4.7 Article

Hydrophilic interaction chromatography combined with ultrasound-assisted ionic liquid dispersive liquid-liquid microextraction for determination of underivatized neurotransmitters in dementia patients' urine samples

期刊

ANALYTICA CHIMICA ACTA
卷 1107, 期 -, 页码 74-84

出版社

ELSEVIER
DOI: 10.1016/j.aca.2020.02.027

关键词

Ultrasound; Ionic liquid dispersive liquid-liquid microextraction; Neurotransmitters; Dementia; Hydrophilic interaction chromatography

资金

  1. National Natural Science Foundation of China [81803699]
  2. Natural Science Foundation of Jiangsu Province [BK20171068]
  3. Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization [012092002006-4]
  4. Naval Medical University military medical innovation [2017JS07]
  5. Science and Technology Action Innovation program by Science and Technology Commission of Shanghai [17411950104]
  6. Science and Technology Support Projects in Biomedicine Field of Shanghai Science and Technology Commission [19441907500]
  7. Priority Academic Program Development of Jiangsu Higher Education Institutions

向作者/读者索取更多资源

A sensitive, rapid, precise and specific analytical method of hydrophilic interaction ultra-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (HILIC-UHPLC-QTRAP (R)/MS2) combined with a high-efficiency and easy sample preparation technology of ultrasound-assisted ionic liquid dispersive liquid-liquid microextraction (UA-IL-DLLME) was developed to investigate neurotransmitters (NTs) in mild cognitive impairment, mild dementia and moderate dementia patients' urine samples. Firstly, the UA-IL-DLLME parameters were optimized using Plackett -Burman screening and rotatable central composite design, and the main optimal conditions were obtained: ultrasound power of 307 W, ultrasound time of 4.3 min and agitation time of 4.8 min. Secondly, HILIC-UHPLC-QTRAP (R)/NMS2 method was developed to simultaneously determine 15 underivatized NTs in urine samples. The analysis results of clinical samples showed that some NTs such as y-aminobutyric acid (GABA), acetylcholine (Ach) and glutamic acid (Glu) presented significant differences in different dementia stages. Finally, multivariate analysis based on the combination of principal component analysis and supervised counter propagation artificial neural network was developed for comprehensive analysis of the obtained clinical data sets. As a result, GABA and Glu were simultaneously presented meaningful contribution for classification of samples, and might be considered as potential differential compounds to the urine samples from cluster patients with different dementia stages. In summary, the presented strategy of preparation, analysis and statistics might be used to investigate NTs in different clinical biological fluids. (C) 2020 Elsevier B.V. All rights reserved.

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