Effect of Bioactive Peptides on Gut Microbiota and Their Relations to Human Health
Bibliographic record
Abstract
Bioactive peptides derived from both exogenous and endogenous origins have been studied extensively to use their beneficial effects in humans and animals. Bioactive peptides exhibit beneficial bodily functions and contribute to a healthy gastrointestinal system by influencing barrier functions, immune responses, and gut microbiota. Gut microbiota is a diverse microbial community that significantly influences the overall well-being and homeostasis of the body. Factors such as diet, age, lifestyle, medication, and environmental circumstances can affect the composition and diversity of the gut microbiota. The disturbances or imbalances in the gut microbiota have been associated with various health problems. The interplays between bioactive peptides and gut microbiota are not fully understood, but bioactive peptides hold promise as modulators of the gut microbiota to promote gut health. Almost all the bioactive research on human health, including the development of therapeutics and nutritional interventions, uses cell culture, even though their direct biofunctional activities can only occur when absorbed in the intestine and into the blood system. This review focuses on the current understanding of bioactive peptides in gut microbiota and their impact and mechanisms on gut and human health. The novelty of this review lies in its comprehensive analysis of the multifaceted interactions between bioactive peptides and gut microbiota, integrating knowledge from diverse disciplines between microbiology and nutrition. By elucidating the underlying mechanisms and identifying current research gaps, this review offers an outlook on the potential of bioactive peptides in promoting gut health and shaping future therapeutic and nutritional interventions.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".