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Record W1962018834 · doi:10.1139/bcb-2015-0012

Targeting skeletal muscle mitochondria to prevent type 2 diabetes in youth

2015· review· en· W1962018834 on OpenAlexafffundvenue
Joseph W. Gordon, Vernon W. Dolinsky, Wajihah Mughal, Grant R. Gordon, Jonathan McGavock

Bibliographic record

VenueBiochemistry and Cell Biology · 2015
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of CalgaryChildren's Hospital Research Institute of Manitoba
FundersCanadian Institutes of Health Research
KeywordsInsulin resistanceSkeletal muscleType 2 diabetesInsulinEndocrinologyMitochondrionInternal medicineDiabetes mellitusHomeostasisMedicineGlucose homeostasisBiologyBioinformaticsCell biology

Abstract

fetched live from OpenAlex

The prevalence of type 2 diabetes (T2D) has increased dramatically over the past two decades, not only among adults but also among adolescents. T2D is a systemic disorder affecting every organ system and is especially damaging to the cardiovascular system, predisposing individuals to severe cardiac and vascular complications. The precise mechanisms that cause T2D are an area of active research. Most current theories suggest that the process begins with peripheral insulin resistance that precedes failure of the pancreatic β-cells to secrete sufficient insulin to maintain normoglycemia. A growing body of literature has highlighted multiple aspects of mitochondrial function, including oxidative phosphorylation, lipid homeostasis, and mitochondrial quality control in the regulation of peripheral insulin sensitivity. Whether the cellular mechanisms of insulin resistance in adults are comparable to that in adolescents remains unclear. This review will summarize both clinical and basic studies that shed light on how alterations in skeletal muscle mitochondrial function contribute to whole body insulin resistance and will discuss the evidence supporting high-intensity exercise training as a therapy to circumvent skeletal muscle mitochondrial dysfunction to restore insulin sensitivity in both adults and adolescents.

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 score0.993
Threshold uncertainty score0.947

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 integrity0.0010.000
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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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

Citations31
Published2015
Admission routes3
Has abstractyes

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