|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 3D bioprinting of a biomimetic meniscal scaffold for application in tissue engineering显示文摘Appropriate biomimetic scaffolds created via 3D bioprinting are promising methods for treating damaged menisci.However,given the unique anatomical structure and complex stress environment of the meniscus,many studies have adopted various techniques to take full advantage of different materials,such as the printing combined with infusion,or electrospining,to chase the biomimetic meniscus,which makes the process complicated to some extent.Some researchers have tried to tackle the challenges only by 3D biopringting,while its alternative materials and models have been constrained.In this study,based on a multilayer biomimetic strategy,we optimized the preparation of meniscus-derived bioink,gelatin methacrylate(GelMA)/meniscal extracellular matrix(MECM),to take printability and cytocompatibility into account together.Subsequently,a customized 3D bioprinting system featuring a dual nozzle+multitemperature printing was used to integrate the advantages of polycaprolactone(PCL)and meniscal fibrocartilage chondrocytes(MFCs)-laden GelMA/MECM bioink to complete the biomimetic meniscal scaffold,which had the best biomimetic features in terms of morphology and components.Furthermore,cell viability,mechanics,biodegradation and tissue formation in vivo were performed to ensure that the scaffold had sufficient feasibility and functionality,thereby providing a reliable basis for its application in tissue engineering. | Zhou Jian Tian Zhuang Tian Qinyu Peng Liqing Li Kun Luo Xujiang Wang Diaodiao Yang Zhen Jiang Shuangpeng Sui Xiang Huang Jingxiang Liu Shuyun Hao Libo Tang Peifu Yao Qi Guo Quanyi | 2021 | Bioactive Materials2021，6，6： | 3 |
| 2 | Hierarchical macro-microporous WPU-ECM scaffolds combined with Microfracture Promote in Situ Articular Cartilage Regeneration in Rabbits显示文摘Tissue engineering provides a promising avenue for treating cartilage defects.However,great challenges remain in the development of structurally and functionally optimized scaffolds for cartilage repair and regeneration.In this study,decellularized cartilage extracellular matrix(ECM)and waterborne polyurethane(WPU)were employed to construct WPU and WPU-ECM scaffolds by water-based 3D printing using low-temperature deposition manufacturing(LDM)system,which combines rapid deposition manufacturing with phase separation techniques.The scaffolds successfully achieved hierarchical macro-microporous structures.After adding ECM,WPU scaffolds were markedly optimized in terms of porosity,hydrophilia and bioactive components.Moreover,the optimized WPU-ECM scaffolds were found to be more suitable for cell distribution,adhesion,and proliferation than the WPU scaffolds.Most importantly,the WPU-ECM scaffold could facilitate the production of glycosaminoglycan(GAG)and collagen and the upregulation of cartilage-specific genes.These results indicated that the WPU-ECM scaffold with hierarchical macro-microporous structures could recreate a favorable microenvironment for cell adhesion,proliferation,differentiation,and ECM production.In vivo studies further revealed that the hierarchical macro-microporous WPU-ECM scaffold combined with the microfracture procedure successfully regenerated hyaline cartilage in a rabbit model.Six months after implantation,the repaired cartilage showed a similar histological structure and mechanical performance to that of normal cartilage.In conclusion,the hierarchical macro-microporous WPU-ECM scaffold may be a promising candidate for cartilage tissue engineering applications in the future. | Mingxue Chen YangYang Li Shuyun Liu Zhaoxuan Feng Hao Wang Dejin Yang Weimin Guo Zhiguo Yuan Shuang Gao Yu Zhang Kangkang Zha Bo Huang Fu Wei Xinyu Sang Qinyu Tian Xuan Yang Xiang sui Yixin Zhou Yufeng Zheng Quanyi Guo | 2021 | Bioactive Materials2021，6，7： | 1 |
| 3 | 基于TLBO算法的不确定性条件下复杂产品协同设计的可靠性拓扑优化显示文摘复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑优化方法。将由板结构形成的支撑物作为拓扑优化对象,重量轻、稳定性好。将不确定性下的可靠性优化和结构拓扑优化协同处理。首先,利用有限差分法将优化问题中的不确定性参数修正为确定性参数。然后,将不确定性可靠性分析和拓扑优化的复杂嵌套解耦。最后,利用TLBO算法求解解耦模型,该算法参数少,求解速度快。TLBO算法采用了自适应教学因子,在初始阶段实现了更快的收敛速度,并在后期进行了更精细的搜索。本文给出了一个液压机基板结构的数值实例,说明了该方法的有效性。 | Zhaoxi Hong Xiangyu Jiang 冯毅雄 Qinyu Tian 谭建荣 | 2023 | Engineering2023，，3： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员