Macrophage plasticity in Duchenne muscular dystrophy: a nexus of pathological remodelling with therapeutic implications
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is characterized by chronic skeletal muscle necrosis, leading to muscle regeneration failure and fibrosis. Although macrophages (MPs) are normally essential for muscle regeneration, dysregulated MP function promotes pathological muscle remodelling. Infiltrating MPs can be predominantly pro-inflammatory (M1 biased), anti-inflammatory (M2 biased) or of a mixed phenotype and can originate from the adult bone marrow (monocyte dependent) or embryonic precursors (monocyte independent). In mdx mice (genetic model of DMD) lacking either Toll-like receptor (Tlr) 2 or Tlr4, it is found that MP infiltration of dystrophic muscle is significantly reduced and that the MP phenotype is shifted toward a more anti-inflammatory profile. This is accompanied by significant improvements in muscle histology and force production. Lack of the chemokine receptor CCR2, which impedes monocyte release from the bone marrow, leads to similar beneficial effects in mdx mice. Evidence was also found for Tlr4-regulated induction of trained innate immunity in MPs cultured from the bone marrow of mdx mice before their entry into the muscle. These MPs exhibit epigenetic and metabolic alterations, accompanied by non-specific hyper-responsiveness to multiple stimuli, which is manifested by potentiated upregulation of both pro- and anti-inflammatory genes. In summary, exaggerated recruitment of monocyte-derived MPs and signs of trained innate immunity at the level of the bone marrow are features of the immunophenotype associated with dystrophic muscle disease. These phenomena are regulated by Toll-like receptors that bind endogenous damage-associated molecular pattern (DAMP) molecules, suggesting that DAMP release from dystrophic muscles modulates MP plasticity at the bone marrow level through Toll-like receptor-driven mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".