Comparative analysis of SARS-CoV-2 variants with two high-throughput sequencing platforms

Whole-genome sequencing of upper respiratory tract specimens from patients with confirmed COVID-19 in Henan Province was performed to compare the performance of the Illumina and Oxford Nanopore sequencing platforms, thus providing a reference for whole-genome monitoring of the novel coronavirus (SARS-CoV-2). Ten samples from COVID-19 cases in Henan Province from June 2021 to January 2022 were collected and sequenced with Illumina and Nanopore high-through-put sequencing technology to obtain full genome sequences of the novel coronavirus, which were compared with the Wuhan reference sequence (Wuhan-Hu-1). Bioinformatics software (CLC) was used for sequence alignment analysis. Three of the ten samples were Omicron (BA.1) variants with 55,61 nucleotide variation sites. One sample was an Alpha (B.1.1.7) variant with 41 nucleotide variation sites. Six samples were Delta (8.1.617.2) variants with 35,42,47 nucleotide variation sites. The sequence identity of mutation sites in six samples was 100%, and the mutation sites in the S genome segment of seven samples were consistent. For samples with a Ct value < 33, both next-generation and third-generation sequencing achieved high genome coverage and sequencing depth. A significant difference in coverage was observed between second-generation sequencing and third-generation sequencing (t=-2.037, P < 0.06). However, the coverage at different time points of the third-generation sequencing did not significantly differ (F=2.498, P > 0.05). The needs for SARS-CoV-2 mutant detection could be met through use of either high-throughput sequencing platform. The identification of mutations in the novel coronavirus through Illumina high-throughput sequencing was more accurate, whereas Nanopore high-throughput sequencing technology could be used for rapid detection and typing of different novel coronaviruses.

Psychological antecedents of COVID-19 information sharing within strong-tie and weak-tie networks.

Objective: This study investigates the psychological mechanisms underlying people's sharing of COVID-19 information within their strong-tie networks and weak-tie networks. Methods: A cross-sectional online survey was conducted between March and April 2020 (N = 609 Chinese adults). Measures included emotions and behavioral beliefs about COVID-19 information sharing, risk perceptions, and COVID-19 information acquisition and sharing behaviors. Multiple linear regression was performed to examine the psychological predictors of COVID-19 information sharing. Results: People were more likely to share COVID-19 information within their strong-tie networks when they experienced more negative emotions (ß = .09, p = .01) and had stronger beliefs that information sharing would promote disease prevention (ß = .12, p = .004). By comparison, negative emotions were the only significant predictor of COVID-19 information sharing (ß = .12, p = .002) within weak-tie networks (ß = .04, p = .31 for beliefs about sharing). Conclusion: People may share COVID-19 information within weak-tie networks to cope with negative emotions regardless of whether they perceive information sharing as beneficial to disease prevention. Innovation: Health educators should raise people's awareness of the psychological motivators of COVID-19 information sharing to create a healthy information environment for disease prevention.

Plasmonic Biosensor Augmented by a Genetic Algorithm for Ultra-Rapid, Label-Free, and Multi-Functional Detection of COVID-19.

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.

COVID-19-associated liver injury: from bedside to bench.

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.

Source Trust and COVID-19 Information Sharing: The Mediating Roles of Emotions and Beliefs About Sharing.

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.

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

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.