,600 0.253 0.328 0.003 21,525 7 2.63E-10 1.49E-07 0.826 OASL 1,192 0.189 0.195 0.001 25,432 7 2.73E-10 1.52E-07 0.788 LAX1 1,114 0.178 0.103 0.097 12,125 7 3.59E-10 1.86E-07 0.769 ACPP - 1,143 0.183 0.135 0.035 15,099 7 4.41E-10 2,19E-07 0,777 TBXA S1 - 1,213 0.195 0.159 0.031 15,409 7 5.43E-10 2.55E-07 0.765 IFIT5 1,076 0.174 0.126 0.027 15,825 7 6.47E-10 3.00E-07 0.760 IFIT3 1,331 0.216 0.269 0.000 32,727 7 7.55E-10 3.42E-07 0.794 KCTD 14 1,163 0.190 0.161 0.011 18,106 7 8,80E-10 3.83E-07 0.739 OAS2 1,379 0.230 0.346 0,000 56,480 7 1.99E-09 7.33E-07 0.830 PGD - 1,121 0.189 0.130 0.062 13,439 7 2.95E-09 1.01E-06 0.752 RTP4 1.084 0.189 0.132 0.059 13,565 7 9.15E-09 2.68E-06 0.741 PARP 12 1.189 0.208 0.193 0.021 16,436 7 1,12E- 08 3.13E-06 0.769 LY6E 1.479 0.260 0.363 0.001 23,586 7 1.29E- 08 3.48E-06 0.818 S100A 12 - 1.067 0.190 0.135 0.056 13,727 7 1.81E-08 4.58E-06 0.737 ADA 1,015 0.183 0.146 0.015 17,395 7 2.79E-08 6.47E-06 0.730 IFI44L 1,727 0.311 0.568 0,000 31,320 7 2.90E- 08 6.63E-06 0.823 SORT 1 - 1.013 0.184 0.161 0.005 20.064 7 4.00E- 08 8.89E-06 0.760, Petition 870260069557, dated 07 / 14 / 2026, page 116 / 336 112 / 157 IFI27 2,299 0,423 1,147 0,000 50,156 7 5,67E- 08 1.16E-05 0,867 RSAD 2 1,573 0,292 0,528 0,000 35,451 7 7,48E-08 1,47E-05 0,825 IFI44 1,519 0,283 0,493 0,000 37,895 7 8.24E-08 1.57E-05 0.816 OAS3 1.285 0.240 0.344 0.000 33,835 7 9.09E-08 1.69E-05 0.808 IFIH1 1.014 0.192 0.183 0.003 21,908 7 1,36E- 07 2.42E-05 0.788 TNIP1 - 1.023 0.194 0.152 0.040 14.735 7 1.42E-07 2.50E-05 0.749 RAB3 1 - 1.167 0.225 0.284 0.000 31.645 7 2.27E-07 3.70E-05 0.753 SIGLEC 1 1.447 0.281 0.493 0.000 38.460 7 2.59E-07 4.13E-05 0.816 SLC1 2A9 - 1.215 0.237 0.306 0.000 27.836 7 2.87E-07 4.43E-05 0.786 JUP 1.008 0.198 0.209 0.000 26.258 7 3.66E-07 5.40E-05 0.783 STAT 1 1.009 0.199 0.260 0.000 59.749 7 3.78E-07 5.51E-05 0.739 CUL1 1.060 0.212 0.225 0.004 20.680 7 5.96E-07 7.91E-05 0.753 PLP2 - 1.246 0.250 0.325 0.002 22.620 7 5.99E- 07 7.92E-05 0.768 IMPA2 - 1.428 0.290 0.485 0.000 29.554 7 8.28E- 07 0.00010 168 0.778 DNMT 1 1.071 0.217 0.222 0.012 18.048 7 8.34E- 07 0.00010 169 0.741 IFIT2 1.103 0.226 0.273 0.001 23.533 7 1.01E- 06 0.00011 836 0.749. Petition 870260069557, dated 07 / 14 / 2026, p. 117 / 336 113 / 157 GPAA 1 - 1,275 0,265 0,432 0,000 43,119 7 1,50E- 06 0,00015 81 0,775 CHST 12 1,177 0,246 0,342 0,000 27,608 7 1,62E- 06 0,00016 794 0,772 LTA4 H - 1,585 0,332 0,666 0,000 36,759 7 1,76E- 06 0,00017 814 0,766 RTN3 - 1,045 0,221 0,307 0,000 46,192 7 2,39E- 06 0,00022 179 0,757 CETP - 1,132 0,242 0,333 0,000 29,766 7 2,86E- 06 0,00025 585 0,728 ISG20 1,214 0,262 0,411 0,000 34,693 7 3,64E- 06 0,00030 743 0,758 TALD O1 - 1,138 0,246 0,344 0,000 30,764 7 3,66E- 06 0,00030 848 0,737 DHX5 8 1,197 0,259 0,370 0,001 24,871 7 3,94E- 06 0,00032 598 0,732 EIF2A K2 1,347 0,293 0,554 0,000 47,713 7 4,28E- 06 0,00034 864 0,796 HK3 - 1,109 0,242 0,304 0,002 22,157 7 4,53E- 06 0,00036 318 0,748 ACAA 1 - 1,077 0,235 0,309 0,000 28,834 7 4,61E- 06 0,00036 811 0,745 XAF1 1,300 0,288 0,552 0,000 55,144 7 6,56E- 06 0,00048 71 0,782 GZMB 1,203 0,267 0,394 0,000 26,203 7 6,72E- 06 0,00049 528 0,770 CAT - 1,034 0,230 0,322 0,000 43,416 7 6,86E- 06 0,00050 173 0,710 DOK3 - 1,035 0,233 0,295 0,001 25,110 7 9,08E- 06 0.00062 004 0.709 SORL 1 - 1.213 0.273 0.487 0.000 56.464 7 9.12E- 06 0.00062 162 0.777 PYGL - 1.157 0.261 0.375 0.001 25.452 7 9.46E- 06 0.00064 062 0.754, Petition 870260069557, dated 07 / 14 / 2026, p. 118 / 336 114 / 157 DYSF - 1,127 0,256 0,359 0,001 24,813 7 1,09E- 05 0,00071 449 0,748 TWF2 - 1,081 0,248 0,326 0,002 23,101 7 1,27E- 05 0.00078 837 0.736 TKT - 1.155 0.266 0.434 0.000 40.903 7 1.40E- 05 0.00085 2 0.728 CTSB - 1.080 0.249 0.403 0.000 64.209 7 1.48E-05 0.00088 313 0.695 FLII - 1.159 0.271 0.461 0.000 46.721 7 1.95E- 05 0.00110 142 0.716 PROS 1 - 1.250 0.296 0.520 0.000 31.989 7 2.37E- 05 0.00127 457 0.708 NRD1 - 1.103 0.261 0.400 0.000 31.123 7 2.40E- 05 0.00128 279 0.730 STAT 5B - 1.013 0.240 0.343 0.000 44,775 7 2,46E- 05 0,00131 36 0,736 CYBR D1 - 1,022 0,242 0,357 0,000 36,401 7 2,48E- 05 0,00131 834 0,715 PTAF R - 1,083 0,257 0,403 0,000 39,437 7 2,55E- 05 0,00134 828 0,727 LAPT M5 - 1,010 0,243 0,341 0,000 31,034 7 3,32E- 05 0,00165 747 0.718

[00260] Supplementary Table 2. Data sets with non-infectious inflammatory conditions used to test IADM. Other data sets are listed in Tables 1 and 2. ICU: intensive care unit. CAP: community-acquired pneumonia. SLE: systemic lupus erythematosus. Petition 870260069557, dated 07 / 14 / 2026, page 119 / 336 115 / 157 Access Number | Condition | Uninfected | Condition | Infected | Number of Uninfected | Number of Infected | GSE28750 | Post-surgical adults | Adults with community-acquired bacterial sepsis | 11 | 10 | GSE40012 | Uninfected SIRS in adult ICU | Adults with CAP in ICU | 24 | 47 | GSE66099 | Uninfected SIRS in pediatric ICU | Pediatric sepsis, severe sepsis and septic shock | 30 | 120 | E-MEXP-3589 | Uninfected patients hospitalized with COPD | Hospitalized patients with COPD with respiratory infections | 14 | 9 | GSE22098 | Children and adults with SLE and Still's disease | Children with Gram-positive infections | 14 | 52 | GSE42834 | Adults with sarcoidosis and lung cancer | Adults with bacterial pneumonia | 99 | 19 Petition 870260069557, dated 07 / 14 / 2026, page 120 / 336 116 / 157

[00261] Supplementary Table 3. Diagnostic gene sets identified using a recursive greedy forward search algorithm. Order in direct recursive search positive for viral infection positive for bacterial infection GSE62 69 gpl2507 AUC GSE62 69 gpl570 AUC GSE62 69 gpl96 AUC GSE203 46 gpl6947 AUC GSE400 12 gpl6947 AUC GSE403 96 gpl1055 8 AUC GSE42026 gpl6947 AUC GSE66099 gpl570 AUC Average finding AUC 1 IFI27, JUP, LAX1 HK3, TNIP1, GPAA1, CTSB 0.992 1 0.976 1 1 0.879 0.938 0.844 0.954 2 OAS2, CUL1 SLC12A9, ACPP, STAT5B 0.977 0.967 0.935 1 0.977 0.896 0.858 0.817 0.928 3 ISG15, CHST12 EMR1, FLII 0.945 0.933 0.938 1 0.949 0.9 0.858 0.796 0.915 4 IFIT1, SIGLEC1, ADA PTAFR, NRD1, PLP2 1 1 0.944 1 0.975 0.907 0.858 0.764 0.931 5 MX1 DYSF, TWF2 1 0.925 0.916 1 0.977 0.961 0.848 0.706 0.917 6 RSAD2 SORT1, TSPO 0.961 0.942 0.947 1 0.952 0.879 0.9 0.736 0.915 7 IFI44L, GZMB, KCTD14 TBXAS1, ACAA1, S100A12 0.938 0.958 0.911 1 0.977 0.918 0.854 0.746 0.913 8 LY6E PGD, LAPTM5 0.984 0.967 0.916 1 0.977 0.864 0.885 0.697 0.911 9 IFI44, HESX1, NINJ2, DOK3, SORL1, 0.961 0.967 0.94 1 0.957 0.889 0.851 0.742 0.913 Petition 870260069557, dated 07 / 14 / 2026, p. 121 / 336 117 / 157 Order in direct recursive search positive for viral infection positive for bacterial infection GSE62 69 gpl2507 AUC GSE62 69 gpl570 AUC GSE62 69 gpl96 AUC GSE203 46 gpl6947 AUC GSE400 12 gpl6947 AUC GSE403 96 gpl1055 8 AUC GSE42026 gpl6947 AUC GSE66099 gpl570 AUC Average finding AUC OASL RAB31 10 OAS1 IMPA2, LTA4H 0.992 0.958 0.858 1 0.939 0.904 0.875 0.716 0.905 11 OAS3, EIF2AK2 TALDO1 0.945 0.992 0.928 0.979 0.851 0.793 0.847 0.717 0.882 12 DDX60, DNMT1 TKT 0.984 0.908 0.898 0.99 0.929 0.829 0.886 0.65 0.884 13 HERC5, IFIH1, SAMD9 PYGL, CETP, PROS1 0.961 0.925 0.925 0.958 0.902 0.811 0.85 0.678 0.876 14 IFI6 RTN3, CAT 0.938 0.983 0.913 1 0.889 0.854 0.79 0.651 0.877 15 IFIT3, IFIT5 CYBRD1 0.938 0.925 0.901 0.958 0.866 0.729 0.858 0.645 0.852 16 XAF1, ISG20, PARP12 null 0.867 0.925 0.944 0.948 0.841 0.764 0.837 0.598 0.84 17 IFIT2, DHX58, STAT1 null 0.883 0.9 0.848 0.938 0.879 0.736 0.833 0.578 0.824

