Efficient SARS-CoV-2 detection in unextracted oro-nasopharyngeal specimens by rRT-PCR with the Seegene AllplexTM 2019-nCoV assay
Bibliographic record
Abstract
Abstract The fight against the COVID-19 pandemic has created an urgent need to detect and isolate infected people. The challenge for clinical laboratories has been finding a high throughput, cheap, and efficient testing method in the context of extraction reagent shortages on a planetary scale. To answer this need, we studied SARS-CoV-2 detection in nasopharyngeal swabs stored in UTM (Universal Transport Media) or RNAse-free water by rRT-PCR with the Seegene Allplex TM 2019-nCoV assay without RNA extraction. Optimal results were obtained with 1/2 dilution for swabs in RNAse free water (30/30 detected) and 1/5 dilution for swabs in UTM (29/30 detected) followed by thermal lysis. In addition, a proteinase K (PK) treatment allows a significant reduction of invalid results and increases sensitivity for detection of low viral load specimens. In a panel of 90 known positives with all 3 viral genes present and N gene Ct values from 15 to 40, our detection rate was 98.9% with PK and 94.4% without. In a panel of 60 low positives with only the N gene detectable at Ct values > 30, the detection rate was 76.7% with PK vs 53.3% without it and the invalid rate fell off from 18.3% to 0%. Furthermore, we demonstrated that our method reliably detects specimens with Ct values up to 35, however false negatives become frequent above this range. Finally, we show that swabs should be stored at -70 o C rather than 4 o C when testing cannot be performed within 72 hours of collection when laboratories are overwhelmed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".