Development and validation of a reverse transcription quantitative PCR for universal detection of viral hemorrhagic septicemia virus
Bibliographic record
Abstract
Viral hemorrhagic septicemia virus (VHSV) infects over 70 fish species inhabiting marine, brackish or freshwater environments throughout the Northern Hemisphere. Over its geographic range, 4 VHSV genotypes and multiple subtypes exist. Here, we describe the development and validation of a rapid, sensitive and specific real-time reverse transcription quantitative PCR assay (RT-qPCR) that amplifies sequence from representative isolates of all VHSV genotypes (I, II, III and IV). The pan-specific VHSV RT-qPCR assay reliably detects 100 copies of VHSV nucleoprotein RNA without cross-reacting with infectious hematopoietic necrosis virus, spring viremia of carp virus or aquatic birnavirus. Test performance characteristics evaluated on experimentally infected Atlantic salmon Salmo salar L. revealed a diagnostic sensitivity (DSe) > or = 93% and specificity (DSp) = 100%. The repeatability and reproducibility of the procedure was exceptionally high, with 93% agreement among test results within and between 2 laboratories. Furthermore, proficiency testing demonstrated the VHSV RT-qPCR assay to be easily transferred to and performed by a total of 9 technicians representing 4 laboratories in 2 countries. The assay performed equivalent to the traditional detection method of virus isolation via cell culture with the advantage of faster turnaround times and high throughput capacity, further suggesting the suitability of the use of this VHSV RT-qPCR in a diagnostic setting.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".