[00262] Supplementary Table 4. Average Area Under the Curve (AUC) for combinations of 2 genes. Each set of 2 genes was taken from the set of genes found by the greedy and iterative advancement search (the pool of 71 genes). The AUC is the average AUC across the sets of findings. Only the Petition 870260069557, dated 07 / 14 / 2026, p. 122 / 336 118 / 157 combinations of two genes with an average AUC >0.80. Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC SIGLEC1 SLC12A 9 0,925 STAT5B LTA4H 0,881 EIF2AK2 NINJ2 0,864 IFIT2 PLP2 0,851 CYBRD1 PGD 0,836 IFI27 HK3 0,921 ADA IFI44L 0,88 GZMB IFI6 0,864 IFIT5 RSAD2 0,851 DYSF SORL1 0,836 IFI27 S100A12 0,919 ADA ISG15 0,88 HERC5 CAT 0,864 IFIT5 CYBRD1 0,851 FLII TSPO 0,836 SIGLEC1 IMPA2 0,916 ADA RSAD2 0,88 HERC5 FLII 0,864 ISG20 OAS3 0,851 IMPA2 TBXAS1 0,836 SIGLEC1 TBXAS1 0,916 DDX60 RAB31 0,88 HERC5 NRD1 0,864 ISG20 CETP 0,851 LAPTM5 S100A12 0,836 IFI27 DYSF 0,915 DDX60 STAT5B 0,88 HERC5 RTN3 0,864 JUP SORT1 0,851 PGD PROS1 0,836 IFI27 TNIP1 0,915 DNMT1 IFI6 0,88 HERC5 TNIP1 0,864 LAX1 TSPO 0,851 PGD TBXAS1 0,836 SIGLEC1 ACAA1 0,914 EIF2AK2 TBXAS1 0,88 IFI44 IFI44L 0,864 LY6E OASL 0,851 ADA NRD1 0,835 SIGLEC1 DYSF 0,914 HERC5 SORL1 0,88 IFI44 MX1 0,864 MX1 XAF1 0,851 ADA SORL1 0,835 IFI27 TSPO 0,913 HERC5 TBXAS1 0,88 IFI44 RSAD2 0,864 OAS1 OAS3 0,851 DNMT1 IFIT2 0,835 OAS2 SLC12A 9 0,913 HESX1 LTA4H 0,88 IFI6 PROS1 0.864 OAS2 IFI44 IMPA2 0.88 IFIT2 EMR1 0.864 STAT1 TBXAS1 0.851 HERC5 SAMD9 0.835 IFI27 SLC12A 0.911 IFI44 RAB31 0.88 IFIT2 SORL1 0.864 XAF1 GPAA1 0.851 HERC5 STAT1 0.835, Petition 870260069557, dated 07 / 14 / 2026, page 123 / 336 119 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC 9 IFI27 SORT1 0,911 IFI44L DOK3 0,88 IFIT3 ACAA1 0,864 XAF1 RTN3 0,851 HESX1 IFIH1 0,835 OAS3 HK3 0,911 IFIH1 TSPO 0,88 IFIT3 LTA4H 0,864 SLC12A 9 LAPTM5 0,851 IFI6 STAT1 0,835 SIGLEC1 STAT5B 0,911 IFIT5 RAB31 0,88 IFIT5 PLP2 0,864 NRD1 SLC12A 9 0,851 KCTD14 SAMD9 0,835 IFIT1 HK3 0,91 IFIT5 SORL1 0,88 ISG15 OAS1 0,864 RTN3 SORL1 0,851 LAX1 HK3 0,835 SIGLEC1 EMR1 0,91 JUP MX1 0,88 ISG20 RAB31 0,864 S100A12 TBXAS1 0,851 SAMD9 SIGLEC1 0,835 IFI27 PGD 0,909 JUP NINJ2 0,88 ISG20 STAT5B 0,864 SLC12A 9 TWF2 0,851 STAT1 GPAA1 0,835 CUL1 IFI27 0,908 JUP STAT5B 0,88 KCTD14 TKT 0,864 TNIP1 SORL1 0,851 ACPP PTAFR 0,835 IFI27 JUP 0,908 KCTD14 ACPP 0,88 LAX1 LTA4H 0,864 ADA SLC12A 9 0,85 CAT DYSF 0,835 IFI27 ACAA1 0,908 KCTD14 GPAA1 0,88 PARP12 DYSF 0,864 CHST12 SAMD9 0,85 CETP NRD1 0,835 IFI27 GPAA1 0,908 KCTD14 LTA4H 0,88 PARP12 NINJ2 0,864 CHST12 CETP 0,85 CTSB NRD1 0,835 IFI27 NRD1 0,908 KCTD14 PLP2 0,88 PARP12 TBXAS1 .864 CUL1 TKT .85 CYBRD1 S100A12 .835 IFI27 STAT5B .908 KCTD14 TNIP1 .88 SAMD9 TBXAS1 .864 DDX60 EIF2AK2 .85 DYSF SLC12A 9 0.835, Petition 870260069557, dated 07 / 14 / 2026, page 124 / 336 120 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFIT1 DYSF 0.908 LAX1 OAS3 0.88 GPAA1 PLP2 0.864 DHX58 SIGLEC1 0.85 EMR1 PTAFR 0.835 OAS1 HK3 0.908 LAX1 SIGLEC1 0.88 LTA4H PGD 0.864 GZMB ACAA1 0.85 EMR1 SORT1 0.835 OAS1 SLC12A 9 0.908 LY6E LTA4H 0.88 S100A12 SLC12A 9 0.864 GZMB ACPP 0.85 FLII GPAA1 0.835 OAS2 PTAFR 0.908 OAS3 NINJ2 0.88 ADA IFIT5 0.863 GZMB FLII 0.85 FLII PLP2 0.835 OAS3 SLC12A 9 0.908 OAS3 TWF2 0.88 CUL1 PLP2 0.863 GZMB PYGL 0.85 HK3 IMPA2 0.835 SIGLEC1 FLII 0.908 OASL RTN3 0.88 DDX60 CAT 0.863 IFIH1 RSAD2 0.85 LAPTM5 RAB31 0.835 SIGLEC1 TSPO 0.908 PARP12 STAT5B 0.88 DNMT1 OAS3 0.863 IFIT1 XAF1 0.85 LAPTM5 RTN3 0.835 CHST12 IFI27 0.907 RSAD2 PGD 0.88 GZMB IFIT3 0.863 IFIT2 RTN3 0,85 NINJ2 RTN3 0,835 DNMT1 IFI27 0,907 RSAD2 PYGL 0,88 HERC5 ISG15 0,863 IFIT5 MX1 0,85 NRD1 PYGL 0,835 IFI27 ACPP 0,907 GPAA1 RAB31 0,88 HESX1 OAS2 0,863 ISG20 TKT 0,85 NRD1 S100A12 0,835 IFI27 CETP 0,907 GPAA1 SLC12A9 0,88 IFI44 OASL 0,863 KCTD14 IFI6 LY6E 0.863 OAS3 CYBRD1 0.85 CHST12 PROS1 0.834 MX1 DYSF 0.907 ADA OAS3 0.879 IFIH1 LAX1 0.863 OASL CAT 0.85 DDX60 DHX58 0.834 SIGLEC1 DOK3 0.907 CHST12 ISG15 0.879 IFIT3 JUP 0.863 OASL CYBRD1 0.85 DNMT1 CYBRD1 0.834, Petition 870260069557, dated 07 / 14 / 2026, page 125 / 336 121 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFI27 LAX1 0,906 CHST12 STAT5B 0,879 IFIT3 RTN3 0,863 PARP12 GPAA1 0,85 DNMT1 PYGL 0,834 IFI27 DOK3 0,906 GZMB OAS3 0,879 IFIT5 LTA4H 0,863 RSAD2 XAF1 0,85 DNMT1 S100A12 0,834 IFI27 PTAFR 0,906 HERC5 TSPO 0,879 ISG15 OASL 0,863 SIGLEC1 XAF1 0,85 GZMB RTN3 0,834 IFI27 RAB31 0,906 IFI44 LY6E 0,879 ISG20 MX1 0,863 STAT1 LTA4H 0,85 GZMB SORT1 0,834 IFI27 SORL1 0,906 IFI44 DYSF 0,879 JUP SORL1 0,863 ACAA1 RTN3 0,85 IFIH1 IFIT3 0,834 IFIT1 SLC12A 9 0,906 IFI44 LTA4H 0,879 KCTD14 EMR1 0,863 ACPP CAT 0,85 IFIT2 GPAA1 0,834 ISG15 SORT1 0,906 IFI44L PGD 0,879 KCTD14 FLII 0,863 ACPP PGD 0,85 IFIT2 LAPTM5 0,834 MX1 EMR1 0,906 IFI44L TWF2 0,879 KCTD14 LAPTM5 0,863 ACPP SLC12A 9 0,85 IFIT3 IFIT5 0,834 MX1 HK3 0,906 IFIH1 DYSF 0,879 KCTD14 SORL1 0,863 CAT IMPA2 0,85 IFIT3 XAF1 0,834 MX1 SLC12A 9 0,906 IFIT1 JUP 0,879 KCTD14 STAT5B 0,863 CTSB IMPA2 0,85 IFIT5 OAS1 0,834 MX1 SORL1 0,906 IFIT1 PROS1 0,879 LY6E OAS3 0.863 EMR1 S100A12 0.85 LAX1 NINJ2 0.834 OAS2 DYSF 0.906 IFIT3 SORT1 0.879 OASL PROS1 0.863 GPAA1 PGD 0.85 LAX1 SORT1 0.834 OAS2 TSPO 0.906 ISG15 CETP 0.879 PARP12 ACPP 0.863 NINJ2 SLC12A 9 0.85 ACPP PYGL 0.834 RSAD2 DYSF 0.906 ISG15 RTN3 0.879 SAMD9 PYGL 0.863 S100A12 TWF2 0.85 CAT NRD1 0.834 Petition 870260069557, dated 07 / 14 / 2026, page 126 / 336 122 / 157 IFI27 NINJ2 0.905 OAS3 GPAA1 0.879 CYBRD1 SLC12A 9 0.863 CHST12 NINJ2 0.849 CAT RAB31 0.834 IFI27 PROS1 0.905 OASL PYGL 0.879 LTA4H NRD1 0.863 CUL1 CTSB 0.849 CTSB PGD 0.834 OAS1 DYSF 0.905 PARP12 SORL1 0.879 LTA4H TBXAS1 0.863 CUL1 NINJ2 0.849 DYSF EMR1 0.834 OASL DYSF 0.905 GPAA1 RTN3 0.879 RTN3 SLC12A 9 0.863 DDX60 OASL 0.849 DYSF PROS1 0.834 RSAD2 SLC12A 9 0.905 ADA IFI6 0.878 ADA IFIT3 0.862 DHX58 ISG15 0.849 DYSF RTN3 0.834 SIGLEC1 ACPP 0.905 CHST12 DDX60 0.878 CHST12 LY6E 0.862 DHX58 OAS2 0.849 DYSF S100A12 0.834 IFI27 FLII 0.904 CHST12 MX1 0.878 CHST12 NRD1 0.862 DHX58 RSAD2 0.849 EMR1 NINJ2 0.834 IFI27 LAPTM5 0.904 DDX60 LAX1 0.878 CUL1 TBXAS1 0.862 DHX58 FLII 0.849 EMR1 TWF2 0.834 IFIT1 EMR1 0.904 DHX58 PTAFR 0.878 CUL1 TWF2 0.862 DNMT1 TSPO 0.849 HK3 STAT5B 0.834 IFIT1 SORL1 0.904 DNMT1 IFIT1 0.878 DDX60 KCTD14 0.862 EIF2AK2 HERC5 0.849 HK3 TNIP1 0.834 MX1 PTAFR 0.904 GZMB IFIT1 0.849 .878 DHX58 PGD 0.862 EIF2AK2 IFIT3 0.849 PTAFR PYGL 0.834 OAS2 SORL1 0.904 GZMB MX1 0.878 DHX58 TBXAS1 0.862 GZMB PARP12 0.8 SORL 49.83 BAT STAT OAS3 DYSF 0.904 GZMB RSAD2 0.878 DNMT1 SORL1 0.862 GZMB CETP 0.849 SORT1 TNIP1 0.834 OASL HK3 0.904 HERC5 RAB31 0.878 EIF2AK2 IFI, 5668 IFI, HERC ADA PGD 0.833, Petition 870260069557, of 14 / 07 / 2026, p. 127 / 336 123 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC RSAD2 HK3 0,904 IFI44 NINJ2 0,878 EIF2AK2 OAS3 0,862 HERC5 OAS3 0,849 CUL1 DHX58 0,833 SIGLEC1 SORT1 0,904 IFI44 STAT5B 0,878 HERC5 GPAA1 0,862 HESX1 NINJ2 0,849 DHX58 EIF2AK2 0,833 CHST12 GPAA1 0,903 IFI44L FLII 0,878 HERC5 TKT 0,862 IFIT1 IFIT5 0,849 DHX58 CETP 0,833 IFI27 CTSB 0,903 IFI44L LTA4H 0,878 HESX1 LY6E 0,862 IFIT2 TALDO1 0,849 DHX58 TALDO1 0,833 IFI27 IMPA2 0,903 IFI44L TNIP1 0,878 IFI44L IFIH1 0,862 IFIT5 SIGLEC1 0,849 DNMT1 EMR1 0,833 IFI27 TBXAS1 0,903 IFI6 DOK3 0,878 IFI44L OAS1 0,862 IFIT5 CTSB 0,849 IFIT2 TWF2 0,833 IFI27 TWF2 0,903 IFI6 LAPTM5 0,878 IFI44L OAS3 0,862 IFIT5 PGD 0,849 IFIT3 PARP12 0,833 IFIT1 SORT1 0,903 IFIH1 EMR1 0,878 IFI6 CYBRD1 0,862 IFIT5 PROS1 0,849 IFIT3 SAMD9 0,833 OAS2 ACAA1 0,903 IFIT3 ACPP 0,878 IFIT3 ISG15 0,862 JUP OASL 0,849 IFIT5 OASL 0,833 OAS2 STAT5B 0,903 ISG15 CAT 0,878 IFIT3 PROS1 0,862 KCTD14 S100A12 0,849 LAX1 CAT 0,833 OAS3 SORT1 0,903 ISG15 TALDO1 0.878 IFIT3 PYGL 0.862 LAX1 PTAFR 0.849 OAS1 SAMD9 0.833 EIF2AK2 HK3 0.902 JUP CTSB 0.878 IFIT3 TKT 0.862 PARP12 RSAD2 0.849 SAMD9 TKT 0.833 IFI27 MX1 0.902 KCTD14 RSAD2 0.878 IFIT5 ISG15 0.862 SAMD9 CAT 0.849 ACAA1 RAB31 0.833 IFI27 OAS2 0.902 LAX1 OASL 0.878 IFIT5 LAX1 0.862 XAF1 PYGL 0.849 DOK3 S100A12 0.833 IFI27 LTA4H 0.902 LY6E RTN3 0.878 IFIT5 S100A12 0.862 ACPP NRD1 0.849 DYSF IMPA2 0.833 IFI27 PLP2 0.902 LY6E TKT 0.878 ISG20 RSAD2 0.862 LTA4H PTAFR 0.849 EMR1 FLII 0.833 IFIT1 RAB31 0.902 MX1 SIGLEC1 0.878 JUP LY6E 0.862 ADA IFIT2 0.848 IMPA2 TWF2 0.833, Petition: 870260069557, on 07 / 14 / 2026, page. 128 / 336 124 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC ISG15 EMR1 0,902 OAS3 S100A12 0,878 MX1 RSAD2 0,862 CHST12 JUP 0,848 PGD PLP2 0,833 ISG15 SLC12A 9 0,902 OASL TWF2 0,878 OAS1 CYBRD1 0,862 CHST12 XAF1 0,848 PLP2 RAB31 0,833 MX1 TSPO 0,902 RSAD2 FLII 0,878 OAS2 RSAD2 0,862 CUL1 IFIT3 0,848 PTAFR RTN3 0,833 OAS2 HK3 0,902 RSAD2 GPAA1 0,878 PARP12 ACAA1 0,862 CUL1 CAT 0,848 PTAFR TSPO 0,833 OAS2 PGD 0,902 RSAD2 TALDO1 0,878 STAT1 HK3 0,862 DDX60 CYBRD1 0,848 PYGL TSPO 0,833 RSAD2 SORT1 0,902 XAF1 DYSF 0,878 XAF1 PLP2 0,862 DHX58 LAPTM5 0,848 SORT1 TALDO1 0,833 SIGLEC1 PGD 0,902 XAF1 SORT1 0,878 ACPP LTA4H 0,862 DNMT1 TALDO1 0,848 TALDO1 TSPO 0,833 SIGLEC1 PLP2 0,902 DDX60 IMPA2 0,877 CTSB GPAA1 0,862 EIF2AK2 OAS1 0,848 ADA NINJ2 0,832 SIGLEC1 PTAFR 0,902 DDX60 PTAFR 0,877 RAB31 STAT5B 0,862 GZMB IFIH1 0,848 CHST12 GZMB 0,832 ADA IFI27 0,901 