Significance of anti-endothelial cell antibodies in paediatrickidney transplant recipients
Bibliographic record
Abstract
Introduction Vascular endothelium, which expresses antigens, could be targeted by various antibodies, and it is the first barrier between the immune system and the allograft in kidney transplant recipients (KTRs). We aimed to outline the clinical significance of anti-endothelial cell antibodies (AECA) in paediatric KTRs. Material and methods Serum AECA IgG titres were measured pre and post renal transplantation in 46 paediatric kidney transplant recipients and in 12 age- and gender-matched healthy controls by ELISA technique. Results In KTRs, AECA titres were significantly increased after transplantation compared to both pre-transplantation (1.66 ±0.90 vs. 0.76 ±0.58 ng/ml, p = 0.002) and healthy controls (1.66 ±0.90 vs. 0.6 ±0.2 ng/ml, p = 0.004). In KTRs, AECA titres were significantly increased in living unrelated compared to living related renal grafts (3.3 ±3.9 vs. 1.09 ±0.87 ng/ml, p = 0.003) and were significantly affected by the type of induction therapy (in anti-thymocyte globulin, n = 30), basiliximab (n = 9) and no antibody induction (n = 7) groups; (1.32 ±1.18, 2.5 ±4.37 and 2.01 ±2.27 ng/ml respectively, p = 0.0372). Anti-endothelial cell antibodies titre was detected positive (≥ 1.2 ng/ml) in 21% (3 patients) of KTRs with acute rejection (AR) (n = 14) and in 28% (2 patients) of KTRs with chronic graft dysfunction (n = 7). Conclusions In KTRs, AECA titre is increased after kidney transplantation without a significant correlation with AR. Anti-endothelial cell antibodies titre is influenced by donor relations and antibody induction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".