Bibliographic record
Abstract
The pathophysiology of Duchenne muscular dystrophy (DMD) is complex and incompletely understood. However it is clear that the primary defect, lack of dystrophin, is necessary but not sufficient on its own to fully account for the onset of muscle fiber destruction, leading to eventual replacement by fibrotic tissue. Dystrophin restoration strategies are currently under clinical development, but the only treatment clearly shown to slow disease progression thus far in human DMD patients is the use of anti‐inflammatory corticosteroids. This presentation will focus on the role of the innate immune system in promoting the dysregulated inflammatory and aberrant muscle repair responses found in DMD. Using the mdx mouse diaphragm as a preclinical model due to its resemblance to the human DMD phenotype, the importance of monocyte‐derived macrophages in driving early disease progression will be outlined. This is exemplified by the therapeutic benefits (improved force generation and reduced fibrosis) noted when monocyte/macrophage recruitment to the dystrophic diaphragm is prevented through genetic or pharmacologic inhibition of the chemokine receptor CCR2. In addition, preventing activation of Toll‐like receptors (TLRs) is similarly beneficial and suggests significant involvement of damage‐associated molecular patterns (DAMPs) in DMD pathogenesis. Data will also be presented indicating that epigenetic alterations occur at the level of macrophage precursors in the bone marrow, which are likely stimulated by TLR4 recognition of DAMPs, and that subsequently play a role in dictating monocyte/macrophage phenotypic characteristics following their entry into the diseased muscles. Although the ideal treatment for DMD would be restoration of dystrophin to all muscles of the body including the diaphragm, significant hurdles remain for this ultimate therapy. In the meanwhile, there are serious drawbacks to the current practice of treating DMD children with corticosteroids, which have major side effects in DMD such as obesity, diabetes and osteoporosis. Future development of a more targeted therapeutic approach based on innate immune system modulation could represent an important advance in the field. Support or Funding Information Canadian Institutes of Health Research
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".