Inside-out: Antibody-binding reveals potential folding hinge-points within the SARS-CoV-2 replication co-factor nsp9.

Nsp9 is a conserved accessory component of the coronaviral replication and transcription complex. It is the predominant substrate of nsp12's nucleotidylation activity while also serving to recruit proteins required for viral 5'-capping. Anti-nsp9 specific nanobodies have been isolated previously. We confirm that their binding mode is centred upon Trp-53 within SARS-CoV-2 nsp9. Antibody binding at this site surprisingly results in large-scale changes to the overall topology of this coronaviral unique fold. We further characterise the antibody-induced structural dynamism within nsp9, identifying a number of potentially flexible regions. A large expansion of the cavity between the s2-s3 and s4-s5 loops is particularly noteworthy. As is the potential for large-scale movements in the C-terminal GxxxG helix.

Characterization of the enhanced infectivity and antibody evasion of Omicron BA.2.75

Recently emerged SARS-CoV-2 Omicron subvariant, BA.2.75, displayed a growth advantage over circulating BA.2.38, BA.2.76 and BA.5 in India. However, the underlying mechanisms for enhanced infectivity, especially compared to BA.5, remain unclear. Here we show BA.2.75 exhibits substantially higher affinity for host receptor ACE2 than BA.5 and other variants. Structural analyses of BA.2.75 Spike shows its decreased thermostability and increased frequency of the receptor binding domain (RBD) in the “up” conformation under acidic conditions, suggesting enhanced low-pH-endosomal cell entry. Relative to BA.4/BA.5, BA.2.75 exhibits reduced evasion of humoral immunity from BA.1/BA.2 breakthrough-infection convalescent plasma, but greater evasion of Delta breakthrough-infection convalescent plasma. BA.5 breakthrough infection plasma also exhibits weaker neutralization against BA.2.75 than BA.5, mainly due to BA.2.75’s distinct neutralizing antibody escape pattern. Antibody therapeutics Evusheld and Bebtelovimab remain effective against BA.2.75. These results suggest BA.2.75 may prevail after BA.4/BA.5, and its increased receptor-binding capability could support further immune-evasive mutations. Graphical SARS-CoV-2 BA.2.75 is growing rapidly and globally. Cao et al. solved the structure of BA.2.75 spike and show it has stronger binding to human ACE2 than previous variants. BA.2.75 also exhibited distinct antigenicity compared to BA.5, escaping neutralizing antibodies targeting various epitopes and evading convalescent plasma from BA.5 breakthrough infections.

Impact analysis of COVID-19 pandemic control measures on nighttime light and air quality in cities.

The COVID-19 pandemic has profoundly affected human society on a global scale. COVID-19 pandemic control measures have led to significant changes in nighttime light (NTL) and air quality. Four cities that were severely impacted by the pandemic and that implemented different pandemic control measures, namely, Wuhan (China), Delhi (India), New York (United States), and Rome (Italy), were selected as study areas. The Visible Infrared Imaging Radiometer Suite (VIIRS) and air quality data were used to study the variation characteristics of NTL and air quality in the four cities in 2020. NTL brightness in Wuhan, Delhi, New York, and Rome decreased by 8.88%, 17.18%, 8.21%, and 6.33%, respectively, compared with pre-pandemic levels; in the resumption phase Wuhan and Rome NTL brightness recovered by 13.74% and 3.38%, but Delhi and New York decreased by 16.23% and 4.99%. Nitrogen dioxide (NO2) concentrations in the lockdown periods of Wuhan, Delhi, New York, and Rome decreased by 65.07%, 68.75%, 55.59%, and 56.81%, respectively; PM2.5 decreased by 49.25%, 69.40%, 52.54%, and 66.67%. Air quality improved, but ozone (O3) concentrations increased significantly during the lockdown periods. The methods presented herein can be used to investigate the impact of pandemic control measures on urban lights and air quality.

Mechanism of High-Tech Enterprises' Technological Practices Affected by the Split Fault of Knowledge Innovation Network.

