Hydrogen sulfide-based therapeutics and gastrointestinal diseases: translating physiology to treatments
Bibliographic record
Abstract
Hydrogen sulfide (H2S) is a gaseous meditator that has various physiological and pathophysiological roles in the body. It has been shown to be an important mediator of gastrointestinal (GI) mucosal defense and contributes significantly to repair of damage and resolution of inflammation. Synthesis of H2S increases markedly after mucosal injury, and inhibition of H2S in such circumstances leads to delayed healing and exacerbated inflammation. The beneficial effects of H2S may be attributable to its ability to elevate mucosal blood flow, prevent leukocyte-endothelial adhesion, reduce oxidative stress, and stimulate angiogenesis. The use of H2S-donating agents and inhibitors of the key enzymes contributing to H2S synthesis have provided strong evidence for the importance of H2S in enhancing mucosal resistance to damage, as well as modulating inflammation and repair. In recent years, significant evidence has been generated to support the notion that these positive aspects of H2S can be exploited in drug design, particularly for arthritis, inflammatory bowel disease, and colon cancer chemoprevention. Thus novel H2S-based therapies have been shown to be effective anti-inflammatories that can promote the resolution of inflammation and accelerate the healing of GI ulcers. Encouraging results have already been seen experimentally with a mesalamine derivative and with H2S-releasing derivatives of nonsteroidal anti-inflammatory drugs.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".