In Vitro Effect of Arsenical Compounds on Glutathione-Related Enzymes
Bibliographic record
Abstract
The mechanism of arsenic toxicity is believed to be due to the ability of arsenite (As(III)) to bind protein thiols. Glutathione (GSH) is the most abundant cellular thiol, and both GSH and GSH-related enzymes are important antioxidants that play an important role in the detoxification of arsenic and other carcinogens. The effect of arsenic on the activity of a variety of enzymes that use GSH has been determined using purified preparations of glutathione reductase (GR) from yeast and bovine glutathione peroxidase (GPx) and equine glutathione S-transferase (GST). The effect on enzyme activity of increasing concentrations (from 1 microM to 100 mM) of commercial sodium arsenite (As(III)) and sodium arsenate (As(V)) and a prepared arsenic(III)-glutathione complex [As(III)(GS)(3)] and methylarsenous diiodide (CH(3)As(III)) has been examined. GR, GPx, and GST are not sensitive to As(V) (IC(50) > 50 mM), and none of the enzymes are inhibited or activated by physiologically relevant concentrations of As(III), As(III)(GS)(3), or CH(3)As(III), although CH(3)As(III) is the most potent inhibitor (0.3 mM < IC(50) < 1.5 mM). GPx is the most sensitive to arsenic treatment and GST the least. Our results do not implicate a direct interaction of As with the glutathione-related enzymes, GR, GPx, and GST, in the mechanism of arsenic toxicity. CH(3)As(III) is the most effective inhibitor, but it is unclear whether this product of arsenic metabolism is produced at a sufficiently high concentration in critical target tissues to play a major role in either arsenic toxicity or carcinogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".