Human MuStem cells are competent to fuse with nonhuman primate myofibers in a clinically relevant transplantation context: A proof-of-concept study
Bibliographic record
Abstract
We previously reported that human muscle-derived stem cells (hMuStem cells) contribute to tissue repair after local administration into injured skeletal muscle or infarcted heart in immunodeficient rodent models. However, extrapolation of these findings to a clinical context is problematic owing to the considerable differences often seen between in vivo findings in humans versus rodents. Therefore, we investigated whether the muscle regenerative behavior of hMuStem cells is maintained in a clinically relevant transplantation context. Human MuStem cells were intramuscularly administered by high-density microinjection matrices into nonhuman primates receiving tacrolimus-based immunosuppression thereby reproducing the protocol that has so far produced the best results in clinical trials of cell therapy in myopathies. Four and 9 weeks after administration, histological analysis of cell injection sites revealed large numbers of hMuStem cell-derived nuclei in all cases. Most graft-derived nuclei were distributed in small myofiber groups in which no signs of a specific immune response were observed. Importantly, hMuStem cells contributed to simian tissue repair by fusing mainly with host myofibers, demonstrating their capacity for myofiber regeneration in this model. Together, these findings obtained in a valid preclinical model provide new insights supporting the potential of hMuStem cells in future cell therapies for muscle diseases.
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 | 0.002 | 0.001 |
| 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".