Knowledge innovation ability is the source of value realization of high-tech enterprises, and the acquisition of high-value knowledge is important. Taking knowledge as the intermediary variable, knowledge field activity and knowledge fermentation as mediating variables, and knowledge mobilization and knowledge network position transition as moderating variables, the conceptual model and theoretical analysis framework of the impact mechanism of knowledge innovation network fragmentation fault on technology practices is constructed and the moderated mediating effect model is derived. Taking high-tech enterprises as empirical samples, 538 valid questionnaires were obtained online and offline and the nonpercentile bootstrap method based on deviation correction was used to empirically investigate the influence mechanism and transmission path of knowledge innovation network fragmentation fault on high-tech enterprises' technological practices. The empirical results show that the main effect of knowledge innovation network fragmentation fault on high-tech enterprise technology practices is significant. Knowledge field activity and knowledge fermentation play a differential mediating role in knowledge innovation network split fault and Technology Convention. Knowledge field activity and knowledge fermentation play a partial mediating role in knowledge innovation network split fault and Technology Convention. Knowledge mobilization partially moderates the split fault of knowledge innovation network and technological practices. Knowledge mobilization positively moderates the positive effect of split fault of knowledge innovation network on technological practices and significantly positively moderates the mediating effect of knowledge field activity and knowledge fermentation, resulting in the moderated mediating effect. Knowledge network location transition plays a part of moderating role in knowledge innovation network split fault and Technology Convention. Knowledge network location transition positively moderates the positive impact of knowledge innovation network split fault on Technology Convention and significantly positively moderates the mediating role of knowledge field activity and knowledge fermentation, resulting in a moderated mediating effect. Knowledge innovation network split fault, knowledge field activity and knowledge fermentation, knowledge mobilization and knowledge network location transition, and the combination of technological practices can be the antecedents of promoting technological practices in high-tech enterprises. Through the research on the mechanism of knowledge innovation network split fault in the technological practices of high-tech enterprises, the connotation of knowledge innovation network split fault is enriched, the influencing factors of technological practices are clarified, and the value-added knowledge is promoted and has guiding and reference significance for the innovation knowledge acquisition and competitiveness improvement of high-tech enterprises.

Predictors of COVID-19 Fatality: A Worldwide Analysis of the Pandemic over Time and in Latin America.

SARS-CoV-2 has infected over one hundred million people worldwide and has affected Latin America particularly severely in terms of both cases and deaths. This study aims to determine the association between SARS-CoV-2 testing and COVID-19 fatality rate worldwide over 8 months and to examine how this relationship differs between Latin America and all other countries. This cross-sectional study used March 2021 data from 169 countries. Multivariate regressions predicted COVID-19 fatality (outcome) from the number of SARS-CoV-2 tests (exposure), while controlling for other predictors. Results for March 2021 were compared to results from June 2020. Additionally, results for Latin America were also compared to all other countries except Latin American for March 2021. SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in both June 2020 and March 2021 (RR = 0.92; 95% CI 0.87-0.96 and RR = 0.86; 95% CI 0.74-1.00, respectively). SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in Latin American countries but not in all other countries (RR = 0.45; 95% CI 0.23-0.89 and RR = 0.95; 95% CI 0.82-1.11, respectively). However, the difference between the risk ratios for June 2020 and March 2021 and between the risk ratios for Latin America and all other countries were not statistically significant. Increased SARS-CoV-2 testing may be a significant predictor of lower COVID-19 case fatality rate, specifically in Latin American countries, due to the existence of a strong association, which may have driven the worldwide results.

Psychological Antecedents and Consequences of Social Integration Based on Self-Disclosure in Virtual Communities: Empirical Evidence From Sina Microblog.

