Corpus overview


Overview

MeSH Disease

Transmission

Seroprevalence
    displaying 1 - 10 records in total 105
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    The repurposed drugs suramin and quinacrine inhibit cooperatively in vitro SARS-CoV-2 3CLpro

    Authors: Raphael J Eberle; Danilo S Olivier; Marcos S Amaral; Dieter Willbold; Raghuvir K Arni; Monika A Coronado; Andrea Benedetti; Jan Larmann; Markus A Weigand; Sean McGrath; Claudia Denkinger; Stefan Baral; Jeff Kwong; Deepali Kumar; Atul Humar; Adrienne Chan; Seham Noureldin; Joshua Booth; Rachel Hong; David Smookler; Wesam Aleyadeh; Anjali Patel; Bethany Barber; Julia Casey; Ryan Hiebert; Henna Mistry; Ingrid Choong; Colin Hislop; Deanna Santer; D. Lorne Tyrrell; Jeffrey S. Glenn; Adam J. Gehring; Harry LA Janssen; Bettina Hansen

    doi:10.1101/2020.11.11.378018 Date: 2020-11-12 Source: bioRxiv

    Since the first report of a new pneumonia disease MESHD pneumonia disease HP in December 2019 (Wuhan, China) up to now WHO reported more than 50 million confirmed cases TRANS and more than one million losses, globally. The causative agent of COVID-19 MESHD (SARS-CoV-2) has spread worldwide resulting in a pandemic of unprecedented magnitude. To date, no clinically safe drug or vaccine is available and the development of molecules to combat SARS-CoV-2 infections MESHD is imminent. A well-known strategy to identify molecules with inhibitory potential against SARS-CoV-2 proteins is the repurposing of clinically developed drugs, e.g., anti-parasitic drugs. The results described in this study demonstrate the inhibitory potential of quinacrine and suramin against SARS-CoV-2 main protease (3CLpro). Quinacrine and suramin molecules present a competitive and non-competitive mode of inhibition, respectively, with IC50 and KD values in low M range. Using docking and molecular dynamics simulations we identified a possible binding mode and the amino acids involved in these interactions. Our results suggested that suramin in combination with quinacrine showed promising synergistic efficacy to inhibit SARS-CoV-2 3CLpro. The identification of effective, synergistic drug combinations could lead to the design of better treatments for the COVID-19 MESHD disease. Drug repositioning offers hope to the SARS-CoV-2 control.

    Nafamostat Mesylate in lipid carrier TRANS for nasal SARS-CoV2 titer reduction MESHD in a hamster model

    Authors: Lisette Cornelissen; Esmee Hoefsmit; Disha Rao; Judith Lijnsvelt; Lucien ven Keulen; Marieke van Es; Volker Grimm; Rene H Medema; Christian U Blank; Marcella Chiari; Alessandro Gori; Marina Cretich

    doi:10.1101/2020.11.09.372375 Date: 2020-11-09 Source: bioRxiv

    Severe acute respiratory syndrome MESHD corona virus 2 (SARS-CoV-2) has been responsible for the largest pandemic in recent decades. After seemingly being in control due to consequent lock-downs and social distancing, the majority of countries faces currently a second wave of exponentially increasing infections, hospital referrals and deaths due to SARS-CoV-2-mediated disease MESHD ( COVID-19 MESHD). To date, no effective vaccination has been found, and wearing masks and social distancing are the only effective approaches to reduce further spreading. However, unwillingness in the societies to distance again and consequently wear masks might be reasons for the second SARS-CoV-2 infection MESHD wave. User-friendly chemicals interfering at the host site with viral entry might be an approach to contain the pandemic. In addition, such an approach would work synergistic with vaccinations that miss new virus mutants. Nafamostat (NM) has been shown in vitro to interfere with cellular virus entry by inhibition of the host transmembrane protease serine 2 (TMPRSS2), an enzyme required for SARS-CoV-2 spike protein cleavage, a prerequisite for cell entry. We hypothesized that nasal application of NM in a liposomal layer (as additional mechanical barrier) could lower the nasal viral load and subsequently reduce the severity of COVID-19 MESHD. We found, indeed, that nasal viral load one day post single NM application, was lowered in a hamster SARS-CoV-2 infection MESHD model. However, severity of subsequent local tissue destruction and weight loss HP weight loss MESHD due to pneumonitis MESHD was not favorably altered. In conclusion, a single NM application reduced nasal viral load, but did not favorably improve the outcome of COVID-19 MESHD, likely due to the short half-time of NM. Improvement of NM stability or repetitive application (which was not permitted in this animal model according to Dutch law) might circumvent these challenges.

    COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19 MESHD

    Authors: Faraz Khoshbakhtian; Ahmed Bilal Ashraf; Shehroz S. Khan

    id:2010.02814v1 Date: 2020-10-06 Source: arXiv

    As of September 2020, the COVID-19 MESHD pandemic continues to devastate the health and well-being of the global population. With more than 33 million confirmed cases TRANS and over a million deaths, global health organizations are still a long way from fully containing the pandemic. This pandemic has raised serious questions about the emergency preparedness of health agencies, not only in terms of treatment of an unseen disease, but also in identifying its early symptoms. In the particular case of COVID-19 MESHD, several studies have indicated that chest radiography images of the infected MESHD patients show characteristic abnormalities. However, at the onset of a given pandemic, such as COVID-19 MESHD, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because the time spent in collecting large amounts of infected peoples' data could lead to the loss of human lives and delays in preventive interventions. Therefore, we formulate this problem within a one-class classification framework, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest ( COVID-19 MESHD in our case). To solve this problem, we present COVIDomaly, a convolutional autoencoder framework to detect unseen COVID-19 MESHD cases from the chest radiographs. We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults TRANS, and (ii) healthy and other non- COVID-19 MESHD pneumonia HP pneumonia MESHD, and detected COVID-19 MESHD as an anomaly MESHD. After performing 3-fold cross validation, we obtain a pooled ROC-AUC of 0.7652 and 0.6902 in the two settings respectively. These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays.

    Broad-spectrum, patient-adaptable inhaled niclosamide-lysozyme particles are efficacious against coronaviruses in lethal murine infection models

    Authors: Ashlee D Brunaugh; Hyojong Seo; Zachary Warnken; Li Ding; Sang Heui Seo; Hugh D.C. Smyth; Justin Rafa O De La Fuente; Megan Mathew; Desmond Green; Sayari Patel; Maria Virginia Perez Bastidas; Sara Haddadi; Mukunthan Murthi; Miguel Santiago Gonzalez; Shweta Kambali; Kayo HM Santos; Huda Asif; Farzaneh Modarresi; Mohammad Faghihi; Mehdi Mirsaeidi

    doi:10.1101/2020.09.24.310490 Date: 2020-09-24 Source: bioRxiv

    Niclosamide (NIC) has demonstrated promising in vitro antiviral efficacy against SARS-CoV-2, the causative agent of the COVID-19 MESHD pandemic. Though NIC is already FDA-approved, the oral formulation produces systemic drug levels that are too low to inhibit SARS-CoV-2. As an alternative, direct delivery of NIC to the respiratory tract as an aerosol could target the primary site of for SARS-CoV-2 acquisition MESHD and spread. We have developed a niclosamide powder suitable for delivery via dry powder inhaler, nebulizer, and nasal spray through the incorporation of human lysozyme (hLYS) as a carrier TRANS molecule. This novel formulation exhibits potent in vitro and in vivo activity against MERS-CoV and SARS-CoV-2 and protects against methicillin-resistance staphylococcus aureus pneumonia MESHD pneumonia HP and inflammatory lung damage MESHD. The suitability of the formulation for all stages of the disease and low-cost development approach will ensure wide-spread utilization.

