Universal reverse-transcriptase real-time PCR for infectious hematopoietic necrosis virus (IHNV)
Bibliographic record
Abstract
Infectious hematopoietic necrosis virus (IHNV) is an acute pathogen of salmonid fishes in North America, Europe and Asia and is reportable to the World Organization for Animal Health (OIE). Phylogenetic analysis has identified 5 major virus genogroups of IHNV worldwide, designated U, M, L, E and J; multiple subtypes also exist within those genogroups. Here, we report the development and validation of a universal IHNV reverse-transcriptase real-time PCR (RT-rPCR) assay targeting the IHNV nucleocapsid (N) gene. Properties of diagnostic sensitivity (DSe) and specificity (DSp) were defined using laboratory-challenged steelhead trout Oncorhynchus mykiss, and the new assay was compared to the OIE-accepted conventional PCR test and virus isolation in cell culture. The IHNV N gene RT-rPCR had 100% DSp and DSe and a higher estimated diagnostic odds ratio (DOR) than virus culture or conventional PCR. The RT-rPCR assay was highly repeatable within a laboratory and highly reproducible between laboratories. Field testing of the assay was conducted on a random sample of juvenile steelhead collected from a hatchery raceway experiencing an IHN epizootic. The RT-rPCR detected a greater number of positive samples than cell culture and there was 40% agreement between the 2 tests. Overall, the RT-rPCR assay was highly sensitive, specific, repeatable and reproducible and is suitable for use 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".