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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| 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".