141 Kidneys under attack: unravelling the mischief of complement in paediatric kidney transplant rejection
Bibliographic record
Abstract
Introduction Complement is a key mediator of inflammation to renal allografts undergoing antibody-mediated rejection (ABMR). Membrane complement regulatory proteins (mCRPs) prevent over-activation of the complement system. Spleen tyrosine kinase (Syk) drives complement-mediated phagocytosis, while C3d is a cleavage product of C3, the central point of convergence of complement activation. This study explores the role of these antigens in monitoring complement activation in paediatric kidney transplant recipients (pKTR) with ABMR.Methods In this retrospective, single centre study, we investigated 55 pKTR from Great Ormond Street Hospital (GOSH) diagnosed with ABMR. Tissue samples underwent immunohistochemical (IHC) staining with pro-inflammatory factors (Syk, C3d) and complement regulatory proteins (CD46, CD55, CD59). Staining intensity was assessed using a semi-quantitative scoring system, with categories of 0 (<10%), 1+ (10–50%), and 2+ (>50%). Five renal allograft biopsies with TCMR or no rejection served as controls.Results CD46 and Syk expression were significantly higher in ABMR tissue compared to controls (p = 0.039 and p < 0.001, respectively). Conversely, C3d expression was significantly lower in ABMR tissue (p = 0.012). CD55 was not expressed in either group, while CD59 showed mainly positive staining in both, with no statistically significant difference.Conclusion These findings highlight complement’s important role in ABMR. CD46, Syk, and C3d could help refine the diagnostic criteria or emerge as potential treatment targets in ABMR. The high CD59 expression suggests a robust natural terminal complement blockade, which might account for the limited efficacy of intravenous eculizumab in ABMR treatment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".