137-OR: Towards Retrievable, Immune-Priviledged, 3D-Printed Hydrogels to Deliver Functional Islets
Bibliographic record
Abstract
Introduction and Objective: Transplantation of free islets is limited by the need for systemic immune suppression. To protect transplanted cells from the autoimmune response and avoid the negative effects of systemic immune suppression, which can include damage to remaining and transplanted beta cells, we are developing physical immune-protective hydrogels. We demonstrated good survival and function in our proprietary synthetic hydrogels in vitro and in vivo and are moving towards a single, retrievable device with a high surface area (LifeRaftsTM). Methods: Fluorescently labelled proteins were used for size exclusions experiments. Blood glucose, C-peptide, and HbA1c of encapsulated donor islets were measured following implantation into streptozotocin (STZ)-induced diabetic immunocompetent rodents. LifeRaftsTM were printed using a commercially available bioprinter and either assessed for viability and protein secretion in vitro or implanted into healthy animals to assess graft survival and host-implant interactions. Results: Protein exclusion could be fine-tuned to excluded IgG, while maintaining good cell viability after encapsulation or printing. Encapsulated rat islets demonstrated rapid and sustained blood glucose control for 110+ days in immunocompetent mice and rats while the functionality of human islet could be extended in mice and large animals, as indicated by reduced blood glucose and increased C-peptide and HbA1c levels. Explanted LifeRaftsTM showed visible blood vessels early post-implant. Conclusion: Our proprietary, retrievable hydrogels showed strong immune protection for both allogenic and xenogeneic transplantation, without any immunosuppression. Early LifeRaftsTM demonstrated promising tissue integration. We are currently preparing for functional studies using LifeRaftsTM, in pursuit of a single retrievable device for functional cell therapy. Disclosure H. Stover: Employee; Allarta Life Science Inc.
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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) | 0.002 | 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".