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274 results on '"Shen, Jianghua"'

40. Dynamic Behavior and Energy Absorption of Typical Porous Materials under Impacts.

Author

Xie, Kui, Li, Menglong, and Shen, Jianghua

Subjects
POROUS materials, SPECIFIC gravity, FINITE element method, STRESS concentration, IMPACT loads
Abstract

Porous materials are known for their excellent energy absorption capability and, thus, are widely used in anti-impact applications. However, how the pore shape and size impact the failure mechanism and overall behavior of the porous materials under impact loading is still unclear or limitedly touched. Instead of using homogeneous solids for the porous material model, pores with various shapes and sizes were implanted in a solid to establish the porous materials that have true porous structures, which permits exploration of the local failure mechanism. The results revealed that differently shaped holes have two different dominant deformation modes. And due to their different local stress distributions, they enter the plastic phase earlier and, thus, have higher specific energy absorption. Meanwhile, the model changes from hardening to a quasi-zero stiffness model as the hole size increases. The application of this work can be extended into the field of impact resistance. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Risk Factors for Postoperative Cognitive Decline After Orthopedic Surgery in Elderly Chinese Patients: A Retrospective Cohort Study

Author

Li,Xian, Lai,Hong, Wang,Peng, Feng,Shuai, Feng,Xuexin, Kong,Chao, Wu,Dewei, Yin,Chunlin, Shen,Jianghua, Yan,Suying, Han,Rui, Liu,Jia, Ren,Xiaoyi, Li,Ying, Tang,Lu, Xue,Dong, Zhao,Ying, Huang,Hao, Li,Xiaoying, Zhang,Yanhong, Wang,Xue, Wang,Chunxiu, Jin,Ping, Lu,Shibao, Wang,Tianlong, Zhao,Guoguang, Wang,Chaodong, Li,Xian, Lai,Hong, Wang,Peng, Feng,Shuai, Feng,Xuexin, Kong,Chao, Wu,Dewei, Yin,Chunlin, Shen,Jianghua, Yan,Suying, Han,Rui, Liu,Jia, Ren,Xiaoyi, Li,Ying, Tang,Lu, Xue,Dong, Zhao,Ying, Huang,Hao, Li,Xiaoying, Zhang,Yanhong, Wang,Xue, Wang,Chunxiu, Jin,Ping, Lu,Shibao, Wang,Tianlong, Zhao,Guoguang, and Wang,Chaodong

Abstract

Xian Li,1,* Hong Lai,2,* Peng Wang,3 Shuai Feng,4 Xuexin Feng,4 Chao Kong,3 Dewei Wu,5 Chunlin Yin,5 Jianghua Shen,6 Suying Yan,6 Rui Han,7 Jia Liu,7 Xiaoyi Ren,8 Ying Li,8 Lu Tang,9 Dong Xue,9 Ying Zhao,9 Hao Huang,10 Xiaoying Li,10 Yanhong Zhang,10 Xue Wang,11 Chunxiu Wang,12 Ping Jin,13 Shibao Lu,3 Tianlong Wang,4 Guoguang Zhao,14 Chaodong Wang1 On behalf of the APPLE-MDT Research Team, Xuanwu Hospital, Capital Medical University and National Clinical Research Center for Geriatric Diseases1Department of Neurology & Neurobiology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, People’s Republic of China; 3Department of Orthopedic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 4Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 5Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 6Department of Pharmacy, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 7Department of Gerontology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 8Department of Nutrition, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 9Department of Oral Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 10Department of Medical Administration, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 11Department of Medical Library, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People’s Republic of China; 12Center for Evidence-Based Medicine, Xuanwu Hospital, C

Published
2024

49. Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms.

Author

Bai, Jiaxuan, Li, Menglong, and Shen, Jianghua

Subjects
ARTIFICIAL neural networks, YOUNG'S modulus, FINITE element method, AEROSPACE materials, STRENGTH of materials
Abstract

The yield strength and Young's modulus of lattice structures are essential mechanical parameters that influence the utilization of materials in the aerospace and medical fields. Currently, accurately determining the Young's modulus and yield strength of lattice structures often requires conduction of a large number of experiments for prediction and validation purposes. To save time and effort to accurately predict the material yield strength and Young's modulus, based on the existing experimental data, finite element analysis is employed to expand the dataset. An artificial neural network algorithm is then used to establish a relationship model between the topology of the lattice structure and Young's modulus (the yield strength), which is analyzed and verified. The Gibson–Ashby model analysis indicates that different lattice structures can be classified into two main deformation forms. To obtain an artificial neural network model that can accurately predict different lattice structures and be deployed in the prediction of BCC-FCC lattice structures, the artificial network model is further optimized and validated. Concurrently, the topology of disparate lattice structures gives rise to a certain discrete form of their dominant deformation, which consequently affects the neural network prediction. In conclusion, the prediction of Young's modulus and yield strength of lattice structures using artificial neural networks is a feasible approach that can contribute to the development of lattice structures in the aerospace and medical fields. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Prophylactic supplementation with Bifidobacterium infantis or its metabolite inosine attenuates cardiac ischemia/reperfusion injury.

Author

Zhang, Hao, Wang, Jiawan, Shen, Jianghua, Chen, Siqi, Yuan, Hailong, Zhang, Xuan, Liu, Xu, Yu, Ying, Li, Xinran, Gao, Zeyu, Wang, Yaohui, Wang, Jun, and Song, Moshi

Subjects
KILLER cells, HEART cells, ADENOSINE triphosphate, CELL death, GUT microbiome
Abstract

Emerging evidence has demonstrated the profound impact of the gut microbiome on cardiovascular diseases through the production of diverse metabolites. Using an animal model of myocardial ischemia–reperfusion (I/R) injury, we found that the prophylactic administration of a well‐known probiotic, Bifidobacterium infantis (B. infantis), exhibited cardioprotective effects in terms of preserving cardiac contractile function and preventing adverse cardiac remodeling following I/R and that these cardioprotective effects were recapitulated by its metabolite inosine. Transcriptomic analysis further revealed that inosine mitigated I/R‐induced cardiac inflammation and cell death. Mechanistic investigations elucidated that inosine suppressed the production of pro‐inflammatory cytokines and reduced the numbers of dendritic cells and natural killer cells, achieved through the activation of the adenosine A2A receptor (A2AR) that when inhibited abrogated the cardioprotective effects of inosine. Additionally, in vitro studies using C2C12 myoblasts revealed that inosine attenuated cell death by serving as an alternative carbon source for adenosine triphosphate (ATP) generation through the purine salvage pathway when subjected to oxygen‐glucose deprivation/reoxygenation that simulated myocardial I/R injury. Likewise, inosine reversed the I/R‐induced decrease in ATP levels in mouse hearts. Taken together, our findings indicate that B. infantis or its metabolite inosine exerts cardioprotective effects against I/R by suppressing cardiac inflammation and attenuating cardiac cell death, suggesting prophylactic therapeutic options for acute ischemic cardiac injury. Highlights: Bifidobacterium infantis, a well‐known probiotic, exhibits prophylactic cardioprotective effects against myocardial I/R injury.The cardioprotective effects of Bifidobacterium infantis are recapitulated by its metabolite inosine.Inosine treatment suppresses cardiac inflammation by reducing the production of pro‐inflammatory cytokines and decreasing the numbers of dendritic cells and natural killer cells after I/R.Inosine treatment attenuates cell death by serving as an alternative carbon source for adenosine triphosphate generation through the purine salvage pathway in stressed myocytes and in I/R‐injured mouse hearts. [ABSTRACT FROM AUTHOR]

Published
2024
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