Brain functional network changes associated with psychological symptoms in emergency psychological responding professionals after the first wave of COVID-19 pandemic.

Background: Emergency psychological responding professionals are recruited to help deal with psychological issues as the Corona Virus Disease 2019 (COVID-19) continues. We aimed to study the neural correlates of psychological states in these emergency psychological responding professionals after exposure to COVID-19 related trauma at baseline and after 1-year self-adjustment. Methods: Resting-state functional MRI (rs-fMRI) and multiscale network approaches were utilized to evaluate the functional brain activities in emergency psychological professionals after trauma. Temporal (baseline vs. follow-up) and cross-sectional (emergency psychological professionals vs. healthy controls) differences were studied using appropriate t-tests. The brain functional network correlates of psychological symptoms were explored. Results: At either time-point, significant changes in the ventral attention (VEN) and the default mode network (DMN) were associated with psychological symptoms in emergency psychological professionals. In addition, the emergency psychological professionals whose mental states improved after 1 year demonstrated altered intermodular connectivity strength between several modules in the functional network, mainly linking the DMN, VEN, limbic, and frontoparietal control modules. Conclusion: Brain functional network alterations and their longitudinal changes varied across groups of EPRT with distinctive clinical features. Exposure to emergent trauma does cause psychological professionals to produce DMN and VEN network changes related to psychological symptoms. About 65% of them will gradually adjust mental states, and the network tends to be rebalanced after a year.

MHC class I links with severe pathogenicity in C57BL/6N mice infected with SARS-CoV-2/BMA8.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes non-symptomatic infection, mild influenza-like symptoms to pneumonia, severe acute respiratory distress syndrome, and even death, reflecting different clinical symptoms of viral infection. However, the mechanism of its pathogenicity remains unclear. Host-specific traits have a breakthrough significance for studying the pathogenicity of SARS-CoV-2. We previously reported SARS-CoV-2/BMA8, a mouse-adapted strain, was lethal to aged BALB/c mice but not to aged C57BL/6N mice. Here, we further investigate the differences in pathogenicity of BMA8 strain against wild-type aged C57BL/6N and BALB/c mice. METHODS: Whole blood and tissues were collected from mice before and after BMA8 strain infection. Viral replication and infectivity were assessed by detection of viral RNA copies and viral titers; the degree of inflammation in mice was tested by whole blood cell count, ELISA and RT-qPCR assays; the pathogenicity of SARS-CoV-2/BMA8 in mice was measured by Histopathology and Immunohistochemistry; and the immune level of mice was evaluated by flow cytometry to detect the number of CD8+ T cells. RESULTS: Our results suggest that SARS-CoV-2/BMA8 strain caused lower pathogenicity and inflammation level in C57BL/6N mice than in BALB/c mice. Interestingly, BALB/c mice whose MHC class I haplotype is H-2Kd showed more severe pathogenicity after infection with BMA8 strain, while blockade of H-2Kb in C57BL/6N mice was also able to cause this phenomenon. Furthermore, H-2Kb inhibition increased the expression of cytokines/chemokines and accelerated the decrease of CD8+ T cells caused by SARS-CoV-2/BMA8 infection. CONCLUSIONS: Taken together, our work shows that host MHC molecules play a crucial role in the pathogenicity differences of SARS-CoV-2/BMA8 infection. This provides a more profound insight into the pathogenesis of SARS-CoV-2, and contributes enlightenment and guidance for controlling the virus spread.

Associations between nighttime light and COVID-19 incidence and mortality in the United States.

COVID-19 has caused almost 770,000 deaths in the United States by November 2021. The nighttime light (NTL), representing the intensity of human activities, may reflect the degree of human contacts and therefore the intensity of COVID-19 transmission. This study intended to assess the associations between NTL differences and COVID-19 incidence and mortality among U.S. counties. The COVID-19 data of U.S. counties as of 31 December 2020 were collected. The average NTL values for each county in 2019 and 2020 were derived from satellite data. A negative binomial mixed model was adopted to assess the relationships between NTL intensity and COVID-19 incidence and mortality. Compared to the counties with the lowest NTL level (0.14-0.37 nW/cm2/sr), those with the highest NTL level (1.78-59.61 nW/cm2/sr) were related with 15% higher mortality rates (mortality rate ratio:1.15, 95 %CI: 1.02-1.30, p-value: 0.02) and 23% higher incidence rates (incidence rate ratio:1.23, 95 %CI: 1.13-1.34, p-value < 0.0001). Our study suggested that more intensive NTL was related with higher incidence and mortality rates of COVID-19, and NTL had a stronger correlation with the COVID-19 incidence rate than mortality rate. Our findings have contributed solid epidemiological evidence to the existing COVID-19 knowledge pool, and would help policymakers develop interventions when faced with the potential risk of the following outbreaks.

