Type-2 innate signals are dispensable for skeletal muscle regeneration and pathology linked to Duchenne muscular dystrophy
Bibliographic record
Abstract
Immune responses play an integral role in skeletal muscle regeneration. In the genetically inherited muscle disease Duchenne muscular dystrophy (DMD), muscle regeneration is disrupted, leading to chronic inflammation, fibrosis, and early mortality. Previously, it has been suggested that type-2 innate immune cells, particularly eosinophils and their production of IL-4, play an essential role in effective muscle regeneration after acute injury. We here re-investigate the role of eosinophils in skeletal muscle repair using mice deficient in eosinophils (ΔdblGATA), or deficient in IL-4R/IL-13R signaling through STAT6 (Stat6-/-). We show that neither deficiency has an impact on skeletal muscle regeneration in response to acute injury as quantified by fiber size, immune cell infiltration, or muscle-resident stem cell proliferation. We also investigate the role of STAT6 signaling in mdx:Stat6-/- mice, a model of DMD and, again, find that ablation of STAT6 signaling has no effect on the rate or severity of fibrotic scar formation or disease progression. In contrast to previous models, our data suggest a negligible role for eosinophils and STAT6 signaling in skeletal muscle regeneration after acute or chronic injury.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".