A Protocol for Simple, Rapid, and Direct Detection of SARS-CoV-2 from clinical samples, using Reverse Transcribed Loop-Mediated Isothermal Amplification (RT-LAMP)
Bibliographic record
Abstract
SARS-CoV-2 has quickly spread all around the globe causing illness and wide damages. Most countries were unprepared for such a rapid spread and crisis. This led to various strategies for effective control of the new pandemic. A key aspect in all countries was to effectively test the population for the virus. Most countries chose a lockdown strategy in which many workplaces and activities are completely closed, leading to substantial economy costs. Here, we present a protocol we recently developed that allows rapid and simple detection of SARS-CoV-2 for the large population, eliminating costs and involvement of professional teams and laboratories. This protocol is based on Reverse Transcribed Loop-Mediated Isothermal Amplification (RT-LAMP). We tested this protocol directly on patient samples, both nasal and throat clinical swabs as well as saliva. Notably, this protocol is simple, cheap and can be easily applied to other pathogens as well.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".