Corpus overview


MeSH Disease

Human Phenotype


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    Evaluating SARS-CoV-2 spike and nucleocapsid proteins as targets for IgG antibody SERO detection in severe and mild COVID-19 cases using a Luminex bead-based assay

    Authors: Joachim Marien; Johan Michiels; Leo Heyndrickx; Karen Kerkhof; Nikki Foque; Marc-Alain Widdowson; Isabelle Desombere; Hilde Jansens; Marjan Van Esbroeck; Kevin K. Arien

    doi:10.1101/2020.07.25.20161943 Date: 2020-07-27 Source: medRxiv

    Large-scale serosurveillance of severe acute respiratory syndrome MESHD coronavirus type 2 (SARS-CoV-2) will only be possible if serological tests SERO are sufficiently reliable, rapid and inexpensive. Current assays are either labour-intensive and require specialised facilities (e.g. virus neutralization assays), or expensive with suboptimal specificity (e.g. commercial ELISAs SERO). Bead-based assays offer a cost-effective alternative and allow for multiplexing to test for antibodies SERO of other pathogens. Here, we compare the performance SERO of four antigens for the detection of SARS-CoV-2 specific IgG antibodies SERO in a panel of sera that includes both severe (n=40) and mild (n=52) cases, using a neutralization and a Luminex bead-based assay. While we show that neutralising antibody SERO levels are significantly lower in mild than in severe cases, we demonstrate that a combination of recombinant nucleocapsid protein (NP), receptor-binding domain (RBD) and the whole spike protein (S1S2) results in a highly sensitive (96%) and specific (99%) bead-based assay that can detect IgG antibodies SERO in both groups. Although S1-specific IgG levels correlate most strongly with neutralizing antibody SERO levels, they fall HP below the detection threshold in 10% of the cases in our Luminex assay. In conclusion, our data supports the use of RBD, NP and S1S2 for the development of SARS-CoV-2 serological bead-based assays. Finally, we argue that low antibody SERO levels in mild/ asymptomatic TRANS cases might complicate the epidemiological assessment of large-scale surveillance studies.

    FIREBall-2: The Faint Intergalactic Medium Redshifted Emission Balloon Telescope

    Authors: Erika Hamden; D. Christopher Martin; Bruno Milliard; David Schiminovich; Shouleh Nikzad; Jean Evrard; Gillian Kyne; Robert Grange; Johan Montel; Etienne Pirot; Keri Hoadley; Donal O'Sullivan; Nicole Melso; Vincent Picouet; Didier Vibert; Philippe Balard; Patrick Blanchard; Marty Crabill; Sandrine Pascal; Frederi Mirc; Nicolas Bray; April Jewell; Julia Blue Bird; Jose Zorilla; Hwei Ru Ong; Mateusz Matuszewski; Nicole Lingner; Ramona Augustin; Michele Limon; Albert Gomes; Pierre Tapie; Xavier Soors; Isabelle Zenone; Muriel Saccoccio

    id:2007.08528v1 Date: 2020-07-16 Source: arXiv

    The Faint Intergalactic Medium Redshifted Emission Balloon (FIREBall) is a mission designed to observe faint emission from the circumgalactic medium of moderate redshift (z~0.7) galaxies for the first time. FIREBall observes a component of galaxies that plays a key role in how galaxies form and evolve, likely contains a significant amount of baryons, and has only recently been observed at higher redshifts in the visible. Here we report on the 2018 flight of the FIREBall-2 Balloon telescope, which occurred on September 22nd, 2018 from Fort Sumner, New Mexico. The flight was the culmination of a complete redesign of the spectrograph from the original FIREBall fiber-fed IFU to a wide-field multi-object spectrograph. The flight was terminated early due to a hole in the balloon, and our original science objectives were not achieved. The overall sensitivity SERO of the instrument and telescope was 90,000 LU, due primarily to increased noise from stray light. We discuss the design of the FIREBall-2 spectrograph, modifications from the original FIREBall payload, and provide an overview of the performance SERO of all systems. We were able to successfully flight test a new pointing control system, a UV-optimized, delta-doped and coated EMCCD, and an aspheric grating. The FIREBall-2 team is rebuilding the payload for another flight attempt in the Fall HP of 2021, delayed from 2020 due to COVID-19.

