Analysis of Allogenicity of Mesenchymal Stem Cells in Engraftment and Wound Healing in Mice
Bibliographic record
Abstract
Studies have shown that allogeneic (allo-) bone marrow derived mesenchymal stem cells (BM-MSCs) may enhance tissue repair/regeneration. However, recent studies suggest that immune rejection may occur to allo-MSCs leading to reduced engraftment. In this study, we compared allo-BM-MSCs with syngeneic BM-MSCs or allo-fibroblasts in engraftment and effect in wound healing. Equal numbers of GFP-expressing allo-BM-MSCs, syngeneic BM-MSCs or allo-fibroblasts were implanted into excisional wounds in GFP-negative mice. Quantification of GFP-expressing cells in wounds at 7, 14 and 28 days indicated similar amounts of allogeneic or syngeneic BM-MSCs but significantly reduced amounts of allo-fibroblasts. With healing progression, decreasing amounts of allogeneic and syngeneic BM-MSCs were found in the wound; however, the reduction was more evident (2 fold) in allo-fibroblasts. Similar effects in enhancing wound closure were found in allogeneic and syngeneic BM-MSCs but not in allo-fibroblasts. Histological analysis showed that allo-fibroblasts were largely confined to the injection sites while allo-BM-MSCs had migrated into the entire wound. Quantification of inflammatory cells in wounds showed that allo-fibroblast- but not allo-BM-MSC-treated wounds had significantly increased CD45+ leukocytes, CD3+ lymphocytes and CD8+ T cells. Our study suggests that allogeneic BM-MSCs exhibit ignorable immunogenicity and are equally efficient as syngeneic BM-MSCs in engraftment and in enhancing wound healing.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".