    Respiratory Rehabilitation After Blood SERO Transfusion in a COVID-19 MESHD Patient: A Case Report

    Authors: Mohammad Javad Mousavi; Narges Obeidi; Saeed keshmiri; Farzan Azodi; Jamile Kiyani; Farhad Abbasi

    doi:10.21203/rs.3.rs-78131/v1 Date: 2020-09-15 Source: ResearchSquare

    Background: The coronavirus disease 2019 MESHD ( COVID-19 MESHD), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been identified as the most crucial threat of the century. Due to severe pneumonia MESHD pneumonia HP and acute respiratory distress syndrome MESHD respiratory distress HP syndrome ( ARDS MESHD), the SARS-CoV-2 can cause shortness of breath MESHD, hypoxemia MESHD hypoxemia HP, and the need to mechanical ventilation, intensive care unit (ICU) management, and eventual death MESHD. We have tried to use a non-invasive approach to prevent patient from needing respiratory support with invasive ventilation (IV). Here, for the first time, improvement of oxygen delivery and oxygen saturation levels were observed in a COVID-19 MESHD patient using packed red blood SERO cells (PRBCs) transfusion.Case presentation: A 63-year-old man with a history of smoking and addiction who came to our hospital facility with fever HP fever MESHD, shortness of breath MESHD and decreased blood SERO oxygen saturation. High-resolution chest CT revealed bilateral and multifocal ground-glass opacities consistent with COVID-19 MESHD. Subsequently, the COVID-19 MESHD infection was confirmed TRANS by real-time polymerase chain reaction (qRT-PCR) assay of the upper respiratory tract. Conclusions: Oxygen delivery and oxygen saturation improvement were observed in the COVID-19 MESHD patient, after PRBCs transfusions.

    COVID-19 MESHD outbreak, social distancing and mass testing in Kenya - Insights from a mathematical model 

    Authors: Rachel Waema Mbogo; John W. Oddhiambo

    doi:10.21203/rs.3.rs-77523/v1 Date: 2020-09-14 Source: ResearchSquare

    As reported by the World Health Organization (WHO), the world is currently facing a devastating pandemic of a novel coronavirus ( COVID -19), which started as an outbreak of pneumonia HP neumonia MESHDof unknown cause in the Wuhan city of China in December 2019. Within days and weeks, the COVID -19 pandemic had spread to over 210 countries. By the end of April, COVID -19 had caused over three million confirmed cases TRANS of i nfections MESHDand 230,000 fatalities globally. The trend poses a huge threat to global public health. Understanding the early transmission TRANS dynamics of the i nfection MESHDand evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission TRANS to occur in new areas.We employed a SEIHCRD delay differential mathematical transmission TRANS model with reported Kenyan data on cases of COVID -19 to estimate how transmission TRANS varies over time and which population to target for mass testing. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak and the vulnerable populations. The results from the model gives insights to the government on the population to target for mass testing. The government should target population in the informal settlement for mass testing. People with pre-existing medical and non-medical conditions should be identified and given special medical care.  With aggressive effective mass testing and adhering to the government directives and guidelines, we can get rid of COVID -19 epidemic.

    COVID-19 MESHD outbreak, social distancing and mass testing in Kenya - Insights from a mathematical model 

    Authors: Rachel Waema Mbogo; John W. Odhiambo

    doi:10.21203/rs.3.rs-77523/v2 Date: 2020-09-14 Source: ResearchSquare

    As reported by the World Health Organization (WHO), the world is currently facing a devastating pandemic of a novel coronavirus ( COVID -19), which started as an outbreak of pneumonia HP neumonia MESHDof unknown cause in the Wuhan city of China in December 2019. Within days and weeks, the COVID -19 pandemic had spread to over 210 countries. By the end of April, COVID -19 had caused over three million confirmed cases TRANS of i nfections MESHDand 230,000 fatalities globally. The trend poses a huge threat to global public health. Understanding the early transmission TRANS dynamics of the i nfection MESHDand evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission TRANS to occur in new areas.We employed a SEIHCRD delay differential mathematical transmission TRANS model with reported Kenyan data on cases of COVID -19 to estimate how transmission TRANS varies over time and which population to target for mass testing. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak and the vulnerable populations. The results from the model gives insights to the government on the population to target for mass testing. The government should target population in the informal settlement for mass testing. People with pre-existing medical and non-medical conditions should be identified and given special medical care.  With aggressive effective mass testing and adhering to the government directives and guidelines, we can get rid of COVID -19 epidemic.

