Tear Protein Test Strip for Dry Eye Diagnosis
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Solution Overview
Problem
Current diagnostic methods for dry eye syndrome are inadequate in accurately classifying subjects, as they often rely on invasive procedures and have limited sensitivity and specificity, particularly in distinguishing between healthy and dry eye conditions.
Innovation Solution
A method and device that classify dry eye by obtaining demographic data and determining the levels of human serum albumin, lactoferrin, and lysozyme in tear samples using immuno-chemical reactions, with a test strip system that produces color intensities proportional to the protein concentrations, allowing for the calculation of cutoff probability scores to accurately diagnose dry eye.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic methods are used for dry eye syndrome, then the diagnostic process can be performed, but the accuracy of classification is inadequate with limited sensitivity and specificity
Solution Approach 1:
The diagnostic method is segmented into multiple independent measurement components: demographic data collection, tear sample analysis, human serum albumin determination, lactoferrin determination, and lysozyme determination. Each component contributes independently to the final diagnostic classification, allowing for more precise and reliable differentiation between dry eye and healthy conditions through cumulative evidence from multiple markers.
2Measurement precision
If invasive procedures are used for diagnosis, then diagnostic information can be obtained, but patient comfort and ease of operation deteriorate
Solution Approach 1:
The test strip system is designed for self-service operation where the patient themselves can collect the tear sample and perform the test without requiring invasive medical procedures or specialized equipment. The strip automatically performs the immunochemical reactions and provides results, making the diagnostic process simple, non-invasive, and comfortable for patients while maintaining high diagnostic accuracy through multiple protein markers.
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 method correctly classifies subjects with dry eye with high accuracy, achieving sensitivity and specificity rates of up to 88% and 86% respectively, providing a non-invasive and effective diagnostic tool for dry eye syndrome.
Implementation Method 1
The determining of the level of the human serum albumin is performed using an immuno-chemical reaction, configured to produce a color, wherein the intensity of the color is proportional to the amount of the human serum albumin in the tear sample
Data Source
AI summary
The present invention provides a method, wherein the method classifies a subject as suffering from dry eye, the method consisting of: a. obtaining demographic data, consisting of the age and gender of the subject; b. obtaining a tear sample from the patient, and determining the level of human serum albumin; c. from the determined level of human serum albumin, assigning a score for the determined amount of human serum albumin; and d. from the assigned score, calculating a cutoff probability score, according to the following equation:exp(-0.6491-1.1142*Albumin)1+exp(-0.6491-1.1142*Albumin)wherein the subject has dry eye, if the calculated cutoff probability score is from 50% to 60%.