Efficacy of Multi-exon Skipping Treatment in Duchenne Muscular Dystrophy Dog Model Neonates
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is caused by mutations in DMD, which codes for dystrophin. Because the progressive and irreversible degeneration of muscle occurs from childhood, earlier therapy is required to prevent dystrophic progression. Exon skipping by antisense oligonucleotides called phosphorodiamidate morpholino oligomers (PMOs), which restores the DMD reading frame and dystrophin expression, is a promising candidate for use in neonatal patients, yet the potential remains unclear. Here, we investigate the systemic efficacy and safety of early exon skipping in dystrophic dog neonates. Intravenous treatment of canine X-linked muscular dystrophy in Japan dogs with a 4-PMO cocktail resulted in ∼3%-27% in-frame exon 6-9 skipping and dystrophin restoration across skeletal muscles up to 14% of healthy levels. Histopathology was ameliorated with the reduction of fibrosis and/or necrosis area and centrally nucleated fibers, significantly in the diaphragm. Treatment induced cardiac multi-exon skipping, though dystrophin rescue was not detected. Functionally, treatment led to significant improvement in the standing test. Toxicity was not observed from blood tests. This is the first study to demonstrate successful multi-exon skipping treatment and significant functional improvement in dystrophic dogs. Early treatment was most beneficial for respiratory muscles, with implications for addressing pulmonary malfunction in patients.
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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".