Affiliation:
1. Department of Chemistry Fudan University Shanghai 200433 China
2. Department of Cardiology Shengjing Hospital of China Medical University NO.36 Sanhao Street, Heping District Shenyang 110004 China
3. School of Chemical Science and Technology Yunnan University No. 2 North Cuihu Road Kunming 650091 P. R. China
Abstract
AbstractAcute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high‐throughput mass spectrometry‐based metabolic analysis is a promising tool for large‐scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO‐66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO‐66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.
Funder
National Natural Science Foundation of China
Subject
General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)
Cited by
6 articles.
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