The physiological stress response and oxidative stress biomarkers in rainbow trout and brook trout from selenium‐impacted streams in a coal mining region
Bibliographic record
Abstract
Selenium (Se) is an essential element that can be toxic at concentrations slightly greater than those required for homeostasis. The main chronic toxic effects of Se in fish are teratogenic deformities, but Se can also activate the physiological stress response and redox cycle with reduced glutathione causing oxidative damage. Rainbow trout, Oncorhynchus mykiss, appear to be more sensitive to Se than brook trout, Salvelinus fontinalis. The objective of this study was to compare the physiological stress response (plasma cortisol, glucose, triiodothyronine, thyroxine, gill Na+/K+ ATPase, cortisol secretory capacity, K and liver somatic index) and oxidative stress biomarkers (liver GSH, GPx, lipid peroxidation, vitamin A and vitamin E) in rainbow trout (RNTR) and brook trout (BKTR) collected from reference and Se-exposed streams. The physiological stress response was not impaired (cortisol secretory capacity unchanged); although there were species differences in plasma cortisol and plasma glucose levels. Liver GSH, GPx and vitamin levels were higher in RNTR than BKTR, but lipid peroxidation levels were not different. The elevated GSH reserves may make RNTR more sensitive to Se-induced lipid peroxidation, but this may be offset by the RNTR's higher antioxidant (GPx and vitamin) levels. Species-specific biochemical differences may mediate differences in Se sensitivity and be used in aquatic Se risk assessments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".