A nucleic acid detection assay combining reverse transcription recombinase-aided amplification with a lateral flow dipstick for the rapid visual detection of porcine deltacoronavirus
Bibliographic record
Abstract
Porcine deltacoronavirus (PDCoV) is an emerging enteropathogen causing severe diarrhoea, dehydration, and death in nursing piglets and enormous economic losses for the global swine industry. Furthermore, it can infect multiple animal species including humans. Therefore, a rapid, definitive diagnostic assay is required for the effective control of this zoonotic pathogen. To identify PDCoV, we developed a nucleic acid detection assay combining reverse transcription recombinase-aided amplification (RT-RAA) with a lateral flow dipstick (LFD) targeting the highly conserved genomic region in the ORF1b gene. The RT-RAA-LFD assay exhibited good PDCoV detection reproducibility and repeatability and could be completed within 11 min. Ten minutes at 40 °C was required for nucleic acid amplification and 1 min at room temperature was needed for the visual LFD readout. The assay specifically detected PDCoV and did not cross-react with any other major swine pathogens. The 95% limit of detection (LOD) was 3.97 median tissue culture infectious dose PDCoV RNA per reaction. This performance was comparable to that of a reference TaqMan-based real-time RT-PCR (trRT-PCR) assay for PDCoV. Of 149 swine small intestine, rectal swab, and serum samples, 71 and 75 tested positive for PDCoV according to RT-RAA-LFD and trRT-PCR, respectively. The diagnostic coincidence rate for both assays was 97.32% (145/149) and the kappa value was 0.946 (p < 0.001). Overall, the RT-RAA-LFD assay is a user-friendly diagnostic tool that can rapidly and visually detect PDCoV.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".