Diets containing corn naturally contaminated with deoxynivalenol reduces the susceptibility of rainbow trout (<i>Oncorhynchus mykiss)</i>to experimental<i>Flavobacterium psychrophilum</i>infection
Bibliographic record
Abstract
The objective of this study was to determine if deoxynivalenol (DON) exposure alters the susceptibility of rainbow trout to bacterial coldwater disease caused by Flavobacterium psychrophilum. Rainbow trout were fed a nutritionally complete diet containing corn that was naturally contaminated with DON at a desired concentration of <0.5 (control and pair-fed treatments), 4 or 6 ppm over 7 weeks to apparent satiation. After 4 weeks, fish were infected by intraperitoneal injection with F. psychrophilum (3.03x106 CFU mL−1) via intraperitoneal injection and monitored for morbidity and mortality. A significant linear reduction in feed intake was associated with increasing dietary levels of DON contamination over the initial 4 weeks. There was a significant reduction (P < 0.05) in cumulative per cent mortality in DON-fed groups (4.1 ppm, 11%; 5.9 ppm, 7%) in comparison to control (46%) and pair-fed (25%) groups at 21 days post infection. Mortality of trout pair-fed the control diet was also significantly lower (P < 0.05) than the control group fed to apparent satiation. A replicate trial using genetically similar fish and the same experimental design produced similar results. These results suggest that DON exposure and restricted feed intake provided a protective effect for rainbow trout infected with F. psychrophilum.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".