Direct on-the-spot detection of SARS-CoV-2 in patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many countries are currently in a state of lockdown due to the SARS-CoV-2 pandemic. One key requirement to safely transition out of lockdown is the continuous testing of the population to identify infected subjects. Currently, detection is performed at points of care using quantitative reverse-transcription PCR, thus requiring dedicated professionals and equipment. Here, we developed a protocol based on reverse transcribed loop-mediated isothermal amplification for the detection of SARS-CoV-2. This protocol is applied directly to SARS-CoV-2 nose and throat swabs, with no RNA purification step required. We tested this protocol on over 180 suspected patients, and compared the results to those obtained using the standard method. We further succeeded in applying the protocol to self-collected saliva samples from confirmed cases. Since the proposed protocol can detect SARS-CoV-2 from saliva and provides on-the-spot results, it allows simple and continuous surveillance of the community. Impact statement Humanity is currently experiencing a global pandemic with devastating implications on human health and the economy. Most countries are gradually exiting their lockdown state. We are currently lacking rapid and simple viral detections, especially methods that can be performed in the household. Here, we applied RT-LAMP directly on human clinical swabs and self-collected saliva samples. We adjusted the method to allow simple and rapid viral detection, with no RNA purification steps. By testing our method on over 180 human samples, we determined its sensitivity, and by applying it to other viruses, we determined its specificity. We believe this method has a promising potential to be applied world-wide as a simple and cheap surveillance test for SARS-CoV-2.
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.
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 | 0.000 |
| 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 it