The global burden of hepatitis B virus(HBV)and hepatitis C virus(HCV)infections and coinfection represents a major public health concern,particularly in resource-limited settings.Elimination of HCV by 2030 has become ...The global burden of hepatitis B virus(HBV)and hepatitis C virus(HCV)infections and coinfection represents a major public health concern,particularly in resource-limited settings.Elimination of HCV by 2030 has become foreseeable,with effective direct-acting antiviral oral therapies and the availability of affordable generics in low-and-middle-income countries(LMICs).However,access to oral nucleos(t)ide therapy for HBV remains critical and is limited outside the existing global HIV program platforms despite affordable prices.Prevention of mother-to-child transmission of HBV through scaling up of birth dose implementation in LMICs is essential to achieve the 2030 elimination goal.Most individuals living with HBV and/or HCV in resource-limited settings are unaware of their infection,and with improved access to medications,the most significant barrier remains access to affordable diagnostics and preventive strategies.The coronavirus disease 2019 pandemic interrupted hepatitis elimination programs,albeit offered opportunities for improved diagnostic capacities and raised political awareness of the critical need for strengthening health care services and universal health coverage.This review underpins the HBV and HCV management challenges in resource-limited settings,highlighting the current status and suggested future elimination strategies in some of these countries.Global efforts should continue to improve awareness and political commitment.Financial resources should be secured to access and implement comprehensive strategies for diagnosis and linkage to care in resource-constrained settings to fulfill the 2030 elimination goal.展开更多
Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this pape...Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.展开更多
文摘The global burden of hepatitis B virus(HBV)and hepatitis C virus(HCV)infections and coinfection represents a major public health concern,particularly in resource-limited settings.Elimination of HCV by 2030 has become foreseeable,with effective direct-acting antiviral oral therapies and the availability of affordable generics in low-and-middle-income countries(LMICs).However,access to oral nucleos(t)ide therapy for HBV remains critical and is limited outside the existing global HIV program platforms despite affordable prices.Prevention of mother-to-child transmission of HBV through scaling up of birth dose implementation in LMICs is essential to achieve the 2030 elimination goal.Most individuals living with HBV and/or HCV in resource-limited settings are unaware of their infection,and with improved access to medications,the most significant barrier remains access to affordable diagnostics and preventive strategies.The coronavirus disease 2019 pandemic interrupted hepatitis elimination programs,albeit offered opportunities for improved diagnostic capacities and raised political awareness of the critical need for strengthening health care services and universal health coverage.This review underpins the HBV and HCV management challenges in resource-limited settings,highlighting the current status and suggested future elimination strategies in some of these countries.Global efforts should continue to improve awareness and political commitment.Financial resources should be secured to access and implement comprehensive strategies for diagnosis and linkage to care in resource-constrained settings to fulfill the 2030 elimination goal.
基金supported by the National Natural Science Foundation of China (7117114371201087)+1 种基金the Tianjin Municipal Research Program of Application Foundation and Advanced Technology of China (10JCY-BJC07300)the Science and Technology Program of FOXCONN Group (120024001156)
文摘Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.
