Recombinase Polymerase Amplification for Diagnostic Applications
Bibliographic record
Abstract
BACKGROUND: First introduced in 2006, recombinase polymerase amplification (RPA) has stirred great interest, as evidenced by 75 publications as of October 2015, with 56 of them just in the last 2 years. The widespread adoption of this isothermal molecular tool in many diagnostic fields represents an affordable (approximately 4.3 USD per test), simple (few and easy hands-on steps), fast (results within 5-20 min), and sensitive (single target copy number detected) method for the identification of pathogens and the detection of single nucleotide polymorphisms in human cancers and genetically modified organisms. CONTENT: This review summarizes the current knowledge on RPA. The molecular diagnostics of various RNA/DNA pathogens is discussed while highlighting recent applications in clinical settings with focus on point-of-care (POC) bioassays and on automated fluidic platforms. The strengths and limitations of this isothermal method are also addressed. SUMMARY: RPA is becoming a molecular tool of choice for the rapid, specific, and cost-effective identification of pathogens. Owing to minimal sample-preparation requirements, low operation temperature (25-42 °C), and commercial availability of freeze-dried reagents, this method has been applied outside laboratory settings, in remote areas, and interestingly, onboard automated sample-to-answer microfluidic devices. RPA is undoubtedly a promising isothermal molecular technique for clinical microbiology laboratories and emergence response in clinical settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".