A High-Performance Liquid Chromatography Assay Method for the Determination of Lidocaine in Human Serum
Bibliographic record
Abstract
Here we report on the development of a selective and sensitive high-performance liquid chromatographic method for the determination of lidocaine in human serum. The extraction of lidocaine and procainamide (internal standard) from serum (0.25 mL) was achieved using diethyl ether under alkaline conditions. After liquid–liquid extraction, the separation of analytes was accomplished using reverse phase extraction. The mobile phase, a combination of acetonitrile and monobasic potassium phosphate, was pumped isocratically through a C18 analytical column. The ultraviolet (UV) wavelength was at 277 nm for the internal standard, and subsequently changed to 210 for lidocaine. The assay exhibited excellent linearity (r2 > 0.999) in peak response over the concentration ranges of 50–5000 ng/mL lidocaine HCl in human serum. The mean absolute recoveries for 50 and 1000 ng/mL lidocaine HCl in serum using the present extraction procedure were 93.9 and 80.42%, respectively. The intra- and inter-day coefficients of variation in the serum were <15% at the lowest, and <12% at other concentrations, and the percent error values were less than 9%. The method displayed a high caliber of sensitivity and selectivity for monitoring therapeutic concentrations of lidocaine in human serum.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".