Anti-CD45RB Monoclonal Antibody Prolongs Renal Allograft Survival in Cynomolgus Monkeys
Bibliographic record
Abstract
Previously, an anti-CD45RB monoclonal antibody (mAb) has been shown to induce murine allograft tolerance. The present study was performed to assess the ability of an anti-human CD45RB mAb to prevent rejection in a monkey MHC-mismatched kidney transplant model. The recipients were allocated into the following treatment groups: (1) isotype control IgG; (2) mouse anti-human CD45RB IgG1 (6G3); (3) human-mouse chimeric anti-CD45RB-IgG1 (C6G3-IgG1); (4) human-mouse chimeric anti-CD45RB-IgG2 (C6G3-IgG2); (5) tacrolimus at a subtherapeutic dose and (6) tacrolimus and C6G3-IgG1 in combination. Monotherapy with anti-CD45RB mAb significantly prolonged renal allograft survival to a median survival of 21 days. Adding a subtherapeutic dose of tacrolimus improved the efficacy of the anti-CD45RB mAb, achieving a median survival of 85 days, whereas a subtherapeutic dose of tacrolimus alone only moderately prolonged survival to 27 days. Treatment with anti-CD45RB mAb resulted in an alteration of the CD45RB(hi) : CD45RB(lo) cell ratio in the peripheral blood. We have, for the first time, demonstrated that an anti-human CD45RB mAb (6G3) can prolong graft survival. Induction with an anti-CD45RB mAb improves the efficacy of tacrolimus in the prevention of rejection. These encouraging results indicate that an anti-CD45RB mAb may be valuable in future clinical 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) | 0.001 | 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".