Chronic Antibody-Mediated Rejection in Nonhuman Primate Renal Allografts: Validation of Human Histological and Molecular Phenotypes
Bibliographic record
Abstract
Molecular testing represents a promising adjunct for the diagnosis of antibody-mediated rejection (AMR). Here, we apply a novel gene expression platform in sequential formalin-fixed paraffin-embedded samples from nonhuman primate (NHP) renal transplants. We analyzed 34 previously described gene transcripts related to AMR in humans in 197 archival NHP samples, including 102 from recipients that developed chronic AMR, 80 from recipients without AMR, and 15 normal native nephrectomies. Three endothelial genes (VWF, DARC, and CAV1), derived from 10-fold cross-validation receiver operating characteristic curve analysis, demonstrated excellent discrimination between AMR and non-AMR samples (area under the curve = 0.92). This three-gene set correlated with classic features of AMR, including glomerulitis, capillaritis, glomerulopathy, C4d deposition, and DSAs (r = 0.39-0.63, p < 0.001). Principal component analysis confirmed the association between three-gene set expression and AMR and highlighted the ambiguity of v lesions and ptc lesions between AMR and T cell-mediated rejection (TCMR). Elevated three-gene set expression corresponded with the development of immunopathological evidence of rejection and often preceded it. Many recipients demonstrated mixed AMR and TCMR, suggesting that this represents the natural pattern of rejection. These data provide NHP animal model validation of recent updates to the Banff classification including the assessment of molecular markers for diagnosing AMR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".