Corpus overview


MeSH Disease

Infections (496)

Disease (450)

Coronavirus Infections (261)

Pneumonia (172)

Death (171)

Human Phenotype

Pneumonia (194)

Fever (60)

Cough (31)

Hypertension (21)

Falls (20)


    displaying 41 - 50 records in total 1372
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    Assessment of multiplex digital droplet RT-PCR as an accurate diagnosis tool for SARS-CoV-2 detection in nasopharyngeal swabs and saliva samples

    Authors: Kevin CASSINARI; Elodie Alessandri; Pascal Chambon; Francoise Charbonnier; Segolene Gracias; Ludivine Beaussire; Kevin Alexandre; Nasrin Sarafan-Vasseur; Claude Houdayer; Manuel Etienne; Francois Caron; Jean-Christophe Plantier; Thierry Frebourg

    doi:10.1101/2020.08.02.20166694 Date: 2020-08-04 Source: medRxiv

    RT-qPCR on nasopharyngeal swabs is currently the reference COVID-19 diagnosis method. We developed a multiplex RT-ddPCR assay, targeting six SARS-CoV-2 genomic regions, and evaluated it on nasopharyngeal swabs and saliva samples collected from 130 COVID-19 positive or negative ambulatory individuals, who presented symptoms suggestive of mild or moderate Sars-CoV2 infection MESHD. The 6-plex RT-ddPCR assay was shown to have 100% sensitivity SERO on nasopharyngeal swabs and a higher sensibility than RT-qPCR on saliva (85% versus 62%). Saliva samples from 2 individuals with negative results on nasopharyngeal swabs were found to be positive. These results show that multiplex RT-ddPCR should represent an alternative and complementary tool for the diagnosis of COVID-19, in particular to control RT-qPCR ambiguous results, and its application to saliva an appropriate strategy for repetitive sampling and testing individuals for whom nasopharyngeal swabbing is not possible.

    A Comprehensive Analysis of COVID-19 Transmission TRANS and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

    Authors: Tanmoy Bhowmik; Sudipta Dey Tirtha; Naveen Chandra Iraganaboina; Naveen Eluru

    doi:10.1101/2020.08.03.20164137 Date: 2020-08-04 Source: medRxiv

    Background: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission TRANS and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission TRANS/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission TRANS/mortality rates considering a comprehensive set of factors in a unified framework. Method: We study two per capita dependent variables: (1) daily COVID-19 transmission TRANS rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity SERO analysis to predict the impact of mobility restrictions at a county level. Findings: Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma MESHD Asthma HP, Cancer, HIV and heart disease MESHD, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission TRANS and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 30% in daily cases. Interpretation: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission TRANS rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission TRANS. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available). Funding: None.

    Highly performing point-of-care molecular testing for SARS-CoV-2 with RNA extraction and isothermal amplification.

    Authors: Pierre Garneret; Etienne Coz; Elian Martin; Jean-Claude Manuguerra; Elodie Brient-Litzler; Vincent Enouf; Daniel Felipe Gonzalez Obando; Jean-Christophe Olivo-Marin; Fabrice Monti; Sylvie Van der Werf; Jessica Vanhomwegen; Patrick Jean Tabeling

    doi:10.1101/2020.08.01.20166538 Date: 2020-08-04 Source: medRxiv

    In order to respond to the urgent request of massive testing, developed countries perform nucleic acid amplification tests (NAAT) of SARS-CoV-2 in centralized laboratories. Real-time RT - PCR (Reverse transcription - Polymerase Chain Reaction) is used to amplify the viral RNA and enable its detection. Although PCR is 37 years old, it is still considered, without dispute, as the gold standard. PCR is an efficient process, but the complex engineering required for automated RNA extraction and temperature cycling makes it incompatible for use in point of care settings. In the present work, by harnessing progress made in the past two decades in DNA amplification, microfluidics and membrane technologies, we succeeded to create a portable test, in which SARS-CoV-2 RNA is extracted, amplified isothermally by RT - LAMP (Loop-mediated Isothermal Amplification), and detected using intercalating dyes or highly fluorescent probes. Depending on the viral load, the detection takes between twenty minutes and one hour. Using pools of naso-pharyngal clinical samples, we estimated a sensitivity SERO comparable to RT-qPCR (up to a Cycle threshold of 39, equivalent to <0.1 TCID50 per mL) and a 100% specificity, for other human coronaviruses and eight respiratory viruses currently circulating in Europe. We designed and fabricated an easy-to-use portable device called COVIDISC to carry out the test at the point of care. The low cost of the materials along with the absence of complex equipment paves the way towards a large dissemination of this device. The perspective of a reliable SARS-CoV-2 point of care detection, highly performing, that would deliver on-site results in less than one hour opens up a new efficient approach to manage the pandemics.

