Restriction of interleukin‐6 alters endothelial cell immunogenicity in an allogenic environment
Bibliographic record
Abstract
Abstract The microvascular endothelium of the renal transplant is the first site of graft interaction with the host immune system and is often injured in chronic Antibody Mediated Rejection (AMR). Microvascular inflammation is an independent determinant of AMR and heightens endothelial expression of HLA molecules thereby increasing the possibility of Donor Specific Antibody (DSA) binding. Endothelial cells produce IL‐6 in the steady‐state and this is increased by inflammation or by HLA‐DR antibody binding in an allogeneic setting. Because IL‐6 has been implicated in AMR, IL‐6 blockade is currently under investigation as a therapeutic target. To further understand the role of IL‐6 in endothelial cell immunogenicity, we have examined whether humanized antibody blockade of IL‐6 altered endothelial cell interactions with allogeneic PBMC and after anti‐HLA or DSA binding to endothelial cells in an in vitro human experimental model. Soluble factors, endothelial phenotype, Stat‐3 activation, CD4+‐T differentiation, and C4d deposition were examined. Blockade of IL‐6 reduced endothelial cell secretion of IL‐6 and of the monocyte chemoattractant MCP‐1. Pre‐activation of endothelial cells by anti‐HLA or DSA binding increased IL‐6 secretion, that was further increased by concurrent binding of both antibodies and this was inhibited by IL‐6 blockade. Activation of Stat‐3 in CD4+‐T mediated by soluble factors produced in endothelial‐PBMC interactions, and endothelial differentiation of CD4+‐T cell subsets (Th1, Th17, Treg), were impaired whereas activation of Complement by anti‐HLA antibody binding remained unchanged by IL‐6 blockade. Together, these data identify EC‐mediated pro‐inflammatory responses (T cell expansion, EC auto‐activation, chemokine secretion) targeted by IL‐6 blockade.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".