Cell therapy in muscular dystrophies: many promises in mice and dogs, few facts in patients
Bibliographic record
Abstract
INTRODUCTION: Muscular dystrophies (MDs) are genetic diseases that produce progressive loss of skeletal muscle fibers. Cell therapy (CT) is an experimental approach to treat MD. The first clinical trials of CT in MD conducted in the 1990s were based on myoblast transplantation (MT). Since they did not yield the expected results, several researchers sought to discover other cells with more advantageous properties than myoblasts whereas others sought to improve MT. AREAS COVERED: We explain the properties that are required for a cell to be used in CT of MD. We briefly review most of the cells that were proposed for this CT, and to what extent these properties were met not only in laboratory animals but also in clinical trials. EXPERT OPINION: Although the repertoire of cells proposed for CT of MD has been expanded since the 1990s, only myoblasts have currently demonstrated unequivocally to significantly engraft in humans. Indeed, MT for MD involves significant technical challenges that need be solved. While it would be ideal to find cells involving less technical challenges for CT of MD, there is so far no clinical evidence that this is possible and therefore the work to improve MT should continue.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".