105.6: Engineering a vascularized bioartificial pancreas for stem cell derived islet maturation
Bibliographic record
Abstract
Background: A possible therapy for type 1 diabetes is pancreatic islet encapsulation, which allows physiological insulin secretion while protecting the transplant against immunological rejection. The effectiveness of encapsulation devices at therapeutic scale is limited by the physical barrier created by encapsulation that restricts oxygen and nutrient transport. Additionally, graft integration into the host vasculature is a slow process that can take several weeks during which oxygen deprivation can result in significant loss of the graft. Devices allowing both immunoprotection and direct vascularization post-transplantation should improve therapeutic efficiency. Methods: By combining 3D printing of sacrificial lattices and polyurethane dip coating, we have created a vascularized biomedical device that can be filled with hydrogel and is intended for anastomosis to the host blood arteries. Pancreatic pseudoislets were first differentiated from human pluripotent stem cells using a multistage protocol yielding ~75% pancreatic progenitors (PDX1+/NKX6.1+ cells; stage 4) and respectively ~25% and ~30% of alpha cells and beta cells at the end of the protocol. To investigate the maturation of pseudoislets inside the device, immature pseudoislets (Stage 6) were cultured for 10 days either in suspension, immobilized in alginate within the device or as slabs. Results: After 10 days of perfused immobilized culture within the device, pseudoislets were highly viable and stained positive for the insulin granule marker dithizone. Flow cytometry analysis demonstrated comparable yield of alpha (glucagon+) and beta (C-peptitde+) cells within the device compared to suspension controls. Pseudoislet maturation efficiency was not significantly different in the encapsulation system as compared to suspension controls based on mRNA-level expression of endocrine commitment markers (NEUROD1, Chromogranin A, NKX6.1). Conclusion: These results show promise for the development of an implantable vascularized pancreas for the treatment of type 1 diabetes. Canadian Institutes of Health Research (CIHR). ThéCell Network.
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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) | 0.001 | 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".