Mesenchymal Stem Cells Enhance Wound Healing Through Differentiation and Angiogenesis
Bibliographic record
Abstract
Although chronic wounds are common, treatment for these disabling conditions remains limited and largely ineffective. In this study, we examined the benefit of bone marrow-derived mesenchymal stem cells (BM-MSCs) in wound healing. Using an excisional wound splinting model, we showed that injection around the wound and application to the wound bed of green fluorescence protein (GFP)(+) allogeneic BM-MSCs significantly enhanced wound healing in normal and diabetic mice compared with that of allogeneic neonatal dermal fibroblasts or vehicle control medium. Fluorescence-activated cell sorting analysis of cells derived from the wound for GFP-expressing BM-MSCs indicated engraftments of 27% at 7 days, 7.6% at 14 days, and 2.5% at 28 days of total BM-MSCs administered. BM-MSC-treated wounds exhibited significantly accelerated wound closure, with increased re-epithelialization, cellularity, and angiogenesis. Notably, BM-MSCs, but not CD34(+) bone marrow cells in the wound, expressed the keratinocyte-specific protein keratin and formed glandular structures, suggesting a direct contribution of BM-MSCs to cutaneous regeneration. Moreover, BM-MSC-conditioned medium promoted endothelial cell tube formation. Real-time polymerase chain reaction and Western blot analysis revealed high levels of vascular endothelial growth factor and angiopoietin-1 in BM-MSCs and significantly greater amounts of the proteins in BM-MSC-treated wounds. Thus, our data suggest that BM-MSCs promote wound healing through differentiation and release of proangiogenic factors. Disclosure of potential conflicts of interest is found at the end of this article.
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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) | 0.003 | 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".