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Antihypertensive Peptides from Food Proteins

2015· review· en· W1982576821 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.026
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