Plant-based antioxidant peptides: impact on oxidative stress and gut microbiota
Bibliographic record
Abstract
Plant-based peptides can be obtained from natural and climate-friendly sources. These peptides show various bioactivities including antioxidant activity. Oxidative stress has an impact on the gut microbiota causing inflammation, insulin resistance, osteoporosis, cancer, and several chronic diseases like type 2 diabetes, arthritis, hypertension, and atherosclerosis. Therefore, antioxidant peptides may significantly affect oxidative stress as a potential alternative to conventional medication. The production of antioxidant peptides from plant-based protein sources through conventional and innovative approaches may provide promising strategies to improve gut microbiota. Recent studies in plant-based antioxidant peptides (PBAP) focus on their advanced identification and characterization techniques, structure-activity relationship, improvement of extraction and purification, cellular and molecular mechanisms, specific health applications in preventing and managing conditions with gut microbiota, and commercial applications in nutraceuticals. Short-chain fatty acids and reactive sulfur species are specific gut-derived metabolites that can improve metabolic function by modulating oxidative stress and the immune system. This review highlights the influence of food oxidants on the gut microbiota and PBAP-induced modulation of gut microbiota. Moreover, the production of PBAP and the challenges in their application will be discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".