Alginate Modification Improves Long-Term Survival and Function of Transplanted Encapsulated Islets
Bibliographic record
Abstract
Despite recent successes in islet transplantation, current immunosuppression protocols required to prevent graft rejection are not suitable for all patients. As a consequence, microencapsulation of islets in alginate has been proposed to protect islets from immune-mediated destruction. Success has been limited, however, due largely to problems with alginate biocompatibility and insufficient immunoprotection by the capsule. The aim of this study was to develop a purified, highly biocompatible, and highly stable alginate from commercially available alginate. We analyzed the chemical properties of the alginate before and after purification and compared in vivo survival and metabolic function of mouse islets encapsulated with either alginate in syngeneic recipients. Recipients of purified alginate capsules exhibited a 105-day graft survival rate of 90.5%, versus 69.2% for recipients of nonpurified alginate, with recipients of purified alginate capsules also showing improved nonfasting blood glucose levels and oral glucose challenges over recipients of nonpurified alginate. On recovery, islets encapsulated in purified alginate capsules demonstrated dramatically reduced capsular overgrowth, and an insulin secretory activity far superior to that of islets in nonpurified alginate capsules. We conclude from this study that alginate purification improves the survival and metabolic function of encapsulated islets. To our knowledge, this is the first paper using pre- and postmodification alginate to demonstrate the direct benefit of purification on transplantation success of islets in simple, open-pore capsules.
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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) | 0.000 | 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".