Indefinite islet protection from autoimmune destruction in nonobese diabetic mice by agarose microencapsulation without immunosuppression1
Bibliographic record
Abstract
BACKGROUND: The recurrence of autoimmunity and allograft rejection act as major barriers to the widespread use of islet transplantation as a cure for type 1 diabetes. The aim of this study was to evaluate the feasibility of immunoisolation by use of an agarose microcapsule to prevent autoimmune recurrence after islet transplantation. METHODS: Highly purified islets were isolated from 6- to 8-week-old prediabetic male nonobese diabetic (NOD) mice and microencapsulated in 5% agarose hydrogel as a semipermeable membrane. Islet function was evaluated by a syngeneic islet transplantation model, in which islets were transplanted into spontaneously diabetic NOD mice. RESULTS: The nonencapsulated islet grafts were destroyed and diabetes recurred within 2 weeks after transplantation in all 12 mice. In contrast, 13 of the 16 mice that underwent transplantation with microencapsulated islets maintained normoglycemia for more than 100 days after islet transplantation. Histologic examination of the nonencapsulated islet grafts showed massive mononuclear cellular infiltration with beta-cell destruction. In contrast, the microencapsulated islets showed well-granulated beta cells with no mononuclear cellular infiltration around the microcapsules or in the accompanying blood capillaries between the microcapsules. CONCLUSIONS: Agarose microcapsules were able to completely protect NOD islet isografts from autoimmune destruction in the syngeneic islet transplantation model.
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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 | 0.000 | 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 | 0.001 |
| 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".