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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".