Intragraft gene expression in native kidney BK virus nephropathy versus T cell–mediated rejection: Prospects for molecular diagnosis and risk prediction
Bibliographic record
Abstract
Novel tools are needed to improve diagnostic accuracy and risk prediction in BK virus nephropathy (BKVN). We assessed the utility of intragraft gene expression testing for these purposes. Eight hundred genes were measured in 110 archival samples, including a discovery cohort of native kidney BKVN (n = 5) vs pure T cell-mediated rejection (TCMR; n = 10). Five polyomavirus genes and seven immune-related genes (five associated with BKVN and two associated with TCMR) were significantly differentially expressed between these entities (FDR < 0.05). These three sets of genes were further evaluated in samples representing a spectrum of BK infection (n = 25), followed by a multicenter validation cohort of allograft BKVN (n = 60) vs TCMR (n = 10). Polyomavirus 5-gene set expression reliably distinguished BKVN from TCMR (validation cohort AUC = 0.992), but the immune gene sets demonstrated suboptimal diagnostic performance (AUC ≤ 0.720). Within the validation cohort, no significant differences in index biopsy gene expression were identified between BKVN patients demonstrating resolution (n = 35), persistent infection (n = 14) or de novo rejection (n = 11) 6 months following a standardized reduction in immunosuppression. These results suggest that, while intragraft polyomavirus gene expression may be useful as an ancillary diagnostic for BKVN, assessment for concurrent TCMR and prediction of clinical outcome may not be feasible with current molecular tools.
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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 | 0.001 | 0.001 |
| 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".