Abnormal Reverse Transcriptase-Polymerase Chain Reaction Amplification Curve as Possible Pre-Selection Tool for SARS-CoV-2 “English Variant” Sequencing
Bibliographic record
Abstract
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are rapidly spreading, and currently represent a consistent worldwide problem in containing coronavirus disease 2019 (COVID-19) pandemic. Methods: We evaluated the Allplex SARS-CoV-2 Assay (Seegene) as a rapid pre-selection screening tool of viral variants by real-time polymerase chain reaction (PCR) amplification, before viral genome sequencing. Results: The analytic platform targets envelope gene (E), ribonucleic acid (RNA)-dependent RNA polymerase (RdRp)/spike (S) gene and nucleocapsid (N) gene of SARS-CoV-2, and reveals the presence of an abnormal (non-sigmoidal) amplification curve in 130 of 1,000 positive samples obtained from nasopharyngeal swabs. Sequencing analysis of a percentage of non-sigmoidal samples confirmed the presence of the new variant B.1.1.7 of SARS-CoV-2. Conclusions: Our results show the potential use of this reverse transcriptase-PCR (RT-PCR) pre-analysis to rapidly identify and track the new lineage B.1.1.7 of SARS-CoV-2, especially when sequencing analysis is not available, and consequently limiting its fast spreading among people. Clin Infect Immun. 2021;6(2):47-50 doi: https://doi.org/10.14740/cii129
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".