Advances in the Use of Biologics and Biomaterials toward the Improvement of Pancreatic Islet Graft Survival in Type 1 Diabetes
Bibliographic record
Abstract
Islet transplantation is a curative treatment for patients suffering from type 1 diabetes and has the potential to replace current treatment strategies involving the exogenous administration of insulin. Despite this potential, there are many hurdles in achieving successful long‐term graft survival due to autoimmune and foreign body reactions leading to graft rejection coupled with donor shortage and potential adverse effects from the need for long‐term administration of immunosuppressive drugs. As a result, various approaches have been proposed to increase the viability and function of islet grafts during isolation and ex vivo culture with the use of growth factors, hormones, and other therapeutic agents. In addition, other strategies have addressed how to enhance or maintain islet graft performance after implantation with improvements on immunosuppressive drug regimens and the use of biomaterials to encapsulate and protect the cells from graft rejection. This review focuses on the recent advances in strategies to improve islet viability and function with the addition of exogenous compounds and the implementation of conformal coating as a promising tool for immunoprotection of islet transplants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".