Caspase Inhibitor IDN6556 Facilitates Marginal Mass Islet Engraftment in a Porcine Islet Autotransplant Model
Bibliographic record
Abstract
BACKGROUND: Large numbers of islets are lost in the early phase after clinical islet transplantation, through apoptosis, necrosis, or innate inflammatory injury. We previously demonstrated the efficacy of a series of caspase inhibitors in mouse models on islet engraftment through reduction in early posttransplant apoptosis. We studied IDN6556, a caspase inhibitor with a first-pass effect, in a large animal (pig) intraportal marginal mass islet autotransplant model. METHODS: Total pancreatectomy and marginal mass islet autotransplantation were carried out in Yucatan miniature swine to explore the effects of IDN6556 on islet engraftment. Pigs were treated with IDN6556 at a dose of 20 mg/kg orally twice daily (n=7) or phosphate-buffered saline control (n=6) orally for 7 days, and blood glucose was monitored for 1 month. Glucose tolerance and acute insulin release were determined at 1 month. RESULTS: There were no differences in islet procurement, isolation, or islet functional parameters between the two groups. Pigs receiving IDN6556 had lower fasting blood glucose level after transplantation and a higher percentage (100% vs. 33.3%) showed fasting blood glucose levels less than 11 mM. This translated into an enhanced metabolic reserve and acute insulin release for pigs in the treatment group. CONCLUSIONS: IDN6556 led to enhanced islet engraftment in this large animal islet transplant model. Although this study has limitations including a short interval of study (1 month) and the use of unpurified islets, the results justify early clinical trials of IDN6556 in islet transplantation.
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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) | 0.000 | 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 | 0.001 |
| 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".