DDX60 TBXAS1 0,877 SORL1 TSPO 0,862 GZMB DYSF 0,848 CUL1 GZMB 0,832 EIF2AK2 DYSF 0,901 IFI44 DNMT1 0,877 ADA SAMD9 0.861 HERC5 IFIT3 0.848 DHX58 ISG20 0.832 JUP PGD 0.901 IFI44L DNMT1 0.877 CHST12 EIF2AK2 0.861 IFI6 PARP12 0.848 DNMTY 0.83 LMTY DYSF 0.901 LAX1 HERC5 0.877 CHST12 TNIP1 0.861 IFIH1 IFIT1 0.848 HESX1 IFIT5 0.832 LY6E TNIP1 0.901 HESX1 SORT1 0.877 CUL1 LY860 LYAPH, LYAP1 H 0.848 IFIT2 OAS1 0.832 MX1 IMPA2 0.901 IFI44L LAPTM5 0.877 CUL1 RSAD2 0.861 IFIT2 MX1 0.848 ISG20 KCTD14 0.832 OAS2 RAB31 0.91 CAT 0.87 CUL17 ACPP 0.861 ISG20 DOK3 0.848 JUP KCTD14 0.832, Petition 870260069557, of 14 / 07 / 2026, p. 129 / 336 125 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFI27 ISG20 0,9 IFIT3 TBXAS1 0,877 CUL1 PTAFR 0,861 ISG20 PTAFR 0,848 OAS3 SAMD9 0,832 IFI27 OAS1 0,9 JUP EMR1 0,877 DDX60 SIGLEC1 0,861 JUP STAT1 0,848 OAS3 STAT1 0,832 IFI27 RSAD2 0,9 KCTD14 PGD 0,877 DNMT1 OASL 0,861 LAX1 ACAA1 0,848 SAMD9 RTN3 0,832 IFI27 TALDO1 0,9 OAS1 LAPTM5 0,877 HERC5 LY6E 0,861 LAX1 TBXAS1 0,848 ACAA1 PGD 0,832 IFI44 SLC12A 9 0,9 OAS1 RTN3 0,877 HESX1 OAS3 0,861 LAX1 TKT 0,848 CAT CETP 0,832 ISG15 HK3 0,9 OASL TALDO1 0,877 HESX1 NRD1 0,861 LY6E PARP12 0,848 CAT RTN3 0,832 LY6E SLC12A 9 0,9 XAF1 EMR1 0,877 IFI44 IFIT1 0,861 OAS2 SAMD9 0,848 CETP S100A12 0,832 MX1 DOK3 0,9 XAF1 PTAFR 0,877 IFI6 ISG20 0,861 PARP12 CETP 0,848 CTSB HK3 0,832 MX1 PGD 0,9 LTA4H EMR1 0,877 IFI6 CAT 0,861 STAT1 ACPP 0,848 CYBRD1 DOK3 0,832 OAS3 EMR1 0,9 CHST12 IFI44 0,876 IFIH1 TALDO1 0,861 ACPP LAPTM5 0,848 DOK3 RTN3 0,832 RSAD2 SORL1 0,9 CUL1 ACAA1 0,876 IFIT3 TWF2 0,861 ACPP PLP2 0,848 DYSF PLP2 0.832 SIGLEC1 TWF2 0.9 DHX58 SORL1 0.876 ISG20 SORL1 0.861 CAT LTA4H 0.848 DYSF RAB31 0.832 GZMB IFI27 0.899 EIF2AK 287 JUP27 JUP 0.861 CETP GPAA1 0.848 DYSF STAT5B 0.832 IFI27 IFI44 0.899 EIF2AK2 RTN3 0.876 KCTD14 HK3 0.861 CYBRD1 EMR1 0.848 DYSF TSPO 0.83 IRDYB 0.8928 HESX1 ISG15 0.876 KCTD14 TWF2 0.861 PLP2 NRD1 0.848 FLII NINJ2 0.832, Petition 870260069557, of 14 / 07 / 2026, p. 130 / 336 126 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFI27 RTN3 0,899 HESX1 EMR1 0,876 LY6E CYBRD1 0,861 PROS1 STAT5B 0,848 FLII RTN3 0,832 ISG15 DYSF 0,899 HESX1 PLP2 0,876 OAS3 RSAD2 0,861 S100A12 TNIP1 0,848 FLII SORT1 0,832 JUP TSPO 0,899 IFI44 KCTD14 0,876 OAS3 CAT 0,861 SLC12A 9 TALDO1 0,848 FLII STAT5B 0,832 LY6E HK3 0,899 IFI44 DOK3 0,876 OAS3 PROS1 0,861 SLC12A 9 TBXAS1 0,848 HK3 PLP2 0,832 LY6E PGD 0,899 IFI44 TNIP1 0,876 SAMD9 STAT5B 0,861 SLC12A 9 TKT 0,848 LAPTM5 TSPO 0,832 OAS1 IMPA2 0,899 IFI44L KCTD14 0,876 STAT1 SLC12A 9 0,861 STAT5B TALDO1 0,848 NINJ2 RAB31 0,832 OAS1 TSPO 0,899 IFI44L LY6E 0,876 XAF1 IMPA2 0,861 STAT5B TKT 0,848 NINJ2 S100A12 0,832 OAS2 IMPA2 0,899 IFI6 LTA4H 0,876 GPAA1 CAT 0,861 ADA TNIP1 0,847 PGD SORT1 0,832 RSAD2 EMR1 0,899 IFI6 STAT5B 0,876 GPAA1 CYBRD1 0,861 DHX58 CHST12 0,847 PTAFR PROS1 0,832 EIF2AK2 SLC12A 9 0,898 IFIT1 KCTD14 0,876 EMR1 GPAA1 0,861 CHST12 TALDO1 0,847 PTAFR SORL1 0,832 IFIT1 IFI27 0,898 IFIT1 GPAA1 0.876 GPAA1 TKT 0.861 CUL1 EMR1 0.847 RTN3 TWF2 0.832 ISG15 IFI27 0.898 JUP LAPTM5 0.876 IMPA2 LTA4H 0.861 IFI44 DDX60 0.847 STAT1 ADA 0,831 SIGLEC1 IFI27 0,898 JUP TKT 0,876 SLC12A9 SORL1 0,861 IFI6 DDX60 0,847 ADA PLP2 0,831, Petition: 870260069557, on 07 / 14 / 2026, page. 131 / 336 127 / 157 IFI27 PYGL 0.898 KCTD14 ACAA1 0.876 STAT5B TBXAS1 0.861 JUP DHX58 0.847 ISG20 CHST12 0.831 IFI44 HK3 0.898 LY6E LAX1 0.876 IFIT3 CHST12 0.86 IFIT5 DNMT1 0.847 IFIT5 DDX60 0.831 IFIT1 DOK3 0.898 SAMD9 LAX1 0.876 OASL CHST12 0.86 DNMT1 CETP 0.847 ISG20 DNMT1 0.831 IFIT1 IMPA2 0.898 LY6E CETP 0.876 CUL1 NRD1 0.86 OASL HERC5 0.847 DNMT1 LAPTM5 0.831 JUP IMPA2 0.898 OASL CTSB 0.876 DDX60 CTSB 0.86 HESX1 CAT 0.847 SAMD9 LAPTM5 0.831 LY6E TSPO 0.898 OASL SORL1 0.876 DDX60 TALDO1 0.86 IFIH1 IFI44 0.847 ACAA1 PROS1 0.831 MX1 ACPP 0.898 XAF1 HK3 0.876 DDX60 TWF2 0.86 XAF1 IFI44 0.847 ACPP EMR1 0.831 MX1 SORT1 0.898 XAF1 SORL1 0.876 DHX58 LAX1 0.86 XAF1 IFI6 0.847 CETP TSPO 0.831 MX1 STAT5B 0.898 IFIT1 CHST12 0.875 DHX58 ACPP 0.86 IFIT2 DOK3 0.847 CYBRD1 FLII 0.831 OAS2 DOK3 0.898 IFI44L CUL1 0.875 DHX58 RAB31 0.86 OAS2 IFIT5 0.847 EMR1 LAPTM5 0.831 OAS2 GPAA1 0.898 DDX60 ACAA1 0.875 LY6E DNMT1 0.898 .86 IFIT5 TNIP1 0.847 HK3 TBXAS1 0.831 OAS3 SORL1 0.898 EIF2AK2 LTA4H 0.875 SIGLEC1 DNMT1 0.86 LAX1 LAPTM5 0.847 NINJ2 NRD1 0.831 PGHERC 0.838 S100A12 0.875 HERC5 CYBRD1 0.86 LAX1 NRD1 0.847 NINJ2 PYGL 0.831 OASL PTAFR 0.898 HESX1 TBXAS1 0.875 HESX1 TWF2 0.86 SAMD9 MX18101010, S101 PGD 0.831 SIGLEC1 SORL1 0.898 HESX1 TSPO 0.875 MX1 IFIH1 0.86 STAT1 MX1 0.847 PLP2 TKT 0.831 SIGLEC1 TALDO1 0.898 IFI44 CTSB 0.817 IFIII FLII 1026 PA SIGLEC1 0.847 RTN3 RAB31 0.831 IFI27 LY6E 0.897 IFI44 GPAA1 0.875 IFIH1 RTN3 0.86 SAMD9 CTSB 0.847 ADA DYSF 0.83, Petition 870260069557, of 14 / 07 / 2026, p. 132 / 336 128 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFI27 OAS3 0,897 ISG15 IFI44L 0,875 IFIH1 TKT 0,86 SAMD9 DOK3 0,847 ADA HK3 0,83 ISG15 TSPO 0,897 SIGLEC1 IFI44L 0,875 IFIH1 TWF2 0,86 ACPP TSPO 0,847 STAT1 CHST12 0,83 LY6E EMR1 0,897 IFI6 SORL1 0,875 IFIT3 IFIT1 0,86 CYBRD1 IMPA2 0,847 IFIH1 CUL1 0,83 LY6E TBXAS1 0,897 MX1 ISG15 0,875 IFIT2 IMPA2 0,86 PGD EMR1 0,847 KCTD14 DHX58 0,83 MX1 RAB31 0,897 ISG20 SLC12A9 0,875 LY6E IFIT3 0,86 PYGL EMR1 0,847 DNMT1 RTN3 0,83 OAS2 ACPP 0,897 KCTD14 TSPO 0,875 MX1 IFIT3 0,86 GPAA1 PROS1 0,847 IFIT2 HERC5 0,83 OAS2 NRD1 0,897 LY6E PYGL 0,875 IFIT3 CTSB 0,86 IMPA2 SORT1 0,847 ISG20 IFIH1 0,83 OAS2 TNIP1 0,897 OAS2 CAT 0,875 IFIT3 NRD1 0,86 LAPTM5 NRD1 0,847 IFIT2 TNIP1 0,83 OAS3 TBXAS1 0,897 OASL STAT5B 0,875 IFIT5 DYSF 0,86 PLP2 RTN3 0,847 LAX1 CYBRD1 0,83 OASL SORT1 0,897 PARP12 PTAFR 0,875 ISG20 ACAA1 0,86 PROS1 SORL1 0,847 LAX1 RTN3 0,83 OASL TSPO 0,897 RSAD2 PROS1 0,875 ISG20 NINJ2 0,86 RAB31 TBXAS1 0,847 SAMD9 OASL 0.83 SIGLEC1 LAPTM5 0.897 RSAD2 RTN3 0.875 LAX1 JUP 0.86 S100A12 TSPO 0.847 XAF1 PARP12 0.83 EIF2AK2 IFI27 0.896 ACPP GPAA1 0.875 LAX1 TNIP1 0.86 JUP ADA 0.846 STAT1 DOK3 0.83 EIF2AK2 SORT1 0.896 CHST12 IFI44L 0.874 OAS1 LY6E 0.86 ADA IMPA2 0.846 STAT1 NRD1 0.83 EIF2AK2 STAT5B 0.896 CHST12 TWF2 0.874 SIGLEC1 OAS1 0.86 ADA TBXAS1 0.846 STAT1 PROS1 0.83 EIF2AK2 TSPO 0.896 CUL1 TSPO 0.874 RSAD2 OASL 0.86 ADA TSPO 0.846 ACAA1 DYSF 0.83 HESX1 IFI27 0.896 JUP DDX60 0.874 SAMD9 DYSF 0.86 KCTD14 CHST12 0.846 ACAA1 S100A12 0.83, Petition 870260069557, dated 07 / 14 / 2026, page 133 / 336 129 / 157 IFIT2 IFI27 0.896 DDX60 DYSF 0.874 SIGLEC1 CAT 0.86 CHST12 CAT 0.846 CAT HK3 0.83 KCTD14 IFI27 0.896 DDX60 LTA4H 0.874 SIGLEC1 PROS1 0.86 CHST12 RTN3 0.846 CAT LAPTM5 0.83 PARP12 IFI27 0.896 DHX58 TSPO 0.874 XAF1 NINJ2 0.86 OAS1 CUL1 0.846 CETP CTSB 0.83 IFI27 STAT1 0.896 EIF2AK2 NRD1 0.874 XAF1 NRD1 0.86 OASL CUL1 0.846 CETP DYSF 0.83 IFI6 SORT1 0.896 HERC5 IMPA2 0.874 XAF1 TBXAS1 0.86 CUL1 PYGL 0.846 CETP IMPA2 0.83 IFIT1 ACPP 0.896 HERC5 STAT5B 0.874 ACAA1 GPAA1 0.86 ISG20 DDX60 0.846 CTSB TBXAS1 0.83 IFIT1 TSPO 0.896 HESX1 ACPP 0.874 ACPP PROS1 0.86 OAS1 DDX60 0.846 CTSB TWF2 0.83 ISG15 PGD 0.896 IFI44 PLP2 0.874 ACPP SORL1 0.86 IFIH1 DNMT1 0.846 CYBRD1 TBXAS1 0.83 ISG15 SORL1 0.896 IFI44 S100A12 0.874 CAT SLC12A 9 0.86 DNMT1 NRD1 0.846 CYBRD1 TWF2 0.83 LY6E PTAFR 0.896 IFI44 TWF2 0.874 EMR1 SORL1 0.86 IFIH1 EIF2AK2 0.846 IMPA2 FLII 0.83 OAS1 SORT1 0.896 IFI44L RTN3 0.874 HK3 LTA4H86 GZMB TNIP1 0.846 HK3 NRD1 0.83 OAS1 TBXAS1 0.896 IFI44L TALDO1 0.874 MPA2 I NRD1 0.86 OAS1 HERC5 0.846 HK3 PROS1 0.83 OAS2 EMR1949 TFITK 0.874 LTA4H NINJ2 0.86 JUP HESX1 0.846 HK3 SLC12A 9 0.83 OAS2 LTA4H 0.896 IFI6 ISG15 0.874 LTA4H TWF2 0.86 PARP12 IFIT1 0.84 TWF2 0.86 HASK, OWF 2032 TBXAS1 0.896 IFI6 SIGLEC1 0.874 NRD1 TSPO 0.86 STAT1 IFIT1 0.846 IMPA2 PROS1 0.83, Petition 870260069557, of 14 / 07 / 2026, p. 134 / 336 130 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OAS3 TSPO 0,896 IFI6 CTSB 0,874 RTN3 STAT5B 0,86 ISG20 RTN3 0,846 IMPA2 PTAFR 0,83 OASL EMR1 0,896 IFIH1 ACAA1 0,874 SLC12A9 TSPO 0,86 ISG20 S100A12 0,846 IMPA2 TALDO1 0,83 OASL SLC12A 9 0,896 IFIH1 SORL1 0,874 CHST12 HK3 0,859 LAX1 PLP2 0,846 PYGL TWF2 0,83 SIGLEC1 GPAA1 0,896 IFIT1 CETP 0,874 HERC5 CUL1 0,859 SAMD9 RSAD2 0,846 RAB31 S100A12 0,83 IFI27 HERC5 0,895 IFIT3 IMPA2 0,874 CUL1 RAB31 0,859 SAMD9 PROS1 0,846 SORT1 TKT 0,83 HESX1 SLC12A 9 0,895 IFIT3 NINJ2 0,874 KCTD14 EIF2AK2 0,859 STAT1 ACAA1 0,846 SORT1 TWF2 0,83 IFI6 HK3 0,895 IFIT5 SORT1 0,874 KCTD14 HERC5 0,859 STAT1 PYGL 0,846 ADA CYBRD1 0,829 IFIT1 NINJ2 0,895 LY6E ISG15 0,874 HERC5 CETP 0,859 ACAA1 NRD1 0,846 LAX1 CUL1 0,829 IFIT1 TBXAS1 0,895 ISG15 CYBRD1 0,874 RSAD2 HESX1 0,859 ACAA1 STAT5B 0,846 OASL DHX58 0,829 ISG15 ACPP 0,895 KCTD14 RTN3 0,874 HESX1 TALDO1 0,859 CETP SORL1 0,846 DHX58 CAT 0,829 MX1 NRD1 0,895 OAS1 LAX1 0,874 HESX1 TKT 0.859 IMPA2 TNIP1 0.846 IFIT2 EIF2AK2 0.829 MX1 PLP2 0.895 LY6E NRD1 0.874 IFI6 IFI44 0.859 LTA4H TKT 0.846 KCTD14 IFIH1 0.829 MX1 TBXAS1 0.895 MX1 CAT 0.874 OAS3 IFI44 0.859 NRD1 PGD 0.846 STAT1 RTN3 0.829 OAS2 FLII 0.895 OAS3 NRD1 0.874 STAT1 IFI44L 0.859 NRD1 RTN3 0.846 ACAA1 PLP2 0.829 OAS2 PLP2 0.895 OAS3 TKT 0.874 IFIH1 PGD 0.859 PGD SLC12A 0.846 CAT TWF2 0.829. Petition 870260069557, 07 / 14 / 2026, p. 135 / 3 131 / 1 