MétaCan
Menu
Back to cohort
Record W2115538617 · doi:10.1139/h09-010

Changes in muscle mass with mechanical load: possible cellular mechanismsThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.

2009· review· en· W2115538617 on OpenAlexvenueno aff
Espen E. Spangenburg

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsPI3K/AKT/mTOR pathwayProtein kinase BSkeletal muscleMechanism (biology)Signal transductionMuscle hypertrophyCell biologyBiologyChemistryEndocrinology

Abstract

fetched live from OpenAlex

Understanding the mechanisms that regulate skeletal muscle mass has remained a focus of numerous researchers for many years. Recent investigations have begun to elucidate cellular signaling mechanisms that regulate skeletal muscle hypertrophy, with significant effort being focused on the Akt/mammalian target of rapamycin (mTOR) signaling pathway. The Akt/mTOR pathway plays a major role in regulating the initiation of protein synthesis after the onset of mechanical loading of skeletal muscle. Although a number of downstream substrates for Akt/mTOR have been elucidated, very little is known about the upstream mechanisms that mechanical load employs to activate the Akt/mTOR signaling pathway. Thus, the purpose of this review is to discuss potential mechanisms that may contribute to the activation of the Akt/mTOR signaling mechanism in mechanically loaded skeletal muscle.

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 categoriesInsufficient payload (model declined to judge)
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 score0.520
Threshold uncertainty score1.000

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations57
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physiology Nutrition and MetabolismSame topicMuscle Physiology and DisordersFrench-language works237,207