目的通过观察丹参多酚酸盐对核苷酸结合寡聚结构域样受体蛋白3(NOD-like receptor thermal protein domain associated protein 3,NLRP3)/天冬氨酸特异性半胱氨酸蛋白酶1(Cysteinyl aspartate specific proteinase-1,Caspase-1)/焦亡效...目的通过观察丹参多酚酸盐对核苷酸结合寡聚结构域样受体蛋白3(NOD-like receptor thermal protein domain associated protein 3,NLRP3)/天冬氨酸特异性半胱氨酸蛋白酶1(Cysteinyl aspartate specific proteinase-1,Caspase-1)/焦亡效应蛋白消皮素D(Gasdermin-D,GSDMD)细胞焦亡通路的影响,探讨改善膜性肾病大鼠肾损伤可能的作用机制。方法SD大鼠通过尾静脉注射阳离子牛血清白蛋白建立膜性肾病大鼠模型,造模成功后分为模型组、盐酸贝那普利组(10 mg/kg)、丹参多酚酸盐低、中、高剂量组(16.7、33.3、66.7 mg/kg),另设有正常组,每组均为10只。各给药组连续给药4周后,检测24小时尿蛋白定量(24-hour urinary protein quantity,24 h UTP)和血肌酐(serum creatinine,Scr)、血尿素氮(blood urea nitrogen,BUN)、血清总蛋白(total protein,TP)、血清白蛋白(serum albumin,ALB)、甘油三酯(triglyceride,TG)、总胆固醇(total cholesterol,TC)的含量;酶联免疫吸附法检测大鼠血清白细胞介素-1β(interleukin-1β,IL-1β)、IL-18;采用光镜、电镜、荧光显微镜下观察肾脏病理学变化;Western blot、实时荧光定量PCR法检测NLRP3、Caspase-1、GSDMD蛋白及mRNA表达。结果与模型组比较,各给药组24 h UTP水平明显降低(P<0.05,P<0.01),丹参多酚酸盐中、高剂量组和盐酸贝那普利组大鼠血清TP和ALB水平显著升高(P<0.01),TC和TG水平明显降低(P<0.05,P<0.01);普通光镜、荧光显微镜及电镜下观察可见模型组大鼠肾组织病理损伤明显,经各组药物治疗后病理损伤逐渐改善。与模型组比较,各给药组大鼠血清IL-1β、IL-18水平明显降低(P<0.05,P<0.01);与正常组比较,模型组大鼠肾脏NLRP3、Caspase-1、GSDMD蛋白及其mRNA表达显著升高(P<0.01);与模型组比较,各给药组大鼠肾组织NLRP3/Caspase-1/GSDMD蛋白及其mRNA表达水平明显降低(P<0.05,P<0.01),以丹参多酚酸盐高剂量组效果最佳。结论丹参多酚酸盐对膜性肾病大鼠肾保护作用与调控NLRP3/Caspase-1/GSDMD细胞焦亡通路,改善炎症状态,减轻肾损伤有关。展开更多
The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications ...The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.展开更多
文摘[目的]基于血清核转录因子2(Nrf2)/血红蛋白氧合酶-1(HO-1)信号通路探讨丹参多酚酸盐对膜性肾病(MN)大鼠氧化应激的影响。[方法] 80只SD雄性大鼠随机选取20只为正常对照组,其余60只大鼠均采用尾静脉注射阳离子化牛血清白蛋白(C-BSA)构建MN模型,MN模型大鼠复制成功后随机分为模型组,盐酸贝那普利组(10 mg/kg),丹参多酚酸盐分为低、中、高剂量组(16.7、33.3、66.7 mg/kg)。药物灌胃连续4周,1次/d,正常组和模型组予以相等体积的生理盐水灌胃。治疗后检测大鼠24 h尿蛋白定量(24 h UTP)。给药结束后,经大鼠腹主动脉取血检测血尿素氮(BUN),血清肌酐(Scr),三酰甘油(TG),总胆固醇(TC),总蛋白(TP),白蛋白(ALB)水平;过碘酸-六胺银(PASM)染色观察大鼠肾组织病理形态;免疫荧光检测肾组织免疫球蛋白G(IgG)、补体C3沉积情况;采用酶联免疫吸附测定法(ELISA)检测大鼠血清中超氧化物歧化酶(SOD)、丙二醛(MDA)表达情况;蛋白免疫印迹法(Western Blot)和实时荧光定量聚合酶链式反应(Real-time PCR)观察肾组织Nrf2、HO-1蛋白及Nrf2、HO-1 mRNA表达。