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Record W2620825787 · doi:10.1155/2017/3165396

Impact of Aging and Exercise on Mitochondrial Quality Control in Skeletal Muscle

2017· review· en· W2620825787 on OpenAlexafffund
Yuho Kim, Matthew Triolo, David A. Hood

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2017
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSkeletal musclePhysical medicine and rehabilitationMedicineInternal medicine

Abstract

fetched live from OpenAlex

Mitochondria are characterized by its pivotal roles in managing energy production, reactive oxygen species, and calcium, whose aging‐related structural and functional deteriorations are observed in aging muscle. Although it is still unclear how aging alters mitochondrial quality and quantity in skeletal muscle, dysregulation of mitochondrial biogenesis and dynamic controls has been suggested as key players for that. In this paper, we summarize current understandings on how aging regulates muscle mitochondrial biogenesis, while focusing on transcriptional regulations including PGC‐1 α , AMPK, p53, mtDNA, and Tfam. Further, we review current findings on the muscle mitochondrial dynamic systems in aging muscle: fusion/fission, autophagy/mitophagy, and protein import. Next, we also discuss how endurance and resistance exercises impact on the mitochondrial quality controls in aging muscle, suggesting possible effective exercise strategies to improve/maintain mitochondrial health.

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)0.001
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.410
Teacher spread0.319 · 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

Citations157
Published2017
Admission routes2
Has abstractyes

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