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Record W2735671187 · doi:10.1093/nutrit/nux028

Proposed mechanisms of the effects of proanthocyanidins on glucose homeostasis

2017· review· en· W2735671187 on OpenAlexaff
Kaiyuan Yang, Catherine B. Chan

Bibliographic record

VenueNutrition Reviews · 2017
Typereview
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProanthocyanidinGlucose homeostasisBiochemistryBioavailabilityHomeostasisBiologyChemistryAntioxidantPharmacologyInsulinEndocrinologyInsulin resistancePolyphenol

Abstract

fetched live from OpenAlex

Proanthocyanidins are a major group of flavonoids in the human diet, known for their strong antioxidant properties. Emerging evidence from clinical studies indicates a role of proanthocyanidins in modulating glucose homeostasis, and higher proanthocyanidin intake has been associated with reduced risk of diabetes. On the other hand, recent studies report limited bioavailability of proanthocyanidins. At relatively low concentrations in the systemic circulation, proanthocyanidins may act as cell-signaling molecules to modulate glucose homeostasis. For example, they affect hepatic glucose production via adenosine monophosphate-activated protein kinase and/or insulin-signaling pathways. There is also evidence for a direct role of proanthocyanidins in modulating several pancreatic β-cell functions: prevention of oxidative stress, enhancement of insulin secretion, and promotion of β-cell survival. Therefore, greater understanding of the potentially beneficial effects of proanthocyanidins on cell-signaling pathways implicated in glucose homeostasis is needed. In addition, further investigation to address the in vivo metabolism of proanthocyanidins and the comparative effectiveness of proanthocyanidin-derived metabolites is warranted. The dosage and the experimental model should be given special attention when results from mechanistic studies using proanthocyanidins are interpreted.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.659
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.088
GPT teacher head0.370
Teacher spread0.282 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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