Minimalist Nanocomplex with Dual Regulation of Endothelial Function and Inflammation for Targeted Therapy of Inflammatory Vascular Diseases.

Vascular disorders, characterized by vascular endothelial dysfunction combined with inflammation, are correlated with numerous fatal diseases, such as coronavirus disease-19 and atherosclerosis. Achieving vascular normalization is an urgent problem that must be solved when treating inflammatory vascular diseases. Inspired by the vascular regulatory versatility of nitric oxide (NO) produced by endothelial nitric oxide synthase (eNOS) catalyzing l-arginine (l-Arg), the eNOS-activating effects of l-Arg, and the powerful anti-inflammatory and eNOS-replenishing effects of budesonide (BUD), we constructed a bi-prodrug minimalist nanoplatform co-loaded with BUD and l-Arg via polysialic acid (PSA) to form BUD-l-Arg@PSA. This promoted vascular normalization by simultaneously regulating vascular endothelial dysfunction and inflammation. Mediated by the special affinity between PSA and E-selectin, which is highly expressed on the surface of activated endothelial cells (ECs), BUD-l-Arg@PSA selectively accumulated in activated ECs, targeted eNOS expression and activation, and promoted NO production. Consequently, the binary synergistic regulation of the NO/eNOS signaling pathway occurred and improved vascular endothelial function. NO-induced nuclear factor-kappa B alpha inhibitor (IκBα) stabilization and BUD-induced nuclear factor-kappa B (NF-κB) response gene site occupancy achieved dual-site blockade of the NF-κB signaling pathway, thereby reducing the inflammatory response and inhibiting the infiltration of inflammation-related immune cells. In a renal ischemia-reperfusion injury mouse model, BUD-l-Arg@PSA reduced acute injury. In an atherosclerosis mouse model, BUD-l-Arg@PSA decreased atherosclerotic plaque burden and improved vasodilation. This represents a revolutionary therapeutic strategy for inflammatory vascular diseases.

Using Pandemic Data to Examine the Government's Pandemic Prevention Measures and Its Support Policies for Vulnerable Groups.

The outbreak of COVID-19 and the uncertainty it brings have created enormous pressure on governments to control the global pandemic and restore economic growth. It is an inevitable choice for governments of various countries to seek to control the pandemic and to provide support such as subsidies to people who lose their jobs or cannot work. However, governments should evaluate their pandemic policies to determine their effectiveness. To maintain social stability and help vulnerable groups, governments also must determine when subsidies are needed and when these support policies should be withdrawn. This research demonstrates that the administration of vaccines and the wearing of masks have a relatively limited impact on preventing the spread of the COVID-19 virus. By contrast, strict school closure policies combined with personal movement restrictions are more helpful in mitigating the spread of the virus. Compared with vaccine policies and wearing masks, controlling internal movement is the most effective way to manage the pandemic in schools. Additionally, economic support such as subsidies for the unemployed and underemployed is not only conducive to prevention of the virus' spread but also to economic recovery and social stability. When the pandemic is brought under control, economic support for vulnerable groups can be gradually reduced or even withdrawn.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.