Growth Factor Coinjection Improves the Migration Potential of Monkey Myogenic Precursors without Affecting Cell Transplantation Success
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is an inherited disease and a main target of myogenic cell transplantation (MT). After the failure of the first clinical trials with DMD patients, the poor migration of transplanted cells has been suspected to be a major problem for a more effective clinical application of MT. Previous investigations suggested that the quantity and dispersion of myofibers containing donor cell nuclei might be improved by increasing the migration of the transplanted cells outside the injection sites. Because the coinjection of motogenic factors with human myoblasts enhanced their intramuscular migration following MT in SCID mice, the present study aimed to investigate whether this approach was appropriate to increase MT success in muscles of nonhuman primates. In vitro studies indicated that IGF-1 or bFGF increased components of proteolytic systems involved in myoblast migration. In vitro and in vivo experiments also demonstrated that coinjection of bFGF or IGF-1 was able to improve monkey myogenic cell migration and invasion. Sixty hours after MT in skeletal muscle tissue, the migration distances reached by monkey myoblasts increased by nearly twofold when one of the growth factors was coinjected with the cells. However, long-term observations in adult monkeys suggest that promigratory treatments are not intrinsically sufficient to improve the success of MT. Even if short-term observations reveal that grafted cells are not always trapped inside the injection site and in spite of the fact that both factors enhanced transplanted cell migration, myofibers including grafted cell nuclei were still restrained to the injection trajectory without notable difference in their amount or their dispersion. The incapacity of transplanted cells to fuse with undamaged myofibers, which are located outside the injection sites, is a priority problem to solve in order to improve transplantation success and reduce the number of injections required for the treatment of DMD patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".