Endothelial Gene Expression in Kidney Transplants with Alloantibody Indicates Antibody-Mediated Damage Despite Lack of C4d Staining
Bibliographic record
Abstract
Anti-HLA alloantibody is a risk factor for graft loss, but does not indicate which kidneys are experiencing antibody-mediated rejection (ABMR). C4d staining in biopsies is specific for ABMR but insensitive. We hypothesized that altered expression of endothelial genes due to alloantibody acting on the microcirculation would be sensitive indicator of ABMR. We identified 119 endothelial-associated transcripts (ENDATs) from literature, and studied their expression by microarrays in 173 renal allograft biopsies for cause. Mean ENDAT expression was increased in all rejection but was higher in ABMR than in T-cell-mediated rejection and correlated with histopathologic lesions of ABMR, and alloantibody. Many individual ENDATs were increased in ABMR and predicted graft loss. Kidneys with high ENDATs and antibody showed increased lesions of ABMR and worse prognosis in comparison to controls. Only 40% of kidneys with high ENDAT expression and chronic ABMR or graft loss were diagnosed by C4d positivity. High ENDAT expression with antibody predicts graft loss with higher sensitivity (77% vs. 31%) and slightly lower specificity (71% vs. 94%) than C4d. The results were validated in independent set of 82 kidneys. High renal endothelial transcript expression in patients with alloantibody is indicator of active antibody-mediated allograft damage and poor graft outcome.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".