Atlas of interactions between SARS-CoV-2 macromolecules and host proteins.

The proteins and RNAs of viruses extensively interact with host proteins after infection. We collected and reanalyzed all available datasets of protein-protein and RNA-protein interactions related to SARS-CoV-2. We investigated the reproducibility of those interactions and made strict filters to identify highly confident interactions. We systematically analyzed the interaction network and identified preferred subcellular localizations of viral proteins, some of which such as ORF8 in ER and ORF7A/B in ER membrane were validated using dual fluorescence imaging. Moreover, we showed that viral proteins frequently interact with host machinery related to protein processing in ER and vesicle-associated processes. Integrating the protein- and RNA-interactomes, we found that SARS-CoV-2 RNA and its N protein closely interacted with stress granules including 40 core factors, of which we specifically validated G3BP1, IGF2BP1, and MOV10 using RIP and Co-IP assays. Combining CRISPR screening results, we further identified 86 antiviral and 62 proviral factors and associated drugs. Using network diffusion, we found additional 44 interacting proteins including two proviral factors previously validated. Furthermore, we showed that this atlas could be applied to identify the complications associated with COVID-19. All data are available in the AIMaP database (https://mvip.whu.edu.cn/aimap/) for users to easily explore the interaction map.

Research on the Design of Virtual Reality Online Education Information Presentation Based on Multi-Sensory Cognition

The popularity of the online teaching model increased during the COVID-19, and virtual reality online education is now firmly established as a future trend in educational growth. Human–computer interaction and collaboration between virtual models and physical entities, as well as virtual multi-sensory cognition, have become the focus of research in the field of online education. In this paper, we analyze the mapping form of teaching information and cue information on users' cognition through an experimental system and investigate the effects of the presentation form of online virtual teaching information, the length of the material, users' memory of the information, and the presentation form of information cues on users' cognitive performance. The experimental results show that different instructional information and cue presentation designs have significant effects on users' learning performance, with relatively longer instructional content being more effective and users being more likely to mechanically remember the learning materials. By studying the impact of multi-sensory information presentation on users' cognition, the output design of instructional information can be optimized, cognitive resources can be reasonably allocated, and learning effectiveness can be ensured, which is of great significance for virtual education research in digital twins.

Study on prescription medication mode and mechanism of traditional Chinese medicine in the treatment of noncritical COVID-19 based on data mining

Background: As of 2023, coronavirus disease 2019 (COVID-19) is still spreading globally. Therefore, we aim to integrate non-critical COVID-19 high-frequency and high-targeting Chinese medicines to provide a reference for clinical prescriptions to improve COVID-19-related symptoms. Materials and methods: The information on non-critical COVID-19 high-frequency Chinese medicines in the diagnosis and treatment of COVID-19 was obtained by the TCM inheritance support platform. Using network pharmacology and molecular docking technology, high-targeting Chinese medicines with good docking activity with COVID-19 receptors angiotensin-converting enzyme-II (ACE2), 3CLpro and tyrosine-protein kinase receptor UFO (AXL) were obtained. A new prescription for non-critical COVID-19 was established by integrating high-frequency and high-targeting Chinese medicines. Rats with acute lung injury induced by lipopolysaccharide were used as the experimental model. The histopathological changes in the lungs of rats in each group were observed by hematoxylin-eosin staining. The lung coefficient of rats was measured. The levels of IL-6, TNF-a, and IL-1ß in serum were detected by enzyme-linked immunosorbent assay. The mRNA and protein levels of ACE2 and AXL in lung tissue were detected by real-time quantitative polymerase chain reaction and western blot. Results: Through data mining, it was found that there were 39 high-frequency traditional Chinese medicines for non-critical COVID-19 in the diagnosis and treatment guidelines. According to network pharmacology and molecular docking, 30 highly targeted traditional Chinese drugs for COVID-19 were found. The new prescriptions for non-critical COVID-19 were comprehensively obtained, including Glycyrrhizae Radix, Ephedra Herba, Amygdalus Communis Vas, Gypsum Fibrosum, Descurainiae Semen, Atractylodes Lancea, Scutellariae Radix, Amomum Tsao-Ko Crevostet, Forsythiae Fructus, Pogostemon cablin, Magnolia Officinalis. Compared with the LPS-induced lung injury model group, the medium dose of the new prescription group had significantly alleviated pathological changes in lung tissue, decreased lung coefficient, decreased contents of IL-6, TNF-a and IL-1ß, and increased mRNA and protein expression of ACE2 and AXL (P < 0.05). Conclusion: Based on data mining, network pharmacology and molecular docking technology, the new prescription for non-critical COVID-19 established by this method has an anti-inflammatory effect on rats with acute lung injury induced by lipopolysaccharide and can provide a reference for clinicians to alleviate the symptoms related to non-critical COVID-19. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Learning Burnout: Evaluating the Role of Social Support in Medical Students.

