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Record W2768424037 · doi:10.1111/boc.201700059

PERK leads a hub dictating pancreatic β cell homoeostasis

2017· review· en· W2768424037 on OpenAlexafffund
George Kefalas, Louise Larose

Bibliographic record

VenueBiology of the Cell · 2017
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health ResearchServier
KeywordsBiologyEndoplasmic reticulumHomeostasisCellCell biologyEmbryonic stem cellKinaseDiabetes mellitusBeta cellGlucose homeostasisPathogenesisBioinformaticsEndocrinologyImmunologyGeneInsulin resistanceGeneticsIslet

Abstract

fetched live from OpenAlex

In humans, the pathogenesis of diabetes is characterised by two major pancreatic β cell defects: a reduction in β cell mass and the failure of β cells to produce enough insulin. Over the past two decades, multiple studies involving cell cultures, animal models and human subjects have established the importance of the protein kinase RNA-like endoplasmic reticulum kinase (PERK) in the adaptive functional capacity of pancreatic β cells during embryonic development and into adulthood. In this review, we will highlight major findings identifying PERK as a crucial player in β cell physiology and in 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.090
GPT teacher head0.354
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2017
Admission routes2
Has abstractyes

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