    Clinical utility of targeted SARS-CoV-2 serology testing to aid the diagnosis and management of suspected missed, late or post-COVID-19 infection MESHD syndromes MESHD: results from a pilot service

    Authors: Nicola Sweeney; Blair Merrick; Suzanne Pickering; Rui Pedro Galao; Alina Botgros; Harry D. Wilson; Adrian W. Signell; Gilberto Betancor; Mark Kia Ik Tan; John Ramble; Neophytos Kouphou; Sam Acors; Carl Graham; Jeffrey Seow; Eithne MacMahon; Stuart J. D. Neil; Michael H. Malim; Katie Doores; Sam Douthwaite; Rahul Batra; Gaia Nebbia; Jonathan D. Edgeworth

    doi:10.1101/2020.07.10.20150540 Date: 2020-07-11 Source: medRxiv

    Objectives: Determine indications and clinical utility of SARS-CoV-2 serology testing in adults TRANS and children TRANS. Design: Prospective evaluation of initial three weeks of a daily Monday to Friday pilot SARS-CoV-2 serology service for patients. Setting: Early post 'first-wave' SARS-CoV-2 transmission TRANS period at single centre London teaching hospital that provides care to the local community, as well as regional and national referral pathways for specialist services. Participants: 110 (72 adults TRANS, 38 children TRANS, age TRANS range 0-83 years, 52.7% female TRANS (n=58)). Interventions: Patient serum SERO from vetted referrals tested on CE marked and internally validated lateral flow immunoassay SERO (LFIA) (SureScreen Diagnostics) detecting antibodies to SARS-CoV-2 SERO spike proteins, with result and clinical interpretation provided to the direct care team. Main outcome measures: Performance SERO characteristics, source and nature of referrals, feasibility and clinical utility of the service, particularly the benefit for clinical decision-making. Results: The LFIA was deemed suitable for clinical advice and decision making following evaluation with 310 serum samples SERO from SARS-CoV-2 PCR positive patients and 300 pre-pandemic samples, giving a sensitivity SERO and specificity of 96.1% and 99.3% respectively. For the pilot, 115 referrals were received leading to 113 tests performed on 108 participants (sample not available for two participants); paediatrics (n=35), medicine (n=69), surgery (n=2) and general practice (n=2). 43.4% participants (n=49) had detectable antibodies to SARS-CoV-2 SERO. There were three main indications for serology; new acute presentations potentially triggered by recent COVID-19 infection MESHD e.g. PIMS-TS (n=26) and pulmonary embolism MESHD pulmonary embolism HP (n=5), potential missed diagnoses in context of a recent compatible illness (n=40), and making infection MESHD control and immunosuppression treatment decisions in persistently SARS-CoV-2 RNA PCR positive individuals (n=6). Conclusions: This study shows acceptable performance SERO characteristics, feasibility and clinical utility of a SARS-CoV-2 serology service using a rapid, inexpensive and portable assay for adults TRANS and children TRANS presenting with a range of clinical indications. Results correlated closely with a confirmatory in-house ELISA SERO. The study showed the benefit of introducing a serology service where there is a reasonable pre-test probability, and the result can be linked with clinical advice or intervention. Experience thus far is that the volume of requests from hospital referral routes are manageable within existing clinical and laboratory services; however, the demand from community referrals has not yet been assessed. Given recent evidence for a rapid decline in antibodies SERO, particularly following mild infection MESHD, there is likely a limited window of opportunity to realise the benefit of serology testing for individuals infected during the 'first-wave' before they potentially fall HP below a measurable threshold. Rapidly expanding availability of serology services for NHS patients will also help understand the long-term implications of serostatus and prior infection MESHD in different patient groups, particularly before emergence of any 'second-wave' outbreak or introduction of a vaccination programme.

    The feasibility of transfer learning for differentiation H1N1 Influenza from COVID-19 on chest CT

    Authors: Houman Sotoudeh; Baharak Tasorian; Seyed Mohsen Tabatabaei; Ehsan Sotoudeh; Abdollatif Moini

    doi:10.21203/ Date: 2020-07-09 Source: ResearchSquare

    Objectives: It is unlikely that by fall HP and winter of 2020, standard vaccine or treatment is available for COVID-19 infection MESHD. In this period, differentiation between COVID-19 and Influenza induced pneumonia MESHD pneumonia HP will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia MESHD pneumonia HP (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall SERO and F1 score of each model were calculated.Results: For differentiation of COVID-19 pneumonia MESHD pneumonia HP versus H1N1 Influenza pneumonia MESHD pneumonia HP versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia MESHD pneumonia HP. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.

