Caspase Inhibitor Therapy Synergizes With Costimulation Blockade to Promote Indefinite Islet Allograft Survival
Bibliographic record
Abstract
OBJECTIVE: Costimulation blockade has emerged as a selective nontoxic maintenance therapy in transplantation. However, these drugs must be combined with other immunomodulatory agents to ensure long-term graft survival. RESEARCH DESIGN AND METHODS: Recent work has demonstrated that caspase inhibitor therapy (EP1013) prevents engraftment phase islet loss and markedly reduces the islet mass required to reverse diabetes. The "danger" hypothesis suggests that reduction in graft apoptosis should reduce the threshold for immunosuppression and increase the possibility for tolerance induction. Thus, the impact of combination of EP1013 treatment with costimulation blockade (CTLA4-Ig) was investigated in this study. RESULTS: Islet allografts were completed in fully major histocompatibility complex (MHC)-mismatched mice (Balb/C to B6). When animals received vehicle or EP1013, there was no difference in graft survival. CTLA4-Ig resulted in prolonged graft survival in 40% of the animals, whereas EP1013+CLTA4-Ig resulted in a significant increase in graft survival (91% >180 days; P = 0.01). Ex vivo analysis revealed that animals receiving EP1013 or EP1013+CTLA4-Ig had a reduced frequency of alloreactive interferon (IFN)-gamma-secreting T-cells and an increased frequency of intragraft Foxp3(+) Treg cells. Alloantibody assays indicated that treatment with EP1013 or CTLA4-Ig prevented allosensitization. CONCLUSIONS: This study suggests that addition of caspase inhibitor therapy to costimulation blockade will improve clinical transplantation by minimizing immune stimulation and thus reduce the requirement for long-term immunosuppressive therapy. The approach also prevents allosensitization, which may be an important component of chronic graft loss in clinical transplantation.
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 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) | 0.000 | 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) | 0.002 | 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".