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- 저자 : ( Xinru Wu ) (검색결과 2 건)
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Echinacoside Prevents Sepsis-Induced Myocardial Damage via Targeting SOD2
Jingxiang Wu,Xingji You,Xin Li,Zuojing Zhang,Xiaoxuan Zhang,Yibo Yin,Xinru Yuan 한국식품영양과학회 2024 Journal of medicinal food Vol.27 No.2
Echinacoside (ECH) is a prominent naturally occurring bioactive compound with effects of alleviatingmyocardial damage. We aimed to explore the beneficial effects of ECH against sepsis-induced myocardial damage andelucidate the potential mechanism. Echocardiography and Masson staining demonstrated that ECH alleviates cardiac functionand fibrosis in the cecal ligation and puncture (CLP) model. Transcriptome profiling and network pharmacology analysisshowed that there are 51 overlapping targets between sepsis-induced myocardial damage and ECH. Subsequently, chemicalcarcinogenesis-reactive oxygen species (ROS) were enriched in multiple targets. Wherein, SOD2 may be the potential targetof ECH on sepsis-induced myocardial damage. Polymerase chain reaction results showed that ECH administration couldmarkedly increase the expression of SOD2 and reduce the release of ROS. Combined with injecting the inhibitor of SOD2, thebeneficial effect of ECH on mortality, cardiac function, and fibrosis was eliminated, and release of ROS was increased afterinhibiting SOD2. ECH significantly alleviated myocardial damage in septic mice, and the therapeutic mechanism of ECH isachieved by upregulating SOD2 which decreased the release of ROS.
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Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model
( Xibin Jia ),( Chen Qian ),( Zhenghan Yang ),( Hui Xu ),( Xianjun Han ),( Hao Ren ),( Xinru Wu ),( Boyang Ma ),( Dawei Yang ),( Hong Min ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1
Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.
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