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Skeletal muscle and resistance exercise training; the role of protein synthesis in recovery and remodeling

2016· review· en· W2530483551 on OpenAlexaff
Chris McGlory, Michaela C. Devries, Stuart M. Phillips

Bibliographic record

VenueJournal of Applied Physiology · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMuscle hypertrophySkeletal muscleProtein turnoverResistance trainingMitochondrial biogenesisBiogenesisProtein biosynthesisRibosome biogenesisPhenotypeBiologyExercise physiologyMedicineInternal medicineEndocrinologyBioinformaticsGeneCell biologyMitochondrionGeneticsRibosomeRNA

Abstract

fetched live from OpenAlex

Exercise results in the rapid remodeling of skeletal muscle. This process is underpinned by acute and chronic changes in both gene and protein synthesis. In this short review we provide a brief summary of our current understanding regarding how exercise influences these processes as well as the subsequent impact on muscle protein turnover and resultant shift in muscle phenotype. We explore concepts of ribosomal biogenesis and the potential role of increased translational capacity vs. translational efficiency in contributing to muscular hypertrophy. We also examine whether high-intensity sprinting-type exercise promotes changes in protein turnover that lead to hypertrophy or merely a change in mitochondrial content. Finally, we propose novel areas for future study that will fill existing knowledge gaps in the fields of translational research and exercise science.

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)0.001
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.244
Teacher spread0.231 · 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

Citations133
Published2016
Admission routes1
Has abstractyes

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