A straightforward molecular strategy to retrospectively investigate the spread of SARS-CoV-2 VOC202012/01 B.1.1.7 variant
Bibliographic record
Abstract
The spread of new SARS-CoV-2 variants represents a serious threat worldwide, thus rapid and cost-effective methods are required for their identification. Since November 2020, the TaqPath COVID-19 assay (Thermo Fisher Scientific) has been used to identify viral strains of the new lineage B.1.1.7, since it fails to detect the S-gene with the ∆69/70 deletion. Here, we proposed S-gene mutations screening with the Allplex SARS-CoV-2 assay (Seegene), another widely used RT-PCR test that targets Sarbecovirus E, SARS-CoV-2 N, and RdRp/S genes. Accordingly, we evaluated the S gene amplification curve pattern compared to those of the other genes. Exploiting an Allplex assay-generated dataset, we screened 663 RT-PCR digital records, including all SARS-CoV-2 respiratory samples tested in our laboratory with the Allplex assay between January 1st and February 25th, 2021. This approach enabled us to detect 64 samples with peculiar non-sigmoidal amplification curves. Sequencing a selected group of 4 RNA viral genomes demonstrated that those curves were associated with B.1.1.7 variant strains. Our results strongly suggest that B.1.1.7 variant spread has begun in this area at least since January and imply the potential of these analytical methods to track and characterize the spread of B.1.1.7 strains in those areas where Allplex SARS-CoV-2 datasets have been previously recorded.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".