    COVID-19 screening strategies that permit the safe re-opening of college campuses

    Authors: A David Paltiel; Amy Zheng; Rochelle P Walensky

    doi:10.1101/2020.07.06.20147702 Date: 2020-07-07 Source: medRxiv

    Importance: The COVID-19 pandemic poses an existential threat to many US residential colleges: either they open their doors to students in September or they risk serious financial consequences. Objective: To define SARS-CoV-2 screening performance SERO standards that would permit the safe return of students to campus for the Fall HP 2020 semester. Design: Decision and cost-effectiveness analysis linked to a compartmental epidemic model to evaluate campus screening using tests of varying frequency (daily-weekly), sensitivity SERO (70%-99%), specificity (98%-99.7%), and cost ($10-$50/test). Reproductive numbers TRANS Rt = {1.5, 2.5, 3.5} defined three epidemic scenarios, with additional infections MESHD imported via exogenous shocks MESHD shocks HP. We generally adhered to US government guidance for parameterization data. Participants: A hypothetical cohort of 5000 college- age TRANS, uninfected students. Main Outcome(s) and Measure(s): Cumulative tests, infections MESHD, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact. All measured over an 80-day, abbreviated semester. Results: With Rt = 2.5, daily screening with a 70% sensitive, 98% specific test produces 85 cumulative student infections MESHD and isolation dormitory daily census averaging 108 (88% false positives). Screening every 2 (7) days nets 135 (3662) cumulative infections MESHD and daily isolation census 66 (252) with 73% (4%) false positives. Across all scenarios, test frequency exerts more influence on outcomes than test sensitivity SERO. Cost-effectiveness analysis selects screening every {2, 1, 7} days with a 70% sensitive test as the preferred strategy for Rt = {2.5, 3.5, 1.5}, implying a screening cost of {$470, $920, $120} per student per semester. Conclusions & Relevance: Rapid, inexpensive and frequently conducted screening (even if only 70% sensitive) would be cost-effective and produce a modest number of COVID-19 infections MESHD. While the optimal screening frequency hinges on the success of behavioral interventions to reduce the base severity of transmission TRANS (Rt), this could permit the safe return of student to campus.

    Reopening universities during the COVID-19 pandemic: A testing strategy to minimize active cases and delay outbreaks

    Authors: Lior Rennert; Corey Andrew Kalbaugh; Lu Shi; Christopher McMahan

    doi:10.1101/2020.07.06.20147272 Date: 2020-07-07 Source: medRxiv

    Background: University campuses present an ideal environment for viral spread and are therefore at extreme risk of serving as a hotbed for a COVID-19 outbreak. While active surveillance throughout the semester such as widespread testing, contact tracing TRANS, and case isolation, may assist in detecting and preventing early outbreaks, these strategies will not be sufficient should a larger outbreak occur. It is therefore necessary to limit the initial number of active cases at the start of the semester. We examine the impact of pre-semester NAT testing on disease MESHD disease spread TRANS spread in a university setting. Methods: We implement simple dynamic transmission TRANS models of SARS-CoV-2 infection MESHD to explore the effects of pre-semester testing strategies on the number of active infections MESHD and occupied isolation beds throughout the semester. We assume an infectious period TRANS of 3 days and vary R0 TRANS to represent the effectiveness of disease MESHD mitigation strategies throughout the semester. We assume the prevalence SERO of active cases at the beginning of the semester is 5%. The sensitivity SERO of the NAT test is set at 90%. Results: If no pre-semester screening is mandated, the peak number of active infections MESHD occurs in under 10 days and the size of the peak is substantial, ranging from 5,000 active infections MESHD when effective mitigation strategies ( R0 TRANS = 1.25) are implemented to over 15,000 active infections MESHD for less effective strategies ( R0 TRANS = 3). When one NAT test is mandated within one week of campus arrival, effective ( R0 TRANS = 1.25) and less effective ( R0 TRANS = 3) mitigation strategies delay the onset of the peak to 40 days and 17 days, respectively, and result in peak size ranging from 1,000 to over 15,000 active infections MESHD. When two NAT tests are mandated, effective ( R0 TRANS = 1.25) and less effective ( R0 TRANS = 3) mitigation strategies delay the onset of the peak through the end of fall HP semester and 20 days, respectively, and result in peak size ranging from less than 1,000 to over 15,000 active infections MESHD. If maximum occupancy of isolation beds is set to 2% of the student population, then isolation beds would only be available for a range of 1 in 2 confirmed cases TRANS ( R0 TRANS = 1.25) to 1 in 40 confirmed cases TRANS ( R0 TRANS = 3) before maximum occupancy is reached. Conclusion: Even with highly effective mitigation strategies throughout the semester, inadequate pre-semester testing will lead to early and large surges of the disease MESHD and result in universities quickly reaching their isolation bed capacity. We therefore recommend NAT testing within one week of campus return. While this strategy is sufficient for delaying the timing of the outbreak, pre-semester testing would need to be implemented in conjunction with effective mitigation strategies to reduce the outbreak size.

