Selenium toxicity in fishes: A current perspective
Bibliographic record
Abstract
Anthropogenic activities have led to increased levels of contaminants that pose significant threats to aquatic organisms, particularly fishes. One such contaminant is Selenium (Se), a metalloid which is released by various industrial activities including mining and fossil fuel combustion. Selenium is crucial for various physiological functions, however it can bioaccumulate and become toxic at elevated concentrations. Given that fishes are key predators in aquatic ecosystems and a major protein source for humans, Se accumulation raises considerable ecological and food safety concerns. Selenium induces toxicity at the cellular level by disrupting the balance between reactive oxygen species (ROS) production and antioxidant capacity leading to oxidative damage. Chronic exposure to elevated Se impairs a wide range of critical physiological functions including metabolism, growth and reproduction. Selenium is also a potent teratogen and induces various types of adverse developmental effects in fishes, mainly due to its maternal transfer to the eggs. Moreover, that can persist across generations. Furthermore, Se-induced oxidative stress in the brain is a major driver of its neurotoxicity, which leads to impairment of several ecologically important behaviours in fishes including cognition and memory functions, social preference and interactions, and anxiety response. Our review provides an up-to-date and in-depth analysis of the various adverse physiological effects of Se in fishes, while identifying knowledge gaps that need to be addressed in future research for greater insights into the impact of Se in aquatic ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".