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Record W2606712557 · doi:10.1097/mco.0000000000000360

Role of muscle stem cells in sarcopenia

2017· review· en· W2606712557 on OpenAlexaff
Tim Snijders, Gianni Parise

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSarcopeniaSkeletal muscleSatelliteStem cellRegeneration (biology)MedicineAutophagyNeuroscienceBiologyCell biologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The review is an update on recent research investigating the role of skeletal muscle stem cells (also known as satellite cells) during muscle fiber regeneration and growth in animal and human skeletal muscle, with an emphasis on their role in age-related sarcopenia. RECENT FINDINGS: Studies indicate clear impairments in satellite cell function with aging, resulting in an impaired muscle fiber regenerative response. The autophagy-mediated switch to an irreversible presenescent state of geriatric satellite cells appears to play a key role in age-related impaired satellite cell function. In addition, inadequate muscle fiber vascularization may be a crucial factor underlying impaired regulation of satellite cells in older adults. Controversy remains on the actual contribution of satellite cells to the development of sarcopenia in later life, this clearly requires further research. Nevertheless, exercise training remains to be a potent intervention strategy, mediated through satellite cells or not, to counteract the ill effects of sarcopenia. SUMMARY: Although important strides are made investigating the importance of satellite cells in the development and/or treatment of sarcopenia, the idea that satellite cell function is a therapeutic target to treat sarcopenia remains controversial.

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 score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.152
GPT teacher head0.463
Teacher spread0.311 · 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

Citations53
Published2017
Admission routes1
Has abstractyes

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