An Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry Method for the Quantification of Vancomycin Requiring Only 2 µL of Rabbit Serum
Bibliographic record
Abstract
A highly sensitive ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was developed for the quantification of vancomycin (VAN) in low volumes of rabbit serum. For each analysis, 2 μL rabbit serum was precipitated with methanol that contained the internal standard teicoplanin (TEI). The supernatant was transferred into a 384 well-plate, diluted with water, covered with a pierceable silicone mat and 5 μL was analyzed in positive ionization mode. The UHPLC-MS/MS consisted of an Agilent 1290 Infinity UHPLC system connected to an AB Sciex QTrap® 5500 hybrid linear ion-trap triple quadrupole mass spectrometer equipped with a Turbo Spray source. Chromatographic separation was achieved using a Waters Acquity UPLC BEH C18 (1.7 μm, 2.1 mm × 100 mm) column, a VanGuard (1.7 μm, 2.1 × 5 mm) guard column and a mobile phase of water and methanol both containing 5 mM ammonium acetate with 0.1% formic acid. VAN was quantified with multiple reaction monitoring using the transitions of m/z 725.5/144.2, and TEI was monitored at m/z 940.6/316.4. The accuracy, precision, linearity, range and lower limit of quantification (LLOQ) were determined. The accuracy was ≤9.93% and the precision was ≤10.6%. The range was established as 0.1 to 40 μg·mL-1. The LLOQ was 0.1 μg·mL-1 VAN requiring 2 μL of sample with an accuracy of -20.2% and precision of 8.39%. The method was applied successfully to determine the VAN concentrations in rabbit serum after the i.v. administration of VAN via implanted ear catheters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".