    Prospective Time Periodic Geographical Covid-19 MESHD Surveillance in Ethiopia Using a Space-time Scan Statistics: Detecting and Evaluating Emerging Clusters

    Authors: Nuredin Nassir Azmach; Tesfay Gebremariam Tesfahannes; Samiya Abrar Abdulsemed; Temam Abrar Hamza

    doi:10.21203/rs.3.rs-76052/v1 Date: 2020-09-11 Source: ResearchSquare

    Background: On December 31, 2019, multiple pneumonia MESHD pneumonia HP cases, subsequently identified as coronavirus disease 2019 MESHD ( COVID-19 MESHD), was reported for the first time in Wuhan, the capital city of Hubei province in China. At that time, the Wuhan Municipal Health Commission had report 27 cases, of which seven are severely ill, and the remaining cases are stable and controllable. Since, then, the spread of COVID-19 MESHD has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. As of September 07, 2020, there had been more than 27 million confirmed cases TRANS and 889,000 total deaths MESHD, with an average mortality of about 3.3%, globally. In Ethiopia, 58,672 confirmed cases TRANS and 918 deaths MESHD and this number are likely to increase exponentially. It is critical to detect clusters of COVID-19 MESHD to better allocate resources and improve decision-making as the pandemics continue to grow.Methods: We have collected the individual-level information on patients with laboratory-confirmed COVID-19 MESHD on daily bases from the official reports of the Ethiopian Federal Ministry of Health (FMOH), regional, and city government of Addis Ababa and Dire Dawa health bureaus. Using the daily case data, we conducted a prospective space-time analysis with SaTScan version 9.6. We detect statistically significant space-time clusters of COVID-19 MESHD at the woreda and sub-city level in Ethiopia between March 13th-June 6th, 2020, and March 13th-June 24th, 2020.Results: The prospective space-time scan statistic detected “alive” and emerging clusters that are present at the end of our study periods; notably, nine more clusters were detected when adding the updated case data.Conclusions: These results can notify public health officials and decision-makers about where to improve the allocation of resources, testing areas; also, where to implement necessary isolation measures and travel TRANS bans. As more confirmed cases TRANS become available, the statistic can be rerun to support timely surveillance of COVID-19 MESHD, demonstrated here. In Ethiopia, our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 MESHD.

    Performance SERO of serum SERO apolipoprotein-A1 as a sentinel of Covid-19 MESHD

    Authors: Thierry Poynard; Olivier Deckmyn; Marika Rudler; Valentina Peta; Yen Ngo; Mathieu Vautier; Sepideh Akhavan; Vincent Calvez; Clemence Franc; Jean Marie Castille; Fabienne Drane; Mehdi Sakka; Dominique Bonnefont-Rousselot; Jean Marc Lacorte; David Saadoun; Yves Allenbach; Olivier Benveniste; Frederique Gandjbakhch; Julien Mayaux; Olivier Lucidarme; Bruno Fautrel; Vlad Ratziu; Chantal Housset; Dominique Thabut; Patrice Cacoub; Fredrik Nyberg; Jose D Posada; Christian G Reich; Lisa M Schilling; Karishma Shah; Nigham H Shah; Vignesh Subbian; Lin Zhang; Hong Zhu; Patrick Ryan; Daniel Prieto-Alhambra; Kristin Kostka; Talita Duarte-Salles

