Corpus overview


MeSH Disease

Infections (473)

Disease (430)

Coronavirus Infections (249)

Pneumonia (164)

Death (162)

Human Phenotype

Pneumonia (185)

Fever (59)

Cough (30)

Hypertension (21)

Falls (20)


    displaying 701 - 710 records in total 1301
    records per page

    Preliminary evaluation of COVID-19 disease MESHD outcomes, test capacities and management approaches among African countries.

    Authors: Adebayo A Otitoloju; Esther O Oluwole; Kafilat A Bawa-Allah; Mayowa J Fasona; Ifeoma P Okafor; Chukwuemeka Isanbor; Vincent O Osunkalu; Abimbola A Sowemimo; Obafemi A Keshinro; Idowu A Aneyo; Olawale S Folarin; Akinbami A Oladokun; Oluwatosin J Akinsola; Christianah I Ayolabi; Tenny O Egwuatu; Victor A Owoyomi; Anthony E Ogbeibu

    doi:10.1101/2020.05.16.20103838 Date: 2020-05-20 Source: medRxiv

    Background: Following the declaration of COVID-19 as a global pandemic and the report of index case in Africa, the number of countries in Africa with confirmed cases TRANS of the infection MESHD has grown tremendously with disease MESHD now being reported in almost all countries on the continent, with the exemption of Lesotho after 75 days. It is therefore necessary to evaluate the disease MESHD outcomes among the African countries as the situation unfolds for early identification of best practices for adoption. Methods: In this study, COVID-19 disease MESHD outcomes ( confirmed cases TRANS, deaths MESHD and recoveries), testing capacities and disease MESHD management approaches among African countries were evaluated. The relationship between COVID-19 infections MESHD in African countries and their performance SERO on global resilient indices including the Human Development Index (HDI), performance SERO on Sustainable Development Goals (SDGs) and the Global Risk Index (GRI) were also examined. Data acquired from various standard databases were evaluated over a period of 75 days from the date of reporting the index case. Results: This study has revealed compelling spatial differences in the incidence, deaths MESHD and recoveries from COVID-19 among African countries. Egypt, South Africa, Morocco and Algeria were clustered as countries with highest values of COVID-19 disease MESHD outcomes on the continent during the 75-day period of observation. The cluster analysis and comparison of countries in terms of percentage recovered cases of confirmed TRANS infections MESHD revealed that Mauritius, Mauritania, Gambia, Burkina Faso, Madagascar, Togo and Uganda had the highest scores. Comparative analysis of COVID-19 across the world revealed that the parameters were relatively inconsequential in Oceania and Africa continents, while Europe, North America and Asia had significantly higher cases of disease MESHD outcomes. For COVID-19 testing capacities, South Africa, Ghana and Egypt are leading in total number of tests carried out. However when the number of tests carried out were related to population number of the countries, Djibouti, Mauritius, Ghana and South Africa are found to be the leading countries. With respect to management of the disease MESHD in Africa, all the countries adopted the WHO protocols, personal hygiene, economic palliatives and social distancing measures. Only three countries in Africa (Madagascar, Togo and Burkina Faso) had a state supported initiative to utilise traditional medicines or herbs as alternatives to control COVID-19. Additionally, most of the countries are providing prompt treatment of the patients with a range of drugs especially Hydroxychloroquine, Chloroquine and Chloroquine-Azithromycin combination. The study found that no strong relationship currently exists between the global resilient indicators (HDI, SDG and GRI) and COVID-19 cases across Africa. Conclusions: This study has revealed compelling spatial differences in disease MESHD outcomes among African countries and also found testing capacities for COVID-19 to be abysmally low in relation to the population. During the 75 days of observation, African countries have recorded significantly low number of deaths MESHD associated with COVID-19 and relatively high recovery rates. Countries in Africa with higher rate of recovery from the disease MESHD were found to have adopted strict adherence to some of WHO protocol to contain the disease MESHD, isolate all those who test positive to the disease MESHD and provide prompt treatment of the patients with a range of drugs especially Hydroxychloroquine, Chloroquine and Chloroquine-Azithromycin combination. The study recommends that the approaches adopted by the African countries which achieved high recovery rates from COVID-19 should be integrated into healthcare management plans for the disease MESHD across the continent even as the situation unfolds.

