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Antihypertensive Peptides from Food Proteins

2015· review· en· W1982576821 on OpenAlexafffund
Rotimi E. Aluko

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeptideChemistryRenin–angiotensin systemNitric oxideBiochemistryAngiotensin IIVasodilationAngiotensin-converting enzymeEnzymeReceptorPharmacologyEndocrinologyBlood pressureBiology

Abstract

fetched live from OpenAlex

Bioactive peptides are encrypted within the primary structure of food proteins where they remain inactive until released by enzymatic hydrolysis. Once released from the parent protein, certain peptides have the ability to modulate the renin-angiotensin system (RAS) because they decrease activities of renin or angiotensin-converting enzyme (ACE), the two main enzymes that regulate mammalian blood pressure. These antihypertensive peptides can also enhance the endothelial nitric oxide synthase (eNOS) pathway to increase nitric oxide (NO) levels within vascular walls and promote vasodilation. The peptides can block the interactions between angiotensin II (vasoconstrictor) and angiotensin receptors, which can contribute to reduced blood pressure. This review focuses on the methods that are involved in antihypertensive peptide production from food sources, including fractionation protocols that are used to enrich bioactive peptide content and enhance peptide activity. It also discusses mechanisms that are believed to be involved in the antihypertensive activity of these peptides.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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

Citations372
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of Food Science and TechnologySame topicProtein Hydrolysis and Bioactive PeptidesFrench-language works237,207