Intramuscular cell transplantation as a potential treatment of myopathies: clinical and preclinical relevant data
Bibliographic record
Abstract
INTRODUCTION: Myopathies produce deficits in skeletal muscle function and, in some cases, literally progressive loss of skeletal muscles. The transplantation of cells able to differentiate into myofibers is an experimental strategy for the potential treatment of some of these diseases. AREAS COVERED: Among the two routes used to deliver cells to skeletal muscles, that is intramuscular and intravascular, this paper focuses on the intramuscular route due to our expertise and because it is the most used in animal experiments and the only tested so far in humans. Given the absence of recent reviews about clinical observations and the profusion based on mouse results, this review prioritizes observations made in humans and non-human primates. The review provides a vision of cell transplantation in myology centered on what can be learned from clinical trials and from preclinical studies in non-human primates and leading mouse studies. EXPERT OPINION: Experiments on myogenic cell transplantation in mice are essential to quickly identify potential treatments, but studies showing the possibility to scale up the methods in large mammals are indispensable to determine their applicability in humans and to design clinical protocols.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".