Dynamic Multi-Stage Gastrointestinal Digestion Model Assessment of Microbial Fermentation Products of Collagen Hydrolysates
Bibliographic record
Abstract
Proteins, peptides and amino acids (AAs) that bypass upper gastrointestinal (GI) digestion can be fermented in the colonic regions. This could lead to microbial production of health promoting short-chain fatty acids (SCFAs). Nitrogenous compounds can also be fermented to generate potentially harmful branched chain fatty acids (BCFAs). As collagen hydrolysate (CH) supplements contain a high peptide content, we evaluated whether peptides that undergo intestinal CH digestion and microbial fermentation can generate SCFAs and BCFAs. Two bovine-sourced CH formulations (CH-GL and CH-OPT) underwent digestive processes and microbial fermentation for 24 h in a dynamic GI digestion model containing human fecal matter. After 24 h, CH-OPT showed a significant (p < 0.05) increase in SCFAs (propionic, butyric and valeric acids) in the ascending colonic vessel with no changes observed with CH-GL. Only CH-OPT showed a significant (p < 0.05) increase in BCFAs, also noted in the ascending colon. No significant (p < 0.05) changes to SCFAs and BCFAs were observed in the transverse and descending colons for both CHs. These findings demonstrate that CHs can induce microbial production of SCFAs and BCFAs although this appears to depend on the CH tested. More studies are needed to determine the physiological significance of these microbial metabolites from intake of CH supplements.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".