期刊
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
卷 133, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2020.116094
关键词
Inorganic mass spectrometry; Disease; Machine learning; Biological samples; Elemental composition
资金
- Sao Paulo Research Foundation (FAPESP, Sao Paulo, Brazil) [2018/25207-0, 2017/500853, 2018/23478-7]
- Brazilian National Council of Scientific and Technological Development (CNPq, Brasilia, Brazil) [401170/2016-0]
- Coordination for the Improvement of Higher Education Personnel (CAPES, Brasilia, Brazil) [88887.115406/2015]
- INCTBio (FAPESP, Sao Paulo, Brazil) [2014/50867-3]
- Graduate School of Arts and Sciences at Wake Forest University
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.
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