Microbial Fermentation of Dietary Protein: An Important Factor in Diet–Microbe–Host Interaction
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
Protein fermentation by gut microbiota contributes significantly to the metabolite pool in the large intestine and may contribute to host amino acid balance. However, we have a limited understanding of the role that proteolytic metabolites have, both in the gut and in systemic circulation. A review of recent studies paired with findings from previous culture-based experiments suggests an important role for microbial protein fermentation in altering the gut microbiota and generating a diverse range of bioactive molecules which exert wide-ranging host effects. These metabolic products have been shown to increase inflammatory response, tissue permeability, and colitis severity in the gut. They are also implicated in the development of metabolic disease, including obesity, diabetes, and non-alcoholic fatty liver disease (NAFLD). Specific products of proteolytic fermentation such as hydrogen sulfide, ammonia, and p-Cresol may also contribute to the development of colorectal cancer. These findings are in conflict with other studies showing that tryptophan metabolites may improve gut barrier function and attenuate severity in a multiple sclerosis model. Further research examining proteolytic fermentation in the gut may be key to our understanding of how microbial and host metabolism interact affecting 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.
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) | 0.001 | 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 | 0.001 | 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