Identification of β2‐microglobulin as a urinary biomarker for chronic allograft nephropathy using proteomic methods
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Chronic allograft nephropathy (CAN) remains the leading cause of renal graft loss after the first year following renal transplantation. This study aimed to identify novel urinary proteomic profiles, which could distinguish and predict CAN in susceptible individuals. EXPERIMENTAL DESIGN: The study included 34 renal transplant patients with histologically proven CAN and 36 patients with normal renal transplant function. High-throughput proteomic profiles were generated from urine samples with three different ProteinChip arrays by surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Following SELDI, a biomarker pattern software analysis was performed which led to the identification of a novel biomarker pattern that could distinguish patients with CAN from those with normal renal function. RESULTS: An 11.7 kDa protein identified as β2 microglobulin was the primary protein of this biomarker pattern, distinguishing CAN from control patients (receiver operator characteristic [ROC]=0.996). SELDI-TOF-MS comparison of purified β2 microglobulin protein and CAN urine demonstrated identical 11.7 kDa protein peaks. Significantly, higher concentrations of 2 microglobulin were found in the urine of patients with CAN compared with the urine of normal renal function transplant recipients (p<0.001). CONCLUSIONS AND CLINICAL RELEVANCE: Although further validation in a larger more diverse patient population is required to determine if this β2 microglobulin protein biomarker will provide a potential means of diagnosing CAN by noninvasive methods in a clinical setting, this study clearly shows a capability to stratify control and disease patients.
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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) | 0.000 | 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 it