| 1 | In vitro and in vivo biodegradation and biocompatibility of an MMT/BSA composite coating upon magnesium alloy AZ31显示文摘The performance of biodegradable magnesium alloy requires special attention to rapid degradation and poor biocompatibility, which can cause the implant to fail. Here, a sodium montmorillonite(MMT)/bovine serum albumin(BSA) composite coating was prepared upon magnesium alloy AZ31 via hydrothermal synthesis, followed by dip coating. We evaluated the surface characterization and corrosion behavior in vitro, and the biocompatibility in vitro and in vivo. Biodegradation progress of the MMT-BSA coated Mg pieces was examined through hydrogen evolution, immersion tests, and electrochemical measurements in Hank’s solution. In vitro biocompatibility studies were evaluated via hemolysis tests, dynamic cruor time tests, platelet adhesion, MTT testing and live-dead stain of osteoblast cells(MC3 T3-E1). It was found that the MMT-BSA coating had good corrosion resistance and a marked improvement in biocompatibility in comparison to bare Mg alloy AZ31. in vivo studies were carried out in rat model and the degradation was characterized by computed tomography scans. Results revealed that the MMT-BSA coated Mg alloy AZ31 implants maintained their structural integrity and slight degradation after 120 d of post-implantation. A100% survival rate for the rats was observed with no obvious toxic damages on the organs and tissues.Additionally, we proposed a sound coating formation mechanism. Considering the good corrosion protection and biocompatibility, the MMT-BSA coated Mg alloy AZ31 is a promising candidate material for biomedical implants. | Jian Wang Lanyue Cui Yande Ren Yuhong Zou Jinlong Ma Chengjian Wang Zhongyin Zheng Xiaobo Chen Rongchang Zeng Yufeng Zheng | 2020 | Journal of Materials Science & Technology2020，47，12： | 7 |
| 2 | DCAU-Net:dense convolutional attention U-Net for segmentation of intracranial aneurysm images显示文摘Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography(MRA)is essential for medical auxiliary treatments,which can effectively prevent subarachnoid hemorrhages.This paper proposes an image segmentation model based on a dense convolutional attention U-Net,which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences.The U-Net model serves as a backbone,combining dense block and convolution block attention module(CBAM).The dense block is composed of a batch normalization layer,an randomly rectified linear unit activation function,and a convolutional layer,for mitigation of vanishing gradients,for multiplexing of aneurysm features,and for improving the network training efficiency.The CBAM is composed of a channel attention module and a spatial attention module,improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information.Owing to the large variation of aneurysm sizes,multi-scale fusion is performed during up-sampling,for adaptive segmentation of MRA-acquired aneurysm images.The model was tested on the MICCAI 2020 ADAM dataset,and its generalizability was validated on the clinical aneurysm dataset(aneurysm sizes:<3 mm,3–7 mm,and>7 mm)supplied by the Affiliated Hospital of Qingdao University.A good clinical application segmentation performance was demonstrated. | Wenwen Yuan Yanjun Peng Yanfei Guo Yande Ren Qianwen Xue | 2022 | Visual Computing for Industry,Biomedicine,and Art2022，5，1： | 0 |
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