Background: Burnout is a stress-induced syndrome considered to be closely related to work. Although social support could relief burnout syndrome, its effect on learning burnout in medical students remains unclear. The objectives of the study are to evaluate the association between learning burnout and social support in Chinese medical students. Methods: A cross-sectional online survey was distributed to students who participated in online learning in a medical college in Wuhan during the COVID-19 epidemic. We used the Lian version of the Maslach Burnout Inventory (MBI) to assess learning burnout and the Social Support Rating Scale (SSRS) to assess social support. Chi-square tests were used to analyze factors associated with burnout. Independent t-test and multiple logistic regression were explored to analyze the relationship between social support and burnout. Results: A total of 684 students have completed the survey (response rate: 30.9%), of which 315 (46.12%) met standard criteria for learning burnout. Multiple logistic regression analysis has revealed that seniors, low family income and low social support were significant predictors of learning burnout (χ2 = 41.983, p < 0.001). After adjusting for the grade and family income, there was a significant and relevant association between social support and learning burnout (OR = 0.937; 95% CI: 0.905-0.970; p < 0.001). Conclusions: Learning burnout was highly prevalent in medical students at our college. Senior students and low family income might be risk factors for learning burnout. Social support, especially subjective support and utilization of support might play a protective role in reducing the risk of learning burnout.

Ultrasensitive Strain Sensor Utilizing a AgF-AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis.

Breath monitoring and pulmonary function analysis have been the prime focus of wearable smart sensors owing to the COVID-19 outbreak. Currently used lung function meters in hospitals are prone to spread the virus and can result in the transmission of the disease. Herein, we have reported the first-ever wearable patch-type strain sensor for enabling real-time lung function measurements (such as forced volume capacity (FVC) and forced expiratory volume (FEV) along with breath monitoring), which can avoid the spread of the virus. The noninvasive and highly sensitive strain sensor utilizes the synergistic effect of two-dimensional (2D) silver flakes (AgFs) and one-dimensional (1D) silver nanowires (AgNWs), where AgFs create multiple electron transmission paths and AgNWs generate percolation networks in the nanocomposite. The nanocomposite-based strain sensor possesses a high optimized conductivity of 7721 Sm-1 (and a maximum conductivity of 83,836 Sm-1), excellent stretchability (>1000%), and ultrasensitivity (GFs of 35 and 87 when stretched 0-20 and 20-50%, respectively), thus enabling reliable detection of small strains produced by the body during breathing and other motions. The sensor patching site was optimized to accurately discriminate between normal breathing, quick breathing, and deep breathing and analyze numerous pulmonary functions, including the respiratory rate, peak flow, FVC, and FEV. Finally, the observed measurements for different pulmonary functions were compared with a commercial peak flow meter and a spirometer, and a high correlation was observed, which highlights the practical feasibility of continuous respiratory monitoring and pulmonary function analysis.

Long-term Association between NO2 and Human Mobility: A Two-year Spatiotemporal Study during the COVID-19 Pandemic in Southeast Asia (preprint)

Since the COVID-19 pandemic, governments have implemented lockdowns and movement restrictions to contain the disease outbreak. Previous studies have reported a significant positive correlation between NO2 and mobility level during the lockdowns in early 2020. Though NO2 level and mobility exhibited similar spatial distribution, our initial exploration indicated that the decreased mobility level did not always result in concurrent decreasing NO2 level during a two-year time period in Southeast Asia with human movement data at a very high spatial resolution (i.e., Facebook origin-destination data). It indicated that factors other than mobility level contributed to NO2 level decline. Our subsequent analysis used a trained Multi-Layer Perceptron model to assess mobility and other contributing factors (e.g., travel modes, temperature, wind speed) and predicted future NO2 levels in Southeast Asia. The model results suggest that, while as expected mobility has a strong impact on NO2 level, a more accurate prediction requires considering different travel modes (i.e., driving and walking). Mobility shows two-sided impacts on NO2 level: mobility above the average level has a high impact on NO2, whereas mobility at a relatively low level shows negligible impact. The results also suggest that spatio-temporal heterogeneity and temperature also have impacts on NO2 and they should be incorporated to facilitate a more comprehensive understanding of the association between NO2 and mobility in the future study.

The relationship between real-life social support and Internet addiction among the elderly in China.

