Macrophages Improve Survival, Proliferation and Migration of Engrafted Myogenic Precursor Cells into MDX Skeletal Muscle
Bibliographic record
Abstract
Transplantation of muscle precursor cells is of therapeutic interest for focal skeletal muscular diseases. However, major limitations of cell transplantation are the poor survival, expansion and migration of the injected cells. The massive and early death of transplanted myoblasts is not fully understood although several mechanisms have been suggested. Various attempts have been made to improve their survival or migration. Taking into account that muscle regeneration is associated with the presence of macrophages, which are helpful in repairing the muscle by both cleansing the debris and deliver trophic cues to myoblasts in a sequential way, we attempted in the present work to improve myoblast transplantation by coinjecting macrophages. The present data showed that in the 5 days following the transplantation, macrophages efficiently improved: i) myoblast survival by limiting their massive death, ii) myoblast expansion within the tissue and iii) myoblast migration in the dystrophic muscle. This was confirmed by in vitro analyses showing that macrophages stimulated myoblast adhesion and migration. As a result, myoblast contribution to regenerating host myofibres was increased by macrophages one month after transplantation. Altogether, these data demonstrate that macrophages are beneficial during the early steps of myoblast transplantation into skeletal muscle, showing that coinjecting these stromal cells may be used as a helper to improve the efficiency of parenchymal cell engraftment.
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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 | 0.001 |
| 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".