06-5: MESENCHYMAL STROMAL CELL-MEDIATED VASCULARIZATION ENHANCES ISLET ENGRAFTMENT AND FUNCTION IN THE SUBCUTANEOUS SPACE
Bibliographic record
Abstract
Introduction: Islet transplantation has demonstrated that cell therapies can reverse hyperglycaemia, but poor engraftment and survival limit long-term success. The subcutaneous (SC) space is an attractive alternative site due to its accessibility and retrievability, yet inadequate vascularization remains a major challenge, restricting islet survival. Mesenchymal stromal cells (MSCs) promote angiogenesis and tissue remodelling, making them a promising strategy to improve vascularization, islet engraftment and function. Herein, we aim to evaluate the impact of MSCs in optimizing the SC space and assess the role of MSC-mediated vascularization in the engraftment and function of neonatal porcine islets (NPIs) transplanted into the SC space. Methods: Human acinar-derived MSCs (5x106) were coated onto electrospun polylactic-co-glycolic acid and gelatin (PLGA+G) scaffolds, followed by implantation into the SC space of B6/Rag−/− mice; uncoated scaffolds were used as control (n=6/group). (Figure1)2-weeks post-implantation, animals were injected with lectin (FITC) intravenously and grafts were recovered for vascularization assessment. In separate cohorts, 3000 NPIs were transplanted into the MSC-conditioned SC space (PGM) of streptozotocin-induced diabetic B6/Rag−/− mice, PLGA+G scaffolds were used as control (n=12/group). Blood glucose levels of transplanted animals were monitored for 28 weeks post-transplant. All groups were balanced for sex. Results: PGM implanted mice showed increased vessel number (p=0.0003), area (p<0.0001) and size (p=0.0001), along with higher lectin+ (p=0.0029), CD31+ (p<0.0001) and SMA+ (p=0.0019) cells. These results correlated with improved diabetes reversal (10/12 vs 4/12; p=0.0139) and decreased reversal time (14 vs 22 days; p=0.0177) in the PGM group, compared to controls. Conclusions: MSCs enhance vascularization in the SC space, which correlates with improved NPI engraftment and function. In the clinical context, improved engraftment could result in better long-term outcomes, reducing the risk of graft failure. Additionally, it would enable a reduction in the volume of cells to be transplanted, lowering manufacturing costs and enhancing the accessibility of islet transplantation.
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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) | 0.003 | 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".