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Fully automated sample preparation procedure to measure drugs of abuse in plasma by liquid chromatography tandem mass spectrometry

Overview of attention for article published in Analytical & Bioanalytical Chemistry, June 2018
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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7 X users

Citations

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35 Mendeley
Title
Fully automated sample preparation procedure to measure drugs of abuse in plasma by liquid chromatography tandem mass spectrometry
Published in
Analytical & Bioanalytical Chemistry, June 2018
DOI 10.1007/s00216-018-1159-7
Pubmed ID
Authors

Tiphaine Robin, Alan Barnes, Sylvain Dulaurent, Neil Loftus, Sigrid Baumgarten, Stéphane Moreau, Pierre Marquet, Souleiman El Balkhi, Franck Saint-Marcoux

Abstract

For the analysis of drugs and pharmaceutical compounds in biological matrices, extraction procedures are typically used for LC-MS/MS analysis often requiring manual steps in sample preparation. In this study, we report a fully automated extraction method carried out by a programable liquid handler directly coupled to an LC-MS/MS system for the determination of 42 components (illicit drugs and/or metabolites) (plus 20 deuterated internal standards). The acquisition was performed in positive ionization mode with up to 15 MRM transitions per compound, each with optimized collision energy (MRM spectrum mode) to enable qualitative library searching in addition to quantitation. After placing the sample tube into the system, no further intervention was necessary: automated preparation used 50 μL of blood or plasma with 3 μL of extracted sample injected for analysis. The method was validated according to the requirements of ISO 15189. The limit of detection and quantification was 1-5 ng/mL depending on the compound. Stability experiments found that historic calibration curve data files could accurately quantify for up to 1 month with less than 20% uncertainty. Comparison to a QuEChERS method was made using patient samples providing a regression correlation R2 = 0.98 between the two methods. This approach was successfully designed to support parallel sample preparation and analysis therefore significantly increasing sample throughput and reduced cycle times. Graphical abstract ᅟ.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 14%
Student > Doctoral Student 3 9%
Researcher 3 9%
Professor > Associate Professor 3 9%
Professor 2 6%
Other 3 9%
Unknown 16 46%
Readers by discipline Count As %
Chemistry 6 17%
Pharmacology, Toxicology and Pharmaceutical Science 4 11%
Biochemistry, Genetics and Molecular Biology 3 9%
Arts and Humanities 1 3%
Unspecified 1 3%
Other 2 6%
Unknown 18 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 July 2018.
All research outputs
#7,304,289
of 25,382,440 outputs
Outputs from Analytical & Bioanalytical Chemistry
#1,674
of 9,619 outputs
Outputs of similar age
#118,921
of 342,171 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#23
of 171 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 9,619 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,171 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 171 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.