ICP-MS and trace element analysis as tools for better understanding medical conditions

被引:42
|
作者
Amais, Renata S. [1 ]
Donati, George L. [2 ]
Zezzi Arruda, Marco A. [1 ]
机构
[1] Univ Estadual Campinas, Inst Chem, Spectrometry Sample Preparat & Mech Grp GEPAM, POB 6154, BR-13083970 Campinas, SP, Brazil
[2] Wake Forest Univ, Dept Chem, Salem Hall,Box 7486, Winston Salem, NC 27109 USA
基金
巴西圣保罗研究基金会;
关键词
Inorganic mass spectrometry; Disease; Machine learning; Biological samples; Elemental composition; INDUCTIVELY-COUPLED PLASMA; ISOTOPE RATIO MEASUREMENTS; SINGLE-CELL ANALYSIS; MASS-SPECTROMETRY; BLOOD-SERUM; WILSONS-DISEASE; BREAST-CANCER; DIAGNOSIS; CU; MS/MS;
D O I
10.1016/j.trac.2020.116094
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Element constitution and distribution in tissues and body fluids have increasingly become key pieces of information in life sciences and medicine, and trace elements may be successfully used as disease biomarkers. Here, we review the most recent advances in inductively coupled plasma mass spectrometry (ICP-MS) and the related state-of-the-art instrumentation and methods (e.g. single-particle and single- cell determination capabilities) used to expand the application of trace element information to the study of diseases. Advanced statistical tools and machine learning used for evaluating, diagnosing, and treating different diseases has highlighted the importance of trace elements in clinical research. In this manuscript, we review recently published studies involving trace element analysis and machine learning applied to better understanding clinical conditions and pathologies, and discuss some perspectives for this field. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:15
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