Molecular Mechanisms of Hydrogen Sulfide Toxicity
Bibliographic record
Abstract
RATIONALE: The toxicity of H2S has been attributed to its ability to inhibit cytochrome c oxidase in a similar manner to HCN. However, the successful use of methemoglobin for the treatment of HCN poisoning was not successful for H2S poisonings even though the ferric heme group of methemoglobin scavenges H2S. Thus, we speculated that other mechanisms contribute to H2S induced cytotoxicity. Experimental procedure. Hepatocyte isolation and viability and enzyme activities were measured as described by Moldeus et al. (1978), and Steen et al. (2001). RESULTS: Incubation of isolated hepatocytes with NaHS solutions (a H2S source) resulted in glutathione (GSH) depletion. Moreover, GSH depletion was also observed in TRIS-HCl buffer (pH 6.0) treated with NaHS. Several ferric chelators (desferoxamime and DETAPAC) and antioxidant enzymes (superoxide dismutase [SOD] and catalase) prevented cell-free and hepatocyte GSH depletion. GSH-depleted hepatocytes were very susceptible to NaHS cytotoxicity, indicating that GSH detoxified NaHS or H2S in cells. Cytotoxicity was also partly prevented by desferoxamine and DETAPC, but it was increased by ferric EDTA or EDTA. Cell-free oxygen consumption experiments in TRIS-HCl buffer showed that NaHS autoxidation formed hydrogen peroxide and was prevented by DETAPC but increased by EDTA. We hypothesize that H2S can reduce intracellular bound ferric iron to form unbound ferrous iron, which activates iron. Additionally, H2S can increase the hepatocyte formation of reactive oxygen species (ROS) (known to occur with electron transport chain). H2S cytotoxicity therefore also involves a reactive sulfur species, which depletes GSH and activates oxygen to form ROS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".