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Record W4312052895 · doi:10.1016/j.smhs.2022.11.004

The skeletal muscle fiber periphery: A nexus of mTOR-related anabolism

2022· review· en· W4312052895 on OpenAlexaff
Cassidy T. Tinline‐Goodfellow, Matthew Lees, Nathan Hodson

Bibliographic record

VenueSports Medicine and Health Science · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsmTORC1AnabolismPI3K/AKT/mTOR pathwaySkeletal muscleCell biologyMechanistic target of rapamycinBiologyCatabolismSignal transductionNeuroscienceAnatomyBiochemistry

Abstract

fetched live from OpenAlex

Skeletal muscle anabolism is driven by numerous stimuli such as growth factors, nutrients (i.e., amino acids, glucose), and mechanical stress. These stimuli are integrated by the mechanistic target of rapamycin (mTOR) complex 1 (mTORC1) signal transduction cascade. In recent years, work from our laboratory and elsewhere has sought to unravel the molecular mechanisms underpinning the mTOR-related activation of muscle protein synthesis (MPS), as well as the spatial regulation of these mechanisms within the skeletal muscle cell. These studies have suggested that the skeletal muscle fiber periphery is a region of central importance in anabolism (i.e., growth/MPS). Indeed, the fiber periphery is replete with the substrates, molecular machinery, and translational apparatus necessary to facilitate MPS. This review provides a summary of the mechanisms underpinning the mTOR-associated activation of MPS from cell, rodent, and human studies. It also presents an overview of the spatial regulation of mTORC1 in response to anabolic stimuli and outlines the factors that distinguish the periphery of the cell as a highly notable region of skeletal muscle for the induction of MPS. Future research should seek to further explore the nutrient-induced activation of mTORC1 at the periphery of skeletal muscle fibers.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.044
GPT teacher head0.360
Teacher spread0.317 · 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 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

Citations10
Published2022
Admission routes1
Has abstractyes

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