Optimization of Reverse Transcription Loop-Mediated Isothermal Amplification for In Situ Detection of SARS-CoV-2 in a Micro-Air-Filtration Device Format
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The Coronavirus disease 2019 (COVID-19) pandemic has supercharged innovation in the field of molecular diagnostics and led to the exploration of systems that permit the autonomous identification of airborne infectious agents. Airborne virus detection is an emerging approach for determining exposure risk, although current methods limit intervention timeliness. Here, we explore reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays for one-pot detection of Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) (SCV2) run on membrane filters suitable for micro-air-filtration of airborne viruses. We use a design of experiments statistical framework to establish the optimal additive composition for running RT-LAMP on membrane filters. Using SCV2 liquid spike-in experiments and fluorescence detection, we show that single-pot RT-LAMP on glass fiber filters reliably detected 0.10 50% tissue culture infectious dose (TCID 50 ) SCV2 per reaction (3600 E-gene copies) and is an order of magnitude more sensitive than conventional RT-LAMP.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".