XIAP Overexpression in Islet β-Cells Enhances Engraftment and Minimizes Hypoxia–Reperfusion Injury
Bibliographic record
Abstract
Recent advances in clinical islet transplantation have allowed patients with type 1 diabetes to become insulin independent, but the procedure is limited since islets from two donors per recipient are typically required. This limitation arises because within a few days of the islets being embolized into the portal circulation, at least half of the transplanted beta-cells have undergone apoptotic cell death triggered by hypoxic and chemokine/cytokine-mediated stress. We hypothesized that the survival of beta-cells in the early post-transplant period would be enhanced if naturally occurring inhibitor of apoptosis proteins (IAPs) were transiently overexpressed in the grafts. In the present study, we used a growth-regulatable beta-cell line (betaTC-Tet) as a model for beta-cells within islets, and examined whether adenovirally delivered XIAP (X-linked IAP-a highly potent IAP) could enhance beta-cell survival. In vitro, XIAP-expressing betaTC-Tet cells were markedly resistant to apoptosis in an ischemia-reperfusion injury model system and following exposure to cytokines. When Ad-XIAP transduced betaTC-Tet cells were transplanted subcutaneously into immunodeficient mice, the grafts were able to reverse diabetes in 3 days, vs. 21 days for Ad-betaGal transduced cells. This approach may allow more efficient use of the limited existing supply of human islets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".