Bibliographic record
Abstract
PURPOSE OF REVIEW: Cell therapies for inherited myopathies are based on the implantation of normal or genetically corrected myogenic cells into the body. This review summarizes the recent progress in this field, systematized according to the factors important for success. RECENT FINDINGS: In the choice of donor cells, myoblasts derived from satellite cells remain the best choice. Some studies on the population of muscle-derived stem cells in mice suggested that these cells may have some advantages over myoblasts; however, no results supporting this advantage have been presented in a primate model. Recent studies on bone marrow transplantation as a systemic source of myogenic precursors for the treatment of myopathies were disappointing. Concerning donor cell delivery, intramuscular myoblast injection remains the only way that can significantly introduce exogenous myogenic cells into the muscles. A recent study in primates showed some parameters of myoblast injection that could be useful in the human. Progress was made in mice to understand the factors that could favor the migration of the donor myoblasts in the host muscles. Concerning donor cell survival, analysis of immune cell infiltration dynamics allowed a better understanding of the factors implicated in early donor cell death. Progress was made on the control of acute rejection for myoblast transplantation in primates. So far, few mouse experiments have advanced the field of tolerance induction toward myogenic cells. SUMMARY: Myoblast transplantation (intramuscular injection of satellite cell-derived myoblasts) currently remains the only cell-based therapy that has produced promising results in the context of a preclinical model such as the nonhuman primate.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".