Therapeutic effects of exon skipping and losartan on skeletal muscle of mdx mice
Bibliographic record
Abstract
Various attempts have been made to find treatments for Duchenne muscular dystrophy (DMD) patients. Exon skipping is one of the promising technologies for DMD treatment by restoring dystropin protein, which is one of the muscle components. It is well known that losartan, an angiotensin II type1 receptor blocker, promotes muscle regeneration and differentiation by lowering the level of transforming growth factor-beta1 signaling. In this study, we illustrated the combined effects of exon skipping and losartan on skeletal muscle of mdx mice. We supplied mdx mice with losartan for 2 weeks before exon skipping treatment. The losartan with the exon skipping group showed less expression of myf5 than the losartan treated group. Also the losartan with exon skipping group recovered normal muscle architecture, in contrast to the losartan group which still showed many central nuclei. However, the exon skipping efficiency and the restoration of dystrophin protein were lower in the losartan with exon skipping group compared to the exon skipping group. We reveal that losartan promotes muscle regeneration and shortens the time taken to restore normal muscle structure when combined with exon skipping. However, combined treatment of exon skipping and losartan decreases the restoration of dystrophin protein meaning decrease of exon skipping efficiency.
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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) | 0.001 | 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".