Recombinase polymerase amplification in the molecular diagnosis of microbiological targets and its applications
Bibliographic record
Abstract
Since the introduction of the polymerase chain reaction (PCR) technique in 1983, nucleic acid amplification has permeated all fields of biological science, particularly clinical research. Despite its importance, PCR has been restricted to specialized centers and its use in laboratories with few resources is limited. In recent decades, there has been a notable increase in the development of new isothermal technologies for molecular diagnosis with the hope of overcoming the traditional limitations of the laboratory. Among these technologies, recombinase polymerase amplification (RPA) has a wide application potential because it does not require thermocyclers and has high sensitivity, specificity, simplicity, and detection speed. This technique has been used for DNA and RNA amplification in various pathogenic organisms such as viruses, bacteria, and parasites. In addition, RPA has been successfully implemented in different detection strategies, making it a promising alternative for performing diagnoses in environments with scarce resources and a high burden of infectious diseases. In this study, we present a review of the use of RPA in clinical settings and its implementation in various research areas.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".