Corpus overview


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MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

ProteinS (1850)

ProteinN (502)

NSP5 (400)

ComplexRdRp (232)

ProteinE (133)


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SARS-CoV-2 Proteins
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    Maturation signatures of conventional dendritic cells in COVID-19 MESHD reflect direct viral sensing

    Authors: Laura Marongiu; Giulia Protti; Fabio Alessandro Facchini; Mihai Valache; Francesca Mingozzi; Valeria Ranzani; Anna Rita Putignano; Lorenzo Salviati; Valeria Bevilacqua; Serena Curti; Mariacristina Crosti; Laura Rachele Bettini; Andrea Biondi; Luca Nespoli; Nicolò Tamini; Nicasio Mancini; Nicola Clementi; Sergio Abrignani; Roberto Spreafico; Francesca Granucci

    doi:10.1101/2021.03.03.433597 Date: 2021-03-03 Source: bioRxiv

    Growing evidence suggests that conventional dendritic cells (cDCs) undergo aberrant maturation in COVID-19 MESHD, and this adversely affects T cell activation. Here, we find that cDC2 HGNC subtypes show similar infection-induced gene signatures with an increasing gradient of expression of interferon-stimulated genes from mild to severe patients and a down-regulation of major histocompatibility complex class II (MHC class II) molecules and some inflammatory cytokines compared to the baseline level of healthy donors. In vitro, the direct exposure of cDC2 HGNCs to the virus recapitulates the type of activation observed in vivo. Our findings provide evidence that SARS-CoV-2 can directly interact with cDC2 HGNCs and, by down-regulating crucial molecules required for T cell activation, implements an efficient immune escape mechanism.

    Dissecting CD8 HGNC+ T cell pathology of severe SARS-CoV-2 infection MESHD by single-cell epitope mapping

    Authors:

    doi:10.1101/2021.03.03.432690 Date: 2021-03-03 Source: bioRxiv

    The current COVID-19 pandemic MESHD represents a global challenge. A better understanding of the immune response against SARS-CoV-2 is key to unveil the differences in disease severity and to develop future vaccines targeting novel SARS-CoV-2 variants. Feature barcode technology combined with CITE-seq antibodies and DNA-barcoded peptide-MHC I Dextramer reagents enabled us to identify relevant SARS-CoV-2-derived epitopes and compare epitope-specific CD8 HGNC+ T cell populations between mild and severe COVID-19 MESHD. We identified a strong CD8 HGNC+ T cell response against an S protein PROTEIN-derived epitope. CD8 HGNC+ effector cells in severe COVID-19 MESHD displayed hyperactivation, T cell exhaustion and were missing characteristics of long-lived memory T cells. We identify A*0101 WTAGAAAYY as an immunogenic CD8 HGNC+ T cell epitope with the ability to drive clonal expansion. We provide an in-depth characterization of the CD8 HGNC+ T cell-mediated response to SARS-CoV-2 infection MESHD which will be relevant for the development of molecular and targeted therapies and potential adjustments of vaccination strategies.

    Comparative analysis of codon usage patterns in SARS-CoV-2, its mutants and other respiratory viruses MESHD

    Authors: Neetu Tyagi; Rahila Sardar; Dinesh Gupta

    doi:10.1101/2021.03.03.433699 Date: 2021-03-03 Source: bioRxiv

    The Coronavirus disease 2019 MESHD ( COVID-19 MESHD) outbreak caused by Severe Acute Respiratory Syndrome Coronavirus 2 MESHD virus (SARS-CoV-2) poses a worldwide human health crisis, causing respiratory illness MESHD with a high mortality rate. To investigate the factors governing codon usage bias in all the respiratory viruses, including SARS-CoV-2 isolates from different geographical locations (~62K) including two recently emerging strains from the United Kingdom (UK), i.e.,VUI202012/01 and South Africa (SA), i.e., 501.Y.V2 codon usage bias (CUBs) analysis was performed. The analysis includes RSCU analysis, GC content calculation, ENC analysis, di-nucleotide frequency and neutrality plot analysis. We were motivated to conduct the study to fulfil two primary aims: first, to identify the difference in codon usage bias amongst all SARS-CoV-2 genomes and secondly, to compare their CUBs properties with other respiratory viruses. A biased nucleotide composition was found as most of the highly preferred codons were A/U-ending in all the respiratory viruses studied here. When compared with human host, the RSCU analysis led to the identification of 11 over-represented codons and 9 under-represented codons in SARS-CoV-2 genomes. Correlation analysis of ENC and GC3s revealed that mutational pressure is the leading force determining the CUBs. The present study results yields a better understanding of codon usage preferences for SARS-CoV-2 genomes and discover the possible evolutionary determinants responsible for the biases found among the respiratory viruses, thus unveils a unique feature of the SARS-CoV-2 evolution and adaptation. To the best of our knowledge, this is the first attempt at comparative CUBs analysis on the worldwide genomes of SARS-CoV-2, including novel emerged strains and other respiratory viruses.

