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Record W1871560978 · doi:10.1139/bcb-2015-0057

Does epigenetic dysregulation of pancreatic islets contribute to impaired insulin secretion and type 2 diabetes?

2015· review· en· W1871560978 on OpenAlexvenueno aff
Tasnim Dayeh, Charlotte Ling

Bibliographic record

VenueBiochemistry and Cell Biology · 2015
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
Fundersnot available
KeywordsEpigenomeEpigeneticsType 2 diabetesLipotoxicityInsulin resistanceBiologyInsulinEndocrinologyEpigenomicsInternal medicineDiabetes mellitusDNA methylationMedicineGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

β cell dysfunction is central to the development and progression of type 2 diabetes (T2D). T2D develops when β cells are not able to compensate for the increasing demand for insulin caused by insulin resistance. Epigenetic modifications play an important role in establishing and maintaining β cell identity and function in physiological conditions. On the other hand, epigenetic dysregulation can cause a loss of β cell identity, which is characterized by reduced expression of genes that are important for β cell function, ectopic expression of genes that are not supposed to be expressed in β cells, and loss of genetic imprinting. Consequently, this may lead to β cell dysfunction and impaired insulin secretion. Risk factors that can cause epigenetic dysregulation include parental obesity, an adverse intrauterine environment, hyperglycemia, lipotoxicity, aging, physical inactivity, and mitochondrial dysfunction. These risk factors can affect the epigenome at different time points throughout the lifetime of an individual and even before an individual is conceived. The plasticity of the epigenome enables it to change in response to environmental factors such as diet and exercise, and also makes the epigenome a good target for epigenetic drugs that may be used to enhance insulin secretion and potentially treat diabetes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.275
Teacher spread0.257 · 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

Citations41
Published2015
Admission routes1
Has abstractyes

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