Zengsheng Chen

Dr. Zengsheng Chen

School of Biological Science and Medical Engineering, Beihang University, Beijing, China

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 August 2017 in International Journal for Numerical Methods in Biomedical Engineering
Verified
Journal Article
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Zengsheng ChenZengsheng Chen
Zengsheng Chen
SJSofen K. Jena
Sofen K. Jena
GGGuruprasad A. Giridharan
Guruprasad A. Giridharan
SKSteven C. Koenig
Steven C. Koenig
MSMark S. Slaughter
Mark S. Slaughter
Zhongjun Jon WuZhongjun Jon Wu
Zhongjun Jon Wu

19 February 2020 in Artificial Organs
Verified
Journal Article
Closed access
PDF file
WSWenji Sun
Wenji Sun
SWShigang Wang
Shigang Wang
Zengsheng ChenZengsheng Chen
Zengsheng Chen
JZJiafeng Zhang
Jiafeng Zhang
TLTieluo Li
Tieluo Li
Zhongjun Jon WuZhongjun Jon Wu
Zhongjun Jon Wu

17 December 2019 in Artificial Organs
Verified
Journal Article
Closed access
PDF file
Zengsheng ChenZengsheng Chen
Zengsheng Chen
JZJiafeng Zhang
Jiafeng Zhang
TLTieluo Li
Tieluo Li
DTDouglas Tran
Douglas Tran
BGBartley P. Griffith
Bartley P. Griffith
Zhongjun Jon WuZhongjun Jon Wu
Zhongjun Jon Wu

12 April 2023 in Computers in Biology and Medicine
Verified
Journal Article
Closed access
XFXingji Fu
Xingji Fu
ZSZihua Su
Zihua Su
YWYawei Wang
Yawei Wang
ASAnqiang Sun
Anqiang Sun
LWLizhen Wang
Lizhen Wang
Yubo FanYubo Fan
Yubo Fan

5 November 2022 in Computers in Biology and Medicine
Verified
Journal Article
Closed access
YLYuan Li
Yuan Li
HWHongyu Wang
Hongyu Wang
YXYifeng Xi
Yifeng Xi
ASAnqiang Sun
Anqiang Sun
XDXiaoyan Deng
Xiaoyan Deng
Yubo FanYubo Fan
Yubo Fan

27 May 2022 in Bioengineering
Verified
Journal Article
Open access
PDF file
Yuan LiYuan Li
Yuan Li
HWHongyu Wang
Hongyu Wang
YXYifeng Xi
Yifeng Xi
ASAnqiang Sun
Anqiang Sun
XDXiaoyan Deng
Xiaoyan Deng
Yubo FanYubo Fan
Yubo Fan
(1) Background: Thrombosis is the main complication in patients supported with ventricular assist devices (VAD). Models that accurately predict the risk of thrombus formation in VADs are still lacking. When VADs are clinically assisted, their complex geometric configuration and high rotating speed inevitably generate complex flow fields and high shear stress. These non-physiological factors can damage blood cells and proteins, release coagulant factors and trigger thrombosis. In this study, a more accurate model for thrombus assessment was constructed by integrating parameters such as shear stress, residence time and coagulant factors, so as to accurately assess the probability of thrombosis in three clinical VADs. (2) Methods: A mathematical model was constructed to assess platelet activation and thrombosis within VADs. By solving the transport equation, the influence of various factors such as shear stress, residence time and coagulation factors on platelet activation was considered. The diffusion equation was applied to determine the role of activated platelets and substance deposition on thrombus formation. The momentum equation was introduced to describe the obstruction to blood flow when thrombus is formed, and finally a more comprehensive and accurate model for thrombus assessment in patients with VAD was obtained. Numerical simulations of three clinically VADs (CH-VAD, HVAD and HMII) were performed using this model. The simulation results were compared with experimental data on platelet activation caused by the three VADs. The simulated thrombogenic potential in different regions of MHII was compared with the frequency of thrombosis occurring in the regions in clinic. The regions of high thrombotic risk for HVAD and HMII observed in experiments were compared with the regions predicted by simulation. (3) Results: It was found that the percentage of activated platelets within the VAD obtained by solving the thrombosis model developed in this study was in high agreement with the experimental data (r² = 0.984), the likelihood of thrombosis in the regions of the simulation showed excellent correlation with the clinical statistics (r² = 0.994), and the regions of high thrombotic risk predicted by the simulation were consistent with the experimental results. Further study revealed that the three clinical VADs (CH-VAD, HVAD and HMII) were prone to thrombus formation in the inner side of the secondary flow passage, the clearance between cone and impeller, and the corner region of the inlet pipe, respectively. The risk of platelet activation and thrombus formation for the three VADs was low to high for CH-VAD, HVAD, and HM II, respectively. (4) Conclusions: In this study, a more comprehensive and accurate thrombosis model was constructed by combining parameters such as shear stress, residence time, and coagulation factors. Simulation results of thrombotic risk received with this model showed excellent correlation with experimental and clinical data. It is important for determining the degree of platelet activation in VAD and identifying regions prone to thrombus formation, as well as guiding the optimal design of VAD and clinical treatment.