Pathogen Detection Using Sample Metadata and Molecular Signatures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current animal health diagnostics lack a systemic approach for efficiently evaluating and identifying harmful agents, such as pathogens, to enable early intervention or prevention, often relying on limited, disease-specific panels that do not leverage genomic sequence or combinatorial information for comprehensive pathogenic burden analysis.
Innovation Solution
A system integrating automated assay design, multiplexed genomic sequencing, intelligent databases, and AI/ML for real-time analysis of biological and environmental data to detect, identify, and predict pathogens, generating actionable intelligence reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive genomic sequencing and AI/ML analysis are implemented, then pathogen identification accuracy and speed are improved, but system complexity and cost increase
Solution Approach 1:
The system divides pathogen detection into multiple independent genomic regions (16S rRNA, ITS1, ITS2, 28S rRNA, etc.) that can be sequenced and analyzed separately. Each region targets specific taxonomic levels (bacteria, fungi, etc.), allowing comprehensive coverage while maintaining manageable analysis complexity through modular processing of discrete genomic segments.
Solution Approach 2:
The patent introduces standardized metadata fields and structured data formats as intermediaries between sample collection and AI/ML analysis. These standardized interfaces (including sample metadata, sequencing metadata, and result metadata schemas) enable automated processing and reduce system complexity by providing uniform data structures that bridge different system components.
2Adaptability or versatility
If multiple microorganisms are assayed simultaneously, then diagnostic comprehensiveness is improved, but analysis time and computational resources increase
Solution Approach 1:
The system performs quality control checks, contamination assessments, and preliminary data validation steps before full AI/ML analysis. Metadata is collected and standardized in advance, and sequencing data is pre-processed and filtered to remove low-quality reads, reducing the computational burden and time required for the main pathogen identification analysis.
Solution Approach 2:
The system implements a tiered analysis approach where essential pathogen identification is performed first using key genomic regions, followed by optional deeper analysis of additional regions if needed. This allows rapid initial diagnosis while providing the option for more comprehensive analysis when clinically indicated, balancing speed and comprehensiveness.
Data Source
AI summary
Provided herein are systems and methods for animal health assessment, surveillance, and enhancement. For example, provided here are systems, devices, and processes that collect and analyze, in an efficient and high-throughput manner, biological information, environmental information, and demographic information about animal subjects to detect, identify, predict, and/or surveil adverse-health-causative or-associated agents, such as pathogens, and to select and identify suitable interventions.