Induction of Accommodation by Anti–complement Component 5 Antibody-based Immunosuppression in ABO-incompatible Heart Transplantation
Bibliographic record
Abstract
BACKGROUND: Plasmapheresis in combination with immunoglobulin and rituximab is often used to induce accommodation in ABO-incompatible (ABOi) living-donor transplantation; however, this regimen cannot be applied to cases of ABOi deceased-donor transplantation. Here, we investigated whether an anti-complement component 5 (C5) antibody-based regimen can induce accommodation in ABOi heart transplantation. METHODS: Both IgM and IgG anti-blood type A antibodies were induced in wild-type mice by sensitization using human blood type A antigen. Heterotopic ABOi heart transplantation was performed from human blood type A-transgenic C57BL/6J mice to sensitized wild-type DBA/2 mice. RESULTS: Either anti-C5 antibody or conventional triple immunosuppressants (corticosteroid, tacrolimus, mycophenolate mofetil) alone did not induce accommodation in majority of ABOi heart allografts, whereas their combination induced accommodation in more than 70% of cases despite the presence of anti-A antibodies. The combination therapy markedly suppressed the infiltration of T cells and macrophages into ABOi allografts, despite mild deposition of IgG and C4d. T-cell activation and differentiation into Th1, Th2, and Th17 cells were suppressed along with CD49dCD4 T and follicular helper T cells in the combination treatment group. CD24 B cells, including both CD24CD23 marginal zone B cells and CD24CD23 T2-marginal zone B cells, were increased in the accommodation group. CONCLUSIONS: C5 inhibitor-based immunosuppression induced accommodation in murine ABOi heart transplantation, presenting a promising strategy for ABOi deceased-donor transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".