Efficient SARS-CoV-2 detection in unextracted oro-nasopharyngeal specimens by rRT-PCR with the Seegene Allplex™ 2019-nCoV assay
Bibliographic record
Abstract
Abstract Background The fight against the COVID-19 pandemic has created an urgent need to rapidly detect infected people. The challenge for clinical laboratories has been finding a high throughput, cost-efficient, and accurate testing method in the context of extraction reagents shortage on a global scale. To answer this need, we studied SARS-CoV-2 detection in oro-nasopharyngeal (ONP) swabs stored in Universal Transport Media (UTM) or in RNase-free water by rRT-PCR with Seegene Allplex™ 2019-nCoV assay without RNA extraction. Results Optimal results were obtained when swabs stored in UTM were diluted 1/5 and 1/2 in RNase-free water. Thermal lysis before rRT-PCR testing slightly improved detection rate. In addition, proteinase K (PK) treatment allowed for a significant reduction of invalid results and increased sensitivity for detection of low viral load specimens. In a panel of positive samples with all 3 viral genes amplified and N gene Cycle threshold values (C t values) from 15 to 40, our detection rate was 98.9% with PK and 94.4% without. In a challenging panel of low positive samples with only the N gene being detectable at C t values > 30, detection rate was increased from 53.3 to 76.7% with the addition of PK, and invalid rate fell off from 18.3 to 0%. Furthermore, we demonstrated that our method reliably detects specimens with C t values up to 35, whereas false negative samples become frequent above this range. Finally, we show that swabs should be stored at − 70 °C rather than 4 °C when testing cannot be performed within 72 h of collection. Conclusion We successfully optimized the unextracted rRT-PCR process using the Seegene Allplex™ 2019-nCoV assay to detect SARS-CoV-2 RNAs in nasopharyngeal swabs. This improved method offers cost savings and turnaround time advantages compared to automated extraction, with high efficiency of detection that could play an important role in the surveillance of Covid-19.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".