Identification of predictive biomarkers of response to treatment in patients with antibody mediated rejection: a case –control proteomics study
Bibliographic record
Abstract
Background: Timely detection and appropriate treatment of Acute antibody mediated rejection (AMR) would affect long-term survival of allograft. This study was designed to discover non-invasive biomarkers in prediction of response to therapy in AMR patients. Material and methods: in this case- control study, urine samples of 21 biopsy proven AMR patients were were subjected to proteomics with label free quantification. Patients were allocated into two groups of responders and non- responders to treatment. Urine proteins were identified and their expressions were compared in two groups in order to discover potential candidate biomarkers. Results: From 1020 identified proteins, 257 proteins were differentially expressed between groups among them 153 and 104 proteins increased and decreased in non- responder patients respectively. Complement pathway was more active in non-responders than responders and, extracellular matrix proteins were mainly reduced in non-responders. IGFBP-6 were determined as the most sensitive and specific biomarker in prediction of non-responder patients. Conclusion: According to the role of IGFBP-6 in apoptosis induction and tubular damage, up-regulation of this protein could be a good predictor of response to treatment in AMR patients and treatment approach could be determined based on IGFBP_6 changes. Further studies are needed to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".