Stress response for disease control in aquaculture
Bibliographic record
Abstract
Abstract Heat shock proteins (Hsps) are robustly induced by diverse stressors that denature proteins. In addition to stress resistance, Hsps are involved in the folding of nascent proteins, plant and animal development, aging, environmental adaptation and the immune response, demonstrating the fundamental importance of these proteins to cell survival. Heat shock proteins are induced in aquatic organisms by perturbations of temperature and salinity, environmental contaminants, handling, hormones and biotic stressors. Exposure to sublethal stress may enhance tolerance to a subsequent stress, a process termed induced thermotolerance, and provide protection to stressors other than the initial stress, known as cross‐tolerance. In the present review, we briefly describe the established approaches that are used to control disease during aquaculture. This is followed by documentation of Hsp induction after exposure to stressors commonly encountered by aquatic organisms. Induced thermotolerance, cross‐tolerance and immune enhancement by Hsps are also considered. Although physiological stress is known to decrease disease resistance it is now becoming clear that stress‐induced Hsps enhance the tolerance of aquatic organisms to disease. Potential applications for Hsps in the commercial production of fish, crustaceans and molluscs are indicated, an issue of significance when the importance of aquaculture in feeding the world’s population is realized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".