A combination of nucleos(t)ides and hepatitis B immunoglobulin (HBIg) has been found to be effective for the prevention of hepatitis B viral (HBV) reinfection after liver transplantation (LT),but its administration is...A combination of nucleos(t)ides and hepatitis B immunoglobulin (HBIg) has been found to be effective for the prevention of hepatitis B viral (HBV) reinfection after liver transplantation (LT),but its administration is costly,and not always available. We report the case of a male,33-year-old cirrhotic patient who has tested positive for serum HBsAg,and HBeAg,with 9.04 × 107 copies/mL of HBV DNA. He suffered from acute liver failure and was near death before undergoing emergency LT. No HBIg was available at the time,so only lamivudine was used. He routinely received immunosuppression medication. Serum HBV DNA and HBsAg still showed positive post-LT,and the graft re-infected. Hepatitis B flared three months later. Adefovir dipivoxil was added to the treatment,but in the 24th mo of treatment,the patient developed lamivudine resistance and a worsening of the hepatitis occurred shortly thereafter. The treatment combination was then changed to a double dosage of entecavir and the disease was gradually resolved. After 60-mo of post-LT nucleos(t)ide analogue therapy,anti-HBs seroconverted,and the antiviral was stopped. By the end of a 12-mo follow-up,the patient had achieved sustained recovery. In conclusion,the case seems to point to evidence that more potent and less resistant analogues like entecavir might fully replace HBIg as an HBV prophylaxis and treatment regimen.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
文摘A combination of nucleos(t)ides and hepatitis B immunoglobulin (HBIg) has been found to be effective for the prevention of hepatitis B viral (HBV) reinfection after liver transplantation (LT),but its administration is costly,and not always available. We report the case of a male,33-year-old cirrhotic patient who has tested positive for serum HBsAg,and HBeAg,with 9.04 × 107 copies/mL of HBV DNA. He suffered from acute liver failure and was near death before undergoing emergency LT. No HBIg was available at the time,so only lamivudine was used. He routinely received immunosuppression medication. Serum HBV DNA and HBsAg still showed positive post-LT,and the graft re-infected. Hepatitis B flared three months later. Adefovir dipivoxil was added to the treatment,but in the 24th mo of treatment,the patient developed lamivudine resistance and a worsening of the hepatitis occurred shortly thereafter. The treatment combination was then changed to a double dosage of entecavir and the disease was gradually resolved. After 60-mo of post-LT nucleos(t)ide analogue therapy,anti-HBs seroconverted,and the antiviral was stopped. By the end of a 12-mo follow-up,the patient had achieved sustained recovery. In conclusion,the case seems to point to evidence that more potent and less resistant analogues like entecavir might fully replace HBIg as an HBV prophylaxis and treatment regimen.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
