Extracellular Matrix-Guided Islet Cell Transplantation Results in Improved Glycemic Control in a NOD-SCID Mouse Model
Bibliographic record
Abstract
Current insulin therapy fails to fully restore physiological glucose homeostasis in type 1 diabetes mellitus, with 75% of patients unable to achieve the desired management targets. While stem cell-derived islets offer promising therapy, they require an enhanced extracellular matrix support for optimal transplantation outcomes. To address this challenge, we developed biofunctional endocrine micro-pancreata using decellularized porcine lung scaffolds seeded with embryonic stem cell-derived islets. In vivo efficacy was evaluated following subcutaneous or intraperitoneal transplantation into NOD-SCID mice, followed by streptozotocin induction of diabetes, with the comprehensive assessment of human insulin secretion, glucose homeostasis, and graft integration over 3 months. Our results demonstrated that endocrine micro-pancreata exhibited 1.4-fold-increased glucose-stimulated insulin secretion in vitro compared to non-responsive free islets. In vivo, endocrine micro-pancreas recipients maintained significantly lower glucose levels than controls throughout the experiment. Subcutaneous endocrine micro-pancreata showed superior performance, with 46% improved glucose tolerance versus 31% improvement for intraperitoneal delivery. Extensive CD31-positive neovascularization as well as insulin staining confirmed successful graft integration and sustained insulin production. Endocrine micro-pancreata provide a scalable platform for diabetes cell therapy, demonstrating sustained insulin secretion and improved glycemic control. The preserved extracellular matrix microenvironment supports islet function and vascularization, offering significant potential for clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".