Recombinase Polymerase Amplification for Diagnostic Applications
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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