Long-term islet allograft survival in nonobese diabetic mice treated with tacrolimus, rapamycin, and anti-interleukin-2 antibody1
Bibliographic record
Abstract
BACKGROUND: Nonobese diabetic (NOD) mice develop autoimmune diabetes with features similar to those observed in the human disease. The concurrence of allorecognition and recurrence of autoimmunity might explain why most of the treatments successful in preventing islet allograft destruction in other nonautoimmune combinations often fail in NOD recipients. To assess the value of the NOD mouse model for the evaluation of treatments relevant to clinical islet transplantation, the authors have tested the effect of a protocol closely resembling the one successfully used in the Edmonton clinical trial on the survival of islet allografts in NOD mice. METHODS: C57BL/6 islets were transplanted under the kidney capsule of spontaneously diabetic NOD mice. Treatment consisted of a combination of rapamycin, tacrolimus, and anti-interleukin (IL)-2 monoclonal antibody. Control groups received each treatment alone, a combination of two agents, or no treatment. RESULTS: Untreated animals invariably lost their graft within 13 days. Administration of rapamycin and tacrolimus significantly prolonged graft survival, with two of seven animals bearing a functional graft longer than 100 days. Addition of anti-IL-2 antibody therapy further improved graft survival, with six of eight grafts functioning longer than 100 days and two of eight grafts functioning longer than 200 days. CONCLUSIONS: In view of the limited success obtained with other treatments in this model, the dramatic prolongation of graft survival observed in the authors' study, by using a therapy that mimics one successfully used in clinical trials, seems to validate the NOD mouse as a meaningful model for the study of therapeutic interventions for the prevention of islet graft loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".