A liquid chromatography–mass spectrometric method for the quantification of azithromycin in human plasma
Bibliographic record
Abstract
A liquid chromatographic mass spectrometric assay for the quantification of azithromycin in human plasma was developed. Azithromycin and imipramine (as internal standard, IS) were extracted from 0.5 mL human plasma using extraction with diethyl ether under alkaline conditions. Chromatographic separation of drug and IS was performed using a C18 column at room temperature. A mobile phase consisting of methanol, water, ammonium hydroxide and ammonium acetate was pumped at 0.2 mL/min. The mass spectrometer was operated in positive ion mode and selected ion recording acquisition mode. The ions utilized for quantification of azithromycin and IS were m/z 749.6 (M + H)(+) and m/z 591.4 (fragment) for azithromycin, and 281.1 m/z for internal standard; retention times were 6.9 and 3.4 min, respectively. The calibration curves were linear (r(2) > 0.999) in the concentration ranges of 10-1000 ng/mL. The mean absolute recoveries for 50 and 500 ng/mL azithromycin and 1 µg/ mL IS were >75%. The percentage coefficient of variation and mean error were <11%. Based on validation data, the lower limit of quantification was 10 ng/mL. The present method was successfully applied to determine azithromycin pharmacokinetic parameters in two obese volunteers. The assay had applicability for use in pharmacokinetic studies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".