Proteomics and Renal Transplantation: Searching for Novel Biomarkers and Therapeutic Targets
Bibliographic record
Abstract
Renal transplantation has emerged as the preferred option for many patients with endstage renal failure. While significant progress has been achieved in short-term outcomes, long-term survival has only marginally improved. Adaptation of immunosuppressive drugs to the individual needs of every patient at every time point after transplant will be essential to improve long-term outcomes. Thus, assays are required that detect allograft injury very early, which implies frequent noninvasive measurements (e.g. in urine or serum). In this review, we describe important general aspects in urine biomarker discovery using proteomics and discuss currently published studies. Although proteomics has the potential to provide insights into complex pathophysiological processes and reveal novel diagnostic biomarkers as well as therapeutic drug targets, the actual status of urine proteomic activities in renal transplantation is still far from reaching these ambitious goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".