Proteomic-Based Detection of Urine Proteins Associated with Acute Renal Allograft Rejection
Bibliographic record
Abstract
At present, the diagnosis of renal allograft rejection requires a renal biopsy. Clinical management of renal transplant patients would be improved by the development of non-invasive markers of rejection that can be measured frequently. This study sought to determine whether such candidate proteins can be detected in urine using mass spectrometry. Four patient groups were rigidly defined on the basis of allograft function, clinical course, and allograft biopsy result: acute clinical rejection group (n = 18), stable transplant group (n = 22), acute tubular necrosis group (n = 5), and recurrent (or de novo) glomerulopathy group (n = 5). Urines collected the day of the allograft biopsy were analyzed by mass spectrometry. As a normal control group, 28 urines from healthy individuals were analyzed the identical manner, as well as 5 urines from non-transplanted patients with lower urinary tract infection. Furthermore, sequential urine analysis was performed in patients in the acute clinical rejection and the stable transplant group. Three prominent peak clusters were found in 17 of 18 patients (94%) with acute rejection episodes, but only in 4 of 22 patients (18%) without clinical and histologic evidence for rejection and in 0 of 28 normal controls (P < 0.001). In addition, the presence or absence of these peak clusters correlated with the clinicopathologic course in most patients. Acute tubular necrosis, glomerulopathies, lower urinary tract infection, and cytomegalovirus viremia were not confounding variables. In conclusion, proteomic technology together with stringent definition of patient groups can detect urine proteins associated with acute renal allograft rejection. Identification of these proteins may prove useful as non-invasive diagnostic markers for rejection and the development of novel therapeutic agents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".