P
Pengfei Dong
Researcher at Florida Institute of Technology
Publications - 52
Citations - 679
Pengfei Dong is an academic researcher from Florida Institute of Technology. The author has contributed to research in topics: Medicine & Stent. The author has an hindex of 6, co-authored 28 publications receiving 545 citations. Previous affiliations of Pengfei Dong include University of Nebraska–Lincoln & Taiyuan University of Technology.
Papers
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Journal ArticleDOI
Optimization of Freeze-FRESH Methodology for 3D Printing of Microporous Collagen Constructs
TL;DR: Freeform reversible embedding of suspended hydrogels (FRESH) is a layer-by-layer extrusion-based technique to enable three-dimensional (3D) printing of soft tissue constructs by using a thermo-reve
Journal ArticleDOI
Serum Homocysteine Level Predictive Capability for Severity of Restenosis Post Percutaneous Coronary Intervention
Jiqiang Guo,Ying Ying Gao,Mohammad Ahmed,Pengfei Dong,Yuping Gao,Zhi-Min Gong,Jinwen Liu,Yajie Mao,Zhi‐ping Yue,Qingli Zheng,Jiansheng Li,JianRong Rong,Yong-Cun Zhou,Meiwen An,Linxia Gu,Jin Zhang +15 more
TL;DR: Compared with coronary angiography (CAG), Hcy levels provided a significantly better clinical detection of ISR severity after PCI, indicating that the serum Hcy concentration could predict ISR.
Journal ArticleDOI
Finite Element Analysis of Soccer Ball-Related Ocular and Retinal Trauma and Comparison with Abusive Head Trauma
TL;DR: In this article , a finite element model of the eye was used to investigate the effects of a collision of a soccer ball on the eye and to compare them with those observed in abusive head trauma (AHT).
Patent
Clamping device of Hopkinson pull bar test-piece and experimental method
TL;DR: In this article, a clamping device of a Hopkinson pull bar test-piece and an experimental method is described, which belongs to the field of shock dynamics experiment and is used in pairs.
Journal ArticleDOI
Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography
Yazan Gharaibeh,Juhwan Lee,Vladislav Zimin,Chaitanya Kolluru,L A O Dallan,Gabriel Tensol Rodrigues Pereira,Armando Vergara-Martel,Justin Kim,Ammar Hoori,Pengfei Dong,Peshala P. T Gamage,Linxia Gu,Hiram G. Bezerra,Sadeer G. Al-Kindi,David L. Wilson +14 more
TL;DR: An automated machine learning approach is created that uses lesion attributes to predict stent under-expansion from pre-stent images, suggesting the need for plaque modification.