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Record W2053347391 · doi:10.1155/2012/642038

Mitochondrial Dysregulation in the Pathogenesis of Diabetes: Potential for Mitochondrial Biogenesis-Mediated Interventions

2011· review· en· W2053347391 on OpenAlexafffund
Bertrand Joseph, Denis R. Joanisse, Richard Baillot, David A. Hood

Bibliographic record

VenueExperimental Diabetes Research · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversité LavalYork University
FundersNational Institute on AgingNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMitochondrial biogenesisMitochondrionCrosstalkCell biologyBiologyMitochondrial DNARetrograde signalingDNAJA3BiogenesisSkeletal muscleSignal transductionmitochondrial fusionGeneticsEndocrinologyGene

Abstract

fetched live from OpenAlex

Muscle mitochondrial metabolism is a tightly controlled process that involves the coordination of signaling pathways and factors from both the nuclear and mitochondrial genomes. Perhaps the most important pathway regulating metabolism in muscle is mitochondrial biogenesis. In response to physiological stimuli such as exercise, retrograde signaling pathways are activated that allow crosstalk between the nucleus and mitochondria, upregulating hundreds of genes and leading to higher mitochondrial content and increased oxidation of substrates. With type 2 diabetes, these processes can become dysregulated and the ability of the cell to respond to nutrient and energy fluctuations is diminished. This, coupled with reduced mitochondrial content and altered mitochondrial morphology, has been directly linked to the pathogenesis of this disease. In this paper, we will discuss our current understanding of mitochondrial dysregulation in skeletal muscle as it relates to type 2 diabetes, placing particular emphasis on the pathways of mitochondrial biogenesis and mitochondrial dynamics, and the therapeutic value of exercise and other interventions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.107
GPT teacher head0.399
Teacher spread0.292 · 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.

Study designBench or experimental
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

Citations114
Published2011
Admission routes2
Has abstractyes

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