Amplification Curve Similarity Modeling for Accurate Ct Estimation
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Solution Overview
Problem
Existing molecular diagnostic methods face challenges in accurately estimating Ct values, quantifying target analytes, and determining positive/negative results due to interference from background signals and noise in amplification reactions, especially when multiple target analytes are present.
Innovation Solution
A method using a computer device with a memory and processor to analyze amplification reaction data by calculating shape similarity between target curves and reference patterns, employing neural networks to estimate molecular diagnostic results, including Ct, quantitative values, and suitability of oligonucleotide candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional molecular diagnostic methods are used to analyze amplification reaction data, then the process requires standard curve experiments and multiple reference materials, but the measurement precision of Ct values and quantitative analysis deteriorates due to background signals and noise
Solution Approach 1:
The patent extracts and removes the problematic background signals and noise components from the amplification curves through signal processing techniques. By separating the useful amplification signal from interfering background components, the method achieves accurate Ct value estimation without requiring complex standard curve experiments or multiple reference materials, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent performs preliminary signal processing and background subtraction on the amplification reaction data before analysis. By preprocessing the data to remove noise and background interference in advance, the method enables direct and accurate Ct value determination without needing subsequent complex experimental procedures, thereby reducing overall process complexity while maintaining high measurement precision
2Measurement precision
If signal processing is performed on amplification curves to remove background signals and noise, then measurement precision improves, but calculation complexity increases
Solution Approach 1:
The patent introduces an intermediary reference curve that represents the expected amplification pattern. By comparing the actual amplification curve against this reference intermediary, the method achieves accurate signal processing and noise removal through straightforward deviation analysis rather than complex mathematical transformations, thus improving quantitative accuracy while keeping calculation complexity manageable
Solution Approach 2:
The patent transforms the amplification curve data into derivative forms (first derivative, second derivative) to enhance signal features and suppress noise. By changing the mathematical representation parameters of the data, the method achieves better signal-to-noise ratio and more accurate quantitative analysis without requiring overly complex processing algorithms
3Adaptability or versatility
If multiple reference patterns are used to account for different amplification scenarios, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal reference amplification curve that can represent multiple amplification scenarios through parameter adjustment rather than requiring separate reference patterns for each case. This single versatile reference can adapt to different amplification conditions, achieving high adaptability while avoiding the complexity of managing multiple distinct reference patterns
Solution Approach 2:
The patent employs dynamic threshold determination that adapts to different amplification scenarios automatically. Rather than using fixed thresholds or multiple static reference patterns, the method dynamically adjusts analysis parameters based on the actual amplification curve characteristics, achieving versatility without the complexity of pre-defining multiple reference patterns
Data Source
AI summary
Proposed is a method for acquiring molecular diagnostic analysis results, performed by a computer device using a memory, a processor, and one or more programs stored in the memory and configured to be executed by the processor. The method may include acquiring a dataset representing results of the amplification reaction for a target analyte in the sample, calculating the shape similarity for each reference pattern by comparing the target curve in the dataset to multiple pre-established reference patterns, and providing the shape similarity for each reference pattern to a pre-trained estimation model, and acquiring, from the estimation model, molecular diagnostic analysis results including at least one of the Ct of the target curve, the quantitative value of the target analyte in the sample, the positive/negative reading result for the target analyte in the sample, and the suitability assessment result of oligonucleotide candidates used in the amplification reaction.