Host-Gene Expression Scoring for Bacterial–Viral Infection Differentiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for rapid, inexpensive, and accurate methods to identify bacterial infections, viral infections, and non-infectious causes of fever, particularly in patients who have recently undergone surgery, to facilitate timely administration of appropriate therapies.
Innovation Solution
The method involves determining the expression levels of RUNX1, ITGAM, PSTPIP2, LY6E, IRF9, and optionally ABL1 in a biological sample, calculating viral and bacterial infection scores using logistic regression models, and comparing these scores to predetermined cutoff values to differentiate between bacterial, viral, and non-infectious causes of fever.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used to identify bacterial and viral infections, then the diagnostic process is simple and inexpensive, but the identification speed is slow and accuracy is insufficient
Solution Approach 1:
The diagnostic method is segmented into distinct modules: sample collection, nucleic acid extraction, PCR amplification of specific gene targets (ly6e for viral, pstPIP2 for bacterial), and digital analysis of amplification curves. Each module performs a specific function, allowing complex infection identification to be broken down into manageable, standardized steps that can be automated and quality-controlled
Solution Approach 2:
The patent introduces digital analysis algorithms as an intermediary between the PCR amplification process and the final diagnostic conclusion. The system uses automated curve analysis, threshold comparisons, and machine learning models to interpret amplification data, eliminating the need for manual interpretation and reducing human error while maintaining diagnostic accuracy
2Loss of time
If rapid diagnostic methods are implemented to quickly identify infections, then the treatment timing is improved, but the cost and complexity of the diagnostic process increase
Solution Approach 1:
The system performs preliminary actions by pre-designing specific primer and probe sets for known pathogen targets (ly6e for viral, pstPIP2 for bacterial), establishing predetermined threshold values for positive identification, and creating automated analysis algorithms before clinical use. This preparation allows rapid diagnosis without requiring complex real-time decision-making during the testing process
Solution Approach 2:
The patent utilizes parameter changes in the PCR amplification process itself as the diagnostic mechanism. By monitoring changes in fluorescence intensity, amplification rate, and cycle threshold (Ct) values during real-time PCR, the system can rapidly distinguish between infected and non-infected samples based on characteristic amplification patterns, achieving fast results without complex additional testing
3Measurement precision
If comprehensive infection screening is performed to differentiate between bacterial, viral, and non-infectious causes, then the diagnostic accuracy is improved, but the complexity of differentiation increases
Solution Approach 1:
The diagnostic approach is segmented into pathogen-specific detection pathways: viral infection detection targets viral genes (ly6e), bacterial infection detection targets bacterial genes (pstPIP2), and non-infectious fever is identified by the absence of pathogen-specific amplification. This segmentation allows each infection type to be detected through dedicated molecular targets, simplifying the differentiation process while maintaining high accuracy
Solution Approach 2:
The patent replaces complex mechanical or manual differentiation processes with molecular biology-based detection. Instead of relying on culture methods, biochemical tests, or clinical judgment alone, the system uses PCR amplification and fluorescence detection to automatically distinguish between bacterial and viral infections based on pathogen-specific genetic signatures, eliminating the need for complex manual differentiation protocols
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure is directed to methods and kits for discriminating between a bacterial infection and viral infection in a human subject. More specifically, the methods can comprise detecting the expression level of a combination of ABL1, IRF9, ITGAM, LY6E, PSTPIP2 and RUNX1 in biological samples from the human subject and determining whether the human subject has a bacterial or viral infection based on those expression levels.