Determination of diuretics in human urine by hollow fiber-based liquid–liquid–liquid microextraction coupled to high performance liquid chromatography
Bibliographic record
Abstract
In competition sports, a diuretic is a substance widely prohibited by the World Anti-Doping Agency (WADA). In this paper, a sensitive, rapid and convenient analytical method for the determination of acidic [furosemide (FUROS) and bumetanide (BUMET)] and basic [triamterene (TRIAM)] diuretics in human urine was developed by hollow fiber-based liquid-liquid-liquid microextraction (LLLME) coupled with HPLC-UV. The LLLME conditions, such as the organic extraction solvent, the acidity and basicity of the donor- and acceptor-phases, stirring speed, extraction time and ionic strength, were studied in detail. Under the optimum conditions, the linear ranges of furosemide, bumetanide and triamterene were 1.2-250, 5.0-250 and 5.0-500 ng mL(-1), respectively. The detection limits were 0.5 ng mL(-1) for furosemide, 1.2 ng mL(-1) for bumetanide and 2.0 ng mL(-1) for triamterene. The LLLME obtained a great improvement of the detection limits for all the analytes considered here, to the ng mL(-1) level, which almost reaches the level of the LC-MS method. This new LLLME method provided very high enrichments: 117-fold for furosemide, 175-fold for bumetanide and 68-fold for triamterene. Since the hollow fiber membrane was sealed, it could be used for extracting the diuretics directly from 'dirty' human urine samples without any clean-up procedures. With LLLME-HPLC, the corresponding recoveries ranged from 79.2 to 109% with the RSDs not exceeding 5.5% for the three diuretics in the spiked urine samples. The method was successfully applied to analyse the amounts of the three diuretics in real urine samples of volunteers after oral drug-taking. This new method proves to be sensitive and reliable and thus renders a very suitable means for the determination of trace diuretics in human urine based on the common HPLC instrument.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".