Pathogen Autofluorescence Detection for Rapid Antimicrobial Susceptibility Testing
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Solution Overview
Problem
Conventional antimicrobial susceptibility testing (AST) methods are cumbersome, time-consuming, and costly, often requiring specialized facilities and not effectively addressing the rapid identification of antimicrobial-resistant pathogens, leading to prolonged patient debility and increased healthcare costs.
Innovation Solution
A method utilizing pathogen autofluorescence, excited with UV-A, UV-B, UV-C, and visible light, combined with machine learning models, to rapidly assess susceptibility to antimicrobial drugs by measuring autofluorescence parameters such as intensity and lifetime, allowing for direct phenotypic drug susceptibility testing with reduced reagent use and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AST methods are used, then reliable susceptibility assessment is achieved, but testing time is prolonged and costs increase
Solution Approach 1:
The patent replaces conventional mechanical/biochemical AST methods with optical detection based on autofluorescence. Instead of using biochemical markers, expensive reagents, and manual reading processes, the system uses UV-A, UV-B, UV-C, or visible light excitation to induce autofluorescence in pathogens, with detection via charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) sensors. This substitution enables rapid susceptibility assessment within 2-5 hours while maintaining reliability through machine learning analysis of autofluorescence parameters.
Solution Approach 2:
The patent measures changes in autofluorescence parameters (intensity, lifetime, spectral characteristics) of pathogens when exposed to antimicrobial drugs. By monitoring these optical parameter changes rather than traditional growth-based parameters, the system achieves rapid susceptibility determination. The machine learning model analyzes these parameter changes to classify susceptibility, enabling fast and reliable results without prolonged incubation periods.
2Reliability
If conventional AST methods are used, then susceptibility results are obtained, but device complexity and facility requirements increase
Solution Approach 1:
The patent replaces complex biochemical testing systems with a simplified optical detection system. Instead of requiring specialized microbiology facilities, automated incubators, and computer-assisted reading systems, the invention uses basic optical components (light sources for UV-A, UV-B, UV-C, or visible light excitation) combined with standard image sensors (CCD or CMOS). This substitution dramatically reduces facility and instrument requirements while maintaining susceptibility testing capability through autofluorescence detection and machine learning analysis.
Solution Approach 2:
The patent eliminates the need for expensive reagents and biochemical markers by utilizing the inherent autofluorescence property of pathogens. The method uses only light excitation and optical detection, replacing costly consumables with reusable optical components. This approach significantly reduces testing costs and simplifies facility requirements, making AST accessible without specialized infrastructure.
3Reliability
If conventional AST methods are used, then susceptibility assessment is performed, but reagent consumption and costs increase
Solution Approach 1:
The patent eliminates the need for expensive biochemical reagents and markers by utilizing the natural autofluorescence properties of pathogens. The method requires only light excitation sources (UV-A, UV-B, UV-C, or visible light) and optical detection equipment, completely removing dependency on costly consumables. This substitution maintains susceptibility assessment accuracy while dramatically reducing reagent consumption and associated costs.
Solution Approach 2:
The patent leverages the pathogen's own autofluorescence property as the detection signal, eliminating the need for external biochemical markers or reagents. The pathogen itself serves as the source of the detection signal through its inherent fluorescent molecules, which are excited by UV or visible light and emit characteristic autofluorescence. This self-service approach eliminates reagent consumption while maintaining reliable susceptibility assessment through analysis of autofluorescence parameter changes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid identification of pathogen susceptibility within 2-5 hours, reducing the need for biochemical markers and expensive instruments, providing accurate and cost-effective AST results.
Implementation Method 1
A method utilizing pathogen autofluorescence, excited with UV-A, UV-B, UV-C, and visible light, combined with machine learning models, to rapidly assess susceptibility to antimicrobial drugs by measuring autofluorescence parameters such as intensity and lifetime
Data Source
AI summary
Examples of assessing susceptibility of a target pathogen based on autofluorescence, are described. In an example, a sample containing a target pathogen and a predefined concentration of a target drug may be obtained. Thereafter, autofluorescence features based on autofluorescence exhibited by the sample in response to the sample being subjected to excitation radiation, may be determined. Once the autofluorescence features are determined, they may be analyzed based on a susceptibility-detection model to determine susceptibility of the target pathogen with respect to the target drug. In an example, the susceptibility-detection model is trained based on a training autofluorescence features correlated with reference data, in which the reference data includes information pertaining to susceptibility or resistance of a reference pathogen with respect to a reference drug.