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Record W4401308499 · doi:10.1002/mnfr.202400251

Interplay of Food‐Derived Bioactive Peptides with Gut Microbiota: Implications for Health and Disease Management

2024· review· en· W4401308499 on OpenAlexaff
Mingyang Hu, Yufeng Du, Wenyue Li, Xiaomei Zong, Wenjuan Du, Hui‐Zeng Sun, Hongyun Liu, Ke Zhao, Jianxiong Li, Muhammad Zahid Farooq, Jianping Wu, Qingbiao Xu

Bibliographic record

VenueMolecular Nutrition & Food Research · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Hubei Province
KeywordsGut floraBiologyDiseaseGut bacteriaImmune systemComputational biologyImmunityBioinformaticsImmunologyMedicine

Abstract

fetched live from OpenAlex

Bioactive peptides (BPs) are protein fragments with beneficial effects on metabolism, physiology, and diseases. This review focuses on proteolytic BPs, which are produced by the action of gut microbiota on proteins in food and have demonstrated to influence the composition of gut microbes. And gut microbiota are candidate targets of BPs to alleviate oxidative stress, enhance immunity, and control diseases, including diabetes, hypertension, obesity, cancer, and immune and neurodegenerative diseases. Despite promising results, further research is needed to understand the mechanisms underlying the interactions between BPs and gut microbes, and to identify and screen more BPs for industrial applications. Overall, BPs offer potential as therapeutic agents for various diseases through their interactions with gut microbes, highlighting the importance of continued research in this area.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.057
GPT teacher head0.410
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2024
Admission routes1
Has abstractyes

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