Internet addiction among the elderly is a novel issue in many countries. However, extant research about excessive use of the Internet is focusing on adolescents and younger adults. There are few studies to explore the topic of the elderly's Internet addiction. The purpose of this study is to investigate the relationship between real-life social support and Internet addiction among older adults during the COVID-19 pandemic. This article adopted a self-reported questionnaire via internet links to collect data. A total of 303 valid samples about Internet addiction for the elderly were obtained in China. The results suggested that real-life social support is significantly and negatively related to Internet addiction among the aged. Moreover, the findings revealed that real-life social support could mitigate Internet addiction by increasing the levels of hopefulness and decreasing the feeling of loneliness. We expect that this study can enrich the understanding of the problematic Internet usage within older populations. Finally, the contributions, practical significance, and limitations of this study were discussed.

The impact of COVID-19 on NO2 and PM2.5 levels and their associations with human mobility patterns in Singapore

The decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, overlooking the meteorological changes that could simultaneously mediate air pollution levels. This pitfall could potentially lead to over- or under-estimation of the effect of COVID-19 on air pollution. This study, thus, aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant levels in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results show that the mean NO2 and PM2.5 declined by 12% and 19%, which were less than the observed drops (i.e. 54% and 29%, respectively) without considering the effect of meteorological parameters. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019. Two correlation analyses were conducted to investigate how human mobility influenced air pollutant levels: one between daily PM2.5 and mobility changes at a regional scale and the other between weekly NO2 and mobility changes at a spatial resolution of 0.01°. The NO2 variation was found to be more associated with the change in human mobility and a cluster of stronger correlations was found in the South and East Coast of Singapore. Contrarily, PM2.5 and mobility had a weak correlation, which could be due to the limit of a coarse spatial resolution. [ FROM AUTHOR] Copyright of Annals of GIS is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by "split in two R-groups" cutting methods and fragment combinations (q2 = 0.703, r2 = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.

Human adenovirus (HAdV) infection in children with acute respiratory tract infections in Guangzhou, China, 2010-2021: a molecular epidemiology study.

BACKGROUND: Human adenovirus (HAdV) infection can cause a variety of diseases. It is a major pathogen of pediatric acute respiratory tract infections (ARIs) and can be life-threatening in younger children. We described the epidemiology and subtypes shifting of HAdV among children with ARI in Guangzhou, China. METHODS: We conducted a retrospective study of 161,079 children diagnosed with acute respiratory illness at the Guangzhou Women and Children's Medical Center between 2010 and 2021. HAdV specimens were detected by real-time PCR and the hexon gene was used for phylogenetic analysis. RESULTS: Before the COVID-19 outbreak in Guangzhou, the annual frequency of adenovirus infection detected during this period ranged from 3.92% to 13.58%, with an epidemic peak every four to five years. HAdV demonstrated a clear seasonal distribution, with the lowest positivity in March and peaking during summer (July or August) every year. A significant increase in HAdV cases was recorded for 2018 and 2019, which coincided with a shift in the dominant HAdV subtype from HAdV-3 to HAdV-7. The latter was associated with a more severe disease compared to HAdV-3. The average mortality proportion for children infected with HAdV from 2016 to 2019 was 0.38% but increased to 20% in severe cases. After COVID-19 emerged, HAdV cases dropped to 2.68%, suggesting that non-pharmaceutical interventions probably reduced the transmission of HAdV in the community. CONCLUSION: Our study provides the foundation for the understanding of the epidemiology of HAdV and its associated risks in children in Southern China.

Dynamic Changes of the Blood Chemistry in Syrian Hamsters Post-Acute COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that causes coronavirus disease 2019 (COVID-19). However, the long-term health consequences of COVID-19 are not fully understood. We aimed to determine the long-term lung pathology and blood chemistry changes in Syrian hamsters infected with SARS-CoV-2. Syrian hamsters (Mesocricetus auratus) were inoculated with 105 PFU of SARS-CoV-2, and changes post-infection (pi) were observed for 20 days. On days 5 and 20 pi, the lungs were harvested and processed for pathology and viral load count. Multiple blood samples were collected every 3 to 5 days to observe dynamic changes in blood chemistry. Infected hamsters showed consistent weight loss until day 7 pi At day 5 pi, histopathology of the lungs showed moderate to severe inflammation and the virus could be detected. These results indicate that SARS-CoV-2 has an acute onset and recovery course in the hamster infection model. During the acute onset, blood triglyceride levels increased significantly at day 3 pi During the recovery course, uric acid and low-density lipoprotein levels increased significantly, but the total protein and albumin levels decreased. Together, our study suggests that SARS-CoV-2 infection in hamsters not only causes lung damage but also causes long-term changes in blood biochemistry during the recovery process. IMPORTANCE COVID-19 is now considered a multiorgan disease with a wide range of manifestations. There are increasing reports of persistent and long-term effects after acute COVID-19, but the long-term health consequences of COVID-19 are not fully understood. This study reported for the first time the use of blood samples collected continuously in a SARS-CoV-2-infected hamster model, which provides more information about the dynamic changes in blood biochemistry during the acute and recovery phases of SARS-CoV-2 infection. Our study suggests that SARS-CoV-2 infection in hamsters not only causes lung damage but also causes long-term changes in blood biochemistry during the recovery process. The study may be used by several researchers and clinicians, especially those who are studying potential treatments for patients with post-acute COVID-19 syndrome.

A siRNA targets and inhibits a broad range of SARS-CoV-2 infections including Delta variant.

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants has altered the trajectory of the COVID-19 pandemic and raised some uncertainty on the long-term efficiency of vaccine strategy. The development of new therapeutics against a wide range of SARS-CoV-2 variants is imperative. We, here, have designed an inhalable siRNA, C6G25S, which covers 99.8% of current SARS-CoV-2 variants and is capable of inhibiting dominant strains, including Alpha, Delta, Gamma, and Epsilon, at picomolar ranges of IC50 in vitro. Moreover, C6G25S could completely inhibit the production of infectious virions in lungs by prophylactic treatment, and decrease 96.2% of virions by cotreatment in K18-hACE2-transgenic mice, accompanied by a significant prevention of virus-associated extensive pulmonary alveolar damage, vascular thrombi, and immune cell infiltrations. Our data suggest that C6G25S provides an alternative and effective approach to combating the COVID-19 pandemic.

The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore (preprint)

The decline in NO 2 and PM 2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO 2 and PM 2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO 2 and PM 2.5 baseline in 2020 using machine learning methods. The results found that NO 2 and PM 2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM 2.5 and carpark availability changes at regional scale and the other between NO 2 and taxi availability changes at a spatial resolution of 0.01°. The NO 2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM 2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO 2 in the South and East Coast area can provide insights into future NO 2 reduction policy in Singapore. Graphical Abstract

Seroprevalence of IgM and IgG Antibodies against SARS-CoV-2 in Asymptomatic People in Wuhan: Data from a General Hospital Near South China Seafood Wholesale Market during March to April in 2020.

The aim of this study was to estimate the seroprevalence of immunoglobulin M (IgM) and G (IgG) antibodies against SARS-CoV-2 in asymptomatic people in Wuhan. This was a cross-sectional study, which enrolled 18,712 asymptomatic participants from 154 work units in Wuhan. Pearson Chi-square test, t-test, and Mann-Whitney test were used to compare the standardized seroprevalence of IgG and IgM for age and gender between different groups. The results indicated the standardized seroprevalence of IgG and IgM showed a downward trend and was significantly higher among females than males. Besides, different geographic areas and workplaces had different seroprevalence of IgG among asymptomatic people, and the number of abnormalities in CT imaging were higher in IgG antibody-positive cases than IgG-negative cases. We hope these findings can provide references for herd immunity investigation and provide basis for vaccine development.

Progressive liver injury and increased mortality risk in COVID-19 patients: A retrospective cohort study in China.

BACKGROUND: Liver injury is common and also can be fatal, particularly in severe or critical patients with coronavirus disease 2019 (COVID-19). AIM: To conduct an in-depth investigation into the risk factors for liver injury and into the effective measures to prevent subsequent mortality risk. METHODS: A retrospective cohort study was performed on 440 consecutive patients with relatively severe COVID-19 between January 28 and March 9, 2020 at Tongji Hospital, Wuhan, China. Data on clinical features, laboratory parameters, medications, and prognosis were collected. RESULTS: COVID-19-associated liver injury more frequently occurred in patients aged ≥ 65 years, female patients, or those with other comorbidities, decreased lymphocyte count, or elevated D-dimer or serum ferritin (P < 0.05). The disease severity of COVID-19 was an independent risk factor for liver injury (severe patients: Odds ratio [OR] = 2.86, 95% confidence interval [CI]: 1.78-4.59; critical patients: OR = 13.44, 95%CI: 7.21-25.97). The elevated levels of on-admission aspartate aminotransferase and total bilirubin indicated an increased mortality risk (P < 0.001). Using intravenous nutrition or antibiotics increased the risk of COVID-19-associated liver injury. Hepatoprotective drugs tended to be of assistance to treat the liver injury and improve the prognosis of patients with COVID-19-associated liver injury. CONCLUSION: More intensive monitoring of aspartate aminotransferase or total bilirubin is recommended for COVID-19 patients, especially patients aged ≥ 65 years, female patients, or those with other comorbidities. Drug hepatotoxicity of antibiotics and intravenous nutrition should be alert for COVID-19 patients.