A neonatal mouse model characterizes transmissibility of SARS-CoV-2 variants and reveals a role for ORF8.

Small animal models have been a challenge for the study of SARS-CoV-2 transmission, with most investigators using golden hamsters or ferrets. Mice have the advantages of low cost, wide availability, less regulatory and husbandry challenges, and the existence of a versatile reagent and genetic toolbox. However, adult mice do not robustly transmit SARS-CoV-2. Here we establish a model based on neonatal mice that allows for transmission of clinical SARS-CoV-2 isolates. We characterize tropism, respiratory tract replication and transmission of ancestral WA-1 compared to variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Omicron BA.1 and Omicron BQ.1.1. We identify inter-variant differences in timing and magnitude of infectious particle shedding from index mice, both of which shape transmission to contact mice. Furthermore, we characterize two recombinant SARS-CoV-2 lacking either the ORF6 or ORF8 host antagonists. The removal of ORF8 shifts viral replication towards the lower respiratory tract, resulting in significantly delayed and reduced transmission in our model. Our results demonstrate the potential of our neonatal mouse model to characterize viral and host determinants of SARS-CoV-2 transmission, while revealing a role for an accessory protein in this context.

High-Spatial-Resolution Methane Emissions Calculation Using TROPOMI Data by a Divergence Method

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km ×7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses. METHODS: Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed. RESULTS: A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies. CONCLUSION: S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

SARS-CoV-2 variant spike and accessory gene mutations alter pathogenesis.

The ongoing COVID-19 pandemic is a major public health crisis. Despite the development and deployment of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pandemic persists. The continued spread of the virus is largely driven by the emergence of viral variants, which can evade the current vaccines through mutations in the spike protein. Although these differences in spike are important in terms of transmission and vaccine responses, these variants possess mutations in the other parts of their genome that may also affect pathogenesis. Of particular interest to us are the mutations present in the accessory genes, which have been shown to contribute to pathogenesis in the host through interference with innate immune signaling, among other effects on host machinery. To examine the effects of accessory protein mutations and other nonspike mutations on SARS-CoV-2 pathogenesis, we synthesized both viruses possessing deletions in the accessory genes as well as viruses where the WA-1 spike is replaced by each variant spike gene in a SARS-CoV-2/WA-1 infectious clone. We then characterized the in vitro and in vivo replication of these viruses and compared them to both WA-1 and the full variant viruses. Our work has revealed that the accessory proteins contribute to SARS-CoV-2 pathogenesis and the nonspike mutations in variants can contribute to replication of SARS-CoV-2 and pathogenesis in the host. This work suggests that while spike mutations may enhance receptor binding and entry into cells, mutations in accessory proteins may alter clinical disease presentation.

Antibodies elicited by SARS-CoV-2 infection or mRNA vaccines have reduced neutralizing activity against Beta and Omicron pseudoviruses.

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that have mutations associated with increased transmission and antibody escape have arisen over the course of the current pandemic. Although the current vaccines have largely been effective against past variants, the number of mutations found on the Omicron (B.1.1.529) spike protein appear to diminish the protection conferred by preexisting immunity. Using vesicular stomatitis virus (VSV) pseudoparticles expressing the spike protein of several SARS-CoV-2 variants, we evaluated the magnitude and breadth of the neutralizing antibody response over time in individuals after infection and in mRNA-vaccinated individuals. We observed that boosting increases the magnitude of the antibody response to wild-type (D614), Beta, Delta, and Omicron variants; however, the Omicron variant was the most resistant to neutralization. We further observed that vaccinated healthy adults had robust and broad antibody responses, whereas responses may have been reduced in vaccinated pregnant women, underscoring the importance of learning how to maximize mRNA vaccine responses in pregnant populations. Findings from this study show substantial heterogeneity in the magnitude and breadth of responses after infection and mRNA vaccination and may support the addition of more conserved viral antigens to existing SARS-CoV-2 vaccines.

COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness.

The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario.

Diagnostic model for the society safety under COVID-19 pandemic conditions.

The aim of this paper is to develop an information-modeling method for assessing and predicting the consequences of the COVID-19 pandemic. To this end, a detailed analysis of official statistical information provided by global and national organizations is carried out. The developed method is based on the algorithm of multi-channel big data processing considering the demographic and socio-economic information. COVID-19 data are analyzed using an instability indicator and a system of differential equations that describe the dynamics of four groups of people: susceptible, infected, recovered and dead. Indicators of the global sustainable development in various sectors are considered to analyze COVID-19 data. Stochastic processes induced by COVID-19 are assessed with the instability indicator showing the level of stability of official data and the reduction of the level of uncertainty. It turns out that the number of deaths is rising with the Human Development Index. It is revealed that COVID-19 divides the global population into three groups according to the relationship between Gross Domestic Product and the number of infected people. The prognosis for the number of infected people in December 2020 and January-February 2021 shows negative events which will decrease slowly.

