The Development and Validation of a Simple HPLC-UV Method for the Determination of Vancomycin Concentration in Human Plasma and Application in Critically Ill Patients
Bibliographic record
Abstract
Vancomycin is an antimicrobial agent that exhibits high efficacy against Gram-positive bacteria. The importance of therapeutic drug monitoring (TDM) for vancomycin has been substantiated in specific patient cohorts, underscoring the significance of determining vancomycin plasma levels. This study presents the development and validation of a simple, reproducible, and practical approach for quantifying vancomycin levels in human plasma samples through high-performance liquid chromatography (HPLC). Deproteinization of plasma samples (0.3 mL) was achieved using 10% perchloric acid. The chromatographic separation was achieved using a C18 column. The mobile phase, consisting of phosphate buffer and acetonitrile (90:10, v/v), was run at a flow rate of 1 mL/min. Ultraviolet detection was conducted at a wavelength of 192 nm and the method was linear in the range of 4.5–80 mg/L (r2 > 0.99). Inter- and intra-day assay precision and accuracy were determined to be within the acceptable range. The run time was noted to be 10 min. This method was evaluated using different greenness tools, which indicated that the method is environmentally friendly. Our method was effectively applied to analyze vancomycin concentrations in critically ill patients. Thus, our approach has the potential for practical implementation in routine TDM procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".