Application of an Endothelialized Modular Construct for Islet Transplantation in Syngeneic and Allogeneic Immunosuppressed Rat Models
Why this work is in the frame
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Bibliographic record
Abstract
Modular tissue engineering is a novel approach to assemble tissues with an inherent vascularization. In this article, we evaluated whether endothelialized module-driven vascularization enhances islet engraftment in diabetic rats. Two thousand islets were transplanted in the omental pouch of syngeneic and allogeneic immunosuppressed diabetic recipients as free islets, islets in collagen modules, or islets in endothelialized modules. Transplantation of islets in endothelialized modules significantly increased the vessel density compared with controls. Donor green fluorescent protein-positive endothelial cells (ECs) formed vessels in proximity to transplanted islets; donor vessels connected to host vasculature as the vessels included erythrocytes in their lumens and were supported by host smooth muscle cells by 21 days. Transplantation of 2000 islets reversed diabetes in two of five of syngeneic recipients until 60 days, although there was no apparent benefit to islet function of adding ECs relative to collagen modules without EC. However, there was a trend toward increased viability when islets were implanted in endothelialized modules compared with collagen modules at 21 days. Meanwhile, 2000 islets in allogeneic immunosuppressed recipients lowered blood glucose levels short term, but there was graft failure within 1 week. This study explored the simultaneous transplantation of primary ECs with islets in diabetic recipients. The endothelialized modular approach increased vessel density around transplanted islets. Further modulation (i.e., acceleration) of vessel maturation, is presumed necessary to improve islet engraftment.
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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 | 0.000 | 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) | 0.000 | 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 it