Inflammasome Activity in the Skeletal Muscle and Heart of Rodent Models for Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is characterized by wasting of muscles that leads to difficulty moving and premature death, mainly from heart failure. Glucocorticoids are applied in the management of the disease, supporting the hypothesis that inflammation may be driver as well as target. However, the inflammatory mechanisms during progression of cardiac and skeletal muscle dysfunction are still not well characterized. Our objective was to characterize the inflammasomes in myocardial and skeletal muscle in rodent models of DMD. Gastrocnemius and heart samples were collected from mdx mice and DMDmdx rats (3 and 9–10 months). Inflammasome sensors and effectors were assessed by immunoblotting. Histology was used to assess leukocyte infiltration and fibrosis. In gastrocnemius, a tendency towards elevation of gasdermin D irrespective of the age of the animal was observed. The adaptor protein was elevated in the mdx mouse skeletal muscle and heart. Increased cleavage of the cytokines was observed in the skeletal muscle of the DMDmdx rats. Sensor or cytokine expression was not changed in the tissue samples of the mdx mice. In conclusion, inflammatory responses are distinct between the skeletal muscle and heart in relevant models of DMD. Inflammation tends to decrease over time, supporting the clinical observations that the efficacy of anti-inflammatory therapies might be more prominent in the early stage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".