Determination of 31 Antimicrobials in Human Serum Using Ultra-High Performance Liquid Chromatography With Diode Array Detection for Application in Therapeutic Drug Monitoring
Bibliographic record
Abstract
BACKGROUND: A versatile ultra-high performance liquid chromatography method with diode array detection was developed to quantify a wide range of antibiotics in human serum. This method addresses the need for rapid and accurate determination of antibiotic levels to ensure effective patient treatment and support the fight against antibiotic resistance. METHODS: This method assesses 31 different compounds covering β-lactams, fluoroquinolones, antifungals, antituberculars, and more. Proteins were precipitated using methanol or acetonitrile, and drugs were extracted by liquid-liquid extraction with dichloromethane. Separation of the antimicrobials was achieved on a pentafluorophenyl column, using a mobile phase of phosphoric acid (0.01 mol/L) and acetonitrile in a gradient elution mode, with a flow rate of 500 μL/min. RESULTS: Almost all compounds were detected at 200 nm. The total analysis time for this method was kept under 18 minutes, including equilibration time. CONCLUSIONS: This efficient method enables fast determination of numerous antimicrobial classes, providing clinicians with an essential tool for ensuring effective patient treatment and combating antimicrobial resistance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".