Effects of Hydrogen Sulfide on the Microbiome: From Toxicity to Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Significance: Hydrogen sulfide (H 2 S), an important regulator of physiology and health, helps resolve inflammation and promotes tissue repair in the gastrointestinal tract. Recent Advances: Gut microbiota live as a multispecies biofilm in close interaction with the upper mucus layer lining the epithelium. The relative abundance, spatial organization, and function of these microorganisms affect a broad range of health outcomes. This article provides a state-of-the-art review of our understanding of the cross talk between H 2 S, the gut microbiota, and health. H 2 S can have toxic or therapeutic effects, depending on its concentration and source. When produced at excessive concentrations by local microbiota, H 2 S may cause mucus disruption and inflammation and contribute to development of cancer. In contrast, low levels of endogenous or exogenous H 2 S directly stabilize mucus layers, prevent fragmentation and adherence of the microbiota biofilm to the epithelium, inhibit the release of invasive pathobionts, and help resolve inflammation and tissue injury. Although scarce, research findings suggest that dietary H 2 S obtained from plants or ingestion of the H 2 S precursor, L-cysteine, may also modulate the abundance and function of microbiota. Critical Issues: A critical issue is the lack of understanding of the metagenomic, transcriptomic, and proteomic alterations that characterize the interactions between H 2 S and gut microbiota to shape health outcomes. Future Directions: The ambivalent roles of H 2 S in the gut offer a fertile ground for research on such critical issues. The findings will improve our understanding of how H 2 S modulates the microbiota to affect body function and will help identify novel therapeutic strategies. Antioxid. Redox Signal . 36, 211–219.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it