Human Muscle Precursor Cells Form Human-Derived Myofibers in Skeletal Muscles of Nonhuman Primates: A Potential New Preclinical Setting to Test Myogenic Cells of Human Origin for Cell Therapy of Myopathies
Bibliographic record
Abstract
This study aimed to verify if human myogenic cells could participate in muscle regeneration in macaques. This experimental setting would grant researchers a model that could better evaluate the effects of cell therapies in myopathies with a better translation to human patients. Human muscle precursor cells (MPCs) were cultured in vitro and transduced with ß-galactosidase. The cells were subsequently injected into 1-cm3 muscle regions of 6 macaques immunosuppressed with tacrolimus and dexamethasone. Allogeneic ß-galactosidase+ MPCs were injected in other regions as positive controls. Some cell-grafted regions were electroporated to induce extensive muscle regeneration. MPC-grafted regions were sampled 1 month later and analyzed by histology. There were ß-galactosidase+ myofibers in both the regions grafted with human and macaque MPCs. Electroporation increased the engraftment of human MPCs in the same way as in macaque allografts. The histological analysis (hematoxylin and eosin, CD8, and CD4 immunodetection) demonstrated an absence of cellular rejection in most MPC-grafted regions, as well as minimal lymphocytic infiltration in the regions transplanted with human MPCs in the individual with the lowest tacrolimus levels. Circulating de novo anti-donor antibodies were not detected. In conclusion, we report the successful engraftment of human myogenic cells in macaques, which was possible using tacrolimus-based immunosuppression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".