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RIG-I,a novel DAMPs sensor for myoglobin,activates NF-κB/caspase-3 signaling in CS-AKI model
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作者 Peng-Tao Wang Ning Li +7 位作者 Xin-Yue Wang Jia-Le Chen Chen-Hao Geng zi-quan liu Hao-Jun Fan Qi Lv Shi-Ke Hou Yan-Hua Gong 《Military Medical Research》 SCIE CSCD 2022年第1期40-52,共13页
Background:Acute kidney injury(AKI)is the main life-threatening complication of crush syndrome(CS),and myoglobin is accepted as the main pathogenic factor.The pattern recognition receptor retinoicacid-inducible gene I... Background:Acute kidney injury(AKI)is the main life-threatening complication of crush syndrome(CS),and myoglobin is accepted as the main pathogenic factor.The pattern recognition receptor retinoicacid-inducible gene I(RIG-I)has been reported to exert anti-viral effects function in the innate immune response.However,it is not clear whether RIG-I plays a role in CS-AKI.The present research was carried out to explore the role of RIG-I in CS-AKI.Methods:Sprague-Dawley rats were randomly divided into two groups:the sham and CS groups(n=12).After administration of anesthesia,the double hind limbs of rats in the CS group were put under a pressure of 3 kg for 16 h to mimic crush conditions.The rats in both groups were denied access to food and water.Rats were sacrificed at 12 h or 36 h after pressure was relieved.The successful establishment of the CS-AKI model was confirmed by serum biochemical analysis and renal histological examination.In addition,RNA sequencing was performed on rat kidney tissue to identify molecular pathways involved in CS-AKI.Furthermore,NRK-52 E cells were treated with 200μmol/L ferrous myoglobin to mimic CS-AKI at the cellular level.The cells and cell supernatant samples were collected at 6 h or 24 h.Small interfering RNAs(siRNA)was used to knock down RIG-I expression.The relative expression levels of molecules involved in the RIG-I pathway in rat kidney or cells samples were measured by quantitative real-time PCR(qPCR),Western blotting analysis,and immunohistochemistry(IHC)staining.Tumor necrosis factor-α(TNF-α)was d etected by ELISA.Co-immunoprecipitation(Co-IP)assays were used to detect the interaction between RIG-I and myoglobin.Results:RNA sequencing of CS-AKI rat kidney tissue revealed that the different expression of RIG-I signaling pathway.qPCR,Western blotting,and IHC assays showed that RIG-I,nuclear factor kappa-B(NF-κB)P65,p-P65,and the a poptotic marker caspase-3 and cleaved caspase-3 were up-regulated in the CS group(P<0.05).However,the levels of interferon regulatory factor 3(IRF3),p-IRF3 and the antiviral factor interferon-beta(IFN-β)showed no significant c hanges between the sham and CS groups.Co-IP assays showed the interaction between RIG-I and myoglobin in the kidneys of the CS group.Depletion of RIG-I could alleviate the myoglobin induced expression of apoptosis-associated molecules via the NF-κB/caspase-3 axis.C onclusions:RIG-I is a novel damage-associated molecular patterns(DAMPs)sensor for myoglobin and participates in the NF-κB/caspase-3 signaling pathway in CS-AKI.In the development of CS-AKI,specific intervention in the RIG-I p athway might be a potential therapeutic strategy for CS-AKI. 展开更多
关键词 Crush syndrome Acute kidney injury Retinoic acid-inducible gene I MYOGLOBIN Nuclear factor kappa-B/caspase-3 Damage-associated molecular patterns
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An error recognition method for power equipment defect records based on knowledge graph technology 被引量:4
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作者 Hui-fang WANG zi-quan liu 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2019年第11期1564-1577,共14页
To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a... To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a method for constructing a knowledge graph of power equipment defects is presented. Then, a graph search algorithm is employed to recognize different kinds of errors in defect records, based on the knowledge graph of power equipment defects. Finally, an error recognition example in terms of transformer defect records is given, by comparing the precision, recall, F1-score, accuracy, and efficiency of the proposed method with those of machine learning methods, and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods, and can satisfy real-time requirements. 展开更多
关键词 Error recognition Power equipment defect record Knowledge graph Machine learning
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