Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (201)

Hypertension (137)

Fever (122)

Cough (105)

Respiratory distress (88)


    displaying 11 - 20 records in total 2402
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    Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis

    Authors: Garyfallos Konstantinoudis; Tullia Padellini; James E Bennett; Bethan Davies; Majid Ezzati; Marta Blangiardo

    doi:10.1101/2020.08.10.20171421 Date: 2020-08-11 Source: medRxiv

    Background: Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design, based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths MESHD up to June 30, 2020 in England using high geographical resolution. Methods: We included 38 573 COVID-19 deaths MESHD up to June 30, 2020 at the Lower Layer Super Output Area level in England (n=32 844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. Findings: We find a 0.5% (95% credible interval: -0.2%-1.2%) and 1.4% (-2.1%-5.1%) increase in COVID-19 mortality rate for every 1g/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect of 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease TRANS disease MESHD during the first wave of the epidemic. Interpretation: Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain. Funding: Medical Research Council, Wellcome Trust, Environmental Protection Agency and National Institutes of Health.

    Telmisartan for treatment of Covid-19 patients: an open randomized clinical trial. Preliminary report.

    Authors: Mariano Duarte; Facundo G Pelorosso; Liliana Nicolosi; M. Victoria Salgado; Hector Vetulli; Analia Aquieri; Francisco Azzato; Mauro Basconcel; Marcela Castro; Javier Coyle; Ignacio Davolos; Eduardo Esparza; Ignacio Fernandez Criado; Rosana Gregori; Pedro Mastrodonato; Maria Rubio; Sergio Sarquis; Fernando Wahlmann; Rodolfo Pedro Rothlin

    doi:10.1101/2020.08.04.20167205 Date: 2020-08-11 Source: medRxiv

    Background. Covid-19, the disease MESHD caused by SARS-CoV-2, is associated with significant respiratory-related morbidity and mortality. Angiotensin receptor blockers (ARBs) have been postulated as tentative pharmacological agents to treat Covid-19-induced lung inflammation MESHD. Trial design. This trial is a parallel group, randomized, two arm, open label, multicenter superiority trial with 1:1 allocation ratio. Methods. Participants included patients who were 18 years of age TRANS or older and who had been hospitalized with confirmed Covid-19 with 4 or fewer days since symptom onset TRANS. Exclusion criteria included intensive care unit admission prior to randomization and use of angiotensin receptor blocker or angiotensin converting enzyme inhibitors at admission. Participants in the treatment arm received telmisartan 80 mg bid during 14 days plus standard care. Participants in the control arm received standard care alone. Primary outcome was to achieve significant reductions in plasma SERO levels of C-reactive protein in telmisartan treated Covid-19 patients at day 5 and 8 after randomization. Key secondary outcomes included time to discharge evaluated at 15 days after randomization and admission to ICU and death MESHD at 15- and 30-days post randomization. We present here a preliminary report. Results. A total of 78 patients were included in the interim analysis, 40 in the telmisartan and 38 in the control groups. CRP levels at day 5 in the control group were 51.1 +/- 44.8 mg/L (mean +/- SD; n=28) and in the telmisartan group were 24.2 +/- 31.4 mg/L (mean +/- SD; n=32, p<0.05). At day 8, CRP levels were 41.6 +/- 47.6 mg/L (mean +/- SD; n=16) and 9.0 +/- 10.0 mg/L (mean +/- SD; n=13, p < 0.05) in the control and telmisartan groups, respectively. Also, analysis of time to discharge by Kaplan-Meier method showed that telmisartan treated patients had statistically significant lower time to discharge (median time to discharge control group=15 days; telmisartan group=9 days). No differences were observed for ICU admission or death MESHD. No significant adverse events related to telmisartan were reported. Conclusions. In the present preliminary report, despite the small number of patients studied, ARB telmisartan, a well-known inexpensive safe antihypertensive drug, administered in high doses, demonstrates anti-inflammatory effects and improved morbidity in hospitalized patients infected with SARS -CoV-2, providing support for its use in this serious pandemia (NCT04355936).

    Comparison of deaths MESHD rates for COVID-19 across Europe

    Authors: Leonardo Villani; Martin McKee; Luca Giraldi; Walter Ricciardi; Stefania Boccia

    doi:10.21203/ Date: 2020-08-11 Source: ResearchSquare

    Europe suffered greatly in the early stages of the COVID-19 pandemic. Italy was in the forefront, with its Lombardy region especially badly affected. However, European countries have been impacted to quite different degrees. We report Crude Mortality Rates (CMRs) and, in five countries supplying comparable age TRANS-specific data, Standardized Mortality Rates (SMRs) from deaths MESHD reported as due to COVID-19 in the European Union and United Kingdom. As of 21st July 2020, Belgium was the country with the highest cumulative CMR (85.6/100,000), but Lombardy region was at almost double this value (167.0/100,000), while corresponding figure for the rest of Italy was 36.3/100,000. SMRs could be calculated for five countries (Italy, Portugal, Sweden, Germany and Netherlands). Among them, Sweden had the highest SMR (60.7/100,000). The corresponding figures for Italy, Netherlands, Portugal and Germany were 48.2/100,000, 41.0/100,000, 15.1/100,000 and 10.0/100,000 respectively. It is clear that countries within Europe have performed very differently in their responses to the COVID-19 pandemic, but the many limitations in the available data must be addressed before a definitive detailed assessment of the reasons can be made.

