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MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

ProteinS (1188)

ProteinN (352)

NSP5 (289)

ComplexRdRp (165)

ProteinE (88)


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SARS-CoV-2 Proteins
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    COVID-19 MESHD- Related Stigma in COVID-19 MESHD Survivors in Kampala, Uganda: A Qualitative Study

    Authors: Kabunga Amir

    doi:10.21203/rs.3.rs-150583/v1 Date: 2021-01-19 Source: ResearchSquare

    Background: COVID-19 MESHD-related stigma is gradually becoming a global problem among COVID-19 MESHD survivors with far-reaching implications. However, this social problem has received little attention in research and policy. This study aimed at exploring the COVID-19 MESHD-related stigma survivors in Kampala, UgandaMethods: A cross-sectional exploratory research design was used. COVID-19 MESHD survivors in Kampala district part of the study. In-depth interviews were used to collect data and analysis was done using thematic approach. Results: The results from the data showed that COVID-19 MESHD-related stigma is prevalent and the common form of stigma was social rejection. Conclusions: The majority of the respondent in the sample endorsed COVID-19 MESHD-related stigma and such behaviors were high in the community. The COVID-19 pandemic MESHD survivors indicated that they faced social rejection and community ostracism. Thus reducing COVID-19 MESHD-related stigma is vital to control the spread of the virus. An all-inclusive effort is needed to address COVID-19 MESHD-related stigma and its debilitating consequences by health workers and policy makers.

    BRD2 HGNC inhibition blocks SARS-CoV-2 infection MESHD in vitro by reducing transcription of the host cell receptor ACE2

    Authors: Ruilin Tian; Avi J Samelson; Veronica V Rezelj; Merissa Chen; Gokul N Ramadoss; Xiaoyan Guo; Alice Mac Kain; Quang Dinh Tran; Shion A Lim; Irene Lui; James Nunez; Sarah J Rockwood; Na Liu; Jared Carlson-Stevermer; Jennifer Oki; Travis Maures; Kevin Holden; Jonathan S Weissman; James A Wells; Bruce Conklin; Marco Vignuzzi; Martin Kampmann; Roshni Patel; Juan P Dizon; Irina Shimeliovich; Anna Gazumyan; Marina Caskey; Pamela J Bjorkman; Rafael Casellas; Theodora Hatziioannou; Paul D Bieniasz; Michel C Nussenzweig

    doi:10.1101/2021.01.19.427194 Date: 2021-01-19 Source: bioRxiv

    SARS-CoV-2 infection MESHD of human cells is initiated by the binding of the viral Spike protein PROTEIN to its cell-surface receptor ACE2 HGNC. We conducted an unbiased CRISPRi screen to uncover druggable pathways controlling Spike protein PROTEIN binding to human cells. We found that the protein BRD2 HGNC is an essential node in the cellular response to SARS-CoV-2 infection MESHD. BRD2 HGNC is required for ACE2 HGNC transcription in human lung epithelial cells and cardiomyocytes, and BRD2 HGNC inhibitors currently evaluated in clinical trials potently block endogenous ACE2 HGNC expression and SARS-CoV-2 infection MESHD of human cells. BRD2 HGNC also controls transcription of several other genes induced upon SARS-CoV-2 infection MESHD, including the interferon response, which in turn regulates ACE2 HGNC levels. It is possible that the previously reported interaction between the viral E protein PROTEIN and BRD2 HGNC evolved to manipulate the transcriptional host response during SARS-CoV-2 infection MESHD. Together, our results pinpoint BRD2 HGNC as a potent and essential regulator of the host response to SARS-CoV-2 infection MESHD and highlight the potential of BRD2 HGNC as a novel therapeutic target for COVID-19 MESHD.

    COVID-Net CT-2 HGNC: Enhanced Deep Neural Networks for Detection of COVID-19 MESHD from Chest CT Images Through Bigger, More Diverse Learning

    Authors: Hayden Gunraj; Ali Sabri; David Koff; Alexander Wong

    id:2101.07433v1 Date: 2021-01-19 Source: arXiv

    The COVID-19 pandemic MESHD continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of CT imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a neural network tailored for detection of COVID-19 MESHD cases from chest CT images as part of the open source COVID-Net initiative. However, one potential limiting factor is restricted quantity and diversity given the single nation patient cohort used. In this study, we introduce COVID-Net CT-2 HGNC, enhanced deep neural networks for COVID-19 MESHD detection from chest CT images trained on the largest quantity and diversity of multinational patient cases in research literature. We introduce two new CT benchmark datasets, the largest comprising a multinational cohort of 4,501 patients from at least 15 countries. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2 HGNC, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The COVID-Net CT-2 HGNC neural networks achieved accuracy, COVID-19 MESHD sensitivity, and COVID-19 MESHD positive predictive value of 98.1%/96.2%/96.7% and 97.9%/95.7%/96.4%, respectively. Explainability-driven performance validation shows that COVID-Net CT-2 HGNC's decision-making behaviour is consistent with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 MESHD assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 HGNC and benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.