INTRODUCTION: With the normalization of COVID-19 prevention and control, a large number of intergenerational audiences with different cognition preferences and value orientations have started to pour into non-acquaintance virtual communities (VCs) to address their social needs by disclosing their own thoughts, feelings and experiences toward certain topics. To avoid the negative impacts of self-disclosure, this study introduced the concept of social integration into cyber society among non-acquaintance VCs, such as the topic-based VCs. Our theoretical model considers both the psychological antecedents and consequences of VC audiences' social integration and our findings have implications for public online (and even offline) social life. Moreover, this research could play a guiding role in improving VC audiences' social integration status in future online learning and telecommuting scenarios. METHOD: To assess the theoretical model constructed in this manuscript, we conducted an online survey in two different topic-based VCs among Microblog and yielded 472 useable responses from intergenerational audiences, among which 28.81% were born before 1985, 26.67% were born from 1985 to 1995, and 48.52% were born after 1995. Our sample consisted of 208 individuals from Health Regimen VC and 264 individuals from Star Chasing VC, 200 (42.37%) were men and 272 (57.63%) were women. RESULTS: Our structural equation model (SEM) indicated that individuals' self-disclosure in topic-based VCs might not directly guide them to acquire social integration. However, intimacy and cognitive communion derived from VC audiences' self-disclosure might not only enhance their social integration, but also improve their psychological well-being. In addition, VC audiences' social integration mediated the relationship between intimacy and psychological well-being, and the relationship between cognitive communion and psychological well-being. Moreover, VC audiences' intimacy was found to have a direct influence on their cognitive communion. CONCLUSION: In the context of topic-based VCs, audiences' self-disclosure could significantly foster their intimacy and cognitive communion with others, and both intimacy and cognitive communion are conductive to VC audiences' social integration. Thus, audiences in topic-based VCs who wish to improve their psychological well-being need to disclose themselves and build corresponding psychological foundations (i.e., intimacy and cognitive communion) to enhance their social integration. Meanwhile, topic-based VCs should pay attention to the cultivation of intimacy and cognitive communion among audiences while encouraging them to reveal themselves.

A Prediction Model for High Risk of Positive RT-PCR Test Results in COVID-19 Patients Discharged From Wuhan Leishenshan Hospital, China.

Several recent studies have reported that a few patients had positive SARS-CoV-2 RNA tests after hospital discharge. The high-risk factors associated with these patients remain to be identified. A total of 463 patients with COVID-19 discharged from Leishenshan Hospital in Wuhan, China, between February 8 and March 8, 2020 were initially enrolled, and 351 patients with at least 2 weeks of follow-up were finally included. Seventeen of the 351 discharged patients had positive tests for SARS-CoV-2 RNA. Based on clinical characteristics and mathematical modeling, patients with shorter hospital stays and less oxygen desaturation were at higher risk of SARS-CoV-2 RNA reoccurrence after discharge. Notably, traditional Chinese medicine treatment offered extensive benefits to reduce risk. Particular attention should be paid to those patients with high risk, and traditional Chinese medicine should be advocated.

A Qualitative Exploration of the Psychological Experience of Patients Hospitalized With COVID-19.

This qualitative study describes the psychological experience of patients hospitalized with COVID-19. These patients went through 3 psychological stages: extremely uncertainties during the initial diagnostic stage, complicated feelings of negativity during the treatment stage, and positive growth in the recovery stage. It is important for nurses to provide holistic care.

Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States.

A novel approach combining time series analysis and complex network theory is proposed to deeply explore characteristics of the COVID-19 pandemic in some parts of the United States (US). It merges as a new way to provide a systematic view and complementary information of COVID-19 progression in the US, enabling evidence-based responses towards pandemic intervention and prevention. To begin with, the Principal Component Analysis (PCA) varimax is adopted to fuse observed time-series data about the pandemic evolution in each state across the US. Then, relationships between the pandemic progress of two individual states are measured by different synchrony metrics, which can then be mapped into networks under unique topological characteristics. Lastly, the hidden knowledge in the established networks can be revealed from different perspectives by network structure measurement, community detection, and online random forest, which helps to inform data-driven decisions for battling the pandemic. It has been found that states gathered in the same community by diffusion entropy reducer (DER) are prone to be geographically close and share a similar pattern and tendency of COVID-19 evolution. Social factors regarding the political party, Gross Domestic Product (GDP), and population density are possible to be significantly associated with the two detected communities within a constructed network. Moreover, the cluster-specific predictor based on online random forest and sliding window is proven useful in dynamically capturing and predicting the epidemiological trends for each community, which can reach the highest.

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia.

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.