An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US.

The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments' decision-making.

Long-term stability and protection efficacy of the RBD-targeting COVID-19 mRNA vaccine in nonhuman primates.

Messenger RNA (mRNA) vaccine technology has shown its power in preventing the ongoing COVID-19 pandemic. Two mRNA vaccines targeting the full-length S protein of SARS-CoV-2 have been authorized for emergency use. Recently, we have developed a lipid nanoparticle-encapsulated mRNA (mRNA-LNP) encoding the receptor-binding domain (RBD) of SARS-CoV-2 (termed ARCoV), which confers complete protection in mouse model. Herein, we further characterized the protection efficacy of ARCoV in nonhuman primates and the long-term stability under normal refrigerator temperature. Intramuscular immunization of two doses of ARCoV elicited robust neutralizing antibodies as well as cellular response against SARS-CoV-2 in cynomolgus macaques. More importantly, ARCoV vaccination in macaques significantly protected animals from acute lung lesions caused by SARS-CoV-2, and viral replication in lungs and secretion in nasal swabs were completely cleared in all animals immunized with low or high doses of ARCoV. No evidence of antibody-dependent enhancement of infection was observed throughout the study. Finally, extensive stability assays showed that ARCoV can be stored at 2-8 °C for at least 6 months without decrease of immunogenicity. All these promising results strongly support the ongoing clinical trial.

Differential immune responses in pregnant patients recovered from COVID-19.

Pregnant women are generally more susceptible to viral infection. Although the impact of SARS-CoV-2 in pregnancy remains to be determined, evidence indicates that the risk factors for severe COVID-19 are similar in pregnancy to the general population. Here we systemically analyzed the clinical characteristics of pregnant and non-pregnant female COVID-19 patients who were hospitalized during the same period and found that pregnant patients developed marked lymphopenia and higher inflammation evident by higher C-reactive protein and IL-6. To elucidate the pathways that might contribute to immunopathology or protective immunity against COVID-19 during pregnancy, we applied single-cell mRNA sequencing to profile peripheral blood mononuclear cells from four pregnant and six non-pregnant female patients after recovery along with four pregnant and three non-pregnant healthy donors. We found normal clonal expansion of T cells in the pregnant patients, heightened activation and chemotaxis in NK, NKT, and MAIT cells, and differential interferon responses in the monocyte compartment. Our data present a unique feature in both innate and adaptive immune responses in pregnant patients recovered from COVID-19.

In silico identification of available drugs targeting cell surface BiP to disrupt SARS-CoV-2 binding and replication: Drug repurposing approach.

AIMS: Cell surface binding immunoglobin protein (csBiP) is predicted to be susceptible to SARS-CoV-2 binding. With a substrate-binding domain (SBD) that binds to polypeptides and a nucleotide-binding domain (NBD) that can initiate extrinsic caspase-dependent apoptosis, csBiP may be a promising therapeutic target for COVID-19. This study aims to identify FDA-approved drugs that can neutralize viral binding and prevent viral replication by targeting the functional domains of csBiP. METHODS: In silico screening of 1999 FDA-approved drugs against the functional domains of BiP were performed using three molecular docking programs to avoid bias from individual docking programs. Top ligands were selected by averaging the ligand rankings from three programs. Interactions between top ligands and functional domains of BiP were analyzed. KEY FINDINGS: The top 10 SBD-binding candidates are velpatasvir, irinotecan, netupitant, lapatinib, doramectin, conivaptan, fenoverine, duvelisib, irbesartan, and pazopanib. The top 10 NBD-binding candidates are nilotinib, eltrombopag, grapiprant, topotecan, acetohexamide, vemurafenib, paritaprevir, pixantrone, azosemide, and piperaquine-phosphate. Among them, Velpatasvir and paritaprevir are antiviral agents that target the protease of hepatitis C virus. Netupitant is an anti-inflammatory drug that inhibits neurokinin-1 receptor, which contributes to acute inflammation. Grapiprant is an anti-inflammatory drug that inhibits the prostaglandin E2 receptor protein subtype 4, which is expressed on immune cells and triggers inflammation. These predicted SBD-binding drugs could disrupt SARS-CoV-2 binding to csBiP, and NBD-binding drugs may falter viral attachment and replication by locking the SBD in closed conformation and triggering apoptosis in infected cells. SIGNIFICANCE: csBiP appears to be a novel therapeutic target against COVID-19 by preventing viral attachment and replication. These identified drugs could be repurposed to treat COVID-19 patients.

Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response.

The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (Δ500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-ß levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-ß responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.

Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model

As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model;then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.

Pregnant women with COVID-19 and risk of adverse birth outcomes and maternal-fetal vertical transmission: a population-based cohort study in Wuhan, China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. However, little is known about the association between pregnant women with COVID-19 and the risk of adverse birth outcomes. METHOD: We conducted a retrospective cohort study based on the Maternal and Child Health Information System (MCHIMS) of Wuhan, China. All pregnant women with singleton live birth recorded by the system between January 13 and March 18, 2020, were included. The adverse birth outcomes were preterm birth, low birth weight, neonatal asphyxia, premature rupture of membrane (PROM), and cesarean section delivery. Multivariate logistic regression was used to evaluate the associations between maternal COVID-19 diagnosis and adverse birth outcomes. RESULTS: Out of 11,078 pregnant women, 65 were confirmed with coronavirus disease 2019 (COVID-19). No deaths occurred from these confirmed cases or their newborns. Compared to pregnant women without COVID-19, pregnant women with a confirmed COVID-19 diagnosis had an increased risk of preterm birth (OR 3.34, 95% CI 1.60-7.00) and cesarean section (OR 3.63, 95% CI 1.95-6.76). There was no statistical difference in low birth weight, neonatal asphyxia, and PROM between the mothers with and without COVID-19. Among these newborns that were born to mothers with confirmed COVID-19, none was tested severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive or had abnormal CT results. Only one had diarrhea and three had a fever. CONCLUSIONS: This population-based cohort study suggests that COVID-19 during the later pregnancy is associated with an increased risk of adverse birth outcomes, including iatrogenic preterm birth and cesarean section delivery. Our data provide little evidence for maternal-fetal vertical transmission of SARS-CoV-2. It is important to monitor the long-term health effects of SARS-CoV-2 infection on pregnant women and their children.

The changes of the peripheral CD4+ lymphocytes and inflammatory cytokines in Patients with COVID-19.

To investigate the clinical value of changes in the subtypes of peripheral blood lymphocytes and levels of inflammatory cytokines in patients with COVID-19, the total numbers of lymphocytes and CD4+ lymphocytes and the ratio of CD4+/CD8+ lymphocytes were calculated and observed in different groups of patients with COVID-19. The results show that the lymphocytopenia in patients with COVID-19 was mainly manifested by decreases in the CD4+ T lymphocyte number and the CD4+/CD8+ ratio. The decreased number of CD4+ T lymphocytes and the elevated levels of TNF-α and IL-6 were correlated with the severity of COVID-19 disease.

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.

COVID-19 in a designated infectious diseases hospital outside Hubei Province, China.

BACKGROUND: The clinical characteristics of novel coronavirus disease (COVID-2019) patients outside the epicenter of Hubei Province are less understood. METHODS: We analyzed the epidemiological and clinical features of all COVID-2019 cases in the only referral hospital in Shenzhen City, China, from January 11, 2020, to February 6, 2020, and followed until March 6, 2020. RESULTS: Among the 298 confirmed cases, 233 (81.5%) had been to Hubei, while 42 (14%) did not have a clear travel history. Only 218 (73.15%) cases had a fever as the initial symptom. Compared with the nonsevere cases, severe cases were associated with older age, those with underlying diseases, and higher levels of C-reactive protein, interleukin-6, and erythrocyte sedimentation rate. Slower clearance of the virus was associated with a higher risk of progression to critical condition. As of March 6, 2020, 268 (89.9%) patients were discharged and the overall case fatality ratio was 1.0%. CONCLUSIONS: In a designated hospital outside Hubei Province, COVID-2019 patients could be effectively managed by properly using the existing hospital system. Mortality may be lowered when cases are relatively mild, and there are sufficient medical resources to care and treat the disease.