Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (231)

Fever (70)

Cough (38)

Hypertension (27)

Falls (24)


Transmission

Seroprevalence
    displaying 681 - 690 records in total 1730
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    The Natural Way Forward: Molecular Dynamics Simulation Analysis of Phytochemicals from Indian Medicinal Plants as Potential Inhibitors of SARS-CoV-2 Targets

    Authors: Pratap Kumar Parida; Dipak Paul; Debamitra Chakravorty

    doi:10.26434/chemrxiv.12581216.v1 Date: 2020-07-01 Source: ChemRxiv

    The natural way forward: Molecular dynamics simulation analysis of phytochemicals from Indian medicinal plants as potential inhibitors of SARS-CoV-2 targetsPratap Kumar Parida MESHD 1#, Dipak Paul 1#, Debamitra Chakravorty 2*# 1 Noor Enzymes Private Limited, 37-B, Darga Road, Kolkata - 700 017, India2 Novel Techsciences (OPC) Private Limited, 37-B, Darga Road, 1st Floor, Kolkata - 700 017, India * Corresponding author:Debamitra Chakravorty, PhD (Project Lead - Computational Biology)Novel Techsciences (OPC) Private Limited, 37-B, Darga Road, 1st Floor, Kolkata - 700 017, IndiaE-mail: dc@noveltechsciences.com#All the authors have contributed equally to the paper.AbstractThe pandemic COVID-19 has become a global panic and health issue forcing our lives towards a compromised "new normal". Research is still ongoing to develop effective antiviral drugs and vaccines against SARS-CoV-2. Thus, to address the current outbreak, development of natural inhibitors as a prophylactic measure is an attractive strategy due to their natural diversity and safety. Phytochemicals that target viral entry (Spike glycoprotein) and replication (3CLPro) are lucrative in terms of both economy and health for the treatment of the deadly virus. In this context, this work explored natural compounds from Indian medicinal plants as potential inhibitors for containing the spread SARS-CoV-2. The phytochemicals were rationally screened from 55 Indian medicinal plants in our previous work. All atom 100 ns molecular dynamics simulations were performed using high performance SERO computing for 8 top scoring rationally screened phytochemicals from Withania somnifera and Azadirachta indica and two repurposed drugs against the spike glycoprotein and the main protease of SARS-CoV-2. MM/PBSA, Principal component analysis and hydrogen bond occupancy were analysed to characterize protein–ligand interactions and to find the binding free energy. Biological pathway enrichment analysis was also carried out to observe the therapeutic efficacy of these phytochemicals. The results revealed that Withanolide R (-141.96 KJ/Mol) and 2,3-Dihydrowithaferin A (-87.60 KJ/Mol) were with the lowest relative free energy of binding for main protease and the spike proteins respectively. It was also observed that the phytochemicals exhibit a remarkable multipotency with the ability to modulate various human biological pathways especially pathways in cancer MESHD. Conclusively we suggest that these compounds need further detailed in vivo experimental evaluation and clinical validation for implementation as potent therapeutic agent for combating SARS-CoV-2.

    Analyzing the Current Status of India in Global Scenario with Reference to COVID-19 Pandemic

    Authors: Dharmendra Kumar Yadav; Sharvari Shukla; S.K. Yadav

    id:10.20944/preprints202007.0001.v1 Date: 2020-07-01 Source: Preprints.org

    The crux of the paper is to present a detailed analysis of COVID-19 data which is available on global basis. This analysis is performed using some specific package of R software. It provides various insights from the data and help to understand the current status of this pandemic in India so that effective measures can be formulated by policymakers. These insights include global summary of this disease, growth rate of this pandemic and performance SERO of SIR model for the given global data. The analysis has been presented in different tables and graphs to understand the outputs of the problem in a more detailed point of view.

    Performance SERO analysis of Zero Black-Derman-Toy interest rate model in catastrophic events: COVID-19 case study

    Authors: Grzegorz Krzyżanowski; Andrés Sosa

    id:2007.00705v2 Date: 2020-07-01 Source: arXiv

    In this paper we continue the research of our recent interest rate tree model called Zero Black-Derman-Toy (ZBDT) model, which includes the possibility of a jump at each step to a practically zero interest rate. This approach allows to better match to risk of financial slowdown caused by catastrophic events. We present how to valuate a wide range of financial derivatives for such a model. The classical Black-Derman-Toy (BDT) model and novel ZBDT model are described and analogies in their calibration methodology are established. Finally two cases of applications of the novel ZBDT model are introduced. The first of them is the hypothetical case of an S-shape term structure and decreasing volatility of yields. The second case is an application of the ZBDT model in the structure of United States sovereign bonds in the current $2020$ economic slowdown caused by the Coronavirus pandemic. The objective of this study is to understand the differences presented by the valuation in both models for different derivatives.

