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Record W2112326292 · doi:10.1242/dev.114223

Intrinsic and extrinsic mechanisms regulating satellite cell function

2015· review· en· W2112326292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDevelopment · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health Research
KeywordsBiologySatelliteCell biologyRegeneration (biology)Stem cellProgenitor cellCell cycleCellNeuroscienceGenetics

Abstract

fetched live from OpenAlex

Muscle stem cells, termed satellite cells, are crucial for skeletal muscle growth and regeneration. In healthy adult muscle, satellite cells are quiescent but poised for activation. During muscle regeneration, activated satellite cells transiently re-enter the cell cycle to proliferate and subsequently exit the cell cycle to differentiate or self-renew. Recent studies have demonstrated that satellite cells are heterogeneous and that subpopulations of satellite stem cells are able to perform asymmetric divisions to generate myogenic progenitors or symmetric divisions to expand the satellite cell pool. Thus, a complex balance between extrinsic cues and intrinsic regulatory mechanisms is needed to tightly control satellite cell cycle progression and cell fate determination. Defects in satellite cell regulation or in their niche, as observed in degenerative conditions such as aging, can impair muscle regeneration. Here, we review recent discoveries of the intrinsic and extrinsic factors that regulate satellite cell behaviour in regenerating and degenerating muscles.

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.

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 score0.998
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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