Improved Success of Myoblast Transplantation in mdx Mice by Blocking the Myostatin Signal
Bibliographic record
Abstract
BACKGROUND: : Duchenne muscular dystrophy (DMD) is caused by a dystrophin gene mutation. Transplantation of normal myoblasts results in long-term restoration of dystrophin. However, the success of this approach is compromised by the limited time of regeneration following muscle damage. Myostatin is known to be responsible for limiting skeletal muscle regeneration. Our purpose is to verify whether blocking the myostatin signal in mdx host mice or in normal myoblasts transplanted in mdx host mice would increase the extent of muscle repair and thus allow the formation of more dystrophin-positive fibers. METHODS: : Transgenic mdx mice carrying a dominant negative form of myostatin receptor (dnActRIIB) were used to test the fiber resistance to damage and to act as a host for normal myoblast transplantation. Myoblasts obtained from nondystrophic transgenic mice carrying the dominant negative myostatin receptor were also transplanted in nontransgenic mdx mice. RESULTS: : Transgenic mdx mice carrying the dnActRIIB gene have bigger muscles than mdx mice with the normal gene of ActRIIB. Their fiber resistance to exercise-induced damage was also greatly improved. Moreover, the success of normal myoblast transplantation was significantly enhanced in mdx/dnActRIIB mice. Finally, nondystrophic dnActRIIB myoblasts formed more abundant and bigger dystrophin positive fibers when transplanted in mdx mice. CONCLUSIONS: : Blocking the myostatin signal in mdx mice allowed the size of muscle fibers to increase, the fiber resistance to damage induced by exercise to increase, and the success of normal myoblast transplantation to improve. The transplantation in mdx mice of dnActRIIB myoblasts formed more abundant and larger dystrophin positive fibers.
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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) | 0.003 | 0.001 |
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".