1503-P: Nano-thin Conformal Coating onto Human Islets to Preserve Cell Function and Induce Immunoprotection for Islet Transplantation
Bibliographic record
Abstract
Allogenic islet transplantation is a promising treatment option for patients with Type 1 Diabetes (T1D). Systemic immunosuppression is required to mitigate donor islet rejection, but this can present with adverse side-effects and reduce patient quality of life. To address this, it has been proposed that biocompatible materials can be used to form a protective capsule around islets and modulate islet-induced immune reactions while maintaining cellular and secretory functions. A key feature of such capsules is minimal implant volume to ensure cells have adequate diffusion across their membrane while allowing for islet infusion into the portal vein. Conformal coatings have been described in the literature to exhibit the desired characteristics of an ideal protective capsule mentioned above. Our work investigates a novel combination of non-immunogenic polyelectrolytes, namely tetrahydropyran triazole phenyl-alginate (TZ-AL) and quaternized phosphocholine-chitosan (PC-QCH), for a nano-thin film formation onto human islet surfaces. After thorough characterization of material properties, we demonstrated successful coating formation (540nm thickness), and biocompatibility using human-derived beta cell lines and donor human islets. Our results indicate that coated islets cultured upto 7 days in vitro preserved their cell viability, GSIS, and gene expression (INS, GLUT2, Glucagon, PDX1) compared to non-coated controls. In vitro immune assays revealed that our materials do not stimulate the innate immune system and reduce pro-inflammatory cytokine secretion. Furthermore, coated islets implanted into STZ-induced diabetic mice restored normoglycemia up to one month and islet morphology is maintained according to histology data. Additional studies to investigate the in vivo immunomodulatory properties of our coating are ongoing and ultimately, the findings from this study can help to further improve the outcomes of islet transplantation. Disclosure M.Y. Yitayew: None. L. Li: None. M. Tabrizian: None. Funding Canadian Institutes of Health Research (91435)
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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 | 0.000 |
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 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".