Automated In-Tube Solid-Phase Microextraction Coupled with Liquid Chromatography-Electrospray Ionization Mass Spectrometry for the Determination of Selected Benzodiazepines
Bibliographic record
Abstract
A simple, rapid, and sensitive method, which allowed us to simultaneously determine seven benzodiazepines (diazepam, nordiazepam, temazepam, oxazepam, 7-aminoflunitrazepam, N-desmethylflunitrazepam, and clonazepam) in buffer solution and in urine and serum samples, was investigated by automated in-tube solid-phase microextraction (SPME) coupled with liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS). In-tube SPME, in which the analytes were extracted from the sample directly into an open tubular capillary column by repeated draw/eject cycles of sample solution, is an extraction technique for organic compounds in aqueous samples. The separation of benzodiazepines was carried out under ion-suppressed reversed-phase conditions by using methanol/50mM ammonium acetate in water (60:40) as a mobile phase with a Supelco LC-18 column. The optimal extraction condition was 10 draw/eject cycles of 30 mL of sample in 100mM Tris-HCl (pH 8.5) at a flow rate of 0.3 mL/min using a piece of 60-cm length Supelco-Q plot capillary column as the extraction capillary. The quantitative study was explored by operating in selected-ion monitoring (SIM) mode. The calibration curves were linear in the range from 0.5 ng/mL or 2 ng/mL to 500 ng/mL. The detection limits were from 0.02 ng/mL to 2 ng/mL. At the optimized capillary and fragmentor voltages, the characteristic ions for each compound clearly showed up in the spectra and it is possible to use the LC-MS to identify these compounds. The method was applied to the analysis of biological samples without interfering peaks. However, the recoveries for some of the compounds in serum samples need to be further improved.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".