Integration of a Cas12a-mediated DNAzyme actuator with efficient RNA extraction for ultrasensitive colorimetric detection of viral RNA
Bibliographic record
Abstract
Developing highly sensitive and specific on-site tests is imperative to strengthen preparedness against future emerging infectious diseases. Here, we describe the construction of a Cas12a-mediated DNAzyme actuator capable of converting the recognition of a specific DNA sequence into an amplified colorimetric signal. To address viral RNA extraction challenges for on-site applications, we developed a rapid and efficient method capable of lysing the viral particles, preserving the released viral RNA, and concentrating the viral RNA. Integration of the DNAzyme actuator with the viral RNA extraction method and loop-mediated isothermal amplification enables a streamlined colorimetric assay for highly sensitive colorimetric detection of respiratory RNA viruses in gargle and saliva. This assay can detect as few as 83 viral particles/100 μL in gargle and 166 viral particles/100 μL in saliva. The entire assay, from sample processing to visual detection, was completed within 1 h at a single controlled temperature. We validated the assay by detecting SARS-CoV-2 in 207 gargle and saliva samples, achieving a clinical sensitivity of 96.3 % and specificity of 100%. The assay is adaptable for detecting specific nucleic acid sequences in other pathogens and is suitable for resource-limited settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".