An online coronavirus analysis platform from the National Genomics Data Center.

Since the first reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in December 2019, coronavirus disease 2019 (COVID-19) has become a global pandemic, spreading to more than 200 countries and regions worldwide. With continued research progress and virus detection, SARS-CoV-2 genomes and sequencing data have been reported and accumulated at an unprecedented rate. To meet the need for fast analysis of these genome sequences, the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB) has established an online coronavirus analysis platform, which includes de novoassembly, BLAST alignment, genome annotation, variant identification, and variant annotation modules. The online analysis platform can be freely accessed at the 2019 Novel Coronavirus Resource (2019nCoVR) (https://bigd.big.ac.cn/ncov/online/tools).

Humanistic care and psychological counseling on psychological disorders in medical students after COVID-19 outbreak: A protocol of systematic review.

BACKGROUND: The objective of this study is to investigate the effects of humanistic care and psychological counseling (HCPC) on psychological disorders (PD) in medical students after coronavirus disease 2019 (COVID-19) outbreak. METHODS: We will search randomized controlled trials or case-controlled studies of HCPC on PD in medical students after COVID-19 outbreak in the following electronic databases: PUBMED/MEDLINE, EMBASE, Cochrane Library, CINAHL, AMED, WANGFANG, and CNKI. The time is restricted from the construction of each database to the present. All process of study selection, data collection, and study quality evaluation will be carried out by two independent authors. Any different opinions will be solved by a third author through discussion. We will employ RevMan 5.3 software to conduct statistical analysis. RESULTS: This study will provide a better understanding of HCPC on PD in medical students after COVID-19 outbreak. CONCLUSIONS: This study may offer strong evidence for clinical practice to treat PD in medical students after COVID-19 outbreak. STUDY REGISTRATION: CRD42020193199.

A comparative-descriptive analysis of clinical characteristics in 2019-coronavirus-infected children and adults.

Acute respiratory disease caused by 2019 novel coronavirus (2019-nCoV) has rapidly spread throughout China. Children and adults show a different clinical course. The purpose of the current study is to comparatively analyze the clinical characteristics of 2019-nCoV infection in children and adults and to explore the possible causes for the discrepancies present. The medical records of 25 adults and 7 children confirmed cases of 2019-2019-nCoV acute respiratory diseases were reviewed retrospectively. All children were family clusters. The total adult patients were differentiated into the local residents of Wuhan, a history of travel to Wuhan and direct contact with people from Wuhan. The numbers were 14 (56%), 10 (40%), and 1 (4%), respectively. The median incubation period of children and adults was 5 days (ranged, 3-12 days) and 4 days (ranged, 2-12 days), respectively. Diarrhoea and/or vomiting (57.1%) were demic by World Health Organiza more common in children, whereas for adults it was myalgia or fatigue (52%). On admission, the percentage of children having pneumonia (5%, 71.4%) was roughly the same as adults (20%, 80%). A total of 20% of adults had leucopoenia, but leukocytosis was more frequently in children (28.6%, P=.014). A higher number of children had elevated creatine kinase isoenzyme (57.1% vs 4%, P=.004). Antiviral therapy was given to all adult patients but to none of the children. In summary, knowledge of these differences between children and adults will not only be helpful for the clinical diagnosis of 2019-nCoV disease, but also for a future discussion on age-specific coronavirus infection.

The 2019 novel coronavirus resource.

An ongoing outbreak of a novel coronavirus infection in Wuhan, China since December 2019 has led to 31,516 infected persons and 638 deaths across 25 countries (till 16:00 on February 7, 2020). The virus causing this pneumonia was then named as the 2019 novel coronavirus (2019-nCoV) by the World Health Organization. To promote the data sharing and make all relevant information of 2019-nCoV publicly available, we construct the 2019 Novel Coronavirus Resource (2019nCoVR, https://bigd.big.ac.cn/ncov). 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information, China National GeneBank, National Microbiology Data Center and China National Center for Bioinformation (CNCB)/National Genomics Data Center (NGDC). It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains. Moreover, by linking seamlessly with related databases in CNCB/NGDC, 2019nCoVR offers virus data submission and sharing services for raw sequence reads and assembled sequences. In this report, we provide comprehensive descriptions on data deposition, management, release and utility in 2019nCoVR, laying important foundations in aid of studies on virus classification and origin, genome variation and evolution, fast detection, drug development and pneumonia precision prevention and therapy.