Biomolecule Detection with Particle Enrichment and Internal Standards
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Solution Overview
Problem
Current methods for detecting and quantifying biomolecules suffer from misclassification, reduced signal-to-noise ratio, interference from high abundance biomolecules, and lack of real-time quality control during nanoparticle enrichment, leading to inaccurate and unreliable disease detection.
Innovation Solution
Incorporating internal standards and particles to adsorb biomolecules, allowing for real-time quality control and normalization of measurements, and using multiple data sets with classifiers to improve detection and classification of biomolecules, particularly for early-stage disease detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If particles are used to adsorb biomolecules for enrichment, then the detection sensitivity is improved, but the measurement accuracy deteriorates due to interference from high abundance biomolecules
Solution Approach 1:
Labeled reference biomolecules are introduced as intermediary substances that compete with endogenous biomolecules for adsorption sites on particles. These reference biomolecules serve as mediators to establish a known relationship between adsorption signal and actual biomolecule concentration, enabling accurate quantification despite the presence of high abundance interfering biomolecules.
Solution Approach 2:
The method changes the parameter of biomolecule concentration by adding known amounts of labeled reference biomolecules to the sample. This parameter change allows the system to calibrate the adsorption process and establish a reference curve that accounts for interference effects, thereby improving measurement accuracy while maintaining detection sensitivity.
2Measurement precision
If internal standards are added to normalize measurements, then the quantitative accuracy is improved, but the device complexity increases
Solution Approach 1:
The labeled reference biomolecules serve multiple functions simultaneously: they act as internal standards for normalization, compete for adsorption sites to establish calibration curves, and provide quality control metrics. This multi-functionality reduces the need for separate control mechanisms, thereby limiting the increase in device complexity while achieving improved quantitative accuracy.
3Reliability
If multiple data sets with classifiers are used to improve disease detection accuracy, then the detection reliability is improved, but the loss of time increases due to processing multiple measurements
Solution Approach 1:
The method performs preliminary classification of biomolecule measurements into distinct data sets before final disease detection analysis. By pre-organizing the data and applying classifiers in advance, the system reduces the computational burden during final interpretation, thereby limiting time loss while maintaining improved detection reliability through multiple data sets.
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
Enhances the accuracy and reproducibility of biomolecule measurement, enabling non-invasive early-stage disease detection and classification, reducing uncertainty and improving the reliability of disease identification.
Implementation Method 1
contacting a biological sample of a subject with particles, thereby adsorbing endogenous biomolecules of the biological sample to the particles
Data Source
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AI summary
Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets, and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.