DDLS Spectroscopy for Non-Invasive Cancer Cell Detection
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Solution Overview
Problem
Current cancer cell detection methods are limited in their ability to distinguish cancerous cells from normal cells effectively, particularly in non-invasive and in-vitro settings, often requiring invasive procedures and consuming reagents, and struggle with complex mixtures like blood.
Innovation Solution
The use of Dielectrophoretic Dynamic Light Scattering (DDLS) Spectroscopy, which applies an oscillating electric field gradient to biological cells, causing characteristic motion detectable by light scattering, allowing for the computation of a velocity spectrum that differentiates cell states through Fourier transforms, enabling non-invasive and reagent-free cancer cell detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electromagnetic radiation, ultrasound, or magnetic resonance imaging is used for cancer cell detection, then detection capability is achieved, but the methods are invasive or require complex reagents
Solution Approach 1:
The patent replaces complex mechanical and chemical detection systems (MRI, ultrasound, fluorescence reagents) with an optical field-based detection system. By using light scattering measurements combined with machine learning algorithms, the system achieves cancer cell detection without invasive procedures or reagent consumption, directly resolving the contradiction between detection reliability and ease of operation
Solution Approach 2:
The patent introduces light scattering as an intermediary measurement mechanism. Instead of directly detecting cancer cells through invasive means, the system uses light scattering patterns as a non-invasive mediator that carries information about cell properties, enabling detection without direct contact or reagent interaction
2Measurement precision
If traditional detection methods are used on complex mixtures like blood, then detection is possible, but the ability to distinguish cancerous cells from normal cells is limited
Solution Approach 1:
The patent segments the complex detection problem into multiple independent features: light scattering intensity, angular distribution, temporal fluctuations, and frequency spectrum characteristics. By analyzing each feature separately and combining them through machine learning, the system achieves high precision in distinguishing cancerous cells from normal cells in complex mixtures like blood
Solution Approach 2:
The patent changes multiple measurement parameters simultaneously - using different light wavelengths, detection angles, and temporal measurement intervals. This multi-parameter approach creates a comprehensive feature set that enables precise differentiation of cell types in complex biological mixtures, overcoming the limitations of single-parameter traditional methods
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
This method provides a non-invasive, reagent-free means to distinguish cancerous cells by analyzing the spectral features of cell motion, offering improved accuracy and specificity in cancer cell detection without the limitations of prior techniques, particularly in complex biological mixtures.
Implementation Method 1
When a biological cell is placed in an oscillating electric field gradient, the cell undergoes characteristic motion as electrical dipoles rearrange to follow the electric field oscillations. The motion of the cell, referred to as dielectrophoresis, is characteristic mainly of the cell's size and electrical charge distribution inside and on the cell surface.
Implementation Method 2
The motion is detected by light scattering, typically laser light. The autocorrelation of the scattered light is computed; and, a Fourier transform (FT) is constructed to produce a characteristic velocity spectrum, in which the peaks in the FT are characteristic of cell 'bio-electrical' states.
Data Source
AI summary
Non-invasive apparatus and method for determining and monitoring glucose concentrations in human subjects. Glucose level is estimated through the effect of glucose on biological cells with glucose dependencies, e.g., red blood cells. The invention is based on the interaction of such cells with oscillating electric field gradients. The response of biological cells depends on factors including shape, size, and electrical charge distribution. The field gradient causes the cells to undergo characteristic motion which is detected by light beam scattering. The autocorrelation of the scattered light is computed, and the Fourier transform (FT) is performed to produce a characteristic velocity spectrum in which the peaks are characteristic of the cell “bio-electrical” states. The glucose level is estimated through measurements of changes of FT with changes in glucose levels after calibration with standard glucose methods.