Simultaneous disease detection system method and devices
US-12092629-B2 · Sep 17, 2024 · US
US12205677B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12205677-B2 |
| Application number | US-201716324562-A |
| Country | US |
| Kind code | B2 |
| Filing date | Aug 10, 2017 |
| Priority date | Aug 10, 2016 |
| Publication date | Jan 21, 2025 |
| Grant date | Jan 21, 2025 |
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A method of analyzing biological data is disclosed. The method comprises obtaining biological data containing at least an expression level of MX dynamin-like GTPase 1 (MX1) and an expression level of C-reactive protein (CRP) in the blood of a subject, calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate along the direction, and correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection.
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What is claimed is: 1. A method of analyzing biological data, the method comprising: obtaining biological data containing at least an expression level of MX dynamin-like GTPase 1 (MX1) and an expression level of C-reactive protein (CRP) in the blood of a subject; calculating by a hardware processor a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated perpendicularly to said axis, between a point on said axis and a corresponding point over said curved line, said points being defined by a coordinate δ along said direction, wherein said coordinate δ, once calculated, equals a 0 +a 1 X+a 2 Z MX1 , and wherein said X is a value of said CRP in μg/ml, and said Z MX1 is a z-score of said MX1 relative to a group of subjects previously diagnosed with a bacterial infection; storing said distance in a memory; by said hardware processor, correlating said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; generating on a graphical user interface an output of said presence, absence or likelihood; obtaining said likelihood based on said distance; comparing said likelihood to a predetermined threshold; and treating the subject for said bacterial infection with an antibiotic agent when said likelihood is above said predetermined threshold; wherein at least 90% of said segment is between a lower bound line f(δ)−ε 0 and an upper bound line f(δ)+ε 1 , wherein said f(δ) equals 1/(1+exp (−δ)), wherein each of said ε 0 and said ε 1 is less than 0.5, and wherein a 0 is from about −2.4 to about −1.9, a 1 is from about 0.04 to about 0.05, and a 2 is from about −0.39 to about −0.43. 2. The method according to claim 1 , wherein at least one of said MX1 and said CRP is measured by an immunoassay. 3. The method according to claim 1 , wherein at least one of said MX1 and CRP is measured by lateral flow immunoassay (LFIA). 4. The method according to claim 1 , wherein at least one of said MX1 and CRP is measured by automated immunoassay. 5. The method according to claim 1 , wherein at least one of said MX1 and CRP is measured by enzyme-linked immunosorbent assay (ELISA). 6. The method according to claim 1 , being executed for distinguishing between a viral infection and a co-infection including both bacterial and viral infections. 7. The method according to claim 1 , wherein said subject has an infection selected from the group consisting of a lower respiratory tract infection, and an upper respiratory tract infection. 8. The method according to claim 1 , wherein said subject has a fever without identifiable source. 9. The method according to claim 1 , wherein said subject has a serious bacterial infection. 10. The method according to claim 1 , wherein said subject is suspected as having at least one of Adenovirus, Coronavirus, Parainfluenza virus, Influenza A, Influenza B, respiratory syncytial virus A, respiratory syncytial virus B, Bocavirus, Enterovirus, Cytomegalovirus (CMV)/Epstein bar virus (EBV). 11. The method according to claim 1 , wherein said subject is suspected as having Mycoplasma pneumoniae. 12. The method according to claim 1 , wherein said subject is suspected as having at least one of E. coli , Group A Strep. 13. The method according to claim 1 , wherein said subject is suspected as having GI virus selected from the group consisting of Rota Virus, Astrovirus, Enteric Adenovirus, Norovirus G I and G II. 14. The method according to claim 1 , wherein said subject is suspected as having at least one of Streptococcus pneumoniae, Staphylococcus aureus and lung disease. 15. The method according to claim 1 , further comprising obtaining an expression level of Neutrophil gelatinase-associated lipocalin (NGAL), wherein said likelihood is based also on said expression level of said NGAL. 16. The method according to claim 1 , further comprising obtaining an expression level of procalcitonin (PCT), wherein said likelihood is based also on said expression level of said PCT. 17. The method according to claim 1 , wherein the subject has Chronic Obstructive Pulmonary Disease (COPD) and the method comprises determining whether said subject is in an infectious exacerbation state or a non-infectious exacerbation state. 18. The method according to claim 1 , further comprising obtaining an age of the subject, and correcting said likelihood based on said age. 19. The method according to claim 1 , wherein at least one of the expression levels is a protein expression level. 20. The method according to claim 1 , wherein at least one of the expression levels is an RNA expression level. 21. The method according to claim 1 , further comprising obtaining said likelihood based on said distance, comparing said likelihood to a predetermined threshold, and prescribing treatment to said subject based on said comparison. 22. The method according to claim 1 , wherein the blood is a blood sample which is a fraction of whole blood. 23. The method according to claim 1 , wherein said calculating and said correlating is executed by a computer remote from the subject. 24. The method according to claim 1 , wherein said calculating and said correlating is executed by a computer near the subject. 25. The method according to claim 1 , wherein said calculating and said correlating is executed by a cloud computing resource of a cloud computing facility. 26. The method according to claim 1 , wherein said obtaining biological data comprises loading a blood sample of the subject onto a cartridge containing reagents for detecting CRP and MX1 in the blood sample, loading said cartridge to a system configured for measuring said expression levels from said cartridge, and receiving said expression levels from said system. 27. The method according to claim 1 , further comprising obtaining an expression level of TNF Related Apoptosis Inducing Ligand (TRAIL), wherein said likelihood is based also on said expression level of said TRAIL. 28. A method of analyzing biological data, the method comprising: obtaining biological data containing at least an expression level of MX dynamin-like GTPase 1 (MX1) and an expression level of C-reactive protein (CRP) in the blood of a subject; calculating by a hardware processor a distance between a segment of a curved line and an axis defined by a direction, said distance being calculated perpendicularly to said axis, between a point on said axis and a corresponding point over said curved line, said points being defined by a coordinate δ along said direction, wherein said coordinate δ, once calculated, equals a 0 +a 1 X+a 2 Y, and wherein said X is a value of said CRP in μg/ml, and said Y is a value of said MX1 in ng/ml; storing said distance in a memory; by said hardware processor, correlating said distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection; generating on a graphical user interface an output of said presence, absence or likelihood; obtaining said likelihood based on said distance; comparing said likelihood to a predetermined threshold; and treating the subject for said bacterial infection with an antibiotic agent when said likelihood is above said predetermined threshold; wherein at least 90% of said segment is between a lower bound line f(δ)−ε 0 and an upper bound line f(δ)+ε 1 , wherein said f(δ) equals 1
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