Combination Therapy With Sirolimus and Interleukin-2 Prevents Spontaneous and Recurrent Autoimmune Diabetes in NOD Mice
Bibliographic record
Abstract
Sirolimus is an immunosuppressant that inhibits interleukin (IL)-2 signaling of T-cell proliferation but not IL-2-induced T-cell apoptosis. Therefore, we hypothesized that administration of IL-2, together with sirolimus, might shift T-cell proliferation to apoptosis and prevent autoimmune destruction of islet beta-cells. We found that sirolimus and IL-2 therapy of female NOD mice, beginning at age 10 weeks, was synergistic in preventing diabetes development, and disease prevention continued for 13 weeks after stopping sirolimus and IL-2 therapy. Similarly, sirolimus and IL-2 were synergistic in protecting syngeneic islet grafts from recurrent autoimmune destruction after transplantation in diabetic NOD mice, and diabetes did not recur after stopping sirolimus and IL-2 combination therapy. Immunocytochemical examination of islet grafts revealed significantly decreased numbers of leukocytes together with increased apoptosis of these cells in mice treated with sirolimus and IL-2, whereas beta-cells were more numerous, and significantly fewer were apoptotic. In addition, Th1-type cells (gamma-interferon-positive and IL-2(+)) were decreased the most, and Th2-type cells (IL-4(+) and IL-10(+)) and Th3-type cells (transforming growth factor-beta1(+)) were increased the most in islet grafts of sirolimus and IL-2-treated mice. We conclude that 1) combination therapy with sirolimus and IL-2 is synergistic in protecting islet beta-cells from autoimmune destruction; 2) diabetes prevention continues after withdrawal of therapy; and 3) the mechanism of protection involves a shift from Th1- to Th2- and Th3-type cytokine-producing cells, possibly due to deletion of autoreactive Th1 cells.
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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".