Nanoparticle Glycoprotein Assessment for Early Cancer Detection
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Solution Overview
Problem
Current methods lack the ability to accurately detect diseases, particularly cancer, at an early stage, which hinders effective treatment and prognosis.
Innovation Solution
Utilizing glycoprotein and glycopeptide information from biomolecule coronas in biofluid samples, combined with machine learning classifiers, to identify disease states such as cancer through mass spectrometry and chromatography techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection methods are used, then the detection process is simple, but the accuracy of early disease detection is insufficient
Solution Approach 1:
The patent segments the detection system into multiple functional components: particle incubation system, biomolecule corona formation system, mass spectrometry system, and machine learning classification system. Each component performs a specific function in the multi-step detection process, allowing for improved measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces particles as intermediaries that facilitate the detection process. Particles are incubated with biofluid samples to form biomolecule coronas, which then serve as carriers for glycoproteins and glycopeptides to the mass spectrometry system. This intermediary mechanism enables sensitive detection of disease markers that would be difficult to detect directly.
2Measurement precision
If multiple glycoproteins and glycopeptides are analyzed, then the detection sensitivity is improved, but the analysis time increases
Solution Approach 1:
The patent performs preliminary actions by incubating particles with biofluid samples before analysis to form biomolecule coronas. This pre-concentration step aggregates multiple glycoproteins and glycopeptides onto particle surfaces, making them more accessible to mass spectrometry and reducing the overall analysis time while maintaining high sensitivity for detecting multiple markers.
Solution Approach 2:
The patent implements continuous processing through automated mass spectrometry analysis and real-time machine learning classification. The system continuously monitors and analyzes glycoprotein and glycopeptide patterns, providing ongoing detection results without requiring repeated sampling or analysis cycles, thus reducing total analysis time while maintaining comprehensive multi-marker detection.
3Measurement precision
If machine learning classifiers are applied to glycoprotein data, then the disease state identification accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent uses machine learning classifiers that have been trained on copies of glycoprotein data from known disease states and healthy controls. The model stores pattern recognition rules and feature weights as computational copies, allowing rapid prediction of disease states in new samples without requiring complex real-time calculations, thus reducing operational computational complexity while maintaining high identification accuracy.
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
Enables early detection of cancer with sensitivity and specificity of about 80% or greater, facilitating timely treatment and reducing the need for invasive procedures.
Implementation Method 1
contacting a sample (e.g. biofluid sample) of a subject with particles to form biomolecule coronas comprising at least 10 distinct glycoproteins or glycopeptides adsorbed to the particles
Implementation Method 2
obtaining a data set comprising amounts of at least 10 glycoproteins or glycopeptides from biomolecule coronas
Data Source
AI summary
Described herein are methods for screening or testing for a disease state using a biological sample. The method may include using glycoprotein, glycopeptide or glycan measurements in evaluating a biological state. The measurements may be obtained through the use of nanoparticles that adsorb glycoproteins, glycopeptides, or glycans.