Multiple Dataset Analysis for False-Positive-Resistant Analyte Detection
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Solution Overview
Problem
Conventional methods for determining the presence or absence of a target analyte in nucleic acid sequences are prone to false positive and false negative errors due to the challenges in setting an optimal signal threshold, and existing data correction methods can distort normal data and fail to identify all errors.
Innovation Solution
A method involving multiple dataset analysis (MDA) that utilizes a dataset pool comprising different types of datasets and two or more determinative factors to accurately determine the presence or absence of a target analyte, minimizing errors through a novel protocol that includes performing an amplification reaction and evaluating these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single signal threshold is used to determine presence or absence of target analyte, then the determination process is simple, but false positive and false negative errors occur
Solution Approach 1:
The patent segments the determination process by using multiple signal thresholds (first threshold and second threshold) instead of a single threshold. This creates distinct decision regions: below the first threshold indicates absence, between the first and second thresholds indicates inconclusive results requiring retesting, and above the second threshold indicates presence. This segmentation resolves the contradiction by maintaining operational simplicity through clear decision rules while significantly improving reliability by eliminating false positives and false negatives through the buffer zone.
2Measurement precision
If data correction methods are applied to amplify weak signals, then detection sensitivity improves, but normal data is distorted and errors are introduced
Solution Approach 1:
The patent applies parameter changes by using mathematical transformations (logarithmic transformation, derivative calculation) on the amplification curve data to enhance detection sensitivity without distorting normal data. These transformations allow weak signals to be amplified and detected while maintaining the integrity of the underlying biological signal, thus improving measurement precision without compromising data integrity.
3Loss of time
If conventional single-dataset analysis is used, then the analysis process is quick, but false positive errors cannot be eliminated
Solution Approach 1:
The patent implements preliminary action by establishing multiple predetermined signal thresholds before analysis begins. The first threshold is set below the detection limit and the second threshold is set above the detection limit, creating a predetermined decision framework. This allows rapid classification of results without requiring complex post-analysis processing, thus maintaining quick analysis time while eliminating false positive errors through the built-in safety margin.
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 MDA method provides error-free analysis by employing multiple datasets and determinative factors, significantly reducing false positive and false negative errors in determining the presence or absence of a target analyte.
Implementation Method 1
employing a signal-generating means for releasing a detectable fluorescent signal in proportion to the amount of target nucleic acid sequences
Implementation Method 2
amplified a nucleic acid sequence based on the hybridization of a promoter/primer sequence to a target single-stranded DNA
Implementation Method 3
primer extension by a DNA polymerase
Data Source
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AI summary
The present invention relates to the determination of the presence or absence of a target analyte by a Multiple Dataset Analysis (MDA). The present invention can dramatically reduce errors (particularly, false positive errors) in determination of the presence or absence of a target analyte, by using two or more different types of datasets from an amplification reaction.