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UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females.

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机构: [1]Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. [2]School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China. [3]Department of Anesthesiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, China. [4]Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
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关键词: propofol female interindividual variability biomarker untargeted metabolomics

摘要:
Although previous studies have shown that certain factors interfere with the sensitivity of propofol, the mechanisms for interindividual variability in response to propofol remain unclear. This study aimed to screen the metabolites to predict patients' sensitivity to propofol and to identify metabolic pathways to explore possible mechanisms associated with propofol resistance. Sera from 40 female patients undergoing elective hysteroscopic surgery in a prospective cohort propofol study were obtained before the administration of propofol. The patients' responsiveness to propofol was differentiated based on propofol effect-site concentration. Serum samples from two sets, a discovery set (n = 24) and an independent validation set (n = 16), were analyzed using ultraperformance liquid chromatography coupled with mass spectrometry based untargeted metabolomics. In the discovery set, 494 differential metabolites were screened out, and then 391 potential candidate biomarkers with the area under receiver operating characteristic curve >0.80 were selected. Pathway analysis showed that the pathway of glycerophospholipid metabolism was the most influential pathway. In the independent validation set, six potential biomarkers enabled the discrimination of poor responders from good and intermediate responders, which might be applied to predict propofol sensitivity. The mass spectrometry data are available via MetaboLights (http://www.ebi.ac.uk/metabolights/login) with the identifier MTBLS2311.

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出版当年[2020]版:
大类 | 2 区 生物
小类 | 2 区 生化研究方法
最新[2025]版:
大类 | 2 区 生物学
小类 | 2 区 生化研究方法
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出版当年[2019]版:
Q1 BIOCHEMICAL RESEARCH METHODS
最新[2023]版:
Q1 BIOCHEMICAL RESEARCH METHODS

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
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通讯机构: [1]Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. [4]Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. [*1]Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China [*2]Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
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