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Record W3199901450 · doi:10.3233/jnd-210737

Macrophages in Skeletal Muscle Dystrophies, An Entangled Partner

2021· article· en· W3199901450 on OpenAlexafffund

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleMichael Smith Health Research BCEuropean Molecular Biology Organization
KeywordsSkeletal muscleRegeneration (biology)InflammationFunction (biology)MyocyteMyogenesis

Abstract

fetched live from OpenAlex

While skeletal muscle remodeling happens throughout life, diseases that result in its dysfunction are accountable for many deaths. Indeed, skeletal muscle is exceptionally capable to respond to stimuli modifying its homeostasis, such as in atrophy, hypertrophy, regeneration and repair. In particular conditions such as genetic diseases (muscular dystrophies), skeletal muscle's capacity to remodel is strongly affected and undergoes continuous cycles of chronic damage. This induces scarring, fatty infiltration, as well as loss of contractibility and of the ability to generate force. In this context, inflammation, primarily mediated by macrophages, plays a central pathogenic role. Macrophages contribute as the primary regulators of inflammation during skeletal muscle regeneration, affecting tissue-resident cells such as myogenic cells and endothelial cells, but also fibro-adipogenic progenitors, which are the main source of the fibro fatty scar. During skeletal muscle regeneration their function is tightly orchestrated, while in dystrophies their fate is strongly disturbed, resulting in chronic inflammation. In this review, we will discuss the latest findings on the role of macrophages in skeletal muscle diseases, and how they are regulated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.501

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 head0.008
GPT teacher head0.256
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations34
Published2021
Admission routes2
Has abstractyes

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