In this study, a hybrid deep-learning model termed as ODANN, built upon neural networks (NN) coupled with data assimilation and natural language processing (NLP) features extraction methods, has been constructed to concurrently process daily COVID-19 time-series records and large volumes of COVID-19 related Twitter data, as representative of the global community's aggregated emotional responses towards the current pandemic, to model the growth rate in the number of confirmed COVID-19 cases globally via a proposed G parameter. Overall, there were 3 key components to ODANN's development phase, namely: (i) data hydration and pre-processing were performed on COVID-19 related Twitter data ranging between 23 January 2020 and 10 May 2020, which amounted to over 100 million Tweets written in English language; (ii) multiple NLP features extraction methods were subsequently leveraged to encode the hydrated Twitter data into useful semantic word vectors for training ODANN under an optimal set of hyperparameters; and (iii) historical time-series data of defined characteristics were also assimilated into ODANN's selected hidden layer(s) to model the G parameter daily with a lead-time of 1 day. By far, our experimental results demonstrated that by adopting a rolling time-window size of 5 days, with respect to the number of historical time-series records for assimilating different data features, enabled ODANN to outperform other traditional time-series models and recent studies, in terms of the computed RMSE and MAE scores attained from the model's testing step. Overall, the summarized results from ODANN demonstrated its competitive edge in modelling and forecasting the growth rate in the number of COVID-19 cases globally.

Daily Microaggressions and Related Distress among Black Women Living with HIV during the Onset of the COVID-19 Pandemic and Black Lives Matter Protests.

Black women living with HIV (BWLWH) in the U.S. face microaggressions based on race, gender, HIV-status, and sexual orientation. We examined changes in daily microaggressions and related distress among 143 BWLWH in Miami, FL. Microaggression-related distress increased from 52% at baseline/October, peaked at 70% during the holidays (November/December), declined to 55% in March when COVID-19 social distancing began, and peaked to 83% in June/July 2020 during widespread Black Lives Matters protests. Baseline viral suppression was associated with lower microaggressions across the 9-months. Microaggression-related distress may change due to social context and research is needed on microaggressions and viral load overtime.

RESUMEN: Las mujeres de raza negra que viven con el VIH (MNVV) en los EE. UU. enfrentan microagresiones basadas en la raza, el género, el estado serológico del VIH y la orientación sexual. Examinamos los cambios en las microagresiones diarias y el estrés relacionado entre 143 MNVV en Miami, FL. El estrés relacionado con la microagresión aumentó del 52% en la línea de base/octubre, alcanzó un máximo del 70% durante las vacaciones (noviembre/diciembre), disminuyó al 55% en marzo cuando comenzó el distanciamiento social por el COVID-19 y alcanzó un máximo del 83% en junio/julio de 2020 durante las protestas generalizadas de Black Lives Matters. La supresión viral inicial se asoció con menores microagresiones durante los 9 meses. El estrés relacionada con la microagresión puede cambiar debido al contexto social y se necesitan investigaciones sobre las microagresiones y la carga viral con el tiempo.

Thymosin Alpha 1 Reduces the Mortality of Severe Coronavirus Disease 2019 by Restoration of Lymphocytopenia and Reversion of Exhausted T Cells.

BACKGROUND: Thymosin alpha 1 (Tα1) had been used in the treatment of viral infections as an immune response modifier for many years. However, clinical benefits and the mechanism of Tα1 treatment for COVID-19 patients are still unclear. METHODS: We retrospectively reviewed the clinical outcomes of 76 severe COVID-19 cases admitted to 2 hospitals in Wuhan, China, from December 2019 to March 2020. The thymus output in peripheral blood mononuclear cells from COVID-19 patients was measured by T-cell receptor excision circles (TRECs). The levels of T-cell exhaustion markers programmed death-1 (PD-1) and T-cell immunoglobulin and mucin domain protein 3 (Tim-3) on CD8+ T cells were detected by flow cytometry. RESULTS: Compared with the untreated group, Tα1 treatment significantly reduced the mortality of severe COVID-19 patients (11.11% vs 30.00%, P = .044). Tα1 enhanced blood T-cell numbers in COVID-19 patients with severe lymphocytopenia. Under such conditions, Tα1 also successfully restored CD8+ and CD4+ T-cell numbers in elderly patients. Meanwhile, Tα1 reduced PD-1 and Tim-3 expression on CD8+ T cells from severe COVID-19 patients compared with untreated cases. It is of note that restoration of lymphocytopenia and acute exhaustion of T cells were roughly parallel to the rise of TRECs. CONCLUSIONS: Tα1 treatment significantly reduced mortality of severe COVID-19 patients. COVID-19 patients with counts of CD8+ T cells or CD4+ T cells in circulation less than 400/µL or 650/µL, respectively, gained more benefits from Tα1. Tα1 reversed T-cell exhaustion and recovered immune reconstitution through promoting thymus output during severe acute respiratory syndrome-coronavirus 2 infection.