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC 9 OAS3 IMPA2 0.895 OASL FLII 0.874 IFIT2 NINJ2 0.859 PGD STAT5B 0.846 CETP RTN3 0.829 OAS3 PTAFR 0.895 RSAD2 CETP 0.874 IFIT2 RAB31 0.859 SLC12A 9 SORT1 0.846 CTSB CYBRD1 0.829 IFI27 DDX60 0.894 GPAA1 S100A12 0.874 IFIT2 TBXAS1 0.859 ADA ACPP 0.845 CTSB PYGL 0.829 EIF2AK2 IMPA2 0.894 LTA4H SORL1 0.874 IFIT2 TSPO 0.859 CUL1 S100A12 0.845 IMPA2 TKT 0.829 EIF2AK2 SORL1 0.894 ADA OASL 0.873 IFIT3 KCTD14 0.859 DNMT1 DHX58 0.845 NRD1 TALDO1 0.829 IFIH1 IFI27 0.894 ADA SIGLEC1 0.873 IFIT3 FLII 0.859 DHX58 S100A12 0.845 PLP2 TNIP1 0.829 IFI27 TKT 0.894 CHST12 IMPA2 0.873 IFIT3 LAPTM5 0.859 DNMT1 CAT 0.845 TKT TSPO 0.829 IFI44L PTAFR 0.894 DDX60 DOK3 0.873 PARP12 ISG15 0.859 GZMB CAT 0.845 ISG20 ADA 0.828 IFIT1 ACAA1 0.894 DNMT1 GPAA1 0.873 SAMD9 ISG15 0.859 GZMB EMR1 0.845 ADA CAT 0.828 LAX1 ISG15 0.894 HERC5 GZMB 0.873 ISG20 PGD 0.859 GZMB PTAFR 0.845 ADA PROS1 0.828 ISG15 DOK3 0.894 HERC5 PLP2 0873 JUP PROS1 0.859 IFIT3 HESX1 0.845 SAMD9 DDX60 0.828 ISG15 STAT5B 0.894 ISG15 IFI44 0.873 XAF1 LAX1 0.859 SIGLEC1 HESX1 0.854 DHXIT 5288 OAS1 RAB31 0.894 IFI44 CAT 0.873 XAF1 LY6E 0.859 JUP IFIH1 0.845 XAF1 DHX58 0.828 OAS2 NINJ2 0.894 IFI44 NRD1 0.873 OAS3 MX1 IFI44 NRD1 0.873 OAS3 MX1 IFI44 NRD1 0.873 OAS3 MX1 SG1 0.8951545 STAT1 DNMT1 0.828 OAS2 SORT1 0.894 OAS2 IFI44L 0.873 PARP12 PGD 0.859 IFIT2 PROS1 0.845 ISG20 GZMB 0.828, Petition 870260069557, of 14 / 07 / 2026, p. 136 / 336 132 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OAS3 STAT5B 0,894 IFI44L CETP 0,873 SAMD9 LTA4H 0,859 IFIT5 TALDO1 0,845 LAX1 GZMB 0,828 SIGLEC1 CTSB 0,894 IFI44L GPAA1 0,873 XAF1 ACAA1 0,859 IFIT5 TKT 0,845 KCTD14 HESX1 0,828 DDX60 SORT1 0,893 IFIH1 ACPP 0,873 IMPA2 STAT5B 0,859 OAS1 ISG20 0,845 OASL IFIT2 0,828 EIF2AK2 PGD 0,893 IFIT1 CYBRD1 0,873 LTA4H RTN3 0,859 ISG20 CTSB 0,845 LAX1 ISG20 0,828 EIF2AK2 PLP2 0,893 IFIT2 HK3 0,873 PROS1 SLC12A 9 0,859 JUP CYBRD1 0,845 PARP12 ISG20 0,828 IFI44L IFI27 0,893 IFIT5 ACPP 0,873 S100A12 SORT1 0,859 KCTD14 CAT 0,845 SAMD9 ISG20 0,828 IFI6 IFI27 0,893 IFIT5 SLC12A9 0,873 SORL1 TBXAS1 0,859 LAX1 ACPP 0,845 STAT1 KCTD14 0,828 OASL IFI27 0,893 L AX1 KCTD14 0,873 SORL1 TWF2 0,859 LAX1 EMR1 0,845 STAT1 OAS1 0,828 IFI27 CAT 0,893 MX1 KCTD14 0,873 HESX1 ADA 0,858 OASL OAS3 0,845 STAT1 CAT 0,828 IFI44L EMR1 0,893 XAF1 STAT5B 0,873 XAF1 ADA 0,858 PARP12 CYBRD1 0,845 ACAA1 EMR1 0,828 IFI44L SLC12A 9 0,893 LTA4H TSPO 0.873 HESX1 CHST12 0.858 SAMD9 CYBRD1 0.845 ACPP DOK3 0.828 IFI6 EMR1 0.893 DDX60 ADA 0.872 IFI6 CUL1 0.858 STAT1 S100A12 0.845 CAT PTAFR 0.828 IFI6 TSPO 0.893 OAS2 ADA 0.872 DDX60 GPAA1 0.858 CYBRD1 SORT1 0.845 CTSB NINJ2 0.828 IFIT1 STAT5B 0.893 ADA GPAA1 0.872 DDX60 LAPTM5 0.858 EMR1 RAB31 0.845 CTSB S100A12 0.828 ISG15 TNIP1 0.893 RSAD2 CHST12 0.872 DDX60 PROS1 0.858 NRD1 TWF2 0.845 CTSB TALDO1 0.828, Petition 870260069557, dated 07 / 14 / 2026, page 137 / 336 133 / 157 MX1 ACAA1 0.893 CHST12 ACAA1 0.872 DHX58 GPAA1 0.858 PLP2 TSPO 0.845 CYBRD1 NINJ2 0.828 MX1 FLII 0.893 CHST12 FLII 0.872 IFIT3 DNMT1 0.858 PYGL SLC12A 9 0.845 CYBRD1 NRD1 0.828 OAS1 EMR1 0.893 CHST12 LAPTM5 0.872 XAF1 DNMT1 0.858 RAB31 TSPO 0.845 DOK3 IMPA2 0.828 OAS1 PGD 0.893 CHST12 PLP2 0.872 EIF2AK2 PROS1 0.858 RTN3 TBXAS1 0.845 DOK3 SLC12A 9 0.828 OAS1 PLP2 0.893 CHST12 PTAFR 0.872 XAF1 GZMB 0.858 SORT1 TSPO 0.845 DOK3 STAT5B 0.828 OAS2 LAPTM5 0.893 CHST12 TBXAS1 0.872 GZMB RAB31 0.858 IFIT5 CHST12 0.844 DYSF TALDO1 0.828 SIGLEC1 RTN3 0.893 CUL1 GPAA1 0.872 HESX1 LAPTM5 0.858 PARP12 CHST12 0.844 DYSF TWF2 0.828 DDX60 SORL1 0.892 CUL1 SORL1 0.872 IFIT3 IFI44L 0.858 CUL1 TNIP1 0.844 PGD RTN3 0.828 IFIT5 IFI27 0.892 DDX60 PYGL 0.872 RSAD2 IFIT1 0.858 OAS3 DDX60 0.844 PLP2 TALDO1 0.828 IFI44 ACPP 0.892 DHX58 DOK3 0.872 IFIT3 TNIP1 0.858 IFI6 DHX58 0.844 PYGL RTN3 0.828 IFI44 PTAFR 0.844.892 DNMT1 LTA4H 0.872 IFIT5 NINJ2 0.858 IFIH1 DHX58 0.844 TALDO1 TBXAS1 0.828 IFI44L ACPP 0.892 SIGLEC1 EIF2AK2 0.872 IFIT5 TBXAS181818 H 0.844 ADA RAB31 0.827 ISG15 GPAA1 0.892 EIF2AK2 PYGL 0.872 SIGLEC1 ISG20 0.858 DNMT1 PGD 0.844 IFIT2 DDX60 0.827 ISG15 S100.895 JUPHERC 0.872 ISG20 EMR1 0.858 IFIH1 HERC5 0.844 DHX58 PROS1 0.827 ISG15 TBXAS1 0.892 HERC5 LTA4H 0.872 ISG20 TALDO1 0.858 OAS3 IFIT43 AKSTAT 0.2012 0.827, Petition 870260069557, of 14 / 07 / 2026, p. 138 / 336 134 / 157 LY6E IMPA2 0.892 HESX1 IMPA2 0.872 OAS1 JUP 0.858 OASL IFIT3 0.844 PARP12 IFIH1 0.827 LY6E SORT1 0.892 SIGLEC1 IFI44 0.872 KCTD14 DYSF 0.858 IFIT5 TWF2 0.844 XAF1 IFIH1 0.827 MX1 TNIP1 0.892 IFI44 TALDO1 0.872 SAMD9 LY6E 0.858 OASL ISG20 0.844 SAMD9 FLII 0.827 OAS1 PTAFR 0.892 IFI44L CYBRD1 0.872 STAT1 LY6E 0.858 ISG20 CAT 0.844 ACAA1 TALDO1 0.827 OAS2 CTSB 0.892 IFI44L PROS1 0.872 OASL MX1 0.858 ISG20 PYGL 0.844 CETP HK3 0.827 RSAD2 STAT5B 0.892 IFI44L PYGL 0.872 OASL NRD1 0.858 OASL KCTD14 0.844 CETP NINJ2 0.827 SIGLEC1 LTA4H 0.892 IFI6 GPAA1 0.872 STAT1 SORL1 0.858 PARP12 KCTD14 0.844 CETP PGD 0.827 SIGLEC1 NRD1 0.892 IFI6 RTN3 0.872 STAT1 TSPO 0.858 STAT1 OAS2 0.844 CETP PTAFR 0.827 SIGLEC1 RAB31 0.892 IFI6 TNIP1 0.872 XAF1 TNIP1 0.858 XAF1 OAS3 0.844 CETP RAB31 0.827 SIGLEC1 TNIP1 0.892 RSAD2 JUP 0.872 ACPP FLII 0.858 PARP12 CAT 0.844 LAPTM5 PLP2 0.827 IFI44 SORT1 0.891 OAS3 KCTD14 0.827 .872 ACPP TNIP1 0.858 PARP12 PROS1 0.844 PTAFR S100A12 0.827 IFI44L DYSF 0.891 KCTD14 RAB31 0.872 SLC12A9 TNIP1 0.858 XAF1 CAT 0.84 0.84 ADA, TAL IFI44L HK3 0.891 LY6E CAT 0.872 SORL1 SORT1 0.858 XAF1 CETP 0.844 SAMD9 EIF2AK2 0.826 IFIT1 LAPTM5 0.891 LY6E PROS1 0.872 DHX5 PRADA, PRADA 0.871 0.844 HERC5 IFIT5 0.826 IFIT1 PGD 0.891 MX1 PROS1 0.872 OAS2 CUL1 0.857 ACAA1 SLC12A 9 0.844 OAS3 IFIT2 0.826 IFIT1 PLP2 0.84 OASK IFIT2 0.826 CUL, H 0.857 CTSB SORL1 0.844 STAT1 TCT 0.826, Petition 870260069557, of 14 / 07 / 2026, p. 139 / 336 135 / 157 ISG15 RAB31 0.891 SIGLEC1 OAS2 0.872 CUL1 PGD 0.857 CTSB STAT5B 0.844 CAT DOK3 0.826 OASL IMPA2 0.891 OAS2 PROS1 0.872 DDX60 FLII 0.857 EMR1 PROS1 0.844 CAT S100A12 0.826 DDX60 SLC12A 9 0.89 OAS3 RTN3 0.872 DDX60 TNIP1 0.857 EMR1 TSPO 0.844 CETP CYBRD1 0.826 EIF2AK2 RAB31 0.89 PARP12 HK3 0.872 DHX58 NINJ2 0.857 FLI RAB31 0.844 CETP FLI 0.826 SAMD9 IFI27 0.89 RSAD2 CYBRD1 0.872 OAS1 DNMT1 0.857 FLI SORL1 0.844 CETP PLP2 0.826 IFI44 SORL1 0.89 RSAD2 TKT 0.872 RSAD2 EIF2AK2 0.857 GPAA1 LAPTM5 0.844 CYBRD1 PTAFR 0.826 IFIH1 SLC12A 9 0.89 SAMD9 SORT1 0.872 EIF2AK2 CAT 0.857 MPA2 IPLP2 0.844 DOK3 PYGL 0.826 IFIT1 NRD1 0.89 SIGLEC1 S100A12 0.872 KCTD14 GZMB 0.857 IMPA2 TSPO 0.844 DYSF PTAFR 0.826 IFIT3 DYSF 0.89 LY6E ADA 0.871 GZMB IMPA2 0.857 RAB31 SORT1 0.844 HK3 RAB31 0.826 ISG15 TWF2 0.89 OAS3 CHST12 0.871 IFI44L HERC5 0.857 RAB31 TNIP1 0.844 HP3 TSPO 0.826 JUP ACPP 0.89 DHX58 HP3 0.844 .871 IFIT5 IFI44L 0.857 TBXAS1 TNIP1 0.844 NRD1 TNIP1 0.826 LY6E FLII 0.89 EIF2AK2 LY6E 0.871 PARP12 IFI44L 0.857 HESX1 DDX60 0.843 PGD TNIP1 0.826 MX1 LTA4H 0.89 EIF2AK2 CETP 0.871 SAMD9 IFI44L 0.857 IFIT3 DDX60 0.843 PGD TSPO 0.826 MX1 S100A12 0.89 EIF2AK2 TALDO1 0.871 IFIT3 IFI6 0.857 DHX58 TKT 0.843 PTAFR STAT5B 0.826 MX1 TALDO1 0.89 EIF2AK2 TKT 0.871 OAS3 IFI6 0.857 DNMT1 TBXAS1 0.843 PTAFR TBXAS1 0.826. Petition 870260069557, 07 / 14 / 2026, p. 140 / 3 136 / 1 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OAS2 TWF2 0,89 HERC5 NINJ2 0,871 IFIH1 CTSB 0,857 OASL HESX1 0,843 PYGL S100A12 0,826 OAS3 ACPP 0,89 IFI6 HESX1 0,871 IFIH1 GPAA1 0,857 IFIT5 IFI44 0,843 ADA RTN3 0,825 OAS3 PLP2 0,89 HESX1 RAB31 0,871 IFIH1 NINJ2 0,857 IFIH1 CYBRD1 0,843 CUL1 PROS1 0,825 PARP12 SLC12A 9 0,89 IFI44L CAT 0,871 IFIT2 SORT1 0,857 IFIT2 TKT 0,843 HESX1 DHX58 0,825 RSAD2 RAB31 0,89 IFI6 FLII 0,871 IFIT3 CAT 0,857 OAS3 IFIT5 0,843 DHX58 CYBRD1 0,825 SIGLEC1 PYGL 0,89 ISG15 IFIT1 0,871 IFIT5 NRD1 0,857 IFIT5 LAPTM5 0,843 DNMT1 HK3 0,825 EIF2AK2 DOK3 0,889 SIGLEC1 IFIT1 0,871 XAF1 JUP 0,857 ISG20 SORT1 0,843 PARP12 HESX1 0,825 HERC5 HK3 0,889 IFIT2 PTAFR 0,871 KCTD14 NINJ2 0,857 LAX1 CETP 0,843 STAT1 HESX1 0,825 IFIT3 IFI27 0,889 IFIT5 HK3 0,871 PARP12 DOK3 0,857 OASL OAS1 0,843 ISG20 CYBRD1 0,825 XAF1 IFI27 0,889 IFIT5 TSPO 0,871 PARP12 LAPTM5 0,857 PARP12 OAS2 0,843 STAT1 TNIP1 0,825 IFIT1 PYGL 0,889 ISG15 PROS1 0,871 SAMD9 ACAA1 0.857 JUP 0.871 0.889 JUP CETP 0.871 ACAA1 SORL1 0.857 CETP EMR1 0.843 FLII TBXAS1 0.825 MX1 GPAA1 0.889 LAX1 STAT5B 0.871 ACPP CETP 0.857 CETP LTA4H 0.843 LAPTM5 PYGL 0.825, Petition 870260069557, 14 / 07 / 2026, pág. 141 / 336 137 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC MX1 NINJ2 0,889 OAS1 CETP 0,871 ACPP RTN3 0,857 CTSB SORT1 0,843 NINJ2 TBXAS1 0,825 OAS1 CTSB 0,889 OASL LAPTM5 0,871 CYBRD1 STAT5B 0,857 CYBRD1 LTA4H 0,843 PGD RAB31 0,825 OAS1 LTA4H 0,889 PARP12 CTSB 0,871 GPAA1 NINJ2 0,857 CYBRD1 PLP2 0,843 PLP2 PROS1 0,825 OAS1 TALDO1 0,889 PARP12 TSPO 0,871 GPAA1 SORT1 0,857 EMR1 NRD1 0,843 PROS1 TSPO 0,825 OAS2 TALDO1 0,889 SAMD9 EMR1 0,871 IMPA2 S100A12 0,857 IMPA2 RAB31 0,843 PTAFR RAB31 0,825 OAS3 PGD 0,889 ACPP STAT5B 0,871 NRD1 TBXAS1 0,857 IMPA2 RTN3 0,843 PTAFR SLC12A 9 0,825 RSAD2 IMPA2 0,889 LTA4H PLP2 0,871 STAT5B TSPO 0,857 LTA4H PYGL 0,843 RAB31 TALDO1 0,825 RSAD2 TBXAS1 0,889 CHST12 IFI6 0,87 ADA LTA4H 0,856 NINJ2 PGD 0,843 TBXAS1 TKT 0,825 SIGLEC1 TKT 0,889 SIGLEC1 CUL1 0,87 CHST12 CTSB 0,856 NINJ2 STAT5B 0,843 CUL1 ADA 0,824 CHST12 SLC12A 9 0,888 DDX60 NINJ2 0,87 CHST12 DOK3 0,856 NINJ2 TSPO 0,843 ADA PYGL 0,824 DDX60 EMR1 0,888 DDX60 NRD1 0,87 CHST12 DYSF 0.856 PROS1 SORT1 0.843 IFIT2 DHX58 0.824 DDX60 HK3 0.888 DHX58 IMPA2 0.87 EIF2AK2 CUL1 0.856 RTN3 S100A18423 EIFIT2 0.824 DDX60 TSPO 0.888 DHX58 STAT5B 0.87 OAS3 CUL1 0.856 RTN3 SORT1 0.843 IFIT3 IFIT2 0.824 IFI44 EMR1 0.888 RSAD2 DNMT1, CULN1 0.856 TALDO TSPO 0.843 STAT1 IFIT3 0.824 IFI44L STAT5B 0.888 OAS2 GZMB 0.87 RSAD2 DDX60 0.856 SORL1 TKT 0.843 LAX1 PROS1 0.824, Petition 870260069557, of 14 / 07 / 2026, p. 142 / 336 138 / 157 LAX1 IFI6 0.888 HERC5 ACAA1 0.87 DDX60 RTN3 0.856 ADAFLII 0.842 SAMD9 CETP 0.824 IFI6 ACPP 0.888 HERC5 DOK3 0.87 DHX58 LTA4H 0.856 CHST12 PYGL 0.842 STAT1 TWF2 0.824 LAX1 IFIT1 0.888 IFI44 HESX1 0.87 DNMT1 STAT5B 0.856 DDX60 CUL1 0.842 CAT CYBRD1 0.824 IFIT1 S100A12 0.888 HESX1 DOK3 0.87 GZMB SORL1 0.856 HERC5 DDX60 0.842 CTSB PROS1 0.824 IFIT1 TWF2 0.888 HESX1 FLII 0.87 OAS2 HERC5 0.856 PARP12 DDX60 0.842 CYBRD1 LAPTM5 0.824 ISG15 ACAA1 0.888 OAS2 IFI44 0.87 IFIT1 HESX1 0.856 LY6E DHX58 0.842 DOK3 RAB31 0.824 ISG15 IMPA2 0.888 IFI44 PGD 0.87 OAS1 IFI44 0.856 EIF2AK2 OASL 0.842 DYSF LAPTM5 0.824 ISG15 LTA4H 0.888 IFI44 PYGL 0.87 IFIT2 IFI44L 0.856 GZMB NRD1 0.842 FLII PYGL 0.824 ISG15 NRD1 0.888 ISG20 IFI44L 0.87 LY6E IFIH1 0.856 HESX1 CYBRD1 0.842 HK3 S100A12 0.824 JUP HK3 0.888 IFI6 NRD1 0.87 IFIT2 S100A12 0.856 IFI44 STAT1 0.842 HK3 TKT 0.824 KCTD14 SLC12A 9 0.888 IFIH1 RAB31 0.824 .87 RSAD2 IFIT3 0.856 IFI6 IFIT5 0.842 LAPTM5 SORT1 0.824 LY6E GPAA1 0.888 MX1 IFIT1 0.87 SIGLEC1 IFIT3 0.856 IFIH1 PROS1 0.844 LTME21 LTME24 LYDO LAPTM5 0.888 LAX1 IFIT3 0.87 XAF1 ISG15 0.856 OAS2 IFIT2 0.842 LAPTM5 TBXAS1 0.824 OAS1 ACPP 0.888 KCTD14 ISG15 0.87 ISG20 LY6E 0.856 OIFIT3 0.842 PTAFR SORT1 0.824 OAS1 TWF2 0.888 RSAD2 ISG15 0.87 ISG20 ACPP 0.856 IFIT5 CETP 0.842 PYGL TNIP1 0.824 OAS2 CETP 0.888 ISG20 80,817 NGPAA ISG1, 2016 PARP12 JUP 0.842 ISG20 CUL1 0.823, Petition 870260069557, of 14 / 07 / 2026, p. 143 / 336 139 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OAS3 ACAA1 0,888 JUP DOK3 0,87 ISG20 TWF2 0,856 KCTD14 CYBRD1 0,842 PARP12 CUL1 0,823 OAS3 DOK3 0,888 KCTD14 CETP 0,87 KCTD14 LY6E 0,856 KCTD14 PROS1 0,842 CUL1 CYBRD1 0,823 OAS3 RAB31 0,888 RSAD2 LY6E 0,87 OAS2 KCTD14 0,856 STAT1 LAX1 0,842 ACAA1 TNIP1 0,823 OASL TNIP1 0,888 OAS2 MX1 0,87 OAS1 MX1 0,856 LAX1 CTSB 0,842 CAT TKT 0,823 RSAD2 PTAFR 0,888 OAS1 GPAA1 0,87 PARP12 NRD1 0,856 LAX1 DYSF 0,842 DOK3 TBXAS1 0,823 RSAD2 TSPO 0,888 OAS1 PROS1 0,87 PARP12 TNIP1 0,856 XAF1 OASL 0,842 DYSF SORT1 0,823 SIGLEC1 NINJ2 0,888 OASL TKT 0,87 XAF1 CTSB 0,856 STAT1 RSAD2 0,842 FLII HK3 0,823 EIF2AK2 ADA 0,887 RSAD2 CAT 0,87 XAF1 S100A12 0,856 STAT1 NINJ2 0,842 NRD1 PTAFR 0,823 IFIT1 ADA 0,887 GPAA1 IMPA2 0,87 ACPP CTSB 0,856 STAT1 RAB31 0,842 PROS1 RTN3 0,823 IFI27 DHX58 0,887 GPAA1 STAT5B 0,87 ACPP SORT1 0,856 CAT SORT1 0,842 PYGL RAB31 0,823 HERC5 EMR1 0,887 LTA4H SORT1 0,87 CAT EMR1 0,856 CAT TSPO 0,842 RAB31 TKT 0.823 HERC5 SORT1 0.887 CHST12 ACPP 0.869 LTA4H TALDO1 0.856 CETP TBXAS1 0.842 STAT5B TNIP1 0.823 IFI44L SORL1 IFI44L SORL1 0.887 ISG 0.869 C PLP2 STAT5B 0.856 CTSB PLP2 0.842 GZMB ADA 0.822 IFI6 SLC12A 9 0.887 CUL1 IMPA2 0.869 KCTD14 ADA 0.855 DOK3 SORL1 0.842 LAX1 CH1 0.821 IFFLII 0.887 LY6E DDX60 0.869 ADA EMR1 0.855 EMR1 PLP2 0.842 IFIT2 CUL1 0.822 IFIT1 LTA4H 0.887 DDX60 PGD 0.869 CHST12 TKT 0.851 EMR1, TKT1042 CUL1 0.822, Petition 870260069557, of 14 / 07 / 2026, p. 144 / 336 140 / 157 JUP PLP2 0.887 DHX58 DYSF 0.869 CUL1 FLII 0.855 LTA4H PROS1 0.842 SAMD9 DHX58 0.822 KCTD14 DOK3 0.887 GZMB EIF2AK2 0.869 CUL1 RTN3 0.855 PLP2 PYGL 0.842 JUP DNMT1 0.822 LY6E ACAA1 0.887 EIF2AK2 GPAA1 0.869 DDX60 TKT 0.855 TSPO TWF2 0.842 ISG20 IFIT2 0.822 MX1 CTSB 0.887 OASL GZMB 0.869 GZMB DHX58 0.855 HESX1 CUL1 0.841 XAF1 IFIT5 0.822 MX1 PYGL 0.887 HERC5 TWF2 0.869 IFIT1 DHX58 0.855 OAS3 DHX58 0.841 SAMD9 TNIP1 0.822 OAS1 ACAA1 0.887 IFI44L HESX1 0.869 DNMT1 TWF2 0.855 KCTD14 DNMT1 0.841 STAT1 CYBRD1 0.822 OAS1 SORL1 0.887 HESX1 PGD 0.869 GZMB PLP2 0.855 GZMB CYBRD1 0.841 CAT TALDO1 0.822 OASL TBXAS1 0.887 IFI44 LAPTM5 0.869 GZMB S100A12 0.855 GZMB LAPTM5 0.841 CETP TNIP1 0.822 RSAD2 ACPP 0.887 IFI44 PROS1 0.869 GZMB TKT 0.855 XAF1 HESX1 0.841 CTSB FLII 0.822 MX1 ADA 0.886 OAS2 IFI6 0.869 MX1 HERC5 0.855 HESX1 S100A12 0.841 CYBRD1 RAB31 0.822 CUL1 LTA4H 0.886 IFI6 TALDO1 0.869 SIGLEC1 IFIH1 0.869.855 SAMD9 IFI44 0.841 FLII PROS1 0.822 EIF2AK2 EMR1 0.886 IFIH1 DOK3 0.869 IFIH1 TNIP1 0.855 SAMD9 IFI6 0.841 NRD1 PROS1 0.822 HERC5 ACPP 0.886 IFIT5 STAT5B 0.869 OAS1 IFIT1 0.855 OASL IFIH1 0.841 PYGL TKT 0.822 HERC5 PTAFR 0.886 OAS2 ISG15 0.869 OAS3 IFIT1 0.855 SAMD9 IFIT1 0.841 BXAS1 T TWF2 0.822 HERC5 SLC12A 9 0.886 ISG20 TNIP1 0.869 OASL IFIT1 0.855 LY6E IFIT2 0.841 ADA S100A12 0.821 LAX1 IFI44 0.886 SIGLEC1 KCTD14 0.869 IFIT2 PYGL 0.855 IFIT2 STAT5B 0.841 SAMD9 HESX1 0.821, Petition 870260069557, 14 / 07 / 2026, pág. 145 / 336 141 / 157 JUP IFI44L 0.886 LY6E OAS2 0.869 IFIT3 OAS2 0.855 JUP IFIT5 0.841 SAMD9 IFIH1 0.821 IFI44L IMPA2 0.886 OAS3 CETP 0.869 IFIT3 GPAA1 0.855 LAX1 TWF2 0.841 STAT1 CETP 0.821 IFI6 DYSF 0.886 OASL CETP 0.869 IFIT5 PTAFR 0.855 XAF1 OAS1 0.841 STAT1 FLII 0.821 IFIH1 SORT1 0.886 SAMD9 TSPO 0.869 IFIT5 PYGL 0.855 PARP12 OAS3 0.841 CAT PYGL 0.821 ISG15 FLII 0.886 FLII LTA4H 0.869 OAS2 ISG20 0.855 SIGLEC1 OASL 0.841 CTSB TKT 0.821 JUP DYSF 0.886 SIGLEC1 CHST12 0.868 OAS1 KCTD14 0.855 STAT1 PLP2 0.841 RTN3 CYBRD1 0.821 JUP FLII 0.886 CHST12 EMR1 0.868 LAX1 FLII 0.855 STAT1 STAT5B 0.841 DOK3 PROS1 0.821 JUP SLC12A 9 0.886 CIFIT1 UL1 0.868 LAX1 IMPA2 0.855 ACPP RAB31 0.841 DYSF PYGL 0.821 LAX1 GPAA1 0.886 DNMT1 DDX60 0.868 LAX1 PGD 0.855 DYSF TBXAS1 0.841 RTN3 TKT 0.821 LY6E PLP2 0.886 ISG15 DDX60 0.868 STAT1 EMR1 0.855 EMR1 TALDO1 0.841 ADA BLACK1 0.82 LY6E TWF2 0.886 EIF2AK2 DNMT1 0.868 STAT1 SORT1 0.886.855 GPAA1 PTAFR 0.841 PARP12 DHX58 0.82 MX1 LAPTM5 0.886 DNMT1 HERC5 0.868 XAF1 TALDO1 0.855 NINJ2 SORT1 0.841 ISG20 IFIT5 0.82 OAS1 NRD1 0.886 IFI44L EIF2AK2 0.868 XAF1 TWF2 0.855 PLP2 SORT1 0.841 ACAA1 FLII 0.82 OAS1 S100A12 0.886 EIF2AK2 FLII 0.868 CETP STAT5B 0.855 SORL1 TALDO1 0.841 ACAA1 LAPTM5 0.82 OAS3 FLII 0.886 EIF2AK2 TWF2 0.868 GPAA1 HK3 0.855 TNIP1 TSPO 0.841 CTSB DOK3 0.82 OASL ACPP 0.886 LY6E GZMB 0.868 GPAA1 PYGL 0.855 ADA ACAA1 0.84 CYBRD1 TALDO1 0.82 Petition 870260069557, dated 14 / 07 / 2026, p. 146 / 336 142 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OASL DOK3 0,886 HERC5 LAPTM5 0,868 IMPA2 SLC12A 9 0,855 ADA STAT5B 0,84 DYSF HK3 0,82 RSAD2 LTA4H 0,886 HERC5 PYGL 0,868 LAPTM5 SORL1 0,855 OAS1 DHX58 0,84 DYSF TKT 0,82 DHX58 EMR1 0,885 HESX1 DYSF 0,868 LTA4H RAB31 0,855 DNMT1 ACAA1 0,84 DYSF TNIP1 0,82 DHX58 SLC12A 9 0,885 IFIT1 IFI44L 0,868 LTA4H S100A12 0,855 DNMT1 TNIP1 0,84 HK3 LAPTM5 0,82 DHX58 SORT1 0,885 MX1 IFI44L 0,868 RAB31 SORL1 0,855 SAMD9 GZMB 0,84 NINJ2 PLP2 0,82 IFI44 TBXAS1 0,885 JUP IFI6 0,868 SORT1 STAT5B 0,855 XAF1 HERC5 0,84 NINJ2 PROS1 0,82 IFI44 TSPO 0,885 IFIH1 LTA4H 0,868 PARP12 ADA 0,854 ISG20 HESX1 0,84 PGD PTAFR 0,82 IFIH1 PTAFR 0,885 IFIT2 DYSF 0,868 CHST12 RAB31 0,854 IFIT2 IFI44 0,84 PGD PYGL 0,82 IFIT3 SLC12A 9 0,885 IFIT2 SLC12A9 0,868 CUL1 DYSF 0,854 IFIT2 IFIT1 0,84 PROS1 S100A12 0,82 MX1 LAX1 0,885 IFIT3 S100A12 0,868 IFI44L DDX60 0,854 RSAD2 IFIT2 0,84 PROS1 TNIP1 0,82 LY6E CTSB 0,885 IFIT3 STAT5B 0,868 DHX58 TNIP1 0.854 SIGLEC1 IFIT2 0.84 PYGL TALDO1 0.82 LY6E DOK3 0.885 IFIT3 TSPO 0.868 DHX58 TWF2 0.854 KCTD14 IFIT5 0.84 ADA LAPTM5 0.819 LY6E S100A12 0.885 IFIT5 EMR1 0.868 HESX1 EIF2AK2 0.854 LAX1 DOK3 0.84 CUL1 CHST12 0.819 LY6E STAT5B 0.885 IFIT5 IMPA2 0.868 EIF2AK2 JUP 0.854 PARP12 OAS1 0.84 SAMD9 CUL1 0.819, Petition 870260069557, dated 07 / 14 / 2026, page 147 / 336 143 / 157 MX1 TKT 0.885 OAS3 ISG15 0.868 JUP GZMB 0.854 ACPP TBXAS1 0.84 STAT1 DDX60 0.819 MX1 TWF2 0.885 JUP NRD1 0.868 GZMB GPAA1 0.854 ACPP TKT 0.84 CETP PYGL 0.819 OAS1 FLII 0.885 JUP PYGL 0.868 GZMB TALDO1 0.854 CAT PLP2 0.84 PYGL CYBRD1 0.819 OAS2 PYGL 0.885 KCTD14 TBXAS1 0.868 GZMB TBXAS1 0.854 CAT STAT5B 0.84 DOK3 EMR1 0.819 OAS2 S100A12 0.885 MX1 LY6E 0.868 HESX1 HERC5 0.854 CAT TBXAS1 0.84 HK3 SORT1 0.819 OASL RAB31 0.885 SIGLEC1 LY6E 0.868 IFIT1 HERC5 0.854 CTSB EMR1 0.84 RTN3 TALDO1 0.819 PARP12 SORT1 0.885 OASL LTA4H 0.868 OAS1 HESX1 0.854 CYBRD1 TNIP1 0.84 DNMT1 GZMB 0.818 RSAD2 NINJ2 0.885 PARP12 IMPA2 0.868 HESX1 PYGL 0.854 FLII SLC12A 9 0.84 IFIT2 HESX1 0.818 RSAD2 S100A12 0.885 PARP12 RAB31 0.868 XAF1 IFI44L 0.854 HK3 NINJ2 0.84 XAF1 IFIT2 0.818 GPAA1 SORL1 0.885 SIGLEC1 RSAD2 0.868 IFIH1 CAT 0.854 HK3 SORL1 0.84 XAF1 SAMD9 0.818 IFI44 ADA 0.884 SAMD9 RAB31 0.868 IFIH1 CETP 0.884.854 IMPA2 PGD 0.84 CTSB CAT 0.818 CUL1 SLC12A 9 0.884 SAMD9 S100A12 0.868 IFIT3 CETP 0.854 LAPTM5 STAT5B 0.84 CETP TKT 0.818 DDX60 ACPP 0.884 ACAA1 LTA4H 0.868 IFIT5 ACAA1 0.854 PGD SORL1 0.84 CTSB PTAFR 0.818 EIF2AK2 ACAA1 0.884 PLP2 SORL1 0.868 STAT1 ISG15 0.854 PLP2 TBXAS1 0.84 TNIP1 CTSB 0.818 EIF2AK2 LAPTM5 0.884 RAB31 SLC12A9 0.868 ISG20 FLII 0.854 PROS1 TBXAS1 0.84 DOK3 NRD1 0.818, Petition 870260069557, dated 07 / 14 / 2026, pp. 148 / 336 144 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC EIF2AK2 S100A12 0,884 CHST12 TSPO 0,867 ISG20 LAPTM5 0,854 PYGL STAT5B 0,84 DOK3 SORT1 0,818 IFI44 GZMB 0,884 IFI44 CUL1 0,867 ISG20 TBXAS1 0,854 RTN3 TNIP1 0,84 DOK3 TSPO 0,818 IFI44L ACAA1 0,884 DDX60 PLP2 0,867 SAMD9 JUP 0,854 S100A12 TALDO1 0,84 HK3 TALDO1 0,818 IFI44L TBXAS1 0,884 SIGLEC1 HERC5 0,867 JUP CAT 0,854 SLC12A 9 STAT5B 0,84 LAPTM5 PGD 0,818 IFI6 PLP2 0,884 HERC5 CTSB 0,867 KCTD14 CTSB 0,854 ADA PTAFR 0,839 PGD TALDO1 0,818 IFI6 PTAFR 0,884 HESX1 GPAA1 0,867 OAS2 OAS1 0,854 JUP CUL1 0,839 PROS1 PYGL 0,818 IFI6 RAB31 0,884 HESX1 RTN3 0,867 OAS3 OAS2 0,854 XAF1 CUL1 0,839 PTAFR TWF2 0,818 IFI6 S100A12 0,884 HESX1 STAT5B 0,867 PARP12 PYGL 0,854 HERC5 DHX58 0,839 TALDO1 TNIP1 0,818 IFIT1 CTSB 0,884 IFI44 FLII 0,867 PARP12 TWF2 0,854 PARP12 EIF2AK2 0,839 ADA DOK3 0,817 IFIT3 PTAFR 0,884 IFI44 TKT 0,867 SAMD9 NRD1 0,854 OAS1 IFIH1 0,839 DNMT1 CUL1 0,817 ISG15 CTSB 0,884 RSAD2 IFI44L 0,867 0.884 IFI6 TKT 0.867 CTSB TSPO 0.854 LAX1 TALDO1 0.839 SAMD9 PARP12 0.817 JUP TBXAS1 0.884 IFIH1 S100A12 0.867 DYSF LTA4H 0.854 STAT1 PGD 0.839 STAT1 LAPTM5 0.817 OAS2 LAX1 0.884 IFIH1 TBXAS1 0.867 EMR1 RTN3 0.854 ACAA1 IMPA2 0.839 ACAA1 HK3 0.817 LY6E SORL1 0.884 ISG20 IFIT1 0.867 EMR1 STAT5B 0.854 ACAA1 TBXAS1 0.839 ACAA1 TWF2 0.817, Petition 870260069557, dated 07 / 14 / 2026, page 149 / 336 145 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC OAS1 NINJ2 0.884 IFIT3 PLP2 0.867 PLP2 SLC12A 9 0.854 ACPP DYSF 0.839 CAT PROS1 0.817 OAS1 TKT 0.884 IFIT3 RAB31 0.867 S100A12 STAT5B 0.854 ACPP NINJ2 0.839 FLII PGD 0.817 OAS2 RTN3 0.884 ISG20 IMPA2 0.867 IFIT1 DDX60 0.853 ACPP TWF2 0.839 HK3 PYGL 0.817 OASL S100A12 0.884 ISG20 TSPO 0.867 MX1 DDX60 0.853 CAT FLII 0.839 LAPTM5 TNIP1 0.817 RSAD2 DOK3 0.884 JUP S100A12 0.867 DDX60 CETP 0.853 CAT PGD 0.839 NINJ2 TKT 0.817 RSAD2 LAPTM5 0.884 KCTD14 IMPA2 0.867 MX1 DHX58 0.853 CYBRD1 DYSF 0.839 TKT TNIP1 0.817 RSAD2 PLP2 0.884 KCTD14 TALDO1 0.867 PARP12 DNMT1 0.853 CYBRD1 TSPO 0.839 CHST12 ADA 0.816 RSAD2 TWF2 0.884 PARP12 PLP2 0.867 DNMT1 SLC12A 9 0.853 DYSF NINJ2 0.839 IFIT5 CUL1 0.816 SIGLEC1 CETP 0.884 XAF1 ACPP 0.867 DNMT1 TKT 0.853 NRD1 RAB31 0.839 PARP12 IFIT2 0.816 GPAA1 LTA4H 0.884 GPAA1 TALDO1 0.867 GZMB CTSB 0.853 NRD1 SORL1 0.839 CAT NINJ2 0.816 ISG15 DNMT1 0.883 OAS1 ADA 0.866 GZMB SLC12A 9 0.853 SORT1 NRD1 0.839 DYSF FLII 0.816 ISG15 EIF2AK2 0.883 EIF2AK2 IFI44 0.866 IFI44 HERC5 0.853 NRD1 TKT 0.85 PGD, TW162 EIF2AK2 PTAFR 0.883 OAS1 GZMB 0.866 ISG20 HERC5 0.853 PLP2 S100A12 0.839 PROS1 TKT 0.816 IFI44L NRD1 0.883 SIGLEC1 GZMBES 0.866 HIPX1 0.866 TIP21012 TCT 0.839 PTAFR TNIP1 0.816, Petition 870260069557, of 14 / 07 / 2026, p. 150 / 336 146 / 157 IFI44L PLP2 0.883 HERC5 PGD 0.866 PARP12 IFI44 0.853 ADA CETP 0.838 TKT TWF2 0.816 IFI44L SORT1 0.883 LAX1 HESX1 0.866 OAS1 IFI6 0.853 ADA TKT 0.838 STAT1 DHX58 0.815 IFI44L TSPO 0.883 MX1 HESX1 0.866 OAS2 IFIH1 0.853 IFIT2 CHST12 0.838 DNMT1 NINJ2 0.815 IFI6 TBXAS1 0.883 HESX1 CTSB 0.866 IFIT3 CYBRD1 0.853 CHST12 CYBRD1 0.838 KCTD14 IFIT2 0.815 IFIT1 TNIP1 0.883 HESX1 HK3 0.866 IFIT5 DOK3 0.853 CUL1 CETP 0.838 ACAA1 NINJ2 0.815 JUP PTAFR 0.883 HESX1 PTAFR 0.866 IFIT5 RTN3 0.853 IFIH1 DDX60 0.838 ACAA1 PTAFR 0.815 JUP TALDO1 0.883 IFI44 ISG20 0.866 ISG20 DYSF 0.853 DNMT1 ACPP 0.838 CYBRD1 PROS1 0.815 JUP TNIP1 0.883 IFI44 CYBRD1 0.866 KCTD14 PYGL 0.853 DNMT1 PTAFR 0.838 DYSF DOK3 0.815 RSAD2 LAX1 0.883 IFI6 IFI44L 0.866 RSAD2 OAS1 0.853 HESX1 PROS1 0.838 HK3 PTAFR 0.815 LY6E ACPP 0.883 KCTD14 IFI6 0.866 PARP12 FLII 0.853 IFI6 IFIT2 0.838 NINJ2 PTAFR 0.815 LY6E RAB31 0.883 MX1 IFI6 0.815 .866 PARP12 S100A12 0.853 IFIH1 OAS3 0.838 NINJ2 TNIP1 0.815 MX1 RTN3 0.883 IFI6 RSAD2 0.866 SAMD9 PGD 0.853 JUP IFIT2 0.838 PROS1 RAB31 0.815 OAS1 PYGL 0.883 IFIH1 IMPA2 0.866 SIGLEC1 CYBRD1 0.853 IFIT2 ACAA1 0.838 CETP DOK3 0.814 OAS2 TKT 0.883 IFIH1 STAT5B 0.866 STAT1 IMPA2 0.853 IFIT2 CAT 0.838 CTSB LAPTM5 0.814 OAS3 CTSB 0.883 LY6E IFIT1 0.866 0.814, Petition 870260069557, 14 / 07 / 2026, pág. 151 / 336 147 / 157 RSAD2 NRD1 0.883 IFIT3 TALDO1 0.866 DYSF GPAA1 0.853 SAMD9 TALDO1 0.838 LAPTM5 NINJ2 0.814 LAX1 EIF2AK2 0.882 ISG20 ISG15 0.866 EMR1 SLC12A 9 0.853 SAMD9 TWF2 0.838 PLP2 TWF2 0.814 IFI44L GZMB 0.882 SIGLEC1 JUP 0.866 IFIH1 ADA 0.852 ACAA1 CETP 0.838 PROS1 TWF2 0.814 ISG15 GZMB 0.882 KCTD14 NRD1 0.866 ADA TWF2 0.852 ACAA1 SORT1 0.838 CETP LAPTM5 0.813 HESX1 ACAA1 0.882 PARP12 LAX1 0.866 CUL1 SORT1 0.852 CTSB RTN3 0.838 CETP TALDO1 0.813 JUP IFI44 0.882 LAX1 SLC12A9 0.866 CUL1 STAT5B 0.852 SLC12A 9 CTSB 0.838 DOK3 NINJ2 0.813 IFI44 ACAA1 0.882 MX1 CYBRD1 0.866 IFI44L DHX58 0.852 GPAA1 DOK3 0.838 DYSF PGD 0.813 LAX1 IFI44L 0.882 SAMD9 ACPP 0.866 DHX58 PYGL 0.852 FLII NRD1 0.838 TNIP1 TWF2 0.813 IFI44L CTSB 0.882 SAMD9 PTAFR 0.866 DNMT1 IMPA2 0.852 GPAA1 TWF2 0.838 DOK3 PLP2 0.812 IFI44L NINJ2 0.882 GPAA1 TSPO 0.866 EIF2AK2 CYBRD1 0.852 IMPA2 NINJ2 0.838 FLIGHT TALDO1 0.812 LIGHT6 ACAA1 0.838 .882 SORL1 S100A12 0.866 HESX1 GZMB 0.852 IMPA2 PYGL 0.838 LAPTM5 PROS1 0.812 IFI6 IMPA2 0.882 CHST12 LTA4H 0.865 GZMB TWF2 0.852 NRD1 STAT5B 0.838 NINJ2 TALDO1 0.812 IFIH1 HK3 0.882 CHST12 PGD 0.865 RSAD2 HERC5 0.852 PYGL SORL1 0.838 PGD TKT 0.812 IFIT1 TALDO1 0.882 CHST12 SORL1 0.865 OASL IFI6 0.852 RAB31 TWF2 0.838 TWF2 TALDO1 0.812 IFIT1 TKT 0.882 CUL1 MX1 0.865 LAX1 IFIT2 0.852 CUL1 DOK3 0.837 SAMD9 IFIT2 0.811, Petition: 870260069557, on 07 / 14 / 2026, page. 152 / 336 148 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC JUP ISG15 0,882 DDX60 GZMB 0,865 IFIT2 CTSB 0,852 IFIT3 DHX58 0,837 STAT1 ISG20 0,811 SIGLEC1 ISG15 0,882 DHX58 ACAA1 0,865 LY6E IFIT5 0,852 EIF2AK2 XAF1 0,837 XAF1 STAT1 0,811 ISG15 TKT 0,882 EIF2AK2 IFIT1 0,865 IFIT5 CAT 0,852 IFIT5 GZMB 0,837 CETP TWF2 0,811 JUP GPAA1 0,882 EIF2AK2 OAS2 0,865 ISG20 HK3 0,852 GZMB DOK3 0,837 HK3 PGD 0,811 JUP LTA4H 0,882 GZMB LTA4H 0,865 OASL OAS2 0,852 PARP12 HERC5 0,837 PTAFR TALDO1 0,811 LY6E NINJ2 0,882 GZMB PGD 0,865 PARP12 RTN3 0,852 IFIT2 NRD1 0,837 PARP12 STAT1 0,81 LY6E TALDO1 0,882 GZMB TSPO 0,865 PARP12 TKT 0,852 IFIT3 ISG20 0,837 ACAA1 DOK3 0,81 MX1 CETP 0,882 HERC5 PROS1 0,865 SAMD9 NINJ2 0,852 LAX1 PYGL 0,837 FLII PTAFR 0,81 OAS1 DOK3 0,882 HERC5 TALDO1 0,865 SAMD9 PLP2 0,852 OASL PARP12 0,837 TKT LAPTM5 0,81 OAS1 STAT5B 0,882 IFI44 CETP 0,865 SIGLEC1 STAT1 0,852 STAT1 OASL 0,837 DNMT1 DOK3 0,809 OAS1 TNIP1 0,882 IFI44 RTN3 0,865 STAT1 DYSF 0,852 STAT1 TALDO1 0.837 PTAFR LAPTM5 0.809 OAS3 LAPTM5 0.882 IFI44L OASL 0.865 XAF1 TKT 0,852 ACAA1 CTSB 0,837 STAT1 IFIT5 0,808 OAS3 TNIP1 0,882 IFIH1 NRD1 0,865 ACPP IMPA2 0,852 ACAA1 PYGL 0,837 TWF2 LAPTM5 0,808 OASL NINJ2 0,882 IFIH1 PLP2 0.865 CAT SORL1 0.852 ACPP S100A12 0.837 PROS1 TALDO1 0.808 RSAD2 ACAA1 0.882 IFIH1 PYGL 0.865 CETP SORT1 0.852 DYSF NRD1 0.837 DNMT1 SORT1 0.807 RSAD2 CTSB 0.882 OAS2 IFIT1 0.865 CTSB LTA4H 0.852 TBXAS1 EMR1 0.837 ADA LAX1 0.806, Petition 870260069557, 14 / 07 / 2026, pág. 153 / 336 149 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC RSAD2 TNIP1 0,882 IFIT2 ACPP 0,865 CTSB RAB31 0,852 EMR1 TKT 0,837 DOK3 HK3 0,806 SAMD9 HK3 0,882 IFIT3 PGD 0,865 DOK3 LTA4H 0,852 HK3 RTN3 0,837 SAMD9 IFIT5 0,805 SAMD9 SLC12A 9 0,882 ISG20 LTA4H 0,865 EMR1 IMPA2 0,852 IMPA2 LAPTM5 0,837 DOK3 PTAFR 0,805 LTA4H SLC12A 9 0,882 KCTD14 PTAFR 0,865 FLII S100A12 0,852 PYGL SORT1 0,837 FLII TKT 0,805 MX1 DNMT1 0,881 OAS2 CYBRD1 0,865 GPAA1 TNIP1 0,852 TBXAS1 SORT1 0,837 DNMT1 CHST12 0,804 EIF2AK2 ACPP 0,881 SIGLEC1 OAS3 0,865 IMPA2 SORL1 0,852 TBXAS1 TSPO 0,837 IFIT2 IFIH1 0,804 EIF2AK2 TNIP1 0,881 OASL GPAA1 0,865 NINJ2 SORL1 0,852 ADA CTSB 0,836 NINJ2 TWF2 0,804 HERC5 DYSF 0,881 PARP12 LTA4H 0,865 STAT5B TWF2 0,852 CUL1 LAPTM5 0,836 STAT1 IFIH1 0,803 IFI44L RAB31 0,881 SAMD9 IMPA2 0,865 CHST12 IFIH1 0,851 XAF1 DDX60 0,836 IFIT5 IFIT2 0,803 IFI44L S100A12 0,881 SAMD9 SORL1 0,865 CHST12 S100A12 0,851 DNMT1 DYSF 0,836 CETP PROS1 0,802 IFI6 NINJ2 0,881 0.802 IFI6 TWF2 0.881 GPAA1 NRD1 0.865 DHX58 NRD1 0.851 GZMB STAT5B 0.836 STAT1 IFIT2 0.801 IFIT1 RTN3 0.881 GPAA1 TBXAS1 0.865 DHX58 RTN3 0.851 IFIT2 CETP 0.836 SAMD9 STAT1 0.8 IFIT3 EMR1 0.881 LAPTM5 LTA4H 0.865 SAMD9 DNMT1 0.851 IFIT2 FLII 0.836 DOK3 TWF2 0.8, Petition: 870260069557, on 07 / 14 / 2026, page. 154 / 336 150 / 157 Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC Gene 1 Gene 2 AUC IFIT3 SORL1 0.881 CHST12 HERC5 0.864 EIF2AK2 ISG20 0.851 IFIT5 FLII 0.836 JUPRTN CHAS 10.838 0.864 IFIT2 GZMB 0.851 IFIT5 GPAA1 0.836 JUP TWF2 0.881 OAS2 CHST12 0.864 GZMB HK3 0.851 XAF1 ISG20 0.836 KCTD14 SORT1 0.881 SORT1 GZMB 626 NINJ2 0.851 LAX1 RAB31 0.836 LAX1 SORL1 0.881 OAS2 DDX60 0.864 GZMB PROS1 0.851 SAMD9 GPAA1 0.836 OAS3 LTA4H 0.881 DDX60 S100XA, HESCE 0.816 0.851 STAT1 CTSB 0.836 OAS3 PYGL 0.881 DHX58 PLP2 0.864 IFIT3 IFI44 0.851 ACAA1 CYBRD1 0.836 OASL ACAA1 0.881 OAS2 DNMT1 0.836 IACHAA, IACHA18181 TSPO 0.836 PARP12 EMR1 0.881 DNMT1 FLII 0.864 IFIT2 LTA4H 0.851 CYBRD1 ACPP 0.836 XAF1 SLC12A 9 0.881 EIF2AK2 MX1 0.864 IFIT2 ACPP083, HPGD 0.836 Petition 870260069557, of 14 / 07 / 2026, p. 155 / 336 151 / 157 REFERENCES 1. 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Claims

1. A method for diagnosing whether an infection in a patient is bacterial or viral, characterized in that it comprises: a) measuring the expression levels of at least two biomarker polynucleotides in a whole blood sample from a patient in vitro; the at least two biomarker polynucleotides selected from one or both of a first set of biomarker polynucleotides in which a higher level of expression indicates a bacterial infection and a second set of biomarker polynucleotides in which a higher level of expression indicates a viral infection; wherein the first set of biomarker polynucleotides comprises at least one of TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALD01, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FLII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR and LAPTM5;and wherein the second set of biomarker polynucleotides comprises at least one of the following: a) OAS1, SAMD9, HERC5, DDX60, HESX1, IFI6, OASL, LAX1, IFIT5, IFIT3, KCTD14, RTP4, PARP12, LY6E, ADA, IFI44L, RSDA2, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1, and GZMB; b) analyze the expression levels of each biomarker polynucleotide in conjunction with the respective range of reference values ​​for each biomarker polynucleotide to determine a viral or bacterial infection; and wherein the upper limit of expression is determined by comparing the levels of the biomarker polynucleotides with the corresponding time reference values ​​for infected or uninfected individuals.

2. Method, according to claim 1, characterized by the fact that at least two biomarker polynucleotides include SIGLEC1 and SLC12A9.

3. Method, according to claim 1 or 2, characterized in that the expression levels of at least two biomarker polynucleotides provide an area under a receptor operating characteristic curve of at least 0.

80.

4. Method according to claim 1, characterized in that the first set of biomarker polynucleotides comprises at least one of HK3, TNIP1, GPAA1 and CTSB; and in that the second set of biomarker polynucleotides comprises at least one of IFI27, JUP and LAX1.

5. Method according to claim 1, characterized in that the biological sample comprises whole blood or peripheral blood mononuclear cells (PBMCs).

6. Method, according to claim 1, characterized in that it further comprises calculating a bacterial / viral metascore for the patient based on the levels of biomarker polynucleotides, wherein a positive bacterial / viral metascore for the patient indicates that the patient has a viral infection and a negative bacterial / viral metascore for the patient indicates that the patient has a bacterial infection.

7. Method, according to claim 1, characterized in that it further comprises normalizing data using COCONUT normalization; wherein COCONUT normalization comprises the steps of: a) separating data from multiple cohorts into healthy and diseased components; b) conormalizing the healthy components using ComBat conormalization without covariates; c) obtaining estimated ComBat parameters for each dataset for the healthy component; and d) applying the estimated ComBat parameters to the diseased component.

8. Method according to claim 1, characterized in that the patient is a human being.

9. A method according to claim 1, characterized in that the measurement of the level of biomarker polynucleotides comprises performing one or more methods that include microarray analysis by fluorescence, chemiluminescence or electrical signal detection, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), digital droplet PCR (ddPCR), solid-state nanopore detection, RNA exchange activation, a Northern blot, isothermal amplification or a serial gene expression analysis (SAGE).

10. Kit, characterized in that it comprises agents for measuring the levels of at least two biomarker polynucleotides in a whole blood or peripheral blood mononuclear cell (PBMC) sample from a patient; wherein the agents are oligonucleotides that hybridize with the biomarkers, the at least two biomarker polynucleotides selected from one or both of a first set of biomarker polynucleotides in which a higher level of expression indicates a bacterial infection and a second set of biomarker polynucleotides in which a higher level of expression indicates a viral infection; wherein the first set of biomarker polynucleotides comprises at least one of TSPO, EMR1, NINJ2, ACPP, TBXAS1, PGD, S100A12, SORT1, TNIP1, RAB31, SLC12A9, PLP2, IMPA2, GPAA1, LTA4H, RTN3, CETP, TALDO1, HK3, ACAA1, CAT, DOK3, SORL1, PYGL, DYSF, TWF2, TKT, CTSB, FEII, PROS1, NRD1, STAT5B, CYBRD1, PTAFR and LAPTM5;and wherein the second set of polynucleotide biomarkers comprises at least one of OAS1, SAMD9, DDX60, HESX1, OASL, LAX1, IFIT5, KCTD14, RTP4, PARP12, LY6E, ADA, IFI44L, IFIH1, SIGLEC1, JUP, STAT1, CUL1, DNMT1, IFIT2, CHST12, ISG20, DHX58, EIF2AK2, XAF1 and GZMB.; 11. Kit, according to claim 10, characterized in that it further comprises agents for measuring the levels of the biomarker polynucleotides CEACAM1, ZDHHC19, C9orf95, GNA15, BATF, C3AR1, KIAA1370, TGFBI, MTCH1, RPGRIP1 and HLA-DPB1.

12. Method, according to claim 1, characterized in that measuring the expression level of biomarker polynucleotides comprises measuring the amount of mRNA, or polynucleotides derived therefrom, present in a biological sample for each of at least two biomarker polynucleotides.

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