Inhibiting Myostatin with Follistatin Improves the Success of Myoblast Transplantation in Dystrophic Mice
Bibliographic record
Abstract
Duchenne muscular dystrophy is a recessive disease due to a mutation in the dystrophin gene. Myoblast transplantation permits to introduce the dystrophin gene in dystrophic muscle fibers. However, the success of this approach is reduced by the short duration of the regeneration following the transplantation, which reduces the number of hybrid fibers. Our aim was to verify whether the success of the myoblast transplantation is enhanced by blocking the myostatin signal with an antagonist, follistatin. Three different approaches were studied to overexpress follistatin in the muscles of mdx mice transplanted with myoblasts. First, transgenic follistatin/mdx mice were generated; second, a follistatin plasmid was electroporated in mdx muscles, and finally, follistatin was induced in mdx mice muscles by a treatment with a histone deacetylase inhibitor. The three approaches improved the success of the myoblast transplantation. Moreover, fiber hypertrophy was also observed in all muscles, demonstrating that myostatin inhibition by follistatin is a good method to improve myoblast transplantation and muscle function. Myostatin inhibition by follistatin in combination with myoblast transplantation is thus a promising novel therapeutic approach for the treatment of muscle wasting in diseases such as Duchenne muscular dystrophy.
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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 | 0.001 | 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) | 0.001 | 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".