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Membranes|November 28, 2019
ZnO Microfiltration Membranes for Desalination by a Vacuum Flow-Through Evaporation MethodShailesh Dangwal, Ruochen Liu, Lyndon D Bastatas, et al.
Computer Assisted Surgery (Abingdon, England)|June 11, 2024
Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methodsBin Zhang, Shengsheng Huang, Chenxing Zhou, et al.
Frontiers in Immunology|March 16, 2026
A multicenter explainable machine learning analysis of autoimmune disease comorbidity in ankylosing spondylitisJichong Zhu, Chengqian Huang, Yang Lin, et al.
Analytical and Bioanalytical Chemistry|September 10, 2024
Identification of novel protein biomarkers and therapeutic targets for ankylosing spondylitis using human circulating plasma proteomics and genome analysisZhongxian Zhou, Chong Liu, Sitan Feng, et al.
Scientific Reports|March 11, 2025
Investigating ferroptosis-related genes NFE2L2 in neutrophils for ankylosing spondylitis: therapeutic potential of cassia twigsTianyou Chen, Jichong Zhu, Xinli Zhan, et al.
Journal of Cellular and Molecular Medicine|December 16, 2024
Exploring T Cell and NK Cell Involvement in Ankylosing Spondylitis Through Single-Cell SequencingTianyou Chen, Shengyu Ning, Jichong Zhu, et al.
International Immunopharmacology|October 2, 2024
Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanismsTianyou Chen, Xinli Zhan, Jichong Zhu, et al.
Biomolecules & Biomedicine|October 28, 2023
Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithmsChengqian Huang, Jing Zhuo, Chong Liu, et al.
JOR Spine|July 29, 2025
Development and Validation of a Machine Learning-Based Online Prognostic Model for Cervical Spondylosis Patients After Anterior Cervical Discectomy and Fusion: A Multicenter StudySitan Feng, Shengsheng Huang, Zhongxian Zhou, et al.
Annals of Medicine|August 23, 2023
Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learningYuanlin Yao, Shaofeng Wu, Chong Liu, et al.
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