    Assessment of a Diagnostic Strategy Based on Chest Computed Tomography in Patients Hospitalized for COVID-19 Pneumonia HP: an observational study

    Authors: Marine Thieux; Anne Charlotte Kalenderian; Aurelie Chabrol; Laurent Gendt; Emma Giraudier; Herve Lelievre; Samir Lounis; Yves Mataix; Emeline Moderni; Laetitia Paradisi; Guillaume Ranchon; Carlos El Khoury

    doi:10.1101/2020.06.29.20140129 Date: 2020-06-30 Source: medRxiv

    Objectives: To assess the relevance of a diagnostic strategy for COVID-19 based on chest computed tomography (CT) in patients with hospitalization criteria. Setting: Observational study with retrospective analysis in a French emergency department (ED). Participants and intervention: From March 3 to April 2, 2020, 385 adult TRANS patients presenting to the ED for suspected COVID-19 underwent an evaluation that included history, physical examination, blood SERO tests, real-time reverse transcription-polymerase chain reaction (RT-PCR) and chest CT. When the time-interval between chest CT and RT-PCR assays was longer than 7 days, patients were excluded from the study. Only patients with hospitalization criteria were included. Diagnosis accuracy was assessed using the sensitivity SERO and specificity of RT-PCR. Outcomes: Sensitivity SERO and specificity of RT-PCR, chest CT (also accompanied by lymphopenia HP lymphopenia MESHD) were measured and were also analyzed by subgroups of age TRANS and sex. Results: Among 377 included subjects, RT-PCR was positive in 36%, while chest CT was compatible with a COVID-19 diagnosis in 59%. In the population with positive RT-PCR, there were more men (55% vs 37%, p=0.015), a higher frequency of reticular and, or, interlobular septal thickening (53% vs 31%, p=0.02) as well as a higher frequency of bilateral lesion distribution (98% vs 86%, p=0.01) compared to the population with negative RT-PCR. The proportion of lymphopenia HP lymphopenia MESHD was higher in men vs women (47% vs 39%, p=0.03) and varies between patients >80 versus 50-80 and p<0.001). Using CT as reference, RT-PCR obtained a sensitivity SERO of 61%, specificity of 93%. There was a significant difference between CT and RT-PCR diagnosis performance SERO (p<0.001). When CT was accompanied by lymphopenia HP lymphopenia MESHD, sensitivity SERO and specificity of RT-PCR were respectively 71% and 94%. CT abnormalities and lymphopenia HP lymphopenia MESHD provided diagnosis in 29% of patients with negative PCR. Conclusions: Chest CT had a superior yield than RT-PCR in COVID-19 hospitalized patients, especially when accompanied by lymphopenia HP lymphopenia MESHD.

    EVALUATION OF THE ABBOTT SARS-COV-2 IG-G ASSAY.

    Authors: CS Lau; SP Hoo; YL Liang; TC Aw

    doi:10.1101/2020.06.28.20132498 Date: 2020-06-30 Source: medRxiv

    Introduction: Antibodies SERO to the novel severe acute respiratory syndrome coronavirus 2 MESHD (SARS-CoV-2) can increase as soon as 10-13 days after infection MESHD. We describe our evaluation of the Abbott SARS-CoV-2 MESHD IgG assay on the Architect immunoassay SERO analyser. Methods: We assessed the precision, sensitivity SERO, and specificity of the Abbott SARS-CoV-2 MESHD IgG assay in samples from polymerase chain reaction (PCR) positive patients and healthy healthcare workers. The manufacturer cut-off index (COI) of 1.4 was adopted to identify positive results. We examined the assay cross-reactivity with other viral antibodies SERO (influenza/dengue/ hepatitis HP hepatitis MESHD C/ hepatitis HP B) and rheumatoid MESHD factor (RF). The sample throughput of the Abbott assay was also assessed. Results: The Abbott assay showed excellent precision, with a CV of 3.4% for the negative control (COI = 0.06) and 1.6% for a high positive serum sample SERO (COI = 8.6). Residual serum SERO was available from 57 inpatients not initially suspected of having COVID-19, 29 of whom tested positive for SARS-CoV-2 IgG. The Abbott assay has a sensitivity SERO of 90.9-100% when tested in 54 subjects [≥]14 days post PCR positive, and a specificity of 100% (N = 358). There was no cross-reactivity with other viral antibodies SERO (influenza/dengue/ hepatitis HP hepatitis MESHD C/ hepatitis HP B) and RF. The Architect Abbott assay has a throughput of 100 samples in 70 minutes. Conclusion: The Abbott SARS-CoV-2 MESHD IgG assay shows excellent performance SERO that is well within FDA and CDC guidelines when testing patients [≥]14 days POS with little cross-reactivity from other viral antibodies SERO. There is some evidence that SARS-CoV-2 IgG develops early in the disease process.

