Microbiome Diagnostics for Locomotor Conditions
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Solution Overview
Problem
Current methods for characterizing locomotor system conditions and providing tailored therapeutics based on microbiome analysis are limited due to technological constraints, making it difficult to effectively diagnose and treat conditions such as rheumatic arthritis and other musculoskeletal disorders.
Innovation Solution
A method and system that involves receiving biological samples from a population, characterizing microbiome composition and functional features, and transforming this data into diagnostic and therapeutic models to identify personalized therapies, including probiotic, phage-based, and small-molecule treatments, to modulate the microbiome towards equilibrium states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current microbiome analysis methods are used, then some level of diagnostic information can be obtained, but the precision and individualization of diagnosis is insufficient
Solution Approach 1:
The patent segments the microbiome analysis into distinct functional modules: sample collection, DNA extraction, 16S rRNA gene amplification, sequencing, and bioinformatics analysis. Each module can be independently optimized and validated, improving diagnostic precision while managing system complexity through modular design.
Solution Approach 2:
The patent implements preliminary actions by establishing standardized protocols for sample collection, DNA extraction, and PCR amplification before actual sequencing. Reference databases and bioinformatics pipelines are pre-configured to enable rapid, precise analysis of sequencing data, reducing variability and improving diagnostic accuracy.
2Reliability
If comprehensive microbiome characterization is performed, then better therapeutic insights are achieved, but the time and resources required increase significantly
Solution Approach 1:
The patent applies partial action by focusing sequencing efforts on the 16S rRNA gene region, which provides sufficient taxonomic resolution for most clinical applications without requiring complete genome sequencing. This targeted approach achieves reliable therapeutic insights while significantly reducing time and computational resources compared to whole-genome analysis.
Solution Approach 2:
The patent implements local quality by applying different levels of analysis depth to different aspects of microbiome characterization. High-resolution taxonomic classification is applied to identify key pathogenic or protective taxa, while functional prediction uses aggregated pathway-level analysis. This differentiated approach optimizes reliability for therapeutic decision-making while minimizing overall analysis time.
3Productivity
If population-wide microbiome analysis is conducted, then general patterns are identified, but individualized diagnostic information is lost
Solution Approach 1:
The patent implements feedback mechanisms where population-level analysis results inform the development of personalized diagnostic criteria. Bioinformatics pipelines compare individual samples against reference databases and population cohorts, providing feedback that highlights both common patterns and individual deviations. This enables high-throughput processing while maintaining individualization accuracy through comparative analysis.
Solution Approach 2:
The patent adds another dimension to the analysis by simultaneously processing data at both population and individual levels. Statistical models incorporate both aggregate patterns and individual variability, allowing the system to identify population-wide trends while preserving and analyzing individual-specific microbiome signatures for personalized diagnostic and therapeutic recommendations.
Data Source
AI summary
A method for at least one of characterizing, diagnosing, and treating a locomotor system condition in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the locomotor system condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the locomotor system condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.