XIAP inhibition of β-cell apoptosis reduces the number of islets required to restore euglycemia in a syngeneic islet transplantation model
Bibliographic record
Abstract
Clinical pancreatic islet transplantation has great promise as a treatment for type 1 diabetes but despite recent advances, it is still limited by the need for lifelong immunosuppression, restricted availability of donor islets, and uncertainty regarding long-term graft survival. Using a syngeneic, suboptimal islet transplantation model, we asked whether adenoviral overexpression of an anti-apoptotic protein, the X-linked inhibitor of apoptosis protein (XIAP) would protect transplanted islet cells from death and reduce the number of islets required for successful transplantation. Transplantation of 100 XIAP-expressing islets into the kidney capsule of syngeneic Balb/c mice restored euglycemia in 86% of recipients, where transplantation of 100 islets transduced with a control adenovirus expressing LacZ restored euglycemia in only 27% of recipients. Analysis of islet grafts by insulin/TUNEL double immunostaining revealed fewer apoptotic beta-cells in recipients of XIAP- compared with LacZ-expressing grafts (0.8±0.5 vs. 2.4±0.8 double-positive cells/graft), suggesting that XIAP enhances graft success by inhibiting β-cell apoptosis in the immediate post-transplant period. In summary, XIAP overexpression inhibits beta cell apoptosis in syngeneic islet transplants, thereby reducing the number of islets and decreasing the number of days required to restore euglycemia. These data raise the possibility that ex vivo XIAP gene transfer in islets prior to transplantation has the potential to increase the number of donor islets available for transplantation and may enhance graft function and long-term transplant success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".