目的:通过在人小梁网细胞(human trabecular meshwork cell,HTMC)中过表达沉默信息调节因子2相关酶1(silent information regulator 1,SIRT1),探讨SIRT1对氧化应激下HTMC功能的影响。方法:将SIRT1过表达慢病毒和GFP阴性对照慢病毒按照最...目的:通过在人小梁网细胞(human trabecular meshwork cell,HTMC)中过表达沉默信息调节因子2相关酶1(silent information regulator 1,SIRT1),探讨SIRT1对氧化应激下HTMC功能的影响。方法:将SIRT1过表达慢病毒和GFP阴性对照慢病毒按照最佳(multiplicity of infection,MOI)分别转染入HTMC,并用实时定量PCR法对SIRT1是否在细胞中过表达进行验证。实验分为以下4组:正常组、H2O2组、H2O2+Lv-SIRT1-OE(过表达)组、H2O2+Lv-GFP组,分别采用Transwell法和CCK8法检测氧化应激下HTMC的迁移能力和活性。两组间比较采用独立样本t检验。结果:在正常组、H2O2组、H2O2+Lv-SIRT1-OE组、H2O2+Lv-GFP组这4组中,Transwell实验结果分别为436±73、254±25、510±51、327±46,H2O2+Lv-SIRT1-OE组分别与H2O2组和H2O2+Lv-GFP组差异均有统计学意义(P<0.01)。CCK8法结果显示,H2O2+Lv-SIRT1-OE组分别与H2O2组和H2O2+Lv-GFP组相比差异均有统计学意义(P<0.01)。H2O2+Lv-SIRT1-OE组分别与H2O2组和阴性对照组(H2O2+Lv-GFP)相比,Bax表达水平明显下降,Bcl-2表达水平明显提高,差异均有统计学意义(P<0.01)。ROS活性氧测定显示H2O2+Lv-SIRT1-OE组比H2O2组的细胞活性氧水平显著降低(P<0.05)。结论:在HTMC中过表达SIRT1能有效降低氧化应激对HTMC迁移能力和活性的影响,从而对HTMC起到一定的保护作用,为后续研究SIRT1保护氧化应激下HTMC的调控机制打下基础。展开更多
The DeGroot model is one of the most classical models in the field of opinion dynamics. The standard DeGroot model assumes that agents are homogeneous and update their opinions in the direction of a weighted average o...The DeGroot model is one of the most classical models in the field of opinion dynamics. The standard DeGroot model assumes that agents are homogeneous and update their opinions in the direction of a weighted average of their neighbors'opinions.One natural question is whether a second type of agents could significantly change the main properties of the model.The authors address this question by introducing rebels,who update their opinions toward the opposite of their neighbors' weighted average.The authors find that the existence of rebels remarkably affects the opinion dynamics. Under certain mild conditions,the existence of a few rebels will lead the group opinion to the golden mean,regardless of the initial opinions of the agents and the structure of the learning network.This result is completely different from that of the standard DeGroot model,where the final consensus opinion is determined by both the initial opinions and the learning topology.The study then provides new insights into understanding how heterogeneous individuals in a group reach consensus and why the golden mean is so common in human society.展开更多
文摘目的:通过在人小梁网细胞(human trabecular meshwork cell,HTMC)中过表达沉默信息调节因子2相关酶1(silent information regulator 1,SIRT1),探讨SIRT1对氧化应激下HTMC功能的影响。方法:将SIRT1过表达慢病毒和GFP阴性对照慢病毒按照最佳(multiplicity of infection,MOI)分别转染入HTMC,并用实时定量PCR法对SIRT1是否在细胞中过表达进行验证。实验分为以下4组:正常组、H2O2组、H2O2+Lv-SIRT1-OE(过表达)组、H2O2+Lv-GFP组,分别采用Transwell法和CCK8法检测氧化应激下HTMC的迁移能力和活性。两组间比较采用独立样本t检验。结果:在正常组、H2O2组、H2O2+Lv-SIRT1-OE组、H2O2+Lv-GFP组这4组中,Transwell实验结果分别为436±73、254±25、510±51、327±46,H2O2+Lv-SIRT1-OE组分别与H2O2组和H2O2+Lv-GFP组差异均有统计学意义(P<0.01)。CCK8法结果显示,H2O2+Lv-SIRT1-OE组分别与H2O2组和H2O2+Lv-GFP组相比差异均有统计学意义(P<0.01)。H2O2+Lv-SIRT1-OE组分别与H2O2组和阴性对照组(H2O2+Lv-GFP)相比,Bax表达水平明显下降,Bcl-2表达水平明显提高,差异均有统计学意义(P<0.01)。ROS活性氧测定显示H2O2+Lv-SIRT1-OE组比H2O2组的细胞活性氧水平显著降低(P<0.05)。结论:在HTMC中过表达SIRT1能有效降低氧化应激对HTMC迁移能力和活性的影响,从而对HTMC起到一定的保护作用,为后续研究SIRT1保护氧化应激下HTMC的调控机制打下基础。
基金supported by the National Natural Science Foundation of China under Grant Nos.71771026,71701058,11471326
文摘The DeGroot model is one of the most classical models in the field of opinion dynamics. The standard DeGroot model assumes that agents are homogeneous and update their opinions in the direction of a weighted average of their neighbors'opinions.One natural question is whether a second type of agents could significantly change the main properties of the model.The authors address this question by introducing rebels,who update their opinions toward the opposite of their neighbors' weighted average.The authors find that the existence of rebels remarkably affects the opinion dynamics. Under certain mild conditions,the existence of a few rebels will lead the group opinion to the golden mean,regardless of the initial opinions of the agents and the structure of the learning network.This result is completely different from that of the standard DeGroot model,where the final consensus opinion is determined by both the initial opinions and the learning topology.The study then provides new insights into understanding how heterogeneous individuals in a group reach consensus and why the golden mean is so common in human society.
