Mass Analysis Data Verification Using Machine-State Trust Records
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Solution Overview
Problem
Current methods for verifying mass analysis results, such as those from mass spectrometry, require significant burdens including re-running tests and searching paper records, which can be impractical when samples are consumed or unavailable.
Innovation Solution
A verification process using a trust-based system that secures and authenticates mass analysis results by storing machine-state data, encrypting them, and utilizing blockchain technology to record and prevent tampering, ensuring integrity and chain of trust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive quality control measures are implemented throughout the genomic analysis workflow, then data reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements feedback mechanisms by sequencing control samples (positive and negative) throughout the genomic analysis workflow. The results from these control samples are compared against expected outcomes, and any deviations trigger alerts or workflow adjustments. This closed-loop feedback system ensures data reliability without requiring complete redesign of the entire system architecture.
Solution Approach 2:
The patent applies preliminary action by preparing and sequencing control samples concurrently with patient samples at the beginning of each batching cycle. This advance preparation of control data establishes a baseline for quality assessment before the main analysis begins, enabling proactive quality assurance rather than reactive correction.
2Measurement precision
If multiple control samples are sequenced throughout the workflow, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent merges the sequencing of control samples with the sequencing of patient samples by processing them in the same batching cycles using identical infrastructure. Multiple control samples are multiplexed together with patient samples in pooled libraries, allowing simultaneous sequencing without requiring separate dedicated time slots or additional sequencing capacity.
Solution Approach 2:
The patent maintains continuity of useful action by integrating control sample sequencing into the continuous patient sample processing workflow. Rather than pausing patient sample analysis to separately process controls, the system continuously sequences both types of samples in parallel batches, ensuring that quality monitoring occurs without interrupting the primary diagnostic function.
3Measurement precision
If control samples are processed in separate batches, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent combines control samples and patient samples into the same sequencing batches and library preparations. Control samples are pooled with patient samples in the same multiplexed libraries, allowing quality control and diagnostic processing to occur simultaneously in a unified workflow rather than requiring separate batch processing lines.
4Reliability
If extensive quality control measures are implemented, then data reliability is improved, but loss of substance increases
Solution Approach 1:
The patent uses synthetic control samples with known sequences that can be regenerated indefinitely at minimal cost. These control materials are not precious biological specimens but rather artificially constructed DNA sequences that serve the quality control function, eliminating the need to consume valuable patient samples for control purposes.
Data Source
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AI summary
Methods and systems for generating verified mass analysis results for a sample. An example method may include acquiring process data for performing a mass analysis test on the sample; performing, by a mass analysis instrument, the mass analysis test based on the acquired process data to generate mass analysis results for the sample; recording machine-level characteristics for the mass analysis instrument during the mass analysis test; generating verification data that includes at least a portion of the recorded machine-level characteristics; generating a secure analytical data file that includes the verification data and at least a portion of the mass analysis results; and transmitting the secure analytical data file.