    A handheld point-of-care system for rapid detection of SARS-CoV-2 in under 20 minutes

    Authors: Jesus Rodriguez-Manzano; Kenny Malpartida-Cardenas; Nicolas Moser; Ivana Pennisi; Matthew Cavuto; Luca Miglietta; Ahmad Moniri; Rebecca Penn; Giovanni Satta; Paul Randell; Frances Davies; Frances Bolt; Wendy Barclay; Alison Holmes; Pantelis Georgiou

    doi:10.1101/2020.06.29.20142349 Date: 2020-06-30 Source: medRxiv

    The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission TRANS and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (< 20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 90.55% sensitivity SERO and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 {+/-} 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n=40), showing average detection times of 12.89 {+/-} 2.59 min for positive samples (n=34), demonstrating a comparable performance SERO to a benchtop commercial instrument. Paired with a smartphone for results visualization and geo-localization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

    Validation and Comparison of a Modified CDC Assay with two Commercially Available Assays for the Detection of SARS-CoV-2 in Respiratory Specimen MESHD

    Authors: Amorce Lima; Vicki Healer; Elaine Vendrone; Suzane Silbert

    doi:10.1101/2020.06.29.179192 Date: 2020-06-30 Source: bioRxiv

    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease MESHD 2019 (COVID-19), has spread rapidly around the globe since it was first identified in December of 2019 in Wuhan, China. In a race to contain the infection MESHD, researchers and healthcare officials have developed several assays to help diagnose individuals with COVID-19. To help laboratories in deciding what assay to bring into testing lines, factors such as assay availability, cost, throughput, and TAT should be considered. Here we validated a modified version of the CDC assay and used it as a reference to evaluate the performance SERO of the NeuMoDx SARS-CoV-2 MESHD and DiaSorin Simplexa Covid-19 Direct assays. In silico analysis and clinical sample testing showed that the primesr/probes designed by the CDC were specific to the SARS-CoV-2 as they accurately detected all reactive samples with an assay LoD of 200 copies/ml. The performance SERO of the three assays were analyzed using 161 nasopharyngeal swabs specimen tested within 24 hours or 5 days from routine testing. A 100% agreement was observed between the commercial assays and the modified CDC SARS-CoV-2 assay. A deeper look at the Ct values showed no significant difference between NeuMoDx and the modified CDC SARS-CoV-2 assay, whereas DiaSorin had lower overall Ct values than the modified CDC SARS-CoV-2 assay. NeuMoDx and DiaSorin workflows were much easier to perform. NeuMoDx has the highest throughput and shortest TAT, whereas although the modified CDC SARS-CoV-2 assay has comparable throughput to DiaSorin, it has the longest hands-on time, and highest TAT.

    Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression

    Authors: Feng Zhou; Tao Chen; Baiying Lei

    id:2006.16942v1 Date: 2020-06-30 Source: arXiv

    Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood SERO biomarkers, including lactic dehydrogenase, lymphocyte (%) and high- sensitivity SERO C-reactive protein, and their interactions. We found that when the fatality probability produced by the logistic regression model was over 0.8, the model had the optimal performance SERO in that it was able to predict patient fatalities more than 11.30 days on average with maximally 34.91 days in advance, an accumulative f1-score of 93.76% and and an accumulative accuracy score of 93.92%. Such a model can be used to identify COVID-19 patients with high risks with three blood SERO biomarkers and help the medical systems around the world plan critical medical resources amid this pandemic.

    Forecasting the Number of Coronavirus (COVID-19) Cases in Ethiopia Using Exponential Smoothing Times Series Model

    Authors: Teshome H Abebe Sr.

    doi:10.1101/2020.06.29.20142489 Date: 2020-06-30 Source: medRxiv

    The main objective of this study is to forecast COVID-19 case in Ethiopiausing the best-fitted model. The time series data of COVID-19 case in Ethiopia from March 14, 2020 to June 05, 2020 were used.To this end, exponential growth, single exponential smoothing method, and doubleexponential smoothing methodwere used. To evaluate the forecasting performance SERO of the model, root mean sum of square error was used. The study showed that double exponential smoothing methods was appropriate in forecasting the future number ofCOVID-19 cases in Ethiopia as dictated by lowest value of root mean sum of square error MESHD. The forecasting model shows that the number of coronavirus cases in Ethiopia grows exponentially. The finding of the results would help the concerned stakeholders to make the right decisions based on the information given on forecasts.

    Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

    Authors: Srilok Srinivasan; Rohit Batra; Henry Chan; Ganesh Kamath; Mathew J. Cherukara; Subramanian Sankaranarayanan

    doi:10.26434/chemrxiv.12581075.v1 Date: 2020-06-30 Source: ChemRxiv

    An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. Computational docking simulations have traditionally been used for in silico ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional in silico ligand design. Here, we present a de novo molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100's) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski's rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance SERO. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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