Pan-Piscine Orthoreovirus (PRV) Detection Using Reverse Transcription Quantitative PCR
Bibliographic record
Abstract
Piscine orthoreovirus (PRV) infects farmed and wild salmon and trout species in North America, South America, Europe, and East Asia. PRV groups into three distinct genotypes (PRV-1, PRV-2, and PRV-3) that can vary in distribution, host specificity, and/or disease potential. Detection of the virus is currently restricted to genotype specific assays such that surveillance programs require the use of three assays to ensure universal detection of PRV. Consequently, herein, we developed, optimized, and validated a real-time reverse transcription quantitative PCR assay (RT-qPCR) that can detect all known PRV genotypes with high sensitivity and specificity. Targeting a conserved region at the 5' terminus of the M2 segment, the pan-PRV assay reliably detected all PRV genotypes with as few as five copies of RNA. The assay exclusively amplifies PRV and does not cross-react with other salmonid viruses or salmonid host genomes and can be performed as either a one- or two-step RT-qPCR. The assay is highly reproducible and robust, showing 100% agreement in test results from an inter-laboratory comparison between two laboratories in two countries. Overall, as the assay provides a single test to achieve highly sensitive pan-specific PRV detection, it is suitable for research, diagnostic, and surveillance purposes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".