Rapid and Technically Simple Detection of SARS-CoV-2 Variants Using CRISPR Cas12 and Cas13
Bibliographic record
Abstract
The worldwide proliferation of the SARS-CoV-2 virus in the past 3 years has allowed the virus to accumulate numerous mutations. Dangerous lineages have emerged one after another, each leading to a new wave of the pandemic. In this study, we have developed the THRASOS pipeline to rapidly discover lineage-specific mutation signatures and thus advise the development of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based diagnostic tests. We also optimized a strategy to modify loop-mediated isothermal amplification amplicons for downstream use with Cas12 and Cas13 for future multiplexing. The close ancestry of the BA.1 and BA.2 variants of SARS-CoV-2 (Omicron) made these excellent candidates for the development of a first test using this workflow. With a quick turnaround time and low requirements for laboratory equipment, the test we have created is ideally suited for settings such as mobile clinics lacking equipment such as Next-Generation Sequencers or Sanger Sequencers and the personnel to run these devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".