Multiscale Element-Doped Nanowire Array-Coupled Machine Learning Reveals Metabolic Fingerprints of Nonreversible Liver Diseases

Autor:	Fangying Shi, Chuwen Huang, Yuan Ren, Chunhui Deng, Nianrong Sun, Xizhong Shen
Rok vydání:	2022
Předmět:	Machine Learning Silicon Nanowires Liver Diseases Quality of Life Humans Analytical Chemistry
Zdroj:	Analytical Chemistry. 94:16204-16212
ISSN:	1520-6882 0003-2700
Popis:	Timely detection of nonreversible liver diseases contributes greatly to reasonable therapy and quality of life. Given the current situation, minimally invasive high-specificity molecular diagnosis based on body fluid can be a good choice. Herein, a mesoporous superstructure is designed using silicon atom-doped nanowire arrays to uniformly load Pt nanoparticles on the surface to produce a desirable ionization effect. We apply the multiscale element-doped nanowire arrays to efficiently assist extraction of high-quality metabolic fingerprints from only 35 nL of serum within seconds. Using different machine learning algorithms, we establish specific biomarker panels to distinguish different liver diseases from the healthy control, with more than 90% accuracy, sensitivity, and specificity. Moreover, from established biomarker panels, we further determine key metabolites of significant difference (
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1863ef279cdd8dc9a4a4d1ee0d6052b https://doi.org/10.1021/acs.analchem.2c03743 Zobrazit plný text záznamu