    On the numbers of infected and deceased in the second Corona wave

    Authors: Juergen Mimkes; Rainer Janssen

    doi:10.1101/2020.08.10.20171553 Date: 2020-08-11 Source: medRxiv

    In Germany and other countries, a second wave of corona infections MESHD has been observed since July 2020, after the first wave has subsided. We have investigated both waves by a modified SIR-SI infection MESHD model, adapted to the data to the Robert-Koch-Institute (RKI) or the Johns- Hopkins-University (JHU). The first wave is characterized by the SIR model: in a perfect lockdown only a small part of the society is infected and the infections MESHD end after a certain time. The SI part considers the incompleteness of any lockdown: at the end of the first wave infections MESHD do not completely go down to zero, but continue to rise again, but only slowly due to mouth protection, hygiene and distance keeping. During this first wave the number of deceased people follows the number of infected persons with a fixed time interval and percentage: mostly symptomatic ill people have been tested. This applied to nearly all countries observed, with different intervals and percentages. In the present second wave, the number of daily infections MESHD has risen again significantly in some countries, and it may be questioned whether this is due to the increased number of tests. The answer may be given by looking at the daily number of deaths MESHD. In Germany, Austria, Italy, Great Britain and others this number has still remained at a constant level for six weeks. In these countries a second wave of died people has not yet arrived. The increased number of tests include obviously mostly asymptomatically TRANS infected persons, who do not fall HP ill or die from coronavirus. However, in some countries, like USA or Israel, the second wave did arrive. The numbers of infected and deceased people both have grown. A real second wave is a permanent threat to all countries.

    A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and the anti-inflammatory response to the drug tranilast

    Authors: Sara J Hamis; Fiona R Macfarlane

    id:2008.04172v1 Date: 2020-08-10 Source: arXiv

    Pyroptosis is an inflammatory mode of cell death MESHD that contributes to the cytokine storm associated with severe cases of coronavirus disease MESHD 2019 (COVID-19). Central to pyroptosis induced by severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) is the formation of the NLRP3 inflammasome. Inflammasome formation, and by extension pyroptosis, may be inhibited by certain anti-inflammatory drugs. One such drug, tranilast, is currently being evaluated as a COVID-19 treatment target in a clinical trial. In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death MESHD and drug-responses to tranilast. The model is formulated in terms of a system of ordinary differential equations (ODEs) that describe the dynamics of proteins involved in pyroptosis. The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death MESHD from inflammatory (pyroptosis) to non-inflammatory (e.g., apoptosis).

    Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions

    Authors: Md Ashraf Ahmed; Arif Mohaimin Sadri; M. Hadi Amini

    id:2008.03866v1 Date: 2020-08-10 Source: arXiv

    Risk and response communication of public agencies through social media played a significant role in the emergence and spread of novel Coronavirus (COVID-19) and such interactions were echoed in other information outlets. This study collected time-sensitive online social media data and analyzed such communication patterns from public health (WHO, CDC), emergency MESHD (FEMA), and transportation (FDOT) agencies using data-driven methods. The scope of the work includes a detailed understanding of how agencies communicate risk information through social media during a pandemic and influence community response (i.e. timing of lockdown, timing of reopening) and disease MESHD outbreak indicators (i.e. number of confirmed cases TRANS, number of deaths MESHD). The data includes Twitter interactions from different agencies (2.15K tweets per agency on average) and crowdsourced data (i.e. Worldometer) on COVID-19 cases and deaths MESHD were observed between February 21, 2020 and June 06, 2020. Several machine learning techniques such as (i.e. topic mining and sentiment ratings over time) are applied here to identify the dynamics of emergent topics during this unprecedented time. Temporal infographics of the results captured the agency-levels variations over time in circulating information about the importance of face covering, home quarantine, social distancing and contact tracing TRANS. In addition, agencies showed differences in their discussions about community transmission TRANS, lack of personal protective equipment, testing and medical supplies, use of tobacco, vaccine, mental health issues, hospitalization, hurricane season, airports, construction work among others. Findings could support more efficient transfer of risk and response information as communities shift to new normal as well as in future pandemics.

