α1-Antitrypsin monotherapy induces immune tolerance during islet allograft transplantation in mice
Bibliographic record
Abstract
Human pancreatic islet transplantation offers diabetic patients tight glucose control but has low graft survival rates. The immunosuppressive drugs that are administered to graft recipients lack the antiinflammatory benefits of corticosteroids because of their diabetogenic effects. The serum protease inhibitor alpha1-antitrypsin (AAT) possesses antiinflammatory properties and reduces cytokine-mediated islet damage. In the present study, diabetic mice were grafted with allogeneic islets and treated with AAT monotherapy (n = 24). After 14 days of treatment, mice remained normoglycemic and islet allografts were functional for up to 120 treatment-free days. After graft removal and retransplantation, mice accepted same-strain islets but rejected third-strain islets, thus confirming that specific immune tolerance had been induced. Explanted grafts exhibited a population of T regulatory cells in transplant sites. According to RT-PCR, grafts contained high levels of mRNA for foxp3, cytotoxic T lymphocyte antigen-4, TGF-beta, IL-10, and IL-1 receptor antagonist; expression of proinflammatory mediators was low or absent. After implantation of skin allografts, AAT-treated mice had greater numbers of foxp3-positive cells in draining lymph nodes (DLNs) compared with control treatment mice. Moreover, dendritic cells in DLNs exhibited an immature phenotype with decreased CD86 activation marker. Although the number of CD3 transcripts decreased in the DLNs, AAT did not affect IL-2 activity in vitro. Thus, AAT monotherapy provides allografts with antiinflammatory conditions that favor development of antigen-specific T regulatory cells. Because AAT treatment in humans is safe, its use during human islet transplantation may be considered.
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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 | 0.001 |
| 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".