6 results match your criteria: "Nanjing Gaochun Hospital of Traditional Chinese Medicine[Affiliation]"
BMJ Open
December 2024
Department of Endocrinology, School of Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
Objectives: To describe the prevalence, clinical characteristics and risk factors of liver steatosis and fibrosis in type 2 diabetes mellitus (T2DM) patients in eastern China.
Design: A cross-sectional, multicentre study based on an ongoing cohort study.
Setting: 16 clinics in eastern China, including primary clinics to tertiary hospitals.
Asian J Surg
November 2024
Department of Urology, Nanjing Gaochun Hospital of Traditional Chinese Medicine, Nanjing, 210029, China. Electronic address:
NPJ Precis Oncol
November 2024
Department of Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
BMC Endocr Disord
November 2024
Department of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Background: The prevalence of steatotic liver disease (SLD) in patients with type 2 diabetes (T2DM) exceeds 50%. This study aimed to investigate the clinical characteristics of SLD and liver fibrosis in Chinese patients with T2DM.
Methods: Inpatients from 2021 to 2023 were included in the study.
Front Biosci (Landmark Ed)
March 2024
Department of Rehabilitation Medicine, Xuancheng People's Hospital, 242000 Xuancheng, Anhui, China.
Background: Severe neurological condition like Alzheimer's disease (AD) has a significantly negative impact on families and society, wherein there is no proven cure. As one of the principal active constituents of Blume, ecdysterone (ECR) has demonstrated antioxidant and cognitive dysfunction improvement effects. Nonetheless, the mechanism underlying the improvement of cognitive dysfunction by ECR remains unclear.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
February 2022
First Clinical Medical College of Nanjing University of Chinese Medicine, Nanjing 210004, China.
Objective: To study the therapeutic mechanism of (LQF) for diabetic kidney disease (DKD) based on GEO database and network pharmacology.
Methods: LQF and DKD targets were obtained using the databases including GEO, TCMSP, CNKI, ChemDraw, and SwissTarget Prediction, and LQF-DKD intersection targets were obtained with VENNY. String was used for protein-protein interaction (PPI) analysis, and R package for KEGG and GO enrichment analysis.