Progress in myoblast transplantation: a potential treatment of dystrophies
Bibliographic record
Abstract
Myoblast transplantation (MT) consists of injecting normal or genetically modified myogenic cells into muscles, where they are expected to fuse and form mature fibers. As an experimental approach to treat severe genetic muscle diseases, MT was tested in dystrophic patients at the beginning of the 1990s. Although these early clinical trials were unsuccessful, MT has progressed through the research on animal models. Many factors that may condition the success of MT were identified in the last years. The present review updates our knowledge on MT and describes the different problems that have limited its success. Factors that were first underestimated, like the specific immune response after MT, are presently well characterized. Destruction of the hybrid fibers by activated T-lymphocytes and production of antibodies against the transplanted myoblasts take place after MT and are responsible for the graft rejection. The choice of the immunosuppression seems to be very important, and FK506 is the best agent known to allow the best results after MT. Under FK506 immunosuppression, very efficient MT were obtained both in mice and monkeys. Moreover, in dystrophic mice it was demonstrated that MT ameliorates some phenotypical characteristics of the disease. The improvement of the survival of the transplanted cells and the increase of their migration into the injected tissue are presently under investigation. Some of the present research is directed also to bypass the immunosuppression by using the patient's own cells for MT. In this sense, efforts are conducted to introduce the normal gene into the patient's myoblasts before MT and to improve the ability of these cells to proliferate in vitro. Micros. Res. Tech. 48:213-222, 2000.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".