    Rapid protection from COVID-19 MESHD in nonhuman primates vaccinated intramuscularly but not intranasally with a single dose of a recombinant vaccine

    Authors: Wakako Furuyama; Kyle Shifflett; Amanda N Pinksi; Amanda J Griffin; Friederike Feldmann; Atsushi Okumura; Tylisha Gourdine; Allen Jankeel; Jamie Lovaglio; Patrick W Hanley; Tina Thomas; Chad S Clancy; Ilhem Messaoudi; Andrea Marzi; Martina Turroja; Kamille A West; Kristie Gordon; Katrina G Millard; Victor Ramos; Justin Da Silva; Jianliang Xu; Robert A Colbert; Roshni Patel; Juan P Dizon; Irina Shimeliovich; Anna Gazumyan; Marina Caskey; Pamela J Bjorkman; Rafael Casellas; Theodora Hatziioannou; Paul D Bieniasz; Michel C Nussenzweig

    doi:10.1101/2021.01.19.426885 Date: 2021-01-19 Source: bioRxiv

    The ongoing pandemic of Coronavirus disease 2019 MESHD ( COVID-19 MESHD) continues to exert a significant burden on health care systems worldwide. With limited treatments available, vaccination remains an effective strategy to counter transmission of severe acute respiratory syndrome coronavirus 2 MESHD (SARS-CoV-2). Recent discussions concerning vaccination strategies have focused on identifying vaccine platforms, number of doses, route of administration, and time to reach peak immunity against SARS-CoV-2. Here, we generated a single dose, fast-acting vesicular stomatitis MESHD virus-based vaccine derived from the licensed Ebola virus (EBOV) vaccine rVSV-ZEBOV, expressing the SARS-CoV-2 spike PROTEIN protein and the EBOV glycoprotein (VSV-SARS2-EBOV). Rhesus macaques vaccinated intramuscularly (IM) with a single dose of VSV-SARS2-EBOV were protected within 10 days and did not show signs of COVID-19 MESHD pneumonia MESHD. In contrast, IN vaccination MESHD resulted in limited immunogenicity and enhanced COVID-19 MESHD pneumonia MESHD compared to control animals. While IM and IN vaccination both induced neutralizing antibody titers MESHD, only IM vaccination resulted in a significant cellular immune response. RNA sequencing data bolstered these results by revealing robust activation of the innate and adaptive immune transcriptional signatures in the lungs of IM-vaccinated animals only. Overall, the data demonstrates that VSV-SARS2-EBOV is a potent single-dose COVID-19 MESHD vaccine candidate that offers rapid protection based on the protective efficacy observed in our study.

    SIR Simulation of COVID-19 Pandemic MESHD in Malaysia: Will the Vaccination Program be Effective?

    Authors: W. K. Wong; Filbert H. Juwono; Tock H. Chua

    id:2101.07494v1 Date: 2021-01-19 Source: arXiv

    Since the end of 2019, COVID-19 MESHD has significantly affected the lives of people around the world. Towards the end of 2020, several COVID-19 MESHD vaccine candidates with relatively high efficacy have been reported in the final phase of clinical trials. Vaccines have been considered as critical tools for opening up social and economic activities, thereby lessening the impact of this disease on the society. This paper presents a simulation of COVID-19 MESHD spread using modified Susceptible-Infected-Removed (SIR) model under vaccine intervention in several localities of Malaysia MESHD, i.e. those cities or states with high relatively COVID-19 MESHD cases such as Kuala Lumpur, Penang, Sabah, and Sarawak. The results show that at different vaccine efficacy levels (0.75, 0.85, and 0.95), the curves of active infection vary slightly, indicating that vaccines with efficacy above 0.75 would produce the herd immunity required to level the curves. In addition, disparity is significant between implementing or not implementing a vaccination program. Simulation results also show that lowering the reproduction number, R0 is necessary to keep the infection curve flat despite vaccination. This is due to the assumption that vaccination is mostly carried out gradually at the assumed fixed rate. The statement is based on our simulation results with two values of R0: 1.1 and 1.2, indicative of reduction of R0 by social distancing. The lower R0 shows a smaller peak amplitude about half the value simulated with R0=1.2. In conclusion, the simulation model suggests a two-pronged strategy to combat the COVID-19 pandemic MESHD in Malaysia: vaccination and compliance with standard operating procedure issued by the World Health Organization (e.g. social distancing).

