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Record W2032084975 · doi:10.1139/h09-073

mTOR function in skeletal muscle: a focal point for overnutrition and exercise

2009· review· en· W2032084975 on OpenAlexvenueno aff
Donato A. Rivas, Sarah J. Lessard, Vernon G. Coffey

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwaySkeletal muscleRPTORmTORC1BiologymTORC2OvernutritionMechanistic target of rapamycinEndocrinologyInternal medicineInsulinAMPKCell biologySirolimusProtein kinase AKinaseMedicineSignal transductionObesity

Abstract

fetched live from OpenAlex

The mammalian target of rapamycin (mTOR) is a highly conserved atypical serine-threonine kinase that controls numerous functions essential for cell homeostasis and adaptation in mammalian cells via 2 distinct protein complex formations. Moreover, mTOR is a key regulatory protein in the insulin signalling cascade and has also been characterized as an insulin-independent nutrient sensor that may represent a critical mediator in obesity-related impairments of insulin action in skeletal muscle. Exercise characterizes a remedial modality that enhances mTOR activity and subsequently promotes beneficial metabolic adaptation in skeletal muscle. Thus, the metabolic effects of nutrients and exercise have the capacity to converge at the mTOR protein complexes and subsequently modify mTOR function. Accordingly, the aim of the present review is to highlight the role of mTOR in the regulation of insulin action in response to overnutrition and the capacity for exercise to enhance mTOR activity in 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 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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations41
Published2009
Admission routes1
Has abstractyes

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