INDUCTION THERAPY WITH MONOCLONAL ANTIBODIES SPECIFIC FOR CD80 AND CD86 DELAYS THE ONSET OF ACUTE RENAL ALLOGRAFT REJECTION IN NON-HUMAN PRIMATES1
Bibliographic record
Abstract
CD80 and CD86 (also known as B7-1 and B7-2, respectively) are both ligands for the T cell costimulatory receptors CD28 and CD152. Both CD80 and CD86 mediate T cell costimulation, and as such, have been studied for their role in promoting allograft rejection. In this study we demonstrate that administering monoclonal antibodies specific for these B7 ligands can delay the onset of acute renal allograft rejection in rhesus monkeys. The most durable effect results from simultaneous administration of both anti-B7 antibodies. The mechanism of action does not involve global depletion of T or B cells. Despite in vitro and in vivo evidence demonstrating the effectiveness of the anti-B7 antibodies in suppressing T cell responsiveness to alloantigen, their use does not result in durable tolerance. Prolonged therapy with murine anti-B7 antibodies is limited by the development of neutralizing antibodies, but that problem was avoided when humanized anti-B7 reagents are used. Most animals develop rejection and an alloantibody response although still on antibody therapy and before the development of a neutralizing antibody response. Anti-B7 antibody therapy may have use as an adjunctive agent for clinical allotransplantation, but using the dosing regimens we used, is not a tolerizing therapy in this non-human primate model.
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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 | 0.001 | 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 | 0.001 |
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".