Determination of phenazopyridine in biological fluids using electromembrane extraction followed by high-performance liquid chromatography
Bibliographic record
Abstract
Recently, electro-assisted extraction of ionic drugs from biological fluids through a supported liquid membrane and into an aqueous acceptor solution was introduced as a new sample preparation technique and has been termed electromembrane extraction (EME). In the present work, this microextraction technique combined with high-performance liquid chromatography and ultraviolet detection has been developed for detection of phenazopyridine (PP) as a local analgesic drug in human plasma and urine samples. From a 6.5 mL neutral aqueous sample, PP was extracted for 20 min through a thin supported liquid membrane of 2-nitrophenyl octyl ether sustained in the pores of the wall of a porous hollow fiber and into an aqueous acidic acceptor solution (25 μL, containing negative electrode) by application of a DC electrical potential. The effects of several factors, including the nature of organic solvent, HCl concentration in donor and acceptor solutions, stirring speed, extraction time, and applied voltage on the extraction efficiency of the drug, were investigated and optimized. Satisfactory linearity ranges with correlation coefficients higher than 0.996 in different extraction media, admissible limits of detection (0.5 and 1.0 ng mL−1 in urine and plasma samples, respectively) and good repeatability and reproducibility (intra- and inter-assay precisions ranged between 3.7%–6.8% and 8.8%–12.5%, respectively) were obtained. The optimized EME procedure was applied to determine the concentration of PP in various matrices, such as plasma and urine samples, and satisfactory results were obtained.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".