B-206 Quality Control Rapid Response in Pre-Pandemic Preparedness Showcasing a Synthetic H5N1 Genetic Template
Bibliographic record
Abstract
Abstract Background The SARS pandemic presented significant challenges globally, primarily due to the initial lack of well characterized patient samples, which impeded the cross-validation of assays and designs. The limited in silico approach did not encompass pre-analytical steps such as extraction, medium format or the analytical sensitivity (LoDs) and specificity, limiting the usability for many assays. Additionally, the absence of External Quality Assessment (EQA) hindered laboratories* ability to evaluate their performance based on peer benchmarking. Recent examples of H5N1 transmission to humans, necessitate rapid response for pandemic preparedness. Microbix employed a synthetic biology approach to design and develop a full process whole genome H5N1 cDNA control that can be used for assay design verification, validation, and ongoing Quality Control (QC). Methods Microbix used a synthetic biology approach to design and developed a whole genome H5N1 cDNA control based on published H5N1 consensus sequence. This whole genome H5N1 cDNA control was confirmed by NGS, quantified by ddPCR, and then formulated on swabs and in liquid format. Both formats were confirmed by testing inhouse on the Cepheid Gene Xpert® SARS/Flu/RSV assay, and additionally the liquid formats were provided for an EQA pilot. Results The timeline for creating the control was 10 days from design to a working prototype. The swab and liquid formulations performed as expected in the internal testing, and the external pilot with liquid samples showed consensus results across multiple platforms. Conclusion Microbix successfully created a non-infectious, quantified whole genome H5N1 cDNA control for use in assay verification/validation and EQA. The synthetic biology approach, along with the short timeline from design to formulation, could be utilized in pandemic emergencies for a rapid response to provide reference materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".