Caspase Inhibitor Therapy Enhances Marginal Mass Islet Graft Survival and Preserves Long-Term Function in Islet Transplantation
Bibliographic record
Abstract
Islet transplantation can provide insulin independence in patients with type 1 diabetes, but islets derived from two or more donors are often required. A significant fraction of the functional islet mass is lost to apoptosis in the immediate posttransplant period. The caspase inhibitor N-benzyloxycabonyl-Val-Ala-Asp-fluoromethyl ketone (zVAD-FMK) has been used therapeutically to prevent apoptosis in experimental animal models of ischemic injury, autoimmunity, and degenerative disease. In the current study, zVAD-FMK therapy was examined in a syngeneic islet transplant model to determine whether caspase inhibition could improve survival of transplanted islets. zVAD-FMK therapy significantly improved marginal islet mass function in renal subcapsular transplantation, where 90% of zVAD-FMK-treated mice became euglycemic with 250 islets, versus 27% of the control animals (P < 0.001). The benefit of zVAD-FMK therapy was further demonstrated after intraportal transplantation, where 75% of zVAD-FMK-treated animals established euglycemia with only 500 islets, and all of the controls remained severely diabetic (P < 0.001). zVAD-FMK pretreatment of isolated islets in the absence of systemic therapy resulted in no significant benefit compared with controls. Long-term follow-up of transplanted animals beyond 1 year posttransplant using glucose tolerance tests confirmed that a short course of zVAD-FMK therapy could prevent metabolic dysfunction of islet grafts over time. In addition, short-term zVAD-FMK treatment significantly reduced posttransplant apoptosis in islet grafts and resulted in preservation of graft insulin reserve over time. Our data suggest that caspase inhibitor therapy will reduce the islet mass required in clinical islet transplantation, perhaps to a level that would routinely allow for insulin independence after single-donor infusion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".