Extraction and Analysis of Methadone in Exhaled Breath Condensate Using a Validated LC-UV Method
Bibliographic record
Abstract
PURPOSE: A combined microextraction and separation method is presented for the determination of methadone in exhaled breath condensate (EBC) which is a promising non-invasive biological component for monitoring drug concentrations. METHODS: In this work, dispersive liquid-liquid microextraction (DLLME) and ultrasonic liquid-liquid microextraction (ULLME) procedure coupled with a validated liquid chromatography method were used for analysis of methadone in EBC collected using an in-house cold trap setup. The method has been validated according to the FDA guidelines using EBC-spiked samples and tested on a number of EBC samples collected from patients. RESULTS: The best DLLME conditions involved the use of a disperser solvent of methanol (1 mL), extraction solvent of chloroform (200 mL), EBC sample pH of 10.0 and centrifugation at 6000 rpm for 5 minutes. The conditions for ULLME were 150 mL of chloroform and the samples were sonicated for 4 minutes. The method was validated over the concentration range of 0.5-10 mg/L-1 in EBC. Inter- and intra-day precision and accuracy were less than 5 % where the acceptable levels are less than 20%. Furthermore, the validated method was successfully applied for the determination of methadone in patients' EBC samples. CONCLUSIONS: The outcomes indicate that the developed LC-UV combined with DLLME and/or ULLME extraction methods can be employed for the extraction and separation of methadone in EBC samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".