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Record W3012610993 · doi:10.3390/ijms21062192

Perspectives on the Potential Benefits of Antihypertensive Peptides towards Metabolic Syndrome

2020· review· en· W3012610993 on OpenAlexafffund
Forough Jahandideh

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEgg Farmers of CanadaAlberta Agriculture and Forestry
KeywordsInsulin resistanceMetabolic syndromeAdipose tissueMedicineMechanism (biology)PathogenesisObesityInsulinBioinformaticsEndocrinologyInternal medicineBlood pressurePharmacologyBiology

Abstract

fetched live from OpenAlex

In addition to the regulation of blood pressure, the renin-angiotensin system (RAS) also plays a key role in the onset and development of insulin resistance, which is central to metabolic syndrome (MetS). Due to the interplay between RAS and insulin resistance, antihypertensive compounds may exert beneficial effects in the management of MetS. Food-derived bioactive peptides with RAS blocking properties can potentially improve adipose tissue dysfunction, glucose intolerance, and insulin resistance involved in the pathogenesis of MetS. This review discusses the pathophysiology of hypertension and the association between RAS and pathogenesis of the MetS. The effects of bioactive peptides with RAS modulating effects on other components of the MetS are discussed. While the in vivo reports on the effectiveness of antihypertensive peptides against MetS are encouraging, the exact mechanism by which these peptides infer their effects on glucose and lipid handling is mostly unknown. Therefore, careful design of experiments along with standardized physiological models to study the effect of antihypertensive peptides on insulin resistance and obesity could help to clarify this relationship.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.028
GPT teacher head0.305
Teacher spread0.277 · 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 designOther design
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

Citations35
Published2020
Admission routes2
Has abstractyes

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