Belatacept and Basiliximab Diminish Human Antiporcine Xenoreactivity and Synergize to Inhibit Alloimmunity
Bibliographic record
Abstract
BACKGROUND: Maintenance immunosuppression with calcineurin inhibitor therapy has improved survival rates in solid organ transplantation over the past decade. However, these drugs are associated with negative side effects, including nephrotoxicity and new-onset diabetes. Selective immunomodulatory agents such as belatacept and basiliximab have shown great promise in promoting allograft survival. In the present study, in vitro experiments were conducted to determine if these agents could synergize to inhibit immune responses. METHODS: Porcine and human lymphocytes were incubated with each drug and analyzed using flow cytometry to measure binding capability. The inhibitory effects of each drug were evaluated using mixed lymphocyte reactions with drug doses comparable to the trough levels observed in treated human patients. RESULTS: Our data demonstrates that belatacept and basiliximab bind to porcine peripheral blood mononuclear cells. Mixed lymphocyte reactions revealed that both belatacept and basiliximab monotherapy potently inhibited allogeneic immune responses and human antipig xenoreactivity. These data also demonstrate that combination of belatacept and basiliximab produces a synergistic inhibition of allogeneic immune responses. CONCLUSIONS: These studies suggest that the combination of belatacept and basiliximab will potently inhibit alloreactivity in vivo when used as maintenance immunosuppression. We have further shown that belatacept and basiliximab are significantly reduce xenoreactivity of human lymphocytes in vitro.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".