Determination of Urine Albumin by New Simple High‐Performance Liquid Chromatography Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 300 SB ‐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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it