[结果]与正常组相比,模型组大鼠肾小球出现体积增大、基底膜增厚、“钉突”形成,沿毛细血管襻有补体C3、IgG弥漫性沉积,24 h UTP、血清TG、TC水平显著升高(P<0.01),TP、ALB水平显著降低(P<0.01),BUN、SCr差异无统计学意义;血清中SOD表达显著降低(P<0.01),MDA表达显著升高(P<0.01);肾组织Nrf2、HO-1 mRNA及蛋白表达显著降低(P<0.01)。与模型组相比,各治疗组大鼠24 h UTP、血清TG、TC水平显著降低(P<0.05或P<0.01),TP、ALB水平显著升高(P<0.01),但丹参多酚酸盐低剂量组改善不明显;各治疗组肾脏病理损害明显改善;SOD表达水平显著升高(P<0.01),MDA表达水平明显降低(P<0.01);Nrf2、HO-1mRNA及蛋白表达显著升高(P<0.05或P<0.01)。[结论]丹参多酚酸盐可能通过调控Nrf2/HO-1信号通路,缓解肾组织氧化应激,进而保护肾脏及延缓疾病进展。
文摘目的通过观察丹参多酚酸盐对核苷酸结合寡聚结构域样受体蛋白3(NOD-like receptor thermal protein domain associated protein 3,NLRP3)/天冬氨酸特异性半胱氨酸蛋白酶1(Cysteinyl aspartate specific proteinase-1,Caspase-1)/焦亡效应蛋白消皮素D(Gasdermin-D,GSDMD)细胞焦亡通路的影响,探讨改善膜性肾病大鼠肾损伤可能的作用机制。方法SD大鼠通过尾静脉注射阳离子牛血清白蛋白建立膜性肾病大鼠模型,造模成功后分为模型组、盐酸贝那普利组(10 mg/kg)、丹参多酚酸盐低、中、高剂量组(16.7、33.3、66.7 mg/kg),另设有正常组,每组均为10只。各给药组连续给药4周后,检测24小时尿蛋白定量(24-hour urinary protein quantity,24 h UTP)和血肌酐(serum creatinine,Scr)、血尿素氮(blood urea nitrogen,BUN)、血清总蛋白(total protein,TP)、血清白蛋白(serum albumin,ALB)、甘油三酯(triglyceride,TG)、总胆固醇(total cholesterol,TC)的含量;酶联免疫吸附法检测大鼠血清白细胞介素-1β(interleukin-1β,IL-1β)、IL-18;采用光镜、电镜、荧光显微镜下观察肾脏病理学变化;Western blot、实时荧光定量PCR法检测NLRP3、Caspase-1、GSDMD蛋白及mRNA表达。结果与模型组比较,各给药组24 h UTP水平明显降低(P<0.05,P<0.01),丹参多酚酸盐中、高剂量组和盐酸贝那普利组大鼠血清TP和ALB水平显著升高(P<0.01),TC和TG水平明显降低(P<0.05,P<0.01);普通光镜、荧光显微镜及电镜下观察可见模型组大鼠肾组织病理损伤明显,经各组药物治疗后病理损伤逐渐改善。与模型组比较,各给药组大鼠血清IL-1β、IL-18水平明显降低(P<0.05,P<0.01);与正常组比较,模型组大鼠肾脏NLRP3、Caspase-1、GSDMD蛋白及其mRNA表达显著升高(P<0.01);与模型组比较,各给药组大鼠肾组织NLRP3/Caspase-1/GSDMD蛋白及其mRNA表达水平明显降低(P<0.05,P<0.01),以丹参多酚酸盐高剂量组效果最佳。结论丹参多酚酸盐对膜性肾病大鼠肾保护作用与调控NLRP3/Caspase-1/GSDMD细胞焦亡通路,改善炎症状态,减轻肾损伤有关。
基金supported by National Key R&D Program of China(No.2022YFB3104500)Natural Science Foundation of Jiangsu Province(No.BK20222013)Scientific Research Foundation of Nanjing Institute of Technology(No.3534113223036)。
文摘The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.
