Hydrogen Sulfide-Based Anti-Inflammatory and Chemopreventive Therapies: An Experimental Approach
Bibliographic record
Abstract
Hydrogen sulfide has potent anti-inflammatory and cytoprotective properties. In the gastrointestinal tract, hydrogen sulfide contributes significantly to mucosal defence and responses to injury. This includes promotion of resolution of inflammation and healing. Inhibition of hydrogen sulfide synthesis increases the susceptibility of the gastrointestinal mucosa to injury and delays healing processes. The beneficial effects of hydrogen sulfide have been exploited in the design of novel anti-inflammatory drugs that cause negligible gastrointestinal damage. Nonsteroidal anti-inflammatory drugs are known to be effective, when used chronically, in reducing the incidence of several types of cancer. However, the toxicity of these drugs, particularly in the gastrointestinal tract, greatly limits this use. On the other hand, the gastrointestinal-safe, hydrogen sulfide-releasing anti-inflammatories show great promise for chemoprevention of cancers. This paper reviews the evidence supporting important anti-inflammatory and cytoprotective effects of hydrogen sulfide, particularly in the gastrointestinal tract. Also reviewed are the approaches taken to develop safer anti-inflammatory and cancer chemopreventive drugs by exploiting the beneficial effects of hydrogen sulfide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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".