Recombinase polymerase amplification in the molecular diagnosis of microbiological targets and its 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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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