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Record W1493023785 · doi:10.5772/34095

Distinct Role for ARNT/HIF-1β in Pancreatic Beta-Cell Function, Insulin Secretion and Type 2 Diabetes

2012· book-chapter· en· W1493023785 on OpenAlexaff
Renjitha Pillai, Whitney E. Jamie

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAryl hydrocarbon receptor nuclear translocatorBeta cellInternal medicineType 2 diabetesEndocrinologyBETA (programming language)InsulinFunction (biology)Diabetes mellitusMedicineChemistryBiologyCell biologyBiochemistryComputer scienceGeneIsletTranscription factor

Abstract

fetched live from OpenAlex

Diabetes mellitus is a common metabolic syndrome that has become an epidemic in modern society and is characterized by either a near-complete lack of insulin production due to autoimmune destruction of pancreatic beta-cells as in type 1 diabetes or abnormal insulin secretion, beta-cell dysfunction and insulin resistance as in type 2 diabetes (T2D). T2D is a complex heterogeneous disease that is characterized by elevated fasting and postprandial blood glucose levels that can result in severe complications including renal failure, cardiovascular disease, blindness and slow wound healing (Lin and Sun, 2010). Pancreatic islet beta-cells play a critical role in maintaining blood glucose levels by secreting the hormone insulin following a meal. Insulin maintains blood glucose levels in the normal physiological range by promoting glucose uptake in muscles, liver and adipose tissue, and by inhibiting hepatic glucose production. Therefore, any defect in insulin secretion in response to a meal or defects in insulin action in peripheral tissues can lead to increased blood glucose levels (Tripathy and Chavez, 2010; Muoio & Newgard, 2008).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.229
Teacher spread0.212 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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