816-P: Bioprinted Allogeneic Islet–Containing Implants Normalize Blood Glucose Control in Diabetic Rat Models without Immune Suppression
Bibliographic record
Abstract
Transplantation of cadaveric islets can reverse type 1 diabetes (T1D) but requires life long immunosuppression. Cell encapsulation has potential to overcome this challenge by blocking immune cell access to islet grafts while allowing nutrient exchange, glucose sensing and insulin release. In this study, 3D tissues consisting of patterned, multi-shell fibres with an islet-containing core and immunoprotective shell were manufactured using Aspect Biosystems’ microfluidic 3D bioprinting technology for implantation in diabetic rats. Blood glucose and animal body weight were monitored for several months following surgery. Upon retrieval, bioprinted tissues were assayed for islet cell viability and function by glucose-stimulated insulin secretion (GSIS) , followed by histology and immunohistochemistry (IHC) to analyze foreign body response (FBR) and immune cell infiltration. 3D bioprinted implants containing reaggregated allogeneic rat islets re-established normoglycemia for > 90 days in streptozotocin-treated diabetic immunodeficient rats, and > 30 days in diabetic immunocompetent rats. Post-retrieval, tissue implants demonstrate high viability and functionality, with absence of immune cell penetration through the shell, confirming its immune protective properties. Modification of the outer shell biomaterial to reduce FBR was shown to extend islet tissue implant functionality. This is the first study describing a 3D bioprinted implant composed of a core/shell micro-fibre used to deliver therapeutic islets into diabetic animals. The promising results obtained in rodent models open the door to further testing in larger animal models and, eventually, T1D patients. Disclosure V.Russo: None. S.Getsios: None. T.J.Kieffer: Consultant; Fractyl Health, Inc., Sigilon Therapeutics, Inc., Employee; ViaCyte, Inc., Research Support; Aspect Biosystems. S.Wadsworth: None. R.Jalili: None. Y.Yu: None. R.Agarwal: None. S.Pan: None. N.Hakimi: None. K.Jahan: None. E.M.Wilts: None. S.Ida: None. Funding National Research Council Canada - Industrial Research Assistance Program (#935249) , Canadian International Innovation Program (#938664) , Genome British Columbia (#GEN009) , Stem Cell Network (#FY20/FBP-7 Kieffer) , Mitacs (#IT19764) ,
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".