    Perception of COVID-19 MESHD Vaccination Amongst Physicians in Colombia, January 2021

    Authors: Jorge L. Alvarado-Socarras; Andrea Liliana Vesga-Varela; Doris Cristina Quintero-Lesmes; Marcela M. Fama-Pereira; Norma C. Serrano-Diaz; Mauricio Vasco; Virgil Carballo-Zarate; Lysien I. Zambrano; Alberto Paniz-Mondolfi; Alfonso J. Rodriguez-Morales

    id:10.20944/preprints202103.0119.v1 Date: 2021-03-03 Source: Preprints.org

    Introduction: The SARS-CoV-2/ COVID-19 pandemic MESHD has triggered the need for developing rapidly effective and safety vaccines to prevent infection, particularly in those at-risk populations such as medical personnel. The objective of this study was to assess perception of COVID-19 MESHD vaccination amongst Colombian physicians featuring two different sceneries of COVID-19 MESHD vaccination. Methods: A cross-sectional analytical study was carried out through an online survey, directed at medical staff in several cities in Colombia. The percentage of physicians who have a positive perception to be vaccinated and the associated factors that determine that decision were determined. A binomial regression analysis adjusted for age and sex was carried out, taking as a dependent variable the acceptance of free vaccination with an effectiveness of 60 and 80%. The most significant factors were determined in the non-acceptance of vaccination. Results: Between 77.1% and 90.8% of physicians in Colombia, accept COVID-19 MESHD vaccination, according to the scenario evaluated where the effectiveness of the vaccine was 60 or 80%, respectively. Medical specialty, have ever paid for a vaccine, recommend administrating the vaccine to their parents or people over 70 years and dispense the vaccine to their children were the factors to be vaccinated for free with an effectiveness of 60% and 80%. Conclusions: There is a high perception of the intention to vaccinate physicians in Colombia against COVID-19 MESHD. But it is very similar to that of the general population, according to results reported in other studies.

    “Clinician’s Probability Calculator” to Convert Pre-Test to Post-Test Probability of COVID-19 MESHD, Based on Method Validation from Each Laboratory

    Authors: Zoe Brooks; Saswati Das; Tom Pliura

    id:10.20944/preprints202012.0094.v3 Date: 2021-03-03 Source: Preprints.org

    Identifying the SARS-CoV-2 virus has been a unique challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians with interpretation of the probability that a positive, or negative, COVID-19 MESHD test result indicates an infected person, based on their clinical estimate of pre-test probability of infection.The authors conducted a small survey on LinkedIn to confirm that hypothesis that that the clinical pre-test probability of COVID-19 MESHD increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database 1, and for selected methods in FDA EUA submissions2,3. Authors calculated likelihood ratios to convert pre-test to post-test probability of disease and designed an online calculator to create graphics and text to report results. Despite best efforts, false positive and false negative Covid-19 MESHD test results are unavoidable4,5. A positive or negative test result from one laboratory has a different probability for the presence of disease than the same result from another laboratory. Likelihood ratios and confidence intervals can convert the physician or other healthcare professional’s clinical estimate of pre-test probability to post-test probability of disease. Ranges of probabilities differ depending on proven method PPA and NPA in each laboratory. We recommend that laboratories verify PPA and NPA and utilize a the “Clinician’s Probability Calculator” to verify acceptable test performance and create reports to help guide clinicians with estimation of post-test probability of COVID-19 MESHD.

    The Effect of Lockdown Period During Covid-19 Pandemic MESHD on Air Quality in Sydney Region, Australia

    Authors: Hiep Duc; David Salter; Merched Azzi; Ningbo Jiang; Loredana Warren; Sean Watt; Matthew Riley; Stephen White; Toan Trieu; Lisa Tzu-Chi Chang; Xavier Barthelemy; David Fuchs; Hubert Nguyen

    id:10.20944/preprints202103.0126.v1 Date: 2021-03-03 Source: Preprints.org

    In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of Covid-19 MESHD virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in term of air quality which is shown to have improved for a number of pollutants, such as nitrogen dioxides (NO2) and carbon monoxide (CO), based on monitoring data on ground and from satellite. Besides primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary ozone (O3) during the lockdown period will also be analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3 and PM2.5 in the Greater Metropolitan Region ( GMR HGNC) of Sydney. COVID-19 MESHD lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR HGNC.

