Experimental and therapeutic approaches to muscular dystrophies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Most patients suffering from muscular dystrophies can now obtain a precise diagnosis of their underlying molecular defect, but no efficient treatment to prevent disability and death. This review summarizes recent progress towards developing efficient treatments for these severe diseases. RECENT FINDINGS: Different levels of progress have been achieved in three main approaches: gene therapy, cell therapy and pharmacological therapy. Gene therapy has progressed by improving different vectors for gene delivery. Adenoviruses (mainly high capacity versions) and adeno-associated viruses were the most explored viral vectors. Progress was made in understanding the factors needed for an efficient transfection of muscle. An understanding of protein structure and function in muscular dystrophies has allowed elegant examples of protein engineering as a way of gene therapy. Non-viral vectors for gene transfer, targeted gene modification and transcription modulation have also been explored recently. Cell therapy (myogenic-cell transplantation) progressed in understanding myoblast transplantation in primates for human applications, evaluating protocols for the control of graft rejection, understanding the biology of donor myogenic cells, and searching for alternative sources of donor cells. Three clinical trials using pharmacological approaches (anabolic agents and gentamicin) show very poor or negative results. Other pharmacological approaches (upregulation of alternative therapeutic proteins) are still being researched in mice. SUMMARY: This panoply of experimental approaches covered all the current possibilities of attacking the problem of treating muscular dystrophies. It is expected that one or more will progress to provide efficient tools for the ultimate clinical goal: to prolong function and life in severe muscular dystrophy patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".