Determination of Urine Albumin by New Simple High‐Performance Liquid Chromatography Method
Bibliographic record
Abstract
Background A simple high‐performance liquid chromatography (HPLC) method was developed for the determination of albumin in patients' urine samples without coeluting proteins and was compared with the immunoturbidimetric determination of albumin. Urine albumin is important biomarker in diabetic patients, but part of it is immuno‐nonreactive. Methods Albumin was determined by high‐performance liquid chromatography (HPLC), UV detection at 280 nm, Zorbax 300SB‐C3 column. Immunoturbidimetric analysis was performed using commercial kit on automatic biochemistry analyzer COBAS INTEGRA® 400, Roche Diagnostics GmbH, Manheim, Germany. Results The HLPC method was fully validated. No significant interference with other proteins (transferrin, α‐1‐acid glycoprotein, α‐1‐antichymotrypsin, antitrypsin, hemopexin) was found. The results from 301 urine samples were compared with immunochemical determination. We found a statistically significant difference between these methods (P = 0.0001, Mann–Whitney test). Conclusion New simple HPLC method was developed for the determination of urine albumin without coeluting proteins. Our data indicate that the HPLC method is highly specific and more sensitive than immunoturbidimetry.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".