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Record W4221099083 · doi:10.1021/acs.jafc.1c06289

Bioactive Peptides: From Basic Research to Clinical Trials and Commercialization

2022· review· en· W4221099083 on OpenAlexafffund
Pauline Duffuler, Khushwant S. Bhullar, Stepheny C. de Campos Zani, Jianping Wu

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCommercializationHydrolysateClinical trialBiotechnologyMedicineFunctional foodPharmacologyNutraceuticalBiologyBusinessBiochemistryPathology

Abstract

fetched live from OpenAlex

Chronic diseases, including metabolic diseases, have become a worldwide public health issue. Research regarding the use of bioactive peptides or protein hydrolysates derived from food, as the diet-based strategies for the prevention and mitigation of chronic diseases, has increased exponentially in the past decades. Numerous in vitro and in vivo studies report the efficacy and safety of food-derived bioactive peptides and protein hydrolysates as antihypertensive, anti-inflammatory, antidiabetic, and antioxidant agents. However, despite promising preclinical results, an inadequate understanding of their mechanisms of action and pharmacokinetics restrict their clinical translation. Commercialization of bioactive peptides can be further hindered due to scarce information regarding their efficacy, safety, bitter taste, as well as the lack of a cost-effective method of production. This review provides an overview of the current clinical evidence and challenges to commercial applications of food-derived bioactive peptides and protein hydrolysates for the prevention and alleviation of chronic diseases.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.978
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.264
GPT teacher head0.459
Teacher spread0.194 · 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 teacher head, 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

Citations99
Published2022
Admission routes2
Has abstractyes

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