Bibliographic record
Abstract
Abstract Eukaryotes and microbiota produce H2S, using the same substrates and enzymes which constitute the reverse-trans-sulfuration and transsulfuration pathways. The homeostasis of gut microbiota impacts on the structural and functional integrity of gut epithelial barrier. Microbiota also serve as signalling sources to inform the host of the metabolism and functional changes. Microbiota dysbiosis negatively affect human health, contributing to diseases like obesity, diabetes, inflammatory bowel diseases, and asthma. Not by coincidence, these pathological conditions are also closely related to the abnormal metabolism and function of H2S signalling.H2S serves as a bacterial signal to the host and the host-produced H2S impacts on the population and size of microbiota. These bi-directional interactions become especially important for the digestion and utilization of sulfur amino acid in diet. Dietary restriction of sulfur amino acid increases the endogenous production of H2S by the host and consequently offers many health benefits. It, on the other hand, decreases the nutritional supply to the microbiota, which could be remedied by the co-application of prebiotics and probiotics. It is strategically sound to target the expression of H2S-producing enzymes in different organs to slow aging processes in our body and promote better health.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".