Enzyme-free temperature resilient amplification assay with toehold stem-loop probe
Bibliographic record
Abstract
Toehold mediated strand displacement reaction (TMSDR) offers a rapid, enzyme-free amplification strategy, providing advantages over traditional methods like RT-PCR, and RT-LAMP. Optimizing TMSDR can significantly enhance sensitivity in point-of-care biosensor applications for target nucleic acid detection. However, achieving optimal performance requires meticulous probe design and stringent quality control. We developed a TMSDR-based system targeting a specific SARS-CoV-2 RNA sequence through testing multiple fluorophore-quencher labeled DNA probes. Following optimization, a probe with a strategically designed: stem, loop, and optimized toehold length emerged as the most effective candidate. Displacer sequence optimization further enhanced amplification efficiency. Ensuring probe purity is crucial, as impurities elevated background noise and diminished sensitivity. This work underscores the importance of rigorous probe quality in achieving reliable and sensitive TMSDR-based viral RNA detection, paving the way for robust point-of-care diagnostic tools.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".