    Low-Cost Enhancement of Facial Mask Filtration to Prevent Transmission TRANS of COVID-19

    Authors: Hari Bhimaraju; Nitish Nag; Ramesh Jain

    doi:10.1101/2020.08.01.20166637 Date: 2020-08-04 Source: medRxiv

    The use of face masks is recommended worldwide to reduce the spread of COVID-19. A plethora HP of facial coverings and respirators, both commercial and homemade, pervade the market, but the true filtration capabilities of many homemade measures against the virus are unclear and continue to be unexplored. In this work, we compare the efficacy of the following masks in keeping out particulate matter below 2.5 microns: N95 respirators, surgical masks, cloth masks, cloth masks with activated carbon air filters, cloth masks with HVAC air filters, lightly starch-enhanced cloth masks, and heavily-starched cloth masks. The experiments utilize an inhalation system and aerosol chamber to simulate a masked individual respiring aerosolized air. COVID-19 disproportionately affects people in low-income communities, who often lack the resources to acquire appropriate personal protective equipment and tend to lack the flexibility to shelter in place due to their public-facing occupations. This work tests low-cost enhancements to homemade masks to assist these communities in making better masks to reduce viral transmission TRANS. Experimental results demonstrate that the filtration efficacy of cloth masks with either a light or heavy starch can approach the performance SERO of much costlier masks. This discovery supports the idea of low-cost enhancements to reduce transmission TRANS and protect individuals from contracting COVID-19.

    FaceOff: Detecting Face Touching with a Wrist-Worn Accelerometer

    Authors: Xiang 'Anthony' Chen

    id:2008.01769v1 Date: 2020-08-04 Source: arXiv

    According to the CDC, one key step of preventing oneself from contracting coronavirus (COVID-19) is to avoid touching eyes, nose, and mouth with unwashed hands. However, touching one's face is a frequent and spontaneous behavior---one study observed subjects touching their faces on average 23 times per hour. Creative solutions have emerged amongst some recent commercial and hobbyists' projects, yet most either are closed-source or lack validation in performance SERO. We develop FaceOff---a sensing technique using a commodity wrist-worn accelerometer to detect face-touching behavior based on the specific motion pattern of raising one's hand towards the face. We report a survey (N=20) that elicits different ways people touch their faces, an algorithm that temporally ensembles data-driven models to recognize when a face touching behavior occurs and results from a preliminary user testing (N=3 for a total of about 90 minutes).

    Analysis of COVID-19 and comorbidity co- infection MESHD Model with Optimal Control

    Authors: Dr. Andrew Omame; Nometa Ikenna

    doi:10.1101/2020.08.04.20168013 Date: 2020-08-04 Source: medRxiv

    The new coronavirus disease MESHD 2019 (COVID-19) infection MESHD is a double challenge for people infected with comorbidities such as cardiovascular and cerebrovascular diseases MESHD and diabetes. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection MESHD in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re- infection MESHD. The model is simulated using data relevant to the dynamics of the diseases MESHD in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection MESHD by comorbid susceptibles as well as the rate of re- infection MESHD by those who have recovered from a previous COVID-19 infection MESHD. Sensitivity SERO analysis of the model when the population of individuals co-infected with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co- infection MESHD model are the effective contact rate for COVID-19 transmission TRANS, $\beta\sst{cv}$, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, $\chi\sst{cm}$, the comorbidity development rate, $\theta\sst{cm}$, the detection rate for singly infected and co-infected individuals, $\eta_1$ and $\eta_2$, as well as the recovery rate from COVID-19 for co-infected individuals, $\varphi\sst{i2}$. Simulations of the model reveal that the cumulative confirmed cases TRANS (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases TRANS (including those co-infected with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re- infection MESHD rates for COVID-19 is 0.1 per day. It may be worse than this if the re- infection MESHD rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection MESHD by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected individuals to 0.7 per day, then the reproduction number TRANS can be brought very low below one, and COVID-19 infection MESHD eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection MESHD by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

    R3T (Rapid Research Response Team) One-step RT-qPCR kit for COVID-19 diagnostic using in-house enzymes

