Mercury-selenium compounds and their toxicological significance: Toward a molecular understanding of the mercury-selenium antagonism
Bibliographic record
Abstract
The interaction between mercury (Hg) and selenium (Se) is one of the best known examples of biological antagonism, yet the underlying mechanism remains unclear. This review focuses on the possible pathways leading to the Hg-Se antagonism, with an emphasis on the potential Hg-Se compounds that are responsible for the antagonism at the molecular level (i.e., bis[methylmercuric]selenide, methylmercury selenocysteinate, selenoprotein P-bound HgSe clusters, and the biominerals HgSe(x)S(1-x)). The presence of these compounds in biological systems has been suggested by direct or indirect evidence, and their chemical properties support their potentially key roles in alleviating the toxicity of Hg and Se (at high Hg and Se exposures, respectively) and deficiency of Se (at low Se exposures). Direct analytical evidences are needed, however, to confirm their in vivo presence and metabolic pathways, as well as to identify the roles of other potential Hg-Se compounds. Further studies are also warranted for the determination of thermodynamic properties of these compounds under physiological conditions toward a better understanding of the Hg-Se antagonism in biota, particularly under real world exposure scenarios.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".