An original HPLC-UV method has been developed for the simultaneous determination of the atypical antipsychotic quetiapine and the geometric isomers of the second-generation antidepressant fluvoxamine. The analytes were separated on a reversed-phase C8 column (150 mm x 4.6mm i.d., 5 microm) using a mobile phase composed of acetonitrile (30%) and a 10.5mM, pH 3.5 phosphate buffer containing 0.12% triethylamine (70%). The flow rate was 1.2 mL min(-1) and the detection wavelength was 245 nm. Sample pretreatment was carried out by an original solid-phase extraction procedure using mixed-mode cation exchange (DSC-MCAX) cartridges; only 300 microL of plasma were needed for one analysis. Citalopram was used as the internal standard. The method was validated in terms of linearity, extraction yield, precision and accuracy. Good linearity was obtained in plasma over the 5.0-160.0 ng mL(-1) concentration range for each fluvoxamine isomer and over the 2.5-400.0 ng mL(-1) concentration range for quetiapine. Extraction yield values were always higher than 93%, with precision (expressed as relative standard deviation values) better than 4.0%. The method was successfully applied to human plasma samples drawn from patients undergoing polypharmacy with the two drugs. Satisfactory accuracy values were obtained, with mean recovery higher than 94%.
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http://dx.doi.org/10.1016/j.jchromb.2006.06.001 | DOI Listing |
ACS Nano
January 2025
CAS Key Laboratory of Bio-inspired Materials and Interfacial Science, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, P.R. China.
Human sweat has the potential to be sufficiently utilized for noninvasive monitoring. Given the complexity of sweat secretion, the sensitivity and selectivity of sweat monitoring should be further improved. Here, we developed an olfactory-inspired separation-sensing nanochannel-based electronic for sensitive and selective sweat monitoring, which was simultaneously endowed with interferent separation and target detection performances.
View Article and Find Full Text PDFPLoS One
January 2025
PHIM, Plant Health Institute of Montpellier, Univ. Montpellier, IRD, CIRAD, INRAE, Institute Agro, Montpellier, France.
Local co-circulation of multiple phylogenetic lineages is particularly likely for rapidly evolving pathogens in the current context of globalisation. When different phylogenetic lineages co-occur in the same fields, they may be simultaneously present in the same host plant (i.e.
View Article and Find Full Text PDFPLoS One
January 2025
GuiZhou Institute of Subtropical Crops, Guizhou Academy of Agricultural Sciences, Guiyang, China.
Background: Fracture disrupts the integrity and continuity of the bone, leading to symptoms such as pain, tenderness, swelling, and bruising. Rhizoma Musae is a medicinal material frequently utilized in the Miao ethnic region of Guizhou Province, China. However, its specific mechanism of action in treating fractures remains unknown.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
End-user feedback early in product development is important for optimizing multipurpose prevention technologies for HIV and pregnancy prevention. We evaluated the acceptability of the 90-day dapivirine levonorgestrel ring (DPV-LNG ring) used for 14 days compared to a dapivirine-only ring (DVR-200mg) in MTN-030/IPM 041 (n = 23), and when used for 90 days cyclically or continuously in MTN-044/IPM 053/CCN019 (n = 25). We enrolled healthy, non-pregnant, HIV-negative women aged 18-45 in Pittsburgh, PA and Birmingham, AL (MTN-030 only).
View Article and Find Full Text PDFJ Thorac Imaging
September 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
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