MétaCan
Menu
Back to cohort
Record W3042472803 · doi:10.3389/fphys.2020.00816

Sarcoplasmic Hypertrophy in Skeletal Muscle: A Scientific “Unicorn” or Resistance Training Adaptation?

2020· review· en· W3042472803 on OpenAlexfundno aff
Michael D. Roberts, Cody T. Haun, Christopher G. Vann, Shelby C. Osburn, Kaelin C. Young

Bibliographic record

VenueFrontiers in Physiology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersJyväskylän YliopistoMcMaster University
KeywordsMyofibrilMuscle hypertrophySarcoplasmSkeletal muscleBiologyCell biologyAnatomyInternal medicineChemistryEndocrinologyEndoplasmic reticulumMedicine

Abstract

fetched live from OpenAlex

Skeletal muscle fibers are multinucleated cells that contain mostly myofibrils suspended in an aqueous media termed the sarcoplasm. Select evidence suggests sarcoplasmic hypertrophy, or a disproportionate expansion of the sarcoplasm relative to myofibril protein accretion, coincides with muscle fiber or tissue growth during resistance training. There is also evidence to support other modes of hypertrophy occur during periods of resistance training including a proportional accretion of myofibril protein with fiber or tissue growth (i.e., conventional hypertrophy), or myofibril protein accretion preceding fiber or tissue growth (i.e., myofibril packing). In this review, we discuss methods that have been used to investigate these modes of hypertrophy. Particular attention is given to sarcoplasmic hypertrophy throughout. Thus, descriptions depicting this process as well as the broader implications of this phenomenon will be posited. Finally, we propose future human and rodent research that can further our understanding in this area of muscle physiology.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.283
Teacher spread0.249 · 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 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

Citations67
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PhysiologySame topicMuscle Physiology and DisordersFrench-language works237,207