IL‐6 receptor blockade for allograft dysfunction after lung transplantation in a patient with COPA syndrome
Bibliographic record
Abstract
Abstract Objective COPA syndrome is a genetic disorder of retrograde cis‐Golgi vesicle transport that leads to upregulation of pro‐inflammatory cytokines (mainly IL‐1β and IL‐6) and the development of interstitial lung disease (ILD). The impact of COPA syndrome on post‐lung transplant (LTx) outcome is unknown but potentially detrimental. In this case report, we describe progressive allograft dysfunction following LTx for COPA‐ILD. Following the failure of standard immunosuppressive approaches, detailed cytokine analysis was performed with the intention of personalising therapy. Methods Multiplexed cytokine analysis was performed on serum and bronchoalveolar lavage (BAL) fluid obtained pre‐ and post‐LTx. Peripheral blood mononuclear cells (PMBCs) obtained pre‐ and post‐LTx were stimulated with PMA, LPS and anti‐CD3/CD28 antibodies. Post‐LTx endobronchial biopsies underwent microarray‐based gene expression analysis. Results were compared to non‐COPA LTx recipients and non‐LTx healthy controls. Results Multiplexed cytokine analysis showed rising type I/II IFNs, and IL‐6 in BAL post‐LTx that decreased following treatment of acute rejection but rebounded with further clinical deterioration. In vitro stimulation of PMBCs suggested that myeloid cells were driving deterioration, through IL‐6 signalling pathways. Tocilizumab (IL‐6 receptor antibody) administration for 3 months (4 mg kg−1, monthly) effectively suppressed IL‐6 levels in BAL. Mucosal gene expression profile following tocilizumab suggested greater similarity to normal. Conclusion Clinical effectiveness of IL‐6 receptor blockade was not observed. However, we identified IL‐6 upregulation associated with graft injury, effective IL‐6 suppression with tocilizumab and evidence of beneficial effect on molecular transcripts. This mechanistic analysis suggests a role for IL‐6 blockade in post‐LTx care that should be investigated further.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".