    Aptamers for Detection and Diagnostics ( ADD MESHD) is a proposed mobile app acquiring optical data from conjugated quantum nanodots to identify molecules indicating presence of SARS-CoV-2 virus: Why public health and healthcare need smartphone sensors as a platform for early detection and prevention

    Authors: Shoumen Datta

    doi:10.26434/chemrxiv.13102877.v29 Date: 2021-03-03 Source: ChemRxiv

    Proposed SARS-CoV-2 surveillance tool using a mobile app for non-invasive monitoring of humans and animals. Engineering a biomedical device as a low-cost, non-invasive, detection, and diagnostic platform for surveillance of infections in humans, and animals. The system embraces the IoT “digital by design” metaphor by incorporating elements of connectivity, data sharing and (secure) information arbitrage. Using an array of aptamers to bind viral targets may help in detection, diagnostics, and potentially prevention in case of SARS-CoV-2. The ADD tool may become part of a broader platform approach.

    Global Daily CO$_2$ emissions for the year 2020

    Authors: Zhu Liu; Zhu Deng; Philippe Ciais; Jianguang Tan; Biqing Zhu; Steven J. Davis; Robbie Andrew; Olivier Boucher; Simon Ben Arous; Pep Canadel; Xinyu Dou; Pierre Friedlingstein; Pierre Gentine; Rui Guo; Chaopeng Hong; Robert B. Jackson; Daniel M. Kammen; Piyu Ke; Corinne Le Quere; Crippa Monica; Greet Janssens-Maenhout; Glen Peters; Katsumasa Tanaka; Yilong Wang; Bo Zheng; Haiwang Zhong; Taochun Sun; Hans Joachim Schellnhuber

    id:2103.02526v1 Date: 2021-03-03 Source: arXiv

    The diurnal cycle CO$_2$ emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVID-19 pandemic MESHD. Here, for the first time we provide a daily CO$_2$ emission dataset for the whole year of 2020 calculated from inventory and near-real-time activity data (called Carbon Monitor project: https://carbonmonitor.org). It was previously suggested from preliminary estimates that did not cover the entire year of 2020 that the pandemics may have caused more than 8% annual decline of global CO$_2$ emissions. Here we show from detailed estimates of the full year data that the global reduction was only 5.4% (-1,901 MtCO$_2$, ). This decrease is 5 times larger than the annual emission drop at the peak of the 2008 Global Financial Crisis. However, global CO$_2$ emissions gradually recovered towards 2019 levels from late April with global partial re-opening. More importantly, global CO$_2$ emissions even increased slightly by +0.9% in December 2020 compared with 2019, indicating the trends of rebound of global emissions. Later waves of COVID-19 MESHD infections in late 2020 and corresponding lockdowns have caused further CO$_2$ emissions reductions particularly in western countries, but to a much smaller extent than the declines in the first wave. That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5.4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era HGNC. These declines are significant, but will be quickly overtaken with new emissions unless the COVID-19 MESHD crisis is utilized as a break-point with our fossil-fuel trajectory, notably through policies that make the COVID-19 MESHD recovery an opportunity to green national energy and development plans.

    Personal Productivity and Well-being -- Chapter 2 of the 2021 New Future of Work Report

    Authors: Jenna Butler; Mary Czerwinski; Shamsi Iqbal; Sonia Jaffe; Kate Nowak; Emily Peloquin; Longqi Yang

    id:2103.02524v1 Date: 2021-03-03 Source: arXiv

    We now turn to understanding the impact that COVID-19 MESHD had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.

    Deep Learning for Virus-Spreading Forecasting: a Brief Survey

    Authors: Federico Baldo; Lorenzo Dall'Olio; Mattia Ceccarelli; Riccardo Scheda; Michele Lombardi; Andrea Borghesi; Stefano Diciotti; Michela Milano

    id:2103.02346v1 Date: 2021-03-03 Source: arXiv

    The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes. In this paper, we will outline the main Deep Learning approaches aimed at predicting the spreading of a disease in space and time. The aim is to show the emerging trends in this area of research and provide a general perspective on the possible strategies to approach this problem. In doing so, we will mainly focus on two macro-categories: classical Deep Learning approaches MESHD and Hybrid models. Finally, we will discuss the main advantages and disadvantages of different models, and underline the most promising development directions to improve these approaches.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins


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