Prevention of Acute Vascular Rejection by a Functionally Blocking Anti-C5 Monoclonal Antibody Combined with Cyclosporine
Bibliographic record
Abstract
BACKGROUND: Inhibition of the complement cascade at C5 prevents formation of pro-inflammatory molecules C5a and C5b-9, which play a key role in allograft rejection. The present study was undertaken to determine whether blocking terminal complement with anti-C5 monoclonal antibody (mAb) alone or combined with cyclosporine (CsA) would prevent acute vascular rejection (AVR) in a mouse cardiac allograft model. METHODS: C3H mouse hearts were transplanted into BALB/c mice and randomized into five groups with the following treatments: (1) no treatment; (2) CsA alone; (3) control mAb; (4) anti-C5 mAb alone; and (5) anti-C5 mAb and CsA. RESULTS: Allografts in untreated or control mAb-treated recipients were rapidly rejected at 8.0+/-0.6 and 8.2+/-0.8 days, respectively. These grafts exhibited typical AVR, characterized by vasculitis, hemorrhage, and thrombosis. A high level of complement activity was also demonstrated in these animals. High-dose CsA was not able to inhibit complement activation or AVR, and grafts were rejected in 15.5+/-1.1 days. Anti-C5 monotherapy completely inhibited complement activation and attenuated AVR, but grafts were rejected in 8.3+/-0.5 days by acute cellular rejection. In contrast, a combination of anti-C5 mAb and CsA successfully achieved indefinite graft survival (>100 days). This combined therapy completely inhibited terminal complement activation and prevented both humoral- and cellular-mediated rejection. CONCLUSIONS: Combination therapy of anti-C5 mAb and CsA achieves indefinite graft survival in a mouse cardiac allograft model. These data suggest that inhibition of terminal complement using anti-C5 mAb may be an effective therapeutic adjunct to prevent AVR in 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) | 0.001 | 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".