Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

Journal: Diabetes, obesity & metabolism
Published Date:

Abstract

BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study aims to develop classification and prognostic models based on multi-modal data.

Authors

  • Xian Shao
    NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
  • Huanqing Xu
    School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
  • Lianqin Chen
    NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
  • Pufei Bai
    NHC Key Lab of Hormones and Development, Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Haizhen Sun
    National Health Commission (NHC) Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
  • Qian Yang
    Center for Advanced Scientific Instrumentation, University of Wyoming, Laramie, WY, United States.
  • Ruixuan Chen
    National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou North Avenue, Baiyun District, Guangzhou 510515, China.
  • Queran Lin
    Clinical Research Design Division, Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Clinical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; WHO Collaborating Centre for Public Health Education and Training, Department of Primary Care and Public Health, School of Public Health, Faculty of Medicine, Imperial College London, London, UK. Electronic address: linqr9@mail.sysu.edu.cn.
  • Lihua Wang
    Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Yao Lin
  • Pei Yu
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Keywords

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