Anakinra Potentiates the Protective Effects of Etanercept in Transplantation of Marginal Mass Human Islets in Immunodeficient Mice
Bibliographic record
Abstract
Anti-inflammatory agents are used routinely in clinical islet transplantation in an attempt to promote islet engraftment. Infliximab, and more recently etanercept, is being used to neutralize tumor necrosis factor alpha, but this tenet is based on limited preclinical data. One group has promoted the potential of combined etanercept with an IL-1 receptor antagonist, anakinra in a small clinical study, but without strong preclinical data to justify this approach. We therefore sought to evaluate the impact of combined anakinra and etanercept in a marginal islet mass transplant model using human islets in immunodeficient mice. The combination of anakinra and etanercept led to remarkable improvement in islet engraftment (control 36.4%; anakinra 53.9%; etanercept 45.45%; anakinra and etanercept 87.5% euglycemia, p < 0.05 by log-rank) compared to single-drug treated mice or controls. This translated into enhanced metabolic function (area under curve glucose tolerance), improved graft insulin content and marked reduction in beta-cell specific apoptotis (0.67% anakinra + etanercept vs. 23.5% control, p < 0.001). These results therefore strongly justify the combined short-term use of anakinra and etanercept in human 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".