    Twitter Subjective Well-Being Indicator During COVID-19 Pandemic MESHD: A Cross-Country Comparative Study

    Authors: Tiziana Carpi; Airo Hino; Stefano Maria Iacus; Giuseppe Porro

    id:2101.07695v1 Date: 2021-01-19 Source: arXiv

    This study analyzes the impact of the COVID-19 pandemic MESHD on the subjective well-being as measured through Twitter data indicators for Japan and Italy. It turns out that, overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019 and even more compared to the historical mean of the indexes. Through a data science approach we try to identify the possible causes of this drop down by considering several explanatory variables including, climate and air quality data, number of COVID-19 MESHD cases and deaths MESHD, Facebook Covid and flu symptoms global survey, Google Trends data and coronavirus-related searches, Google mobility data, policy intervention measures, economic variables and their Google Trends proxies, as well as health and stress proxy variables based on big data. We show that a simple static regression model is not able to capture the complexity of well-being and therefore we propose a dynamic elastic net approach to show how different group of factors may impact the well-being in different periods, even over a short time length, and showing further country-specific aspects. Finally, a structural equation modeling analysis tries to address the causal relationships among the COVID-19 MESHD factors and subjective well-being showing that, overall, prolonged mobility restrictions,flu and Covid-like symptoms, economic uncertainty, social distancing and news about the pandemic have negative effects on the subjective well-being.

    CoVaxxy: A global collection of English Twitter posts about COVID-19 MESHD vaccines

    Authors: Matthew DeVerna; Francesco Pierri; Bao Truong; John Bollenbacher; David Axelrod; Niklas Loynes; Cristopher Torres-Lugo; Kai-Cheng Yang; Fil Menczer; John Bryden

    id:2101.07694v1 Date: 2021-01-19 Source: arXiv

    With a large proportion of the population currently hesitant to take the COVID-19 MESHD vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about the vaccines spreading on social media. In this paper, we present a dataset of English-language Twitter posts about COVID-19 MESHD vaccines. We show statistics for our dataset regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also demonstrate how we are able to perform analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. We have developed a live dashboard to allow people to track hashtag changes over time. The dataset can be used in studies about the impact of online information on COVID-19 MESHD vaccine uptake and health outcomes.

    Continual Deterioration Prediction for Hospitalized COVID-19 MESHD Patients

    Authors: Jiacheng Liu; Meghna Singh; Catherine ST. Hill; Vino Raj; Lisa Kirkland; Jaideep Srivastava

    id:2101.07581v1 Date: 2021-01-19 Source: arXiv

    Leading up to August 2020, COVID-19 MESHD has spread to almost every country in the world, causing millions of infected MESHD and hundreds of thousands of deaths. In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 MESHD outcomes. Then, we develop a temporal stratification approach to make daily predictions on patients' outcome at the end of hospital stay. Training data is segmented by the remaining length of stay, which is a proxy for the patient's overall condition. Based on this, a sequence of predictive models are built, one for each time segment. Thanks to the publicly shared data, we were able to build and evaluate prototype models. Preliminary experiments show 0.98 AUROC, 0.91 F1 score and 0.97 AUPR on continuous deterioration prediction, encouraging further development of the model as well as validations on different datasets. We also verify the key assumption which motivates our method. Clinical variables could have time-varying effects on COVID-19 MESHD outcomes. That is to say, the feature importance of a variable in the predictive model varies at different disease stages.

    Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 MESHD Diagnosis at the Edge

    Authors: Adnan Qayyum; Kashif Ahmad; Muhammad Ahtazaz Ahsan; Ala Al-Fuqaha; Junaid Qadir

    id:2101.07511v1 Date: 2021-01-19 Source: arXiv

    Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19 MESHD. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic MESHD emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 MESHD imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.

    COVID-19 MESHD and Digital Transformation -- Developing an Open Experimental Testbed for Sustainable and Innovative Environments (ETSIE) using Fuzzy Cognitive Maps

    Authors: Wolfgang Höhl

    id:2101.07509v1 Date: 2021-01-19 Source: arXiv

    This paper sketches a new approach using Fuzzy Cognitive Maps (FCMs) to operably map and simulate digital transformation in architecture and urban planning. Today these processes are poorly understood. Many current studies on digital transformation are only treating questions of economic efficiency. Sustainability and social impact only play a minor role. Decisive definitions, concepts and terms stay unclear. Therefore this paper develops an open experimental testbed for sustainable and innovative environments (ETSIE) for three different digital transformation scenarios using FCMs. A traditional growth-oriented scenario, a COVID-19 MESHD scenario and an innovative and sustainable COVID-19 MESHD scenario are modeled and tested. All three scenarios have the same number of components, connections and the same driver components. Only the initial state vectors are different and the internal correlations are weighted differently. This allows for comparing all three scenarios on an equal basis. The mental modeler software is used (Gray et al. 2013). This paper presents one of the first applications of FCMs in the context of digital transformation. It is shown, that the traditional growth-oriented scenario is structurally very similar to the current COVID-19 MESHD scenario. The current pandemic is able to accelerate digital transformation to a certain extent. But the pandemic does not guarantee for a distinct sustainable and innovative future development. Only by changing the initial state vectors and the weights of the connections an innovative and sustainable turnaround in a third scenario becomes possible.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins


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