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Record W3026313967 · doi:10.1098/rsob.200048

Satellite cells in ageing: use it or lose it

2020· review· en· W3026313967 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

VenueOpen Biology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersInstitute of GeneticsNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsBiologySkeletal muscleSenescenceSatelliteRegeneration (biology)Cell biologySarcopeniaPopulationAtrophyMuscle atrophyStem cellMyocyteAgeingNeuroscienceAnatomyGeneticsMedicine

Abstract

fetched live from OpenAlex

Individuals that maintain healthy skeletal tissue tend to live healthier, happier lives as proper muscle function enables maintenance of independence and actuation of autonomy. The onset of skeletal muscle decline begins around the age of 30, and muscle atrophy is associated with a number of serious morbidities and mortalities. Satellite cells are responsible for regeneration of skeletal muscle and enter a reversible non-dividing state of quiescence under homeostatic conditions. In response to injury, satellite cells are able to activate and re-enter the cell cycle, creating new cells to repair and create nascent muscle fibres while preserving a small population that can return to quiescence for future regenerative demands. However, in aged muscle, satellite cells that experience prolonged quiescence will undergo programmed cellular senescence, an irreversible non-dividing state that handicaps the regenerative capabilities of muscle. This review examines how periodic activation and cycling of satellite cells through exercise can mitigate senescence acquisition and myogenic decline.

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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.111
GPT teacher head0.391
Teacher spread0.281 · 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