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Single-cell analyses uncover granularity of muscle stem cells

2020· preprint· en· W3002269053 on OpenAlexafffund
John Saber, Alexander Y. Lin, Michael A. Rudnicki

Bibliographic record

VenueF1000Research · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchOntario Institute for Regenerative MedicineStem Cell NetworkMuscular Dystrophy Association
KeywordsCellSatelliteBiologyStem cellNeuroscienceCell biologyRegeneration (biology)PopulationMyocyteComputational biologyMedicineGeneticsEngineering

Abstract

fetched live from OpenAlex

Satellite cells are the main muscle-resident cells responsible for muscle regeneration. Much research has described this population as being heterogeneous, but little is known about the different roles each subpopulation plays. Recent advances in the field have utilized the power of single-cell analysis to better describe and functionally characterize subpopulations of satellite cells as well as other cell groups comprising the muscle tissue. Furthermore, emerging technologies are opening the door to answering as-yet-unresolved questions pertaining to satellite cell heterogeneity and cell fate decisions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
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 science0.0010.002
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 head0.103
GPT teacher head0.352
Teacher spread0.248 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations15
Published2020
Admission routes2
Has abstractyes

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