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Record W1987122034 · doi:10.2174/1874609811104030234

The Impact of Old Age on Skeletal Muscle Energetics: Supply and Demand

2011· review· en· W1987122034 on OpenAlexaff
David W. Russ, Ian R. Lanza

Bibliographic record

VenueCurrent Aging Science · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
Fundersnot available
KeywordsSkeletal muscleWastingSarcopeniaOxidative phosphorylationGlycolysisBiologyEnergy demandBioenergeticsEndocrinologyInternal medicineMetabolismCell biologyBiochemistryMedicineMitochondrionEconomics

Abstract

fetched live from OpenAlex

Properly functioning skeletal muscle is critical for locomotion and performance of many activities of daily living. Muscle wasting and decreased function of skeletal muscle are important factors in many age-related morbidities. There are several pathways for generating ATP in skeletal muscle that allow adequate ATP supply to meet increased demand during muscle activity. A growing body of literature provides evidence that the aging process may be accompanied by changes in metabolic supply and demand during muscle contractions. Herein, we review a body of evidence that several pathways of ATP synthesis (anaerobic glycolysis, oxidative phosphorylation) may be impaired in aging skeletal muscle as well as several underlying molecular and cellular mechanisms. However, detrimental effects of aging on muscle energy metabolism are not universally accepted, particularly when physical inactivity is accounted for. We discuss this important concept as well as several potential countermeasures that may compress the period of morbidity in old age. In the second half of this review, we discuss how energetic demand of skeletal muscle is affected by aging, with specific focus on basal and contractile ATPase activity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.347
Teacher spread0.312 · 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 designOther design
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

Citations27
Published2011
Admission routes1
Has abstractyes

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