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Record W4205125070 · doi:10.1111/febs.16352

Decline of regenerative potential of old muscle stem cells: contribution to muscle aging

2022· review· en· W4205125070 on OpenAlexafffund
Korin Sahinyan, Felicia Lazure, Darren M. Blackburn, Vahab D. Soleimani

Bibliographic record

VenueFEBS Journal · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsStem cellRegenerative medicineCell biologyAgeingMyocyteBiologyGenetics

Abstract

fetched live from OpenAlex

Muscle stem cells (MuSCs) are required for life-long muscle regeneration. In general, aging has been linked to a decline in the numbers and the regenerative potential of MuSCs. Muscle regeneration depends on the proper functioning of MuSCs, which is itself dependent on intricate interactions with its niche components. Aging is associated with both cell-intrinsic and niche-mediated changes, which can be the result of transcriptional, posttranscriptional, or posttranslational alterations in MuSCs or in the components of their niche. The interplay between cell intrinsic alterations in MuSCs and changes in the stem cell niche environment during aging and its impact on the number and the function of MuSCs is an important emerging area of research. In this review, we discuss whether the decline in the regenerative potential of MuSCs with age is the cause or the consequence of aging skeletal muscle. Understanding the effect of aging on MuSCs and the individual components of their niche is critical to develop effective therapeutic approaches to diminish or reverse the age-related defects in muscle regeneration.

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 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 score0.986
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.300
Teacher spread0.276 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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