Transplanted Myoblasts Can Migrate Several Millimeters to Fuse With Damaged Myofibers in Nonhuman Primate Skeletal Muscle
Bibliographic record
Abstract
A major restriction of the intramuscular transplantation of myoblasts is that the grafted cells fuse mostly with myofibers along the injection trajectories. This has been attributed to a "lack of migration ability" of the grafted myoblasts. It has been assumed that grafted myoblasts remain motionless in the sites of delivery and fuse only with myofibers with which they come into contact. In the present study, we analyzed this phenomenon in 17 cynomolgus monkeys. We found that intramuscularly injected myoblasts within 1 hour after their injection are mainly located in the perimysium and not distributed along the injection trajectories. This suggested that the grafted myoblasts later migrate from the perimysium to fuse with myofibers that are damaged by the injections. Therefore, we analyzed whether β-galactosidase-labeled myoblasts injected subcutaneously over skeletal muscles migrate in needle-damaged and nondamaged muscle regions. We observed that grafted myoblasts migrated up to 1cm in depth from the muscle surface into the muscles, although they seemingly fused mainly with damaged myofibers. Our findings suggest that myoblast transplantation is not necessarily restricted bya "lack of migration ability" of the grafted cells but by the fact that myoblasts fuse with regenerating myofibers and not with undamaged myofibers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".