Wen Jin
Research Subjects
Wen Jin has a total of 46 co-authors. Here are the top co-authors by number of shared publications:
Hiroshi Kurihara
3 shared publications- Rong-Rong He
State Key Laboratory of Mechanism and Quality of Chinese Medicine/School of Pharmacy/Faculty of Chinese Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
3 shared publications - Weibin Cheng
Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
3 shared publications - Yan-Ping Wu
Guangdong Engineering Research Center of Chinese Medicine & Disease Susceptibility, Jinan University, Guangzhou, P. R. China
3 shared publications Yi-Fang Li
3 shared publicationsYu Sun
Department of Cardiac Intensive Care Unit, the Cardiovascular Hospital, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, Guangdong, China
3 shared publications
Latest Publications
- Open Access
- 5 December 2025
- Published by International Society of Global Health in Journal of Global Health
- Vol. 15, 04314
Low physical activity-related disease burden, 1990–2021: assessment of global trends and social determinants based on GBD 2021 data
Ischemic heart disease (IHD), stroke, lower extremity peripheral arterial disease (LEPAD), diabetes mellitus, and chronic kidney disease (CKD) have become the main causes of death and disability globally [1,2]. According to research conducted by Jie Li and colleagues in 2021, IHD caused approximately 9.0 million deaths. Stroke accounted for ~ 7.3 million deaths, diabetes mellitus led to […]
- 5 December 2025
- Published by International Society of Global Health in Journal of Global Health
- Vol. 15, 04314
Open AccessLow physical activity-related disease burden, 1990–2021: assessment of global trends and social determinants based on GBD 2021 data
Ischemic heart disease (IHD), stroke, lower extremity peripheral arterial disease (LEPAD), diabetes mellitus, and chronic kidney disease (CKD) have become the main causes of death and disability globally [1,2]. According to research conducted by Jie Li and colleagues in 2021, IHD caused approximately 9.0 million deaths. Stroke accounted for ~ 7.3 million deaths, diabetes mellitus led to […]
- Open Access
AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure
- 15 June 2025
- Published by Elsevier in Phytomedicine
- Vol. 145, 156967
Chaihu Shugan San formula alleviates psychological stress-induced ovarian cancer susceptibility by inhibiting ubiquitin degradation of TLR2 in macrophages
- 15 June 2025
- Published by Elsevier in Phytomedicine
- Vol. 145, 156967
Chaihu Shugan San formula alleviates psychological stress-induced ovarian cancer susceptibility by inhibiting ubiquitin degradation of TLR2 in macrophages
- 9 November 2024
- Published by Elsevier in Phytomedicine
- Vol. 136, 156207
Corrigendum to “Mitigating phospholipid peroxidation of macrophages in stress-induced tumor microenvironment by natural ALOX15/PEBP1 complex inhibitors”
- 9 November 2024
- Published by Elsevier in Phytomedicine
- Vol. 136, 156207
Corrigendum to “Mitigating phospholipid peroxidation of macrophages in stress-induced tumor microenvironment by natural ALOX15/PEBP1 complex inhibitors”
- 22 February 2024
- Published by Elsevier in Phytomedicine
- Vol. 128, 155475
Mitigating phospholipid peroxidation of macrophages in stress-induced tumor microenvironment by natural ALOX15/PEBP1 complex inhibitors
- 22 February 2024
- Published by Elsevier in Phytomedicine
- Vol. 128, 155475
Mitigating phospholipid peroxidation of macrophages in stress-induced tumor microenvironment by natural ALOX15/PEBP1 complex inhibitors
- 3 November 2022
- Published by Elsevier in Current Problems in Cardiology
- Vol. 48 (2) , 101480
Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach
- 3 November 2022
- Published by Elsevier in Current Problems in Cardiology
- Vol. 48 (2) , 101480
Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach