A Urinary Proteomics Approach to Predict Treatment Response in Acute Antibody-Mediated Rejection
Bibliographic record
Abstract
Acute antibody-mediated rejection (AMR) is a severe complication affecting kidney allografts' long-term survival. Timely detection and appropriate treatment of AMR are crucial for improving graft outcomes. This study aimed to discover non-invasive urinary biomarkers that can predict the response to therapy in patients with AMR. Materials and Methods: In this case-control study, urine samples from 21 biopsy-proven AMR patients underwent proteomics using label-free quantification. The patients were divided into two groups: responders and non-responders to treatment based on their graft function. Urinary proteins were identified, and their expressions were compared between the two groups to identify potential candidate biomarkers. Out of the 1020 identified proteins, 257 proteins were found to be differentially expressed between the two groups. Among these, 153 proteins showed increased expression in non-responder patients, while 104 proteins showed decreased expression. Non-responder patients exhibited higher activation of complement pathway and extracellular matrix degradation than responders. Insulin-like growth factor binding protein 6 (IGFBP-6) emerged as the most sensitive and specific biomarker for predicting non-response to treatment in patients with AMR. Our study has successfully identified urinary proteome biomarkers that can distinguish and predict non-responder patients with AMR. These biomarkers are associated with various biological processes that reflect the pathogenesis and severity of AMR. Further research is necessary to validate these findings.
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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 | 0.002 | 0.001 |
| 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".