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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".