Determination of Diuretics in Urine Using Immobilized Multi‐Walled Carbon Nanotubes in Hollow Fiber Liquid‐Phase Microextraction Combined with Liquid Chromatography‐Tandem Mass Spectrometry
Bibliographic record
Abstract
Abstract A novel technique utilizing the adsorptive potential of immobilized multi‐walled carbon nanotubes (I‐MWCNT) in hollow fiber liquid‐phase microextraction (HF‐LPME) was developed for the determination of diuretics in urine. In this study, the potential of carbon nanotubes as a sorbent for three‐phase liquid‐phase microextraction of diuretics from urine samples was evaluated. Analysis was performed using liquid chromatography‐tandem mass spectrometry (LC‐MS/MS). A novel method was applied to detect acetazolamide (AAA), chlorothiazide (CTA), hydrochlorothiazide (HCT), hydroflumethiazide (HFT), clopamide (CA), trichlormethiazide (TCM), althiazide (AT) and bendroflumethiazide (BFT) in urine. Two‐step extractions using different times and temperatures for each step were adopted. Parameters influencing the extraction efficiency, including the extraction solvent, sample pH, salt concentration, extraction time and extraction temperature were systematically optimized. Under the resulting optimal extraction conditions, this method showed good linearity over an analytes concentration range of 1 to 1000 ng/mL, high extraction repeatability with relative standard deviations of less than 6%, and low detection limits (0.09 to 0.51 ng/mL). The application of the methods to the determination of diuretics in real samples was tested by analyzing urine samples of patient.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".