    Authors: Masateru Takahashi; Muhammad Tehseen; Rahul Salunke; Etsuko Takahashi; Sara Mfarrej; Mohamed A. Sobhy; Fatimah Alhamlan; Sharif Hala; Gerardo R. Mandujano; Ahmed A. Al-Qahtani; Fadwa S. Alofi; Afrah Alsomali; Anwar M. Hashem; Asim Khogeer; Naif A. M. Almontashiri; Jae Man Lee; Hiroaki Mon; Kosuke Sakashita; Mo Li; Takahiro Kusakabe; Arnab Pain; Samir M. Hamdan

    doi:10.1101/2020.07.31.20165704 Date: 2020-08-04 Source: medRxiv

    One-step RT-qPCR is the most widely applied method for COVID-19 diagnostics. Designing in-house one-step RT-qPCR kits is restricted by the patent-rights for the production of enzymes and the lack of information about the components of the commercial kits. Here, we provide a simple, economical, and powerful one-step RT-qPCR kit based on patent-free, specifically-tailored versions of Moloney Murine Leukemia MESHD Leukemia HP Virus Reverse Transcriptase and Thermus aquaticus DNA polymerase termed the R3T (Rapid Research Response Team) One-step RT-qPCR. Our kit was routinely able to reliably detect as low as 10 copies of the synthetic RNAs of the SARS-CoV-2. More importantly, our kit successfully detected COVID-19 in clinical samples of broad viral titers with similar reliability and selectivity as that of the Invitrogen SuperScript III Platinum One-step RT-qPCR and TaqPath 1-Step RT-qPCR kits. Overall, our kit has shown robust performance SERO in both of laboratory settings and the Saudi Ministry of Health-approved testing facility.

    An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency MESHD department

    Authors: Farah E. Shamout; Yiqiu Shen; Nan Wu; Aakash Kaku; Jungkyu Park; Taro Makino; Stanisław Jastrzębski; Duo Wang; Ben Zhang; Siddhant Dogra; Meng Cao; Narges Razavian; David Kudlowitz; Lea Azour; William Moore; Yvonne W. Lui; Yindalon Aphinyanaphongs; Carlos Fernandez-Granda; Krzysztof J. Geras

    id:2008.01774v1 Date: 2020-08-04 Source: arXiv

    During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency MESHD department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance SERO in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

    Integration of heparin-binding protein and interleukin-6 in the early prediction of respiratory failure HP and mortality in pneumonia MESHD pneumonia HP by SARS-CoV-2 (COVID-19)

    Authors: Maria Saridaki; Simeon Metallidis; Sotiria Grigiropoulou; Emmanouil Vrentzos; Malvina Lada; Katerina Argyraki; Olga Tsachouridou; Anna Georgiadou; Anil Vasishta; Evangelos J. Giamarellos-Bourboulis

    doi:10.21203/ Date: 2020-08-03 Source: ResearchSquare

    Purpose Recent publications on the probable role of heparin-binding protein (HBP) as a biomarker in sepsis MESHD sepsis HP prompted us to investigate its diagnostic and prognostic performance SERO in severe COVID-19Methods HBP and IL-6 were measured by immunoassays SERO at admission and on day 7 in 178 patients with pneumonia MESHD pneumonia HP by SARS-CoV-2. Patients were classified into non- sepsis MESHD sepsis HP and sepsis MESHD sepsis HP as per the Sepsis MESHD Sepsis HP-3 definitions and were followed-up for the development of severe respiratory failure HP (SRF) and for outcome. Results were confirmed by multivariate analyses.Results HBP was significantly higher in patients classified as having sepsis MESHD sepsis HP and was negatively associated with the oxygenation ratio and positively associated with creatinine and lactate. Logistic regression analysis evidenced admission HBP more than 18 ng/ml and IL-6 more than 30 pg/ml as independent risk factors for the development of SRP. Their integration prognosticated SRF with respective sensitivity SERO, specificity, positive predictive value SERO and negative predictive 59.1%, 96.3%, 83.9% and 87.8%. Cox regression analysis evidenced admission HBP more than 35 ng/ml and IL-6 more than 30 pg/ml as independent risk factors for 28-day mortality. Their integration prognosticated 28-day mortality with respective sensitivity SERO, specificity, positive predictive value SERO and negative predictive 69.2%, 92.7%, 42.9% and 97.5%. HBP remained unchanged over-time course. Conclusion A prediction score of the disposition of patients with COVID-19 is proposed taking into consideration admission levels of IL-6 and HBP. Using different cut-offs the score may predict the likelihood for SRF and for 28-day outcome. 

    Interpretable Sequence Learning for COVID-19 Forecasting

    Authors: Sercan O. Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; Elli Kanal; Tomas Pfister

    id:2008.00646v1 Date: 2020-08-03 Source: arXiv

    We propose a novel approach that integrates machine learning into compartmental disease MESHD modeling to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance SERO. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and here we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts, in metrics averaged across the entire US, than state-of-the-art alternatives, and that it provides qualitatively meaningful explanatory insights. Lastly, we analyze the performance SERO of our model for different subgroups based on the subgroup distributions within the counties.

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MeSH Disease
Human Phenotype

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