为确定H9N2猪流感病毒(H9N2-SIV)通过瞬时受体电位通道M2(TRPM2)介导肺微血管上皮细胞(PMVEC)铁死亡的分子机制,使用H9N2-SIV接种PMVEC,构建TRPM2-siRNA质粒并转染细胞。用透射电镜观察细胞超微结构,用荧光探针法检测活性氧(ROS)、Ca^(...为确定H9N2猪流感病毒(H9N2-SIV)通过瞬时受体电位通道M2(TRPM2)介导肺微血管上皮细胞(PMVEC)铁死亡的分子机制,使用H9N2-SIV接种PMVEC,构建TRPM2-siRNA质粒并转染细胞。用透射电镜观察细胞超微结构,用荧光探针法检测活性氧(ROS)、Ca^(2+)和Fe^(2+);用生化试剂盒检测丙二醛(MDA)和谷胱甘肽(GSH)含量,并通过荧光定量PCR和Western-blot检测葡萄糖调节蛋白78(GRP78)、TRPM2、蛋白激酶R样内质网激酶(PERK)、活化转录因子4(ATF4)、阳离子转运调控样蛋白1(CHAC1)、谷胱甘肽过氧化物酶4(GPX4)的m RNA和蛋白表达水平。结果显示,H9N2-SIV感染可诱导细胞铁死亡,敲低TRPM2可以减少细胞内ROS水平,降低Ca^(2+)、Fe^(2+)及MDA含量,GSH水平明显增加;此外,GRP78、PERK、ATF4、CHAC1 m RNA和蛋白表达水平下调,GPX4的m RNA和蛋白表达水平上调。结果表明,H9N2-SIV感染可诱导细胞铁死亡,其可通过激活TRPM2使Ca^(2+)内流增多,进而激活PERK/ATF4/CHAC1信号通路,加速GSH耗竭,抑制GPX4的活性,促进细胞铁死亡。展开更多
With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet...With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet detection is critical.However,conventional semiconductor materials suffer from limited UV-visible detection capabilities owing to their narrow bandgaps and high dark currents.To address these challenges,wide-bandgap semiconductors have emerged as promising alternatives.Here,we fabricated a horizontally structured n–n heterojunction photodetector by growingβ-Ga_(2)O_(3) on Si–GaN via plasma-enhanced chemical vapor deposition.The device exhibits a self-powered photocurrent of 3.5 nA at zero bias,enabled by the photovoltaic effect of the space charge region.Under 254-nm and 365-nm illumination,it exhibits rectification behavior,achieving a responsivity of 0.475 m A/W(0 V,220??W/cm~2 at 254 nm)and 257.6 mA/W(-5 V),respectively.Notably,the photodetector demonstrates a high photocurrent-to-dark current ratio of 10~5 under-5-V bias,highlighting its potential for self-powered and high-performance UV detection applications.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.展开更多
文摘为确定H9N2猪流感病毒(H9N2-SIV)通过瞬时受体电位通道M2(TRPM2)介导肺微血管上皮细胞(PMVEC)铁死亡的分子机制,使用H9N2-SIV接种PMVEC,构建TRPM2-siRNA质粒并转染细胞。用透射电镜观察细胞超微结构,用荧光探针法检测活性氧(ROS)、Ca^(2+)和Fe^(2+);用生化试剂盒检测丙二醛(MDA)和谷胱甘肽(GSH)含量,并通过荧光定量PCR和Western-blot检测葡萄糖调节蛋白78(GRP78)、TRPM2、蛋白激酶R样内质网激酶(PERK)、活化转录因子4(ATF4)、阳离子转运调控样蛋白1(CHAC1)、谷胱甘肽过氧化物酶4(GPX4)的m RNA和蛋白表达水平。结果显示,H9N2-SIV感染可诱导细胞铁死亡,敲低TRPM2可以减少细胞内ROS水平,降低Ca^(2+)、Fe^(2+)及MDA含量,GSH水平明显增加;此外,GRP78、PERK、ATF4、CHAC1 m RNA和蛋白表达水平下调,GPX4的m RNA和蛋白表达水平上调。结果表明,H9N2-SIV感染可诱导细胞铁死亡,其可通过激活TRPM2使Ca^(2+)内流增多,进而激活PERK/ATF4/CHAC1信号通路,加速GSH耗竭,抑制GPX4的活性,促进细胞铁死亡。
基金Project supported by the Joints Fund of the National Natural Science Foundation of China(Grant No.U23A20349)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.62204126,62305171,62304113)。
文摘With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet detection is critical.However,conventional semiconductor materials suffer from limited UV-visible detection capabilities owing to their narrow bandgaps and high dark currents.To address these challenges,wide-bandgap semiconductors have emerged as promising alternatives.Here,we fabricated a horizontally structured n–n heterojunction photodetector by growingβ-Ga_(2)O_(3) on Si–GaN via plasma-enhanced chemical vapor deposition.The device exhibits a self-powered photocurrent of 3.5 nA at zero bias,enabled by the photovoltaic effect of the space charge region.Under 254-nm and 365-nm illumination,it exhibits rectification behavior,achieving a responsivity of 0.475 m A/W(0 V,220??W/cm~2 at 254 nm)and 257.6 mA/W(-5 V),respectively.Notably,the photodetector demonstrates a high photocurrent-to-dark current ratio of 10~5 under-5-V bias,highlighting its potential for self-powered and high-performance UV detection applications.
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
