Isothermal Amplification and Ambient Visualization in a Single Tube for the Detection of SARS-CoV-2 Using Loop-Mediated Amplification and CRISPR Technology
Bibliographic record
Abstract
We have developed a single-tube assay for SARS-CoV-2 in patient samples. This assay combined advantages of reverse transcription (RT) loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPRs) and the CRISPR-associated (Cas) enzyme Cas12a. Our assay is able to detect SARS-CoV-2 in a single tube within 40 min, requiring only a single temperature control (62 °C). The RT-LAMP reagents were added to the sample vial, while CRISPR Cas12a reagents were deposited onto the lid of the vial. After a half-hour RT-LAMP amplification, the tube was inverted and flicked to mix the detection reagents with the amplicon. The sequence-specific recognition of the amplicon by the CRISPR guide RNA and Cas12a enzyme improved specificity. Visible green fluorescence generated by the CRISPR Cas12a system was recorded using a smartphone camera. Analysis of 100 human respiratory swab samples for the N and/or E gene of SARS-CoV-2 produced 100% clinical specificity and no false positive. Analysis of 50 samples that were detected positive using reverse transcription quantitative polymerase chain reaction (RT-qPCR) resulted in an overall clinical sensitivity of 94%. Importantly, this included 20 samples that required 30-39 threshold cycles of RT-qPCR to achieve a positive detection. Integration of the exponential amplification ability of RT-LAMP and the sequence-specific processing by the CRISPR-Cas system into a molecular assay resulted in improvements in both analytical sensitivity and specificity. The single-tube assay is beneficial for future point-of-care applications.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".