目的构建转录因子E2-2基因腺病毒载体,观测内皮祖细胞(endothelial progenitor cells,EPCs)过表达E2-2基因对DNA结合抑制因子-1(inhibitor of DNA binding/differentiation,ID1)表达的影响。方法分离、培养并鉴定小鼠骨髓EPCs。RT-PCR...目的构建转录因子E2-2基因腺病毒载体,观测内皮祖细胞(endothelial progenitor cells,EPCs)过表达E2-2基因对DNA结合抑制因子-1(inhibitor of DNA binding/differentiation,ID1)表达的影响。方法分离、培养并鉴定小鼠骨髓EPCs。RT-PCR法扩增E2-2基因CDs全长DNA,克隆入载体pTG19-T后,亚克隆入腺病毒穿梭载体pAdTrack-CMV中,构建pAdTrack/E2-2重组载体,与pAdEasy-1骨架质粒同源重组形成重组病毒pAd/E2-2,经293细胞包装,获具高效感染力的重组pAd/E2-2病毒。将该病毒感染EPCs,倒置显微镜观测经感染的EPCs的GFP表达情况。CCK-8(cell count kit-8)法检测病毒pAd/E2-2对EPCs生长、增殖的影响。RT-PCR、Western blot分别检测经感染的EPCs中E2-2与ID1基因及其编码蛋白的表达情况,并予以定量分析。结果分离、培养并鉴定到小鼠骨髓EPCs。克隆到2013 bp的E2-2基因,并获得高效感染力的重组pAd/E2-2病毒。CCK-8法检测表明,与对照比较,过表达E2-2的EPCs的生长、增殖速度减慢,48h开始变得尤为明显(P<0.01);RT-PCR、Western blot及定量分析结果显示,E2-2能下调ID1的表达,与对照比较,差异具统计学意义(P<0.01)。结论分离、培养并鉴定小鼠骨髓EPCs,克隆出E2-2基因,证实E2-2能明显抑制EPCs的生长、增殖,并能下调ID1基因的表达。展开更多
The natural history of chronic hepatitis B is characterized by different phases of infection,and patients may evolve from one phase to another or may revert to a previous phase.The hepatitis B e antigen(HBeAg)-negativ...The natural history of chronic hepatitis B is characterized by different phases of infection,and patients may evolve from one phase to another or may revert to a previous phase.The hepatitis B e antigen(HBeAg)-negative form is the predominant infection worldwide,which consists of individuals with a range of viral replication and liver disease severity.Although alanine transaminase(ALT)remains the most accessible test available to clinicians for monitoring the liver disease status,further evaluations are required for some patients to assess if treatment is warranted.Guidance from practice guidelines together with thorough investigations and classifications of patients ensure recognition of who needs which level of care.This article aims to assist physicians in the assessment of HBeAgnegative individuals using liver biopsy or non-invasive tools such as hepatitis B s antigen quantification and transient elastography in addition to ALT and hepatitis B virus DNA,to identify who will remain stable,who will reactivate or at risk of disease progression hence will benefit from timely initiation of anti-viral therapy.展开更多
BACKGROUND Hepatitis B e antigen-negative chronic hepatitis B patients under nucleos(t)ids analogues(NAs)rarely achieve hepatitis B surface antigen(HBsAg)loss.AIM To evaluate if the addition of pegylated interferon(Pe...BACKGROUND Hepatitis B e antigen-negative chronic hepatitis B patients under nucleos(t)ids analogues(NAs)rarely achieve hepatitis B surface antigen(HBsAg)loss.AIM To evaluate if the addition of pegylated interferon(Peg-IFN)could decrease HBsAg and hepatitis B core-related antigen(HBcrAg)levels and increase HBsAg loss rate in patients under NAs therapy.METHODS Prospective,non-randomized,open-label trial evaluating the combination of Peg-IFN 180μg/week plus NAs during forty-eight weeks vs NAs in monotherapy.Hepatitis B e antigen-negative non-cirrhotic chronic hepatitis B patients of a tertiary hospital,under NAs therapy for at least 2 years and with undetectable viral load,were eligible.Patients with hepatitis C virus,hepatitis D virus or human immunodeficiency virus co-infection and liver transplanted patients were excluded.HBsAg and HBcrAg levels(log10 U/mL)were measured at baseline and during ninety-six weeks.HBsAg loss rate was evaluated in both groups.Adverse events were recorded in both groups.The kinetic of HBsAg for each treatment group was evaluated from baseline to weeks 24 and 48 by the slope of the HBsAg decline(log10 IU/mL/week)using a linear regression model.RESULTS Sixty-five patients were enrolled,61%receiving tenofovir and 33%entecavir.Thirty-six(55%)were included in Peg-IFN-NA group and 29(44%)in NA group.After matching by age and treatment duration,baseline HBsAg levels were comparable between groups(3.1 vs 3.2)(P=0.25).HBsAg levels at weeks 24,48 and 96 declined in Peg-IFN-NA group(-0.26,-0.40 and-0.44)and remained stable in NA group(-0.10,-0.10 and-0.10)(P<0.05).The slope of HBsAg decline in Peg-IFN-NA group(-0.02)was higher than in NA group(-0.00)(P=0.015).HBcrAg levels did not change.Eight(22%)patients discontinued Peg-IFN due to adverse events.The HBsAg loss was achieved in 3(8.3%)patients of the Peg-IFN-NA group and 0(0%)of the NA group.CONCLUSION The addition of Peg-IFN to NAs caused a greater and faster decrease of HBsAg levels compared to NA therapy.Side effects of Peg-IFN can limit its use in clinical practice.展开更多
Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For...Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary defiance system for mobile ad-hoc networks vs.attacks since they monitor network traffic and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).展开更多
文摘The natural history of chronic hepatitis B is characterized by different phases of infection,and patients may evolve from one phase to another or may revert to a previous phase.The hepatitis B e antigen(HBeAg)-negative form is the predominant infection worldwide,which consists of individuals with a range of viral replication and liver disease severity.Although alanine transaminase(ALT)remains the most accessible test available to clinicians for monitoring the liver disease status,further evaluations are required for some patients to assess if treatment is warranted.Guidance from practice guidelines together with thorough investigations and classifications of patients ensure recognition of who needs which level of care.This article aims to assist physicians in the assessment of HBeAgnegative individuals using liver biopsy or non-invasive tools such as hepatitis B s antigen quantification and transient elastography in addition to ALT and hepatitis B virus DNA,to identify who will remain stable,who will reactivate or at risk of disease progression hence will benefit from timely initiation of anti-viral therapy.
文摘BACKGROUND Hepatitis B e antigen-negative chronic hepatitis B patients under nucleos(t)ids analogues(NAs)rarely achieve hepatitis B surface antigen(HBsAg)loss.AIM To evaluate if the addition of pegylated interferon(Peg-IFN)could decrease HBsAg and hepatitis B core-related antigen(HBcrAg)levels and increase HBsAg loss rate in patients under NAs therapy.METHODS Prospective,non-randomized,open-label trial evaluating the combination of Peg-IFN 180μg/week plus NAs during forty-eight weeks vs NAs in monotherapy.Hepatitis B e antigen-negative non-cirrhotic chronic hepatitis B patients of a tertiary hospital,under NAs therapy for at least 2 years and with undetectable viral load,were eligible.Patients with hepatitis C virus,hepatitis D virus or human immunodeficiency virus co-infection and liver transplanted patients were excluded.HBsAg and HBcrAg levels(log10 U/mL)were measured at baseline and during ninety-six weeks.HBsAg loss rate was evaluated in both groups.Adverse events were recorded in both groups.The kinetic of HBsAg for each treatment group was evaluated from baseline to weeks 24 and 48 by the slope of the HBsAg decline(log10 IU/mL/week)using a linear regression model.RESULTS Sixty-five patients were enrolled,61%receiving tenofovir and 33%entecavir.Thirty-six(55%)were included in Peg-IFN-NA group and 29(44%)in NA group.After matching by age and treatment duration,baseline HBsAg levels were comparable between groups(3.1 vs 3.2)(P=0.25).HBsAg levels at weeks 24,48 and 96 declined in Peg-IFN-NA group(-0.26,-0.40 and-0.44)and remained stable in NA group(-0.10,-0.10 and-0.10)(P<0.05).The slope of HBsAg decline in Peg-IFN-NA group(-0.02)was higher than in NA group(-0.00)(P=0.015).HBcrAg levels did not change.Eight(22%)patients discontinued Peg-IFN due to adverse events.The HBsAg loss was achieved in 3(8.3%)patients of the Peg-IFN-NA group and 0(0%)of the NA group.CONCLUSION The addition of Peg-IFN to NAs caused a greater and faster decrease of HBsAg levels compared to NA therapy.Side effects of Peg-IFN can limit its use in clinical practice.
文摘Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary defiance system for mobile ad-hoc networks vs.attacks since they monitor network traffic and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).
