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您的检索式：作者名="Pengchao Fang"
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| 1 | Multi-functional vesicles improve Helicobacter pylori eradication by a comprehensive strategy based on complex pathological microenvironment显示文摘Helicobacter pylori(H.pylori),creating a global infection rate over 50%,presents great challenges in clinical therapies due to its complex pathological microenvironment in vivo.To improve the eradication efficacy,herein we fabricated a pharmaceutical vesicle RHL/Cl-Ch-cal where cholesterol-PEG,calcitriol and first-line antibiotic clarithromycin were co-loaded in the rhamnolipid-composed outer lipid layer.RHL/Cl-Ch-cal could quickly penetrate through gastric mucus layer to reach H.pylori infection sites,and then effectively destroyed the architecture of H.pylori biofilms,killed dispersed H.pylori and inhibited the re-adhesion of residual bacteria(called biofilms eradication tetralogy).Moreover,RHL/Cl-Ch-cal activated the host immune response to H.pylori by replenishing cholesterol to repair lipid raft on the cell membrane of host epithelial cells.Finally,RHL/Cl-Ch-cal killed the intracellular H.pylori through recovering the lysosomal acidification and assisting degradation.In experiments,RHL/Cl-Ch-cal demonstrated prominent anti-H.pylori efficacy in the classical H.pylori-infected mice model.Therefore,the study provides a“comprehensive attack”strategy for anti-H.pylori therapies including biofilms eradication tetralogy,immune activation and intracellular bacteria killing. | Xiaonan Chen Yiqing Zou Shuqi Zhang Pengchao Fang Shuxuan Li Pengyu Li Yingying Sun Gang Yuan Haiyan Hu | 2022 | Acta Pharmaceutica Sinica B2022，12，9： | 2 |
| 2 | DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection显示文摘We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations. | Pengchao Li Fang Xu Jintao Wang Haibing Guo Mingmin Liu Zhenjun Du | 2024 | Computers, Materials & Continua2024，78，2： | 0 |
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