Optimization of Droplet Digital PCR from RNA and DNA extracts with direct comparison to RT-qPCR: Clinical implications for quantification of Oseltamivir-resistant subpopulations
Bibliographic record
Abstract
The recent introduction of Droplet Digital PCR (ddPCR) has provided researchers with a tool that permits direct quantification of nucleic acids from a wide range of samples with increased precision and sensitivity versus RT-qPCR. The sample interdependence of RT-qPCR stemming from the measurement of Cq and ΔCq values is eliminated with ddPCR which provides an independent measure of the absolute nucleic acid concentration for each sample without standard curves thereby reducing inter-well and inter-plate variability. Well-characterized RNA purified from H275-wild type (WT) and H275Y-point mutated (MUT) neuraminidase of influenza A (H1N1) pandemic 2009 virus was used to demonstrate a ddPCR optimization workflow to assure robust data for downstream analysis. The ddPCR reaction mix was also tested with RT-qPCR and gave excellent reaction efficiency (between 90% and 100%) with the optimized MUT/WT duplexed assay thus enabling the direct comparison of the two platforms from the same reaction mix and thermal cycling protocol. ddPCR gave a marked improvement in sensitivity (>30-fold) for mutation abundance using a mixture of purified MUT and WT RNA and increased precision (>10 fold, p<0.05 for both inter- and intra-assay variability) versus RT-qPCR from patient samples to accurately identify residual mutant viral population during recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".