Surface‐enhanced laser desorption/ionization‐time of flight‐mass spectrometry (SELDI‐TOF‐MS): A new proteomic urinary test for patients with urolithiasis
Bibliographic record
Abstract
SELDI-TOF-MS is a highly sensitive protein-analysis tool capable of detecting minute protein profile differences between biological samples. As proteins have been associated with urinary tract calculi, protein-based urinalysis may offer insights into their diagnosis. The purpose of this study was to evaluate SELDI-TOF-MS as a potential method for identifying urinary biomarkers of urolithiasis. Midstream sterile urine samples were obtained from 25 male patients with a confirmed diagnosis of urolithiasis (test group) and 25 male subjects with no known history of the disease (controls). Urinary levels of oxalate, total protein, albumin, and osteopontin were determined. Protein profiles were generated using SELDI-TOF-MS.SELDI-TOF-MS profiling revealed a relationship between protein peak intensities at 67 and 24 kDa that differed between the two groups. The ratio of p67:p24 was found to be less than 1.0 in all of the control samples (mean 0.26), while 18 out of 25 (72%) of the test group samples displayed a ratio greater than 1.0 (total group mean 4.75, P<0.001). Albumin, total protein, and oxalate levels were higher in the test group than the controls. Although SELDI-TOF-MS is not yet in widespread use in hospital and diagnostic laboratories, this system represents a promising new method for rapidly identifying patients with urolithiasis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".