Resolvin-D2 targets myogenic cells and improves muscle regeneration in Duchenne muscular dystrophy
Bibliographic record
Abstract
Lack of dystrophin causes muscle degeneration, which is exacerbated by chronic inflammation and reduced regenerative capacity of muscle stem cells in Duchenne Muscular Dystrophy (DMD). To date, glucocorticoids remain the gold standard for the treatment of DMD. These drugs are able to slow down the progression of the disease and increase lifespan by dampening the chronic and excessive inflammatory process; however, they also have numerous harmful side effects that hamper their therapeutic potential. Here, we investigated Resolvin-D2 as a new therapeutic alternative having the potential to target multiple key features contributing to the disease progression. Our in vitro findings showed that Resolvin-D2 promotes the switch of macrophages toward their anti-inflammatory phenotype and increases their secretion of pro-myogenic factors. Moreover, Resolvin-D2 directly targets myogenic cells and promotes their differentiation and the expansion of the pool of myogenic progenitor cells leading to increased myogenesis. These effects are ablated when the receptor Gpr18 is knocked-out, knocked-down, or blocked by the pharmacological antagonist O-1918. Using different mouse models of DMD, we showed that Resolvin-D2 targets both inflammation and myogenesis leading to enhanced muscle function compared to glucocorticoids. Overall, this preclinical study has identified a new therapeutic approach that is more potent than the gold-standard treatment for DMD.
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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) | 0.001 | 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) | 0.001 | 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".