    Comparative analyses of SARS-CoV-2 binding (IgG, IgM, IgA) and neutralizing antibodies SERO from human serum samples SERO

    Authors: Livia Mazzini; Donata Martinuzzi; Inesa Hyseni; Giulia Lapini; Linda Benincasa; Pietro Piu; Claudia Maria Trombetta; Serena Marchi; Ilaria Razzano; Alessandro Manenti; Emanuele Montomoli

    doi:10.1101/2020.08.10.243717 Date: 2020-08-10 Source: bioRxiv

    A newly identified coronavirus, named SARS-CoV-2, emerged in December 2019 in Hubei Province, China, and quickly spread throughout the world; so far, it has caused more than 18 million cases of disease MESHD and 700,000 deaths MESHD. The diagnosis of SARS-CoV-2 infection MESHD is currently based on the detection of viral RNA in nasopharyngeal swabs by means of molecular-based assays, such as real-time RT-PCR. Furthermore, serological assays SERO aimed at detecting different classes of antibodies SERO constitute the best surveillance strategy for gathering information on the humoral immune response to infection MESHD and the spread of the virus through the population, in order to evaluate the immunogenicity of novel future vaccines and medicines for the treatment and prevention of COVID-19 disease MESHD. The aim of this study was to determine SARS-CoV-2-specific antibodies SERO in human serum samples SERO by means of different commercial and in-house ELISA SERO kits, in order to evaluate and compare their results first with one another and then with those yielded by functional assays using wild-type virus. It is important to know the level of SARS-CoV-2-specific IgM, IgG and IgA antibodies SERO in order to predict population immunity and possible cross-reactivity with other coronaviruses and to identify potentially infectious subjects. In addition, in a small sub-group of samples, we performed a subtyping Immunoglobulin G ELISA SERO. Our data showed an excellent statistical correlation between the neutralization titer and the IgG, IgM and IgA ELISA SERO response against the receptor-binding domain of the spike protein, confirming that antibodies SERO against this portion of the virus spike protein are highly neutralizing and that the ELISA SERO Receptor-Binding Domain-based assay can be used as a valid surrogate for the neutralization assay in laboratories which do not have Biosecurity level-3 facilities.

    COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform

    Authors: Zhou Yang; Jiwei Xu; Zhenhe Pan; Fang Jin

    id:2008.04285v1 Date: 2020-08-10 Source: arXiv

    The Coronavirus Disease MESHD 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths MESHD. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases TRANS and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases TRANS per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease MESHD disease spreading TRANS spreading dynamics in China and showing how the epidemic is taken under control in China.

    Individualized Prediction of COVID-19 Adverse outcomes with MLHO

    Authors: Hossein Estiri; Zachary H. Strasser; Shawn N. Murphy

    id:2008.03869v1 Date: 2020-08-10 Source: arXiv

    The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of the COVID-19 adverse outcomes on individual patients could have led to better allocation of healthcare resources and more efficient targeted preventive measures. We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death MESHD from patients' past (before COVID-19 infection MESHD) medical records. MLHO is an end-to-end Machine Learning pipeline that implements iterative sequential representation mining and feature and model selection to predict health outcomes. MLHO's architecture enables a parallel and outcome-oriented calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes. Using clinical data from a large cohort of over 14,000 patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records. Overall, the best predictions were obtained from extreme and gradient boosting models. The median AUC ROC for mortality prediction was 0.91, while the prediction performance SERO ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence on predicting each outcome. As COVID-19 cases are re-surging in the U.S. and around the world, a Machine Learning pipeline like MLHO is crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases MESHD that may emerge in the near future.

    How Efficient is Contact Tracing TRANS in Mitigating the Spread of Covid-19? A Mathematical Modeling Approach

    Authors: T. A. Biala; Y. O. Afolabi; A. Q. M. Khaliq

    id:2008.03859v1 Date: 2020-08-10 Source: arXiv

    Contact Tracing TRANS (CT) is one of the measures taken by government and health officials to mitigate the spread of the novel coronavirus. In this paper, we investigate its efficacy by developing a compartmental model for assessing its impact on mitigating the spread of the virus. We describe the impact on the reproduction number TRANS $\mathcal{R}_c$ of Covid-19. In particular, we discuss the importance and relevance of parameters of the model such as the number of reported cases, effectiveness of tracking and monitoring policy, and the transmission TRANS rates to contact tracing TRANS. We describe the terms ``perfect tracking'', ``perfect monitoring'' and ``perfect reporting'' to indicate that traced contacts TRANS will be tracked while incubating, tracked contacts are efficiently monitored so that they do not cause secondary infections MESHD, and all infected persons are reported, respectively. We consider three special scenarios: (1) perfect monitoring and perfect tracking of contacts of a reported case, (2) perfect reporting of cases and perfect monitoring of tracked reported cases and (3) perfect reporting and perfect tracking of contacts of reported cases. Furthermore, we gave a lower bound on the proportion of contacts to be traced TRANS to ensure that the effective reproduction, $\mathcal{R}_c$, is below one and describe $\mathcal{R}_c$ in terms of observable quantities such as the proportion of reported and traced TRANS cases. Model simulations using the Covid-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission TRANS of Covid-19 and reduce peak hospitalizations and deaths MESHD. In particular, our findings suggest that effective monitoring policy of tracked cases and tracking of traced contacts TRANS while incubating are more crucial than tracing TRANS more contacts.

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MeSH Disease
Human Phenotype

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