Accelerated <scp>ISAV</scp> replication detection by cell culture methods combined with time‐monitoring <scp>RT</scp>‐<scp>qPCR</scp>
Bibliographic record
Abstract
Infectious salmon anaemia (ISA) is a viral disease that affects farmed Atlantic salmon (Salmo salar L.), often leading to mass mortalities. A quick detection of the ISA virus (ISAV) is crucial for decision-making and can prevent the occurrence of future outbreaks. Screening done by Canada's National Aquatic Animal Health Laboratory System (NAAHLS) uses quantitative reverse transcription PCR (RT-qPCR) followed by sequencing of PCR amplicons. As neither technique provides information regarding the infectivity of the virus, suspected virulent strains are subsequently tested using viral isolation. However, this stepwise process can require significant time to deliver results. To speed up this delivery, we have improved on these pre-existing techniques by combining the use of cell culture with RT-qPCR to detect replicative virus in as little as 5 days. Preliminary assays enabled the establishment of a minimal shift in Ct values over time, which is representative of viral replication in cultured cells. Subsequent blind panel analyses allowed the establishment of the optimal sampling days, as well as diagnostic sensitivity (DSe) and specificity (DSp) estimates. This method could be adopted not only by laboratories conducting diagnostic analyses for ISAV, but also for other slow-replicating viral agents that replicate through a budding mechanism.
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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) | 0.001 | 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) | 0.003 | 0.003 |
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".