    A New Radiomic Study on Lung CT Images of Patients with Covid-19 using LBP and Deep Learning (Convolutional Neural Networks (CNN))

    Authors: Hüseyin Yaşar; Murat Ceylan

    doi:10.21203/ Date: 2020-05-20 Source: ResearchSquare

    The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease MESHD, pneumonia MESHD pneumonia HP develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease MESHD occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs MESHD were revealed by the help of lung CT imaging. In this study, the amount of 25 lung CT images in total (15 of Covid-19 patients and 10 of normal) was multiplied (250 images in total) using three data augmentation methods which relate to contrast change, brightness change and noise addition, and these images were subjected to automatic classification. Within the scope of the study, experiments were made for each case which include the use of the CT images of lungs (gray-level and RGB) directly, the images obtained by applying Local Binary Pattern (LBP) to these images (gray-level and RGB) and the images obtained by combining these images (gray-level and RGB). In the study, a 23-layer Convolutional Neural Networks (CNN) architecture was developed and used in classification processes. Leave-one-group-out cross validation method was used to test the proposed system. In this context, the result of the study indicated that the best AUC and EER values were obtained for the combination of original (RGB) and LBP applied (RGB) images, and these figures are 0,9811 and 0,0445 respectively. It was observed that, applying LBP to images, the use of CNN input causes an increase in sensitivity SERO values while it causes a decrease in values of specificity. The highest sensitivity SERO was obtained for the case of using LBP-applied (RGB) images and has a value of 0,9947. Within the scope of the study, the highest values of specificity and accuracy were obtained by the help of CT of lungs (gray-level) with 0,9120 and 95,32%, respectively. The results of the study indicate that analyzing images of lung CT using deep learning methods in diagnosing Covid-19 disease MESHD will speed up the diagnosis and significantly reduce the burden on healthcare workers.

    A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

    Authors: Huseyin Yaşar; Murat Ceylan

    doi:10.21203/ Date: 2020-05-20 Source: ResearchSquare

    At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia MESHD Pneumonia HP develops due to Covid-19 virus in patients having severe disease MESHD symptoms. Many literature studies have been carried out in the process where the effects of the disease MESHD-induced pneumonia MESHD pneumonia HP in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease MESHD by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity SERO, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity SERO, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

    Performance SERO of progressive and adaptive COVID-19 exit strategies: a stress test analysis for managing intensive care unit rates

    Authors: Jan-Diederik van Wees; Martijn van der Kuip; Sander Osinga; David van Westerloo; Michael Tanck; Maurice Hanegraaf; Maarten Pluymaekers; Olwijn Leeuwenburgh; Lonneke van Bijsterveldt; Pien Verreijdt; Logan Brunner; Marceline Tutu van Furth

    doi:10.1101/2020.05.16.20102947 Date: 2020-05-20 Source: medRxiv

    Background: In May 2020, many European countries have begun to introduce an exit strategy for the coronavirus disease MESHD 2019 (COVID-19) pandemic which involves relaxing social distancing measures. Predictive epidemiological modeling indicates that chances for resurgence are high. However, parametrization of the epidemiological nature of COVID-19 and the effect of relaxing social distancing is not well constrained, resulting in highly uncertain outcomes in view of managing future intensive care unit (ICU) needs. Methods and findings: For performance SERO analysis of exit strategies we developed an open-source ensemble-based Susceptible-Exposed-Infectious-Removed (SEIR) model. It takes into account uncertainties for the COVID-19 parametrization and social distancing measures. The model is calibrated to data of the outbreak and lockdown phase. For the exit phase, the model includes the capability to activate an emergency MESHD brake, reinstating lockdown conditions. Alternatively, the model uses an adaptive COVID-19 cruise control (ACCC) capable to retain a targeted ICU level. The model is demonstrated for the Netherlands and we analyzed progressive and adaptive exit strategies through a stress test of managing ICU rates. The progressive strategy reflects the outcome of social and economic pressure to use one-way steering toward progressively relaxing measures at an early stage. It is marked by a high probability for the activation of the emergency MESHD brake due to an unsolicited growth of ICU needs in the following months. Alternatively, the two-way steering ACCC can flatten ICU needs in a more gradual way and avoids activation of the emergency MESHD brake. It also performs well for seasonal variation in the reproduction number TRANS of severe acute respiratory syndrome MESHD-coronavirus. Conclusions: The adaptive strategy (ACCC) is favored, as it avoids the use of the emergency MESHD brake at the expense of small steps of restrictive measures and allows the exploration of riskier and potentially rewarding measures in the future pathways of the exit strategy.

    Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

    Authors: Xuanyu Mao; Xiao-Ping Liu; Miao Xiong; Xu Yang; Xiaoqing Jin; Zhiqiang Li; Shuang Zhou; Hang Chang

    doi:10.1101/2020.05.15.20103473 Date: 2020-05-20 Source: medRxiv

    Coronavirus Disease MESHD 2019 (COVID-19) is currently a global pandemic, and the early screening of COVID-19 is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia MESHD pneumonia HP (CAP) patients, the COVID-19 patients revealed significantly higher abundance of these signals. Furthermore, unsupervised feature learning leads to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that has been double-blindly validated in an independent hospital ( sensitivity SERO: 0.941, specificity: 0.904, AUC: 0.952). Our findings could open a new avenue to assist screening of COVID-19 patients.

    Can we use these masks? Rapid Assessment of the Inhalation Resistance Performance SERO of Uncertified Medical Face Masks in the Context of Restricted Resources Imposed during a Public Health Emergency MESHD

    Authors: Steven Begg; Nwabueze G Emekwuru; Nicolas Miche; Bill Whitney; Obuks Ejohwomu

    doi:10.1101/2020.05.13.20100602 Date: 2020-05-19 Source: medRxiv

    In the case of a public health emergency MESHD such as the COVID-19 pandemic, access to large quantities of appropriate personal protection equipment (PPE) has presented a significant problem. A shortage of face masks and respirators has been widely reported across the world. A concerted effort to manufacture high volumes has not unsurprisingly put pressure on the supply chain and the important certification processes. PPE procured or donated as uncertified stock requires rigorous, expedient and scientifically informed evidence before decisions can be made regarding suitable deployment, expensive certification, return or possible destruction of stock. This paper reports a series of experiments devised in reaction to this situation. In this study, an experimental methodology for the assessment of the filtration performance SERO of samples of real-world, uncertified, fluid resistant surgical masks (FRSM type IIR) was evaluated in the resource limited (lockdown) environment of the COVID-19 pandemic. A steady-state flow rig was adapted to incorporate a bespoke filter flow chamber for mounting face masks in order to evaluate the resistance to air flow as an indicator of mask inhalation performance SERO. Pure air was drawn through a specified control surface area at known flow rate conditions; the resistance to the air flow through the masks was measured as the resulting pressure drop. Over 60 tests were performed from 4 different, randomly sampled batches and compared to a control sample of EN Type IIR certified FRSM masks. Steady-state volumetric airflow rates of 30 and 95 lmin-1 were chosen to represent deep breathing and vigorous exercise conditions respectively. The results showed that the sample masks produced a pressure drop of between 26% to 58% compared to the control batch at the lower flow rate and 22% to 55% at the higher rate. The results for each sample were consistent across both flow rates. Within the group of masks tested, two sets (between 48% and 58% of the reference set) showed the potential to be professionally assessed for appropriate deployment in a suitable setting. Although the absolute values of pressure drop measured by this method are unlikely to correlate with other testing approaches, the observed, indicative trends and relative performance SERO of the masks is key to this approach. Critically, this method does not replace certification but it has enabled a public body to quickly make decisions; certify, re-assign, refund, thus saving time and resources. The total time spent conducting the tests was less than 8 hours and the low cost method proposed can be repurposed for low resource regions.

    Diagnostic Performance SERO of a Blood SERO Urea Nitrogen to Creatinine Ratio-Based Nomogram for Predicting in-Hospital Mortality in COVID-19 Patients

    Authors: Qingquan Liu; Yiru Wang; Xuecheng Zhao; Lixuan Wang; Feng Liu; Yongman Lv; Tao Wang; Dawei Ye

    doi:10.21203/ Date: 2020-05-19 Source: ResearchSquare

    Background: The novel coronavirus disease MESHD (COVID-19) is leading to high morbidity and mortality. This study aimed to test whether blood SERO urea nitrogen-to-creatinine ratios (BCR) is a predictor of poor prognosis in patients with COVID-19. Method: From 9,165 generally healthy subjects, we calculated ranges of “normal” BCR values. 416 COVID-19 patients were randomly assigned to training cohort and validation cohort contained 337, 79 patients, respectively. The prognostic ability of abnormal BCR range was assessed using a Logistic regression. Development a nomogram for predicting in-hospital mortality incorporated age TRANS, sex and BCR. The model discrimination was assessed using the calibration curves and concordance index in training and validation cohort. The predictive accuracy and clinical values of the nomogram was measured by decision curve analysis (DCA) and clinical impact curve analysis (CICA). Results: Among 337 COVID-19 patients, 13.4% and 11.3% were classified into higher and lower than normal range group, respectively. The BCR was identified as an independent risks factor for death MESHD in COVID-19 patients (P<0.0001), with area under the curve (AUