Urine proteomics: the present and future of measuring urinary protein components in disease
Bibliographic record
Abstract
For centuries, physicians have attempted to use the urine for noninvasive assessment of disease. Today, urinalysis, in particular the measurement of proteinuria, underpins the routine assessment of patients with renal disease. More sophisticated methods for assessing specific urinary protein losses have emerged; however, albumin is still the principal urinary protein measured. Changes in the pattern of urinary protein excretion are not necessarily restricted to nephrourological disease; for instance, the appearance of beta-human chorionic gonadotropin in the urine of pregnant women is the basis for all commercially available pregnancy kits. Similarly, microalbuminuria is a clinically important marker not only of early diabetic nephropathy but also of concomitant cardiovascular disease. With the emergence of newer technologies, in particular mass spectrometry, it has become possible to study urinary protein excretion in even more detail. A variety of techniques have been used both to characterize the normal complement of urinary proteins and also to identify proteins and peptides that may facilitate earlier detection of disease, improve assessment of prognosis and allow closer monitoring of response to therapy. Such proteomics-based approaches hold great promise as the basis for new diagnostic tests and as the means to better understand disease pathogenesis. In this review, we summarize the currently available methods for urinary protein analysis and describe the newer approaches being taken to identify urinary biomarkers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".