    A Case Study in Model Failure? COVID-19 Daily Deaths MESHD and ICU Bed Utilisation Predictions in New York State

    Authors: Vincent Chin; Noelle I. Samia; Roman Marchant; Ori Rosen; John P. A. Ioannidis; Martin A. Tanner; Sally Cripps

    id:2006.15997v1 Date: 2020-06-26 Source: arXiv

    Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death MESHD counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths MESHD against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death MESHD counts. Two additional data sources that we examined also provided different death MESHD counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell HP within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance SERO tests, before their results are provided to policy makers and public health officials.

    Examining the Effect of COVID-19 on Foreign Exchange Rate and Stock Market -- An Applied Insight into the Variable Effects of Lockdown on Indian Economy

    Authors: Indrajit Banerjee; Atul Kumar; Rupam Bhattacharyya

    id:2006.14499v1 Date: 2020-06-23 Source: arXiv

    The relationship between a pandemic and the concurrent economy is quite comparable to the relation observed among health and wealth in general. Since 25th March 2020, India has been under a nation-wide lockdown. This work attempts to examine the effect of COVID-19 on the foreign exchange rates and stock market performances SERO of India using secondary data over a span of 48 days. The study explores whether the causal relationships among the growth rate of confirmed cases TRANS (GrowthC), exchange rate (GEX) and SENSEX value (GSENSEX) are remaining the same across different pre and post-lockdown phases, attempting to capture any potential changes over time via the Vector Auto Regressive (VAR) models. A positive correlation is found between the growth rate of confirmed cases TRANS and the growth rate of exchange rate, and a negative correlation between the growth rate of confirmed cases TRANS and the growth rate of SENSEX value. A naive interpretation from this could be that with the rising growth rate of the number of confirmed cases TRANS, the economy took a toll, reflected by the Indian currency being depreciated while the stock exchange index suffered from a fall HP. However, on applying a VAR model, it is observed that an increase in the confirmed COVID-19 cases causes no significant change in the values of the exchange rate and SENSEX index. The result varies if the analysis is split across different time periods - before lockdown, first phase of lockdown and extension of lockdown. To compare the three periods, we had undertaken five rounds of analyses. Nuanced and sensible interpretations of the numeric results indicate significant variability across time in terms of the relation between the variables of interest. This detailed knowledge about the varying patterns of dependence could potentially help the policy makers and investors of India in order to develop their policies to cope up the situation.

    Can medication mitigate the need for a strict lock down?: A mathematical study of control strategies for COVID-19 infection MESHD

    Authors: Mohsin Ali; Mudassar Imran; Adnan Khan

    doi:10.21203/ Date: 2020-06-14 Source: ResearchSquare

    BackgroundCOVID-19 is a pandemic that has swept across the world in 2020. To date the only effective control mechanisms were non-pharmaceutical interventions, however there have been encouraging reports regarding possible medication in the literature, with emergency MESHD approval given to some drugs in various countries.MethodsWe formulate a deterministic epidemic model to study the effects of medication on the transmission TRANS dynamics of Corona Virus Disease MESHD (COVID-19). We are especially interested in how the availability of medication could change the necessary quarantine measures for effective control of the disease MESHD. We model the transmission TRANS by extending the SEIR model to include asymptomatic TRANS, quarantined, isolated and medicated population compartments.ResultsWe calculate the basic reproduction number TRANS R0 TRANS and show that for R0 TRANS<1 the disease MESHD dies out and for R0 TRANS>1 the disease MESHD is endemic. Using sensitivity SERO analysis we establish that R0 TRANS is most sensitive to the rates of quarantine and medication. We also study how the effectiveness and the rate of medication along with the quarantine rate affect R0 TRANS. We devise optimal quarantine, medication and isolation strategies, noting that availability of medication reduces the duration and severity of the lock-down needed for effective disease MESHD control.ConclusionOur study also reinforces the idea that with the availability of medication, while the severity of the lock downs can be eased over time some social distancing protocols need to be observed, at least till a vaccine is found. We also analyze the COVID-19 outbreak data for four different countries, in two of these, India and Pakistan the curve is still rising, and in he other two, Italy and Spain, the epidemic curve is now falling HP due to effective quarantine measures. We provide estimates of R0 and the proportion of asymptomatic TRANS individuals in the population for these countries.

    Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

    Authors: Khair Ahammed; Md. Shahriare Satu; Mohammad Zoynul Abedin; Md. Auhidur Rahaman; Shiekh Mohammed Shariful Islam

    doi:10.1101/2020.06.07.20124594 Date: 2020-06-08 Source: medRxiv

    This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity SERO (94.03%) and specificity (97.01%) as well as the lowest fall HP out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall HP out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission TRANS compared to traditional methods.

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MeSH Disease
Human Phenotype

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