Evaluation of recombinase-based isothermal amplification assays for point-of-need detection of SARS-CoV-2 in resource-limited settings
Bibliographic record
Abstract
OBJECTIVES: The democratization of diagnostics is one of the key challenges towards containing the transmission of coronavirus disease 2019 (COVID-19) around the globe. The operational complexities of existing PCR-based methods, including sample transfer to advanced central laboratories with expensive equipment, limit their use in resource-limited settings. However, with the advent of isothermal technologies, the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is possible at decentralized facilities. METHODS: In this study, two recombinase-based isothermal techniques, reverse transcription recombinase polymerase amplification (RT-RPA) and reverse transcription recombinase-aided amplification (RT-RAA), were evaluated for the detection of SARS-CoV-2 in clinical samples. A total of 76 real-time reverse transcription PCR (real-time RT-PCR) confirmed COVID-19 cases and 100 negative controls were evaluated to determine the diagnostic performance of the isothermal methods. RESULTS: This investigation revealed equally promising diagnostic accuracy of the two methods, with a sensitivity of 76.32% (95% confidence interval 65.18-85.32%) when the target genes were RdRP and ORF1ab for RT-RPA and RT-RAA, respectively; the combination of N and RdRP in RT-RPA augmented the accuracy of the assay at a sensitivity of 85.53% (95% confidence interval 75.58-92.55%). Furthermore, high specificity was observed for each of the methods, ranging from 94.00% to 98.00% (95% confidence interval 87.40-9.76%). CONCLUSIONS: Considering the diagnostic accuracies, both RT-RPA and RT-RAA appear to be suitable assays for point-of-need deployment for the detection of the pathogen, understanding its epidemiology, case management, and curbing transmission.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".