Clinical Features and Short-Term Outcomes in COVID-19-Infected Patients with Cancer.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an infectious disease that has been spreading very fast worldwide. Up to now, there is scarce information regarding the clinical features and short-term outcomes of infected patients with cancer. METHODS: We performed a retrospective study in Wuhan Union Hospital from Feb 14, 2020, to Mar 15, 2020, China. Data were retrieved including demographic and clinical features, laboratory findings, and outcome data. Patients were classified into the discharged group and undischarged group by the 4-week outcomes from admission. Difference analysis and correlation analysis were performed between the two groups. RESULTS: A total of 37 patients were enrolled in the study, including 27 cancer survivors in routine follow-up. Breast cancer (18.9%) was the most frequent cancer type, and common symptoms included cough (54.1%), fever (48.6%), and fatigue (27%). Lymphocytopenia and hypoproteinemia were much frequent in patients who had received chemotherapy, radiotherapy, or surgery within the past month. However, the concentration of D-dimer (median: 3.75 vs 0.43, P =0.010) and fibrin degradation products (median: 23.60 vs 1.80, P =0.002) were evidently increased in this population compared with cancer survivors. At the end of follow-up, 83.8% of the enrolled patients were discharged. Among the discharged, women (48.6%) and cancer survivors (67.6%) showed better short-term outcomes. The elevated level of FDP was significantly higher in the undischarged group (median: 21.85 vs 2.00, P =0.049). The proportion of CD3-positive lymphocyte cells and CD4-positive lymphocytes was correlated with short-term outcomes. CONCLUSION: Peripheral lymphocyte subset (CD3-positive and CD4-positive) on admission as a novel biomarker had a potential association with early efficacy. Cancer survivors in routine follow-up would achieve better short-term outcomes. COVID-19 patients with cancer should gain more attention and close monitoring.

Impact of renin-angiotensin system inhibitors use on mortality in severe COVID-19 patients with hypertension: a retrospective observational study.

OBJECTIVE: Association of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) use with coronavirus disease 2019 (COVID-19) remains controversial. We aimed to investigate the impact of ACEI/ARB use on all-cause mortality in severe COVID-19 patients with hypertension. METHODS: We enrolled 650 COVID-19 patients from Changsha and Wuhan city between 17 January 2020 and 8 March 2020. Demographic, clinical characteristics, and outcomes were collected. Multivariable analysis and propensity-score matching were performed to assess the impact of ACEI/ARB therapy on mortality. RESULTS: Among the 650 patients, 126 who had severe COVID-19 concomitant with hypertension were analyzed. The average age was 66 years and 56 (44.4%) were men. There were 37 ACEI/ARB users and 21 in-hospital deaths (mortality rate, 16.7%). Male sex (odds ratio [OR], 5.13; 95% confidence interval [CI], 1.75 to 17.8), but not ACEI/ARB use (OR, 1.09; 95%CI, 0.31 to 3.43), was an independent risk factor for mortality in severe COVID-19 patients with hypertension. After propensity-score matching, 60 severe COVID-19 patients were included and no significant correlation between use of ACEI/ARB and mortality was observed. CONCLUSIONS: There was no significant association of ACEI/ARB use with mortality in severe COVID-19 patients with hypertension. These findings support the continuation of ACEI/ARB therapy for such patients.

Obstetric Management of COVID-19 in Pregnant Women.

The 2019 novel coronavirus disease (COVID-19), which is caused by the novel beta coronavirus, SARS-CoV-2, is currently prevalent all over the world, causing thousands of deaths with relatively high virulence. Like two other notable beta coronaviruses, severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) and Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV-2 can lead to severe contagious respiratory disease. Due to impaired cellular immunity and physiological changes, pregnant women are susceptible to respiratory disease and are more likely to develop severe pneumonia. Given the prevalence of COVID-19, it is speculated that some pregnant women have already been infected. However, limited data are available for the clinical course and management of COVID-19 in pregnancy. Therefore, we conducted this review to identify strategies for the obstetric management of COVID-19. We compared the clinical course and outcomes of COVID-19, SARS, and MERS in pregnancy and discussed several drugs for the treatment of COVID-19 in pregnancy.