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Record W4315490377 · doi:10.3389/fphys.2022.1106425

Potential mechanisms involved in regulating muscle protein turnover after acute exercise: A brief review

2023· review· en· W4315490377 on OpenAlexaff
Guy Hajj‐Boutros, Antony D. Karelis, Marina Cefis, José A. Morais, Juliette Casgrain, Gilles Gouspillou, Vita Sonjak

Bibliographic record

VenueFrontiers in Physiology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcGill UniversityUniversité du Québec à MontréalMcGill University Health Centre
Fundersnot available
KeywordsSkeletal muscleAMPKResistance trainingPI3K/AKT/mTOR pathwayMedicineExercise physiologyMuscle massMuscle hypertrophyEndocrinologyProtein turnoverPhysical exerciseSignal transductionInternal medicineBioinformaticsBiologyPhosphorylationProtein kinase ACell biologyProtein biosynthesisBiochemistry

Abstract

fetched live from OpenAlex

It is well established that resistance training increases muscle mass. Indeed, there is evidence to suggest that a single session of resistance training is associated with an increase in muscle protein synthesis in young adults. However, the fundamental mechanisms that are involved in regulating muscle protein turnover rates after an acute bout of physical exercise are unclear. Therefore, this review will briefly focus on summarizing the potential mechanisms behind the growth of skeletal muscle after physical exercise. We also present mechanistic differences that may exist between young and older individuals during muscle protein synthesis and breakdown after physical exercise. Pathways leading to the activation of AKT/mTOR signals after resistance exercise and the activation of AMPK signaling pathway following a HIIT (High intensity interval training) are discussed.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.266
Teacher spread0.253 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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