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The novel coronavirus (COVID-19) is spreading globally due to its super contagiousness, and the pandemic caused by it has caused serious damage to the health and social economy of all countries in the world. However, conventional diagnostic methods are not conducive to large-scale screening and early identification of infected persons due to their long detection time. Therefore, there is an urgent need to develop a new COVID-19 test method that can deliver results in real time and on-site. In this work, we develop a fast, ultra-sensitive, and multi-functional plasmonic biosensor based on surface-enhanced infrared absorption for COVID-19 on-site diagnosis. The genetic algorithm intelligent program is utilized to automatically design and quickly optimize the sensing device to enhance the sensing performance. As a result, the quantitative detection of COVID-19 with an ultra-high sensitivity (1.66%/nm), a wide detection range, and a diverse measurement environment (gas/liquid) is achieved. In addition, the unique infrared fingerprint recognition characteristics of the sensor also make it an ideal choice for mutant virus screening. This work can not only provide a powerful diagnostic tool for the ultra-rapid, label-free, and multi-functional detection of COVID-19 but also help gain new insights into the field of label-free and ultrasensitive biosensing.

Health information sharing has become especially important during the COVID-19 (coronavirus disease 2019) pandemic because people need to learn about the disease and then act accordingly. This study examines the perceived trust of different COVID-19 information sources (health professionals, academic institutions, government agencies, news media, social media, family, and friends) and sharing of COVID-19 information in China. Specifically, it investigates how beliefs about sharing and emotions mediate the effects of perceived source trust on source-specific information sharing intentions. Results suggest that health professionals, academic institutions, and government agencies are trusted sources of information and that people share information from these sources because they think doing so will increase disease awareness and promote disease prevention. People may also choose to share COVID-19 information from news media, social media, and family as they cope with anxiety, anger, and fear. Taken together, a better understanding of the distinct psychological mechanisms underlying health information sharing from different sources can help contribute to more effective sharing of information about COVID-19 prevention and to manage negative emotion contagion during the pandemic.

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global challenge since December 2019. Although most patients with COVID-19 exhibit mild clinical manifestations, in approximately 5% of these patients, the disease eventually progresses to severe lung injury or even multiorgan dysfunction. This situation represents various challenges to hepatology. In the context of liver injury in patients with COVID-19, several key problems need to be solved. For instance, it is important to determine whether SARS-CoV-2 can directly invade liver, especially when ACE2 appears to be negligibly expressed on hepatocytes. In addition, the mechanisms underlying liver dysfunction in COVID-19 patients are not fully understood, which are likely multifactorial and related to hyperinflammation, dysregulated immune responses, abnormal coagulation and drugs. Here, we systematically describe the potential pathogenesis of COVID-19-associated liver injury and propose several hypotheses about its etiopathogenesis.

Health information sharing has become especially important during the COVID-19 (coronavirus disease 2019) pandemic because people need to learn about the disease and then act accordingly. This study examines the perceived trust of different COVID-19 information sources (health professionals, academic institutions, government agencies, news media, social media, family, and friends) and sharing of COVID-19 information in China. Specifically, it investigates how beliefs about sharing and emotions mediate the effects of perceived source trust on source-specific information sharing intentions. Results suggest that health professionals, academic institutions, and government agencies are trusted sources of information and that people share information from these sources because they think doing so will increase disease awareness and promote disease prevention. People may also choose to share COVID-19 information from news media, social media, and family as they cope with anxiety, anger, and fear. Taken together, a better understanding of the distinct psychological mechanisms underlying health information sharing from different sources can help contribute to more effective sharing of information about COVID-19 prevention and to manage negative emotion contagion during the pandemic.

Background: Although current studies have identified sleep disorders as an independent risk factor for suicide, the relationship between sleep disorders and suicide risk has not been well established. This study explored whether anxiety and depressive symptoms are used as mediators to participate in the impact of sleep quality on suicide risk. Methods: This is a cross-sectional study. We administered a psychological questionnaire to the participants, using a combination of self-assessment and psychiatrist assessment.Sleep quality, suicide risk, level of anxiety and depressive symptoms were assessed by PSQI, NGASR, SAS and SDS.The study subjects were 391 hospitalized COVID-19 patients from Wuhan hospitals. We used model 6 in the PROCESS (version 3.5) plug-in of SPSS software to conduct mediation test with sleep quality as the independent variable, suicide risk as the dependent variable, level of anxiety and depressive symptoms as intermediate variables. Results: The severity of anxiety and depressive symptoms and the risk of suicide in the sleep disorder group (63.15 ± 13.71, 59.85 ± 13.38, 6.52 ± 3.67) were higher than those in the non-sleep disorder group (49.83 ± 13.14, 44.87 ± 10.19, 2.87 ± 3.26) (P

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19. [ABSTRACT FROM AUTHOR]