    doi:10.1101/2020.09.01.20186213 Date: 2020-09-03 Source: medRxiv

    Background Since 1920, a decrease in serum SERO cholesterol has been identified as a marker of severe pneumonia HP pneumonia MESHD. We have assessed the performance SERO of serum SERO apolipoprotein-A1, the main transporter of HDL-cholesterol, to identify the early spread of coronavirus disease 2019 MESHD ( Covid-19 MESHD) in the general population and its diagnostic performance SERO for the Covid-19 MESHD. Methods We compared the daily mean serum SERO apolipoprotein-A1 during the first 34 weeks of 2020 in a population that is routinely followed for a risk of liver fibrosis MESHD risk in the USA (212,297 sera) and in France (20,652 sera) in relation to a local increase in confirmed cases TRANS, and in comparison to the same period in 2019 (266,976 and 28,452 sera, respectively). We prospectively assessed the sensitivity SERO of this marker in an observational study of 136 consecutive hospitalized cases and retrospectively evaluated its specificity in 7,481 controls representing the general population. Results The mean serum SERO apolipoprotein-A1 levels in the survey populations began decreasing in January 2020, compared to the same period in 2019. This decrease was highly correlated with the daily increase in confirmed Covid-19 MESHD cases in the following 34 weeks, both in France and USA, including the June and mid-July recovery periods in France. Apolipoprotein-A1 at the 1.25 g/L cutoff had a sensitivity SERO of 90.6% (95%CI84.2-95.1) and a specificity of 96.1% (95.7-96.6%) for the diagnosis of Covid-19 MESHD. The area under the characteristics curve was 0.978 (0.957-0.988), and outperformed haptoglobin and liver function tests. The adjusted risk ratio of apolipoprotein-A1 for survival without transfer to intensive care unit was 5.61 (95%CI 1.02-31.0;P=0.04). Conclusion Apolipoprotein-A1 could be a sentinel of the pandemic in existing routine surveillance of the general population. NCT01927133, CER-2020-14.

    Viral and Bacterial Pneumonia HP Detection using Artificial Intelligence in the Era of COVID-19 MESHD

    Authors: Mehmet Ozsoz; Abdullahi Umar Ibrahim; Sertan Serte; Fadi Al-Turjman; Polycarp Shizawaliyi Yakoi

    doi:10.21203/rs.3.rs-70158/v1 Date: 2020-09-01 Source: ResearchSquare

    Background: The outbreak of COVID-19 MESHD on the eve of January 2020 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) in mid-March. Currently the outbreak has affected more than 150 countries with more than 20 million confirmed cases TRANS and more than 700,000 death tolls. The standard method for detection of COVID-19 MESHD is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) which is less sensitive, expensive and required specialized health expert. As the number of cases continue to grow, there is high need for developing rapid screening method that is accurate, fast and cheap. Methods: We proposed the use of Deep Learning approach based on Pretrained AlexNet Model for classification of COVID-19 MESHD, non- COVID-19 MESHD viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP and normal Chest X-rays Images (CXR) scans obtained from different public databases. Result and Conclusion: For non- COVID-19 MESHD viral pneumonia HP pneumonia MESHD and healthy datasets, the model achieved 94.43% accuracy, 98.19% Sensitivity SERO and 95.78% Specificity. For bacterial pneumonia MESHD pneumonia HP and healthy datasets, the model achieved 91.43% accuracy, 91.94% sensitivity SERO and 100% Specificity. For COVID-19 MESHD pneumonia HP pneumonia MESHD and healthy CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity SERO and 100% Specificity. For classification of COVID-19 MESHD pneumonia HP pneumonia MESHD and non- COVID-19 MESHD viral pneumonia MESHD pneumonia HP, the model achieved 99.62% accuracy, 90.63% sensitivity SERO and 99.89% Specificity. For multiclass datasets the model achieved 94.00% accuracy, 91.30% sensitivity SERO and 84.78% specificity for COVID-19 MESHD, bacterial pneumonia MESHD pneumonia HP and healthy. For 4 classes ( COVID-19 MESHD, non- COVID-19 MESHD viral pneumonia HP, bacterial pneumonia HP and healthy, the model achieved accuracy of 93.42%, sensitivity SERO of 89.18% and specificity of 98.92%.

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MeSH Disease
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