Non-invasive Chemokine Detection: Improved Prediction of Antibody-Mediated Rejection in Donor-Specific Antibody-Positive Renal Allograft Recipients
Bibliographic record
Abstract
Background: Screening for donor-specific antibodies (DSA) has limited diagnostic value in patients with late antibody-mediated rejection (ABMR). Here, we evaluated whether biomarkers reflecting microcirculation inflammation or tissue injury -as an adjunct to DSA detection -are able to improve noninvasive ABMR monitoring. Methods: Upon prospective cross-sectional antibody screening of 741 long-term kidney transplant recipients with a silent clinical course, 86 DSA-positive patients were identified and biopsied. Serum and urine levels of E-selectin/CD62E, vascular cell adhesion molecule 1 (VCAM-1), granzyme B, hepatocyte growth factor (HGF), C-C motif chemokine ligand (CCL)3, CCL4, C-X-C motif chemokine ligand (CXCL)9, CXCL10 and CXCL11 in DSA-positive recipients were investigated applying multiplexed bead-based immunoassays. Results: Diagnosis of ABMR (50 patients) was associated with significantly higher levels of CXCL9 and CXCL10 in blood and urine and of HGF in blood. Overall, urinary CXCL9 had the highest diagnostic accuracy for ABMR (area under the receiver operating characteristic curve: 0.77; accuracy: 80%) and its combined evaluation with the mean fluorescence intensity of the immunodominant DSA (DSAmax MFI) revealed a net reclassification improvement of 73% compared to DSAmax MFI alone.
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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 | 0.002 |
| 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 | 0.000 |
| 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".