Biological Sample Aliquoting for Parallel Lab Testing
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Solution Overview
Problem
Existing technologies face challenges in efficiently handling biological samples for laboratory testing, particularly in balancing the need to minimize sample material removal while maximizing throughput and minimizing turnaround time.
Innovation Solution
The technology involves using matrices to optimize the allocation of tubes to laboratory machines, incorporating aliquoting to enable parallel testing, and dynamically determining aliquoting and test assignments to meet constraints such as turnaround time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple tubes of biological material are collected from a patient, then tests can be run on different instruments in parallel enabling faster results, but more sample material is removed from the patient
Solution Approach 1:
The system segments a single biological sample into multiple aliquots (portions) that can be distributed to different instruments for parallel testing. The aliquoting device automatically divides the sample into multiple tubes, enabling simultaneous analysis across multiple instruments while using only one original sample collection
Solution Approach 2:
The system performs preliminary determination of whether aliquoting is necessary by evaluating if parallel processing would meet turnaround time constraints. The matrix optimization algorithm pre-calculates the benefits of aliquoting before actual sample processing, deciding in advance whether to divide samples into multiple portions for parallel instrument processing
2Loss of time
If samples are separated into separate tubes for parallel testing, then turnaround time is reduced, but the complexity of sample handling and allocation increases
Solution Approach 1:
The system dynamically determines aliquoting strategies based on real-time conditions including instrument availability, test requirements, and turnaround time constraints. The matrix optimization algorithm continuously evaluates and adjusts sample allocation decisions, transitioning from static pre-defined protocols to dynamic adaptive management of sample processing workflows
Solution Approach 2:
The system uses feedback from instrument status, test completion times, and sample requirements to continuously optimize the aliquoting and allocation decisions. The matrix algorithm incorporates actual processing data to refine future allocation decisions, creating a closed-loop system that learns from past performance to improve turnaround time while managing complexity
3Productivity
If a single sample is tested sequentially on multiple instruments, then sample handling is simpler, but the analysis takes more time
Solution Approach 1:
The system segments the testing process by dividing a single sample into multiple aliquots that can be processed simultaneously on different instruments. This parallel processing approach replaces sequential testing, dramatically increasing analysis speed while the matrix optimization algorithm manages the coordination complexity of distributing and tracking multiple aliquots across instruments
Solution Approach 2:
The aliquoting device serves multiple functions: it divides samples into aliquots, allocates them to appropriate instruments based on test requirements, and tracks their processing status. This multi-functional approach consolidates what would otherwise require separate systems for sample division, allocation, and tracking, reducing overall system complexity while enabling parallel processing
Data Source
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AI summary
In a scenario where a laboratory is required to perform a plurality of tests on biological samples from a plurality of tubes in a manner that satisfies certain constraints, it is possible that the laboratory could handle the samples and assign them to machines in a manner which ensures that the relevant constraints are met. This could include using matrices and optimization functions to represent tubes, tests, machines and prescriptions, and could also include dynamically determining whether and how to aliquot the samples so as to meet the constraints given the conditions under which the samples would be processed.