Multiplexed Flow Assay Cartridge with Computational Spot Optimization
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Solution Overview
Problem
Point-of-care (POC) tests, such as paper-based immuno-assays, face challenges with sensitivity and specificity due to reagent stability, fabrication variability, and matrix effects, particularly in complex samples like blood, leading to inaccurate results, especially with the hook-effect phenomenon.
Innovation Solution
A computational paper-based flow assay cartridge using a vertical or lateral flow assay with a multiplexed sensing membrane and machine learning-based algorithms to optimize immunoreaction spots and conditions, enabling accurate CRP concentration quantification and mitigating the hook-effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional paper-based immuno-assays are used for POC testing, then cost-effectiveness and ease of operation are improved, but sensitivity and measurement precision deteriorate due to reagent stability issues, fabrication variability, and matrix effects
Solution Approach 1:
The patent combines multiple immunoreaction spots with different conditions (antibody concentrations, antigen concentrations, pH levels) into a single multiplexed sensing membrane. This merging allows the system to capture diverse binding responses simultaneously, improving sensitivity and precision while maintaining the simplicity and low cost of paper-based assays.
Solution Approach 2:
The multiplexed sensing membrane serves multiple functions: it detects analyte concentration, compensates for fabrication variability through multiple measurement points, and mitigates the hook-effect by having redundant sensing channels. This multi-functionality allows a single low-cost device to achieve performance previously requiring complex laboratory equipment.
2Ease of operation
If traditional POC tests are used, then rapid operation and user-friendliness are improved, but accuracy deteriorates due to the hook-effect phenomenon and matrix effects in complex samples
Solution Approach 1:
The system uses machine learning algorithms that process signals from multiple immunoreaction spots to infer analyte concentration. The computational model learns from the patterns across different spots and conditions, providing feedback that compensates for the hook-effect and matrix effects, thereby improving accuracy while maintaining ease of operation.
Solution Approach 2:
The patent varies multiple parameters across different immunoreaction spots including antibody concentrations, antigen concentrations, pH levels, and spot locations. This parameter diversity creates a robust measurement system that can accurately determine analyte concentration even in the presence of the hook-effect, without complicating the user interface.
3Measurement precision
If computational algorithms with multiple sensing channels are implemented, then measurement precision and hook-effect mitigation are improved, but device complexity increases
Solution Approach 1:
The sensing membrane is segmented into multiple discrete immunoreaction spots, each with specific conditions. This segmentation allows the complex computational task to be divided into independent parallel measurements, simplifying the overall system architecture while maintaining high quantification accuracy through the collective data from all spots.
4Measurement precision
If machine learning-based optimization of spot configurations is used, then sensitivity and specificity are improved, but manufacturing precision requirements increase
Solution Approach 1:
The patent varies multiple parameters across different immunoreaction spots including antibody concentrations, antigen concentrations, pH levels, and spot locations. This parameter diversity creates a robust measurement system that can accurately determine analyte concentration even in the presence of the hook-effect, without complicating the user interface.
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
The system provides rapid, cost-effective, and accurate high-sensitivity CRP testing, achieving a coefficient of variation of 11.2% and a coefficient of determination of 0.95, effectively overcoming limitations of traditional POC tests and expanding access to diagnostic information for underserved populations.
Implementation Method 1
a multiplexed sensing membrane having a plurality of immunoreaction or biological reaction spots of varying conditions spatially arranged across the surface of the membrane
Data Source
AI summary
A system for detecting the presence of and/or quantifying the amount or concentration of one or more analytes in a sample includes a flow assay cartridge having a multiplexed sensing membrane that has immunoreaction or biological reaction spots of varying conditions spatially arranged across the surface of the membrane defining an optimized spot map. A reader device is provided that uses a camera to image the multiplexed sensing membrane. Image processing software obtains normalized pixel intensity values of the plurality of immunoreaction or biological reaction spots and which are used as inputs to one or more trained neural networks configured to generate one or more outputs that: (i) quantify the amount or concentration of the one or more analytes in the sample; and/or (ii) indicate the presence of the one or more analytes in the sample; and/or (ii) determines a diagnostic decision or classification of the sample.