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Record W2210232346 · doi:10.1002/mnfr.201500783

<i>Trans</i>‐11 vaccenic acid improves insulin secretion in models of type 2 diabetes in vivo and in vitro

2015· article· en· W2210232346 on OpenAlexaff
Xiaofeng Wang, Joel Gupta, Matthew Kerslake, Gina R. Rayat, Spencer D. Proctor, Catherine B. Chan

Bibliographic record

VenueMolecular Nutrition & Food Research · 2015
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFree fatty acid receptor 1Internal medicineEndocrinologyIsletInsulinType 2 diabetesIn vivoDiabetes mellitusBiologyFatty acidReceptorIn vitroSecretionChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

SCOPE: Trans-11 vaccenic acid (VA) is a fatty acid produced by ruminants entering the human food supply through meat and dairy products, which appears not to have the health risks associated with industrially produced trans-fatty acids. In this study, we investigated the effect of VA on insulin secretion in vivo in rats and in vitro in human and rat islets after diabetogenic insult. METHODS AND RESULTS: Hyperglycemic clamp showed that VA dietary supplementation for 8 weeks significantly increased glucose turnover in rats with type 2 diabetes (T2D), accompanied by an elevated plasma C-peptide concentration, indicating improved insulin secretion. The β-cell area and proliferation rate were higher in T2D+VA than T2D group. Isolated islets from T2D+VA rats had higher glucose-stimulated insulin secretion (GSIS) than T2D group. In vitro, VA treatment for 24 and 48 h significantly enhanced GSIS in rat and human islets after diabetogenic challenges. The mRNA expression of G-protein-coupled receptor 40 (GPR40) and regenerating islet-derived 1α (REG-1α) were consistently increased by VA in both rat and human islets. CONCLUSION: These results indicate that VA may improve insulin secretion and growth of islets in T2D, at least partly by altering GPR40 and REG-1α mRNA expression.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations31
Published2015
Admission routes1
Has abstractyes

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