Spectral Diagnostic System Using State-Based Sample Distributions
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Solution Overview
Problem
Existing methods struggle to accurately identify and analyze changes in biological samples over time, particularly in identifying causing agents such as viruses, bacteria, or diseases like cancer, which generate different states in the sample, without directly attempting to identify the agent from its spectrum.
Innovation Solution
A method involving the separation of biological samples into sub-samples, interaction with electromagnetic radiation to obtain spectra, and comparison with reference spectra to calculate population distributions, allowing for the identification of causing agents by analyzing changes in state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing spectral methods are used to identify causing agents, then the analysis can be performed, but the accuracy is insufficient to reliably identify agents at genus, species, or strain level
Solution Approach 1:
The biological sample is separated into multiple sub-samples, each representing different states or stages of the causing agent's effect. This segmentation allows for more precise spectral analysis of individual states, improving identification accuracy at the genus, species, or strain level while enhancing reliability through comparative analysis across multiple sub-samples.
2Measurement precision
If direct spectral identification of the causing agent is attempted, then the process is simplified, but the accuracy is insufficient
Solution Approach 1:
Instead of attempting to directly identify the causing agent from its spectrum, the method extracts and analyzes the spectral signatures of the biological sample at different states. This indirect approach through sub-sample separation and spectral comparison actually improves identification accuracy while managing complexity through systematic processing.
Solution Approach 2:
The method performs preliminary separation of the biological sample into sub-samples representing different states before spectral analysis. This preliminary action prepares the sample in a way that enhances the accuracy of subsequent spectral identification, allowing for more reliable detection of causing agents at fine taxonomic levels.
3Measurement precision
If population distribution analysis is implemented to identify causing agents, then diagnostic accuracy is enhanced, but the complexity of the method increases
Solution Approach 1:
The method uses spectral comparisons across multiple sub-samples to calculate population distributions, creating a feedback loop where the spectral data from different states informs the identification process. This feedback mechanism enhances diagnostic accuracy by providing multiple data points for verification, while the systematic approach manages complexity through structured analysis.
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 the identification of causing agents like bacteria, viruses, or diseases at the genus, species, or strain level, and provides insights into their effects by analyzing population distributions over time, enhancing diagnostic accuracy.
Implementation Method 1
testing at least some of the sub-samples by interaction with electromagnetic radiation to obtain a spectrum of sub-sample properties
Implementation Method 2
directing probe electromagnetic radiation into the sub-samples to interact with said sub-samples; collecting separately electromagnetic radiation transmitted, reflected, scattered or emitted from each sub-sample tested
Data Source
AI summary
A biological sample is analyzed for presence of a causing agent such as a virus, bacteria or cancer which generates a series of different states in the sample. The sample is separated into sub-samples where at least some of the sub-samples include portions of the sample at different states. Electromagnetic radiation interacts with the samples to obtain a spectrum of sub-sample properties which is compared with a plurality of reference spectra obtained from testing a plurality of sub-samples in respective ones of the different states to calculate a population distribution of the states of the sub-samples to obtain information about the causing agent. The population distribution can be analyzed at a single time or temporally over time to generate data relating to an effect of the causing agent. The population distribution is compared with a plurality of reference population distributions to generate data identifying the causing agent.