Cross-reactivity and inclusivity analysis of CRISPR-based diagnostic assays of coronavirus SARS-CoV-2
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; initially named as 2019-nCoV) is the cause of the novel coronavirus disease 2019 (COVID-19) pandemic. Its diagnosis relies on the molecular detection of the viral RNA by polymerase chain reaction (PCR) while newer rapid CRISPR-based diagnostic tools are being developed. As molecular diagnostic assays rely on the detection of unique sequences of viral nucleic acid, the target regions must be common to all coronavirus SARS-CoV-2 circulating strains, yet unique to SARS-CoV-2 with no cross-reactivity with the genome of the host and other normal or pathogenic organisms potentially present in the patient samples. This stage 1 protocol proposes in silico cross-reactivity and inclusivity analysis of the recently developed CRISPR-based diagnostic assays. Cross-reactivity will be analyzed through comparison of target regions with the genome sequence of the human, seven coronaviruses and 21 other organisms. Inclusivity analysis will be performed through the verification of the sequence variability within the target regions using publicly available SARS-CoV-2 sequences from around the world. The absence of cross-reactivity and any mutations in target regions of the assay used would provide a higher degree of confidence in the CRISPR-based diagnostic tests being developed while the presence could help guide the assay development efforts. We believe that this study would provide potentially important information for clinicians, researchers, and decision-makers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".