Disruption of selenium transport and function is a major contributor to mercury toxicity in zebrafish larvae
Bibliographic record
Abstract
Mercury is one of the most toxic elements threatening the biosphere, with levels steadily rising due to both natural and human activities. Selenium is an essential micronutrient, required for normal development and functioning of many organisms. While selenium is known to counteract mercury's toxicity under some conditions, to date information about the mercury-selenium relationship is fragmented and often controversial. As part of a systematic study of mercury and selenium interactions, zebrafish (Danio rerio) larvae (a model verterbrate) were exposed to methylmercury chloride or mercuric chloride. The influence of pre- and post-treatment of selenomethionine on the level and distribution of mercury and selenium in the brain and eye sections, as well as on toxicity, were examined. Selenomethionine treatment decreased the amount of maternally transfered mercury in the larval brain. Selenomethionine treatment prior to exposure to mercuric chloride increased both mercury and selenium levels in the brain but decreased their toxic effects. Conversely, methylmercury levels were not changed as a result of selenium pre-treatment, while toxicity was increased. Strikingly, both forms of mercury severely disrupted selenium metabolism, not only by depleting selenium levels due to formation of Hg-Se complexes, but also by blocking selenium transport into and out of tissues, suggesting that restoring normal selenium levels by treating the organism with selenium after mercury exposure may not be possible. Disruption of selenium metabolism by mercury may lead to disruption in function of selenoproteins. Indeed, the production of thyroid hormones by selenoprotein deiodinases was found to be severely impaired as a result of mercury exposure, with selenomethionine not always being a suitable source of selenium to restore thyroid hormone levels.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".