Regulatory Potential of Food-Derived Bioactive Peptides on Gut Microbiota: A New Perspective against Immune-Mediated Inflammatory Diseases
Bibliographic record
Abstract
Immune-mediated inflammatory diseases (IMIDs) are characterized by chronic inflammation and frequent recurrence, and intervention strategies are urgently needed. Gut microbiota has been shown to play an active role in immune activation and regulation, with food-derived bioactive peptides (FDBPs) effectively improving the homeostasis of the gut microbiota. The potential role of FDBPs in IMIDs intervention is receiving more and more attention due to their ability to regulate gut microbiota. This review first addresses the way FDBPs regulate the gut microbiota, comprehensively summarizing the factors that affect its homeostasis and focusing on the interactions between gut microbiota and IMIDs through signaling pathways and the microbiota-gut-X axis. The review on the role of gut microbiota metabolites emphasizes the importance of FDBPs in regulating the gut microbiota and their potential for nutritional intervention in IMIDs. There can be direct and indirect between gut microbiota and IMIDs. Since FDBPs can regulate the gut microbiota through multiple pathways, dietary intervention, especially peptide diets as dietary supplements, seems to be a reliable strategy for the improvement and alleviation of IMIDs. In the future, we will focus on the structure and bioavailability of FDBPs, aiming to demonstrate, through this structure-function relationship, the different effects of the gut microbiota on IMIDs.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".