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Record W2957102087 · doi:10.1155/2019/4761427

Macrophages Are Key Regulators of Stem Cells during Skeletal Muscle Regeneration and Diseases

2019· review· en· W2957102087 on OpenAlexafffund
Junio Dort, Paul Fabre, Thomas Molina, Nicolas A. Dumont

Bibliographic record

VenueStem Cells International · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCHU Sainte-Justine FoundationStem Cell Network
KeywordsSkeletal muscleMyogenesisRegeneration (biology)Cell biologyInflammationProgenitor cellBiologyMyocyteProinflammatory cytokineHomeostasisStem cellMacrophage polarizationMacrophageImmunologyAnatomyIn vitroGenetics

Abstract

fetched live from OpenAlex

Muscle regeneration is a closely regulated process that involves a variety of cell types such as satellite cells, myofibers, fibroadipogenic progenitors, endothelial cells, and inflammatory cells. Among these different cell types, macrophages emerged as a central actor coordinating the different cellular interactions and biological processes. Particularly, the transition of macrophages from their proinflammatory to their anti-inflammatory phenotype was shown to regulate inflammation, myogenesis, fibrosis, vascularization, and return to homeostasis. On the other hand, deregulation of macrophage accumulation or polarization in chronic degenerative muscle disorders was shown to impair muscle regeneration. Considering the key roles of macrophages in skeletal muscle, they represent an attractive target for new therapeutic approaches aiming at mitigating various muscle disorders. This review aims at summarizing the novel insights into macrophage heterogeneity, plasticity, and functions in skeletal muscle homeostasis, regeneration, and disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations191
Published2019
Admission routes2
Has abstractyes

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Same venueStem Cells InternationalSame topicMuscle Physiology and DisordersFrench-language works237,207