Diagnosis of subclinical and clinical acute T‐cell‐mediated rejection in renal transplant patients by urinary proteome analysis
Bibliographic record
Abstract
PURPOSE: Noninvasive diagnosis of acute renal allograft rejection may be advantageous compared with the allograft biopsy. EXPERIMENTAL DESIGN: In this study, a multi-marker classification model for rejection was defined on a training set of 39 allograft patients by statistical comparison of capillary electrophoresis mass spectrometry (CE-MS) peptide spectra in urine samples from 16 cases with subclinical acute T-cell-mediated tubulointerstitial rejection and 23 nonrejection controls. RESULTS: Application of the rejection model to a blinded validation set (n=64) resulted in an AUC value of 0.91 (95% CI: 0.82-0.97, p=0.0001). In total, 16 out of 18 subclinical and 10 out of 10 clinical rejections (BANFF grades Ia/Ib), and 28 out of 36 controls without rejection were correctly classified. Acute tubular injury in the biopsies or concomitant urinary tract infection did not interfere with CE-MS-based diagnosis. Sequence information of identified altered collagen α(I) and α (III) chain fragments in rejection samples suggested an involvement of matrix metalloproteinase-8 (MMP-8). Biopsy stainings revealed matrix metalloproteinase-8 exclusively in neutrophils located within peritubular capillaries and sparsely, in the tubulointerstitium during rejection. CONCLUSIONS AND CLINICAL RELEVANCE: The established marker set contains peptides related to tubulointerstitial infiltration seen in acute rejection. The set of urinary peptide markers will be used for early diagnosis of acute kidney allograft rejection marker in a multicenter phase III prospective study.
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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 | 0.000 |
| 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".