Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning

Author:

Wang Weichao12,Liu Xian1,Zhang Changwen3,Sheng Fei3,Song Shanjun4,Li Penghui5,Dai Shaoqing6ORCID,Wang Bin7,Lu Dawei12,Zhang Luyao12,Yang Xuezhi12,Zhang Zhihong3,Liu Sijin1ORCID,Zhang Aiqian12,Liu Qian182ORCID,Jiang Guibin12

Affiliation:

1. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin Institute of Urology, Tianjin 300211, China

4. National Institute of Metrology, Beijing 100029, China

5. Tianjin University of Technology, Tianjin 300384, China

6. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands

7. Institute of Reproductive and Child Health, National Health Commission's Key Laboratory of Reproductive Health, Peking University, Beijing 100191, China

8. Institute of Environment and Health, Jianghan University, Wuhan 430056, China

Abstract

The blood Cu concentration and isotopic composition enable new insights into Cu imbalance and diagnosis of bladder cancer with machine learning.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Chinese Academy of Sciences

Sanming Project of Medicine in Shenzhen

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

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