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Record W2095404298 · doi:10.3389/fphys.2012.00273

The role of the Wnt signaling pathway in incretin hormone production and function

2012· article· en· W2095404298 on OpenAlexafffund
Yu‐Ting Chiang, Wilfred Ip, Tianru Jin

Bibliographic record

VenueFrontiers in Physiology · 2012
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Toronto
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health Research
KeywordsIncretinWnt signaling pathwayFunction (biology)HormoneCell biologySignal transductionProduction (economics)BiologyEndocrinologyInternal medicineBioinformaticsMedicineDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

Glucose metabolism is tightly controlled by multiple hormones and neurotransmitters in response to nutritional, environmental, and emotional changes. In addition to insulin and glucagon produced by pancreatic islets, two incretin hormones, namely glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP, also known as glucose-dependent insulinotropic peptide), also play important roles in blood glucose homeostasis. The incretin hormones mainly exert their regulatory effects via their corresponding receptors, which are expressed in pancreatic islets as well as many other extra-pancreatic organs. Recent studies have shown that the genes which encode these two incretin hormones can be regulated by the effectors of the Wnt signaling pathway, including TCF7L2, a transcription factor identified recently by extensive genome wide association studies as an important type 2 diabetes risk gene. Interestingly, TCF7L2 and β-catenin (β-cat), another effector of Wnt signaling pathway, may also mediate the function of the incretin hormones as well as the expression of their receptors in pancreatic β-cells. In this review, we have introduced the incretin hormones and the Wnt signaling pathway, summarized recent findings in the field, and provided our perspectives.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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 designObservational
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

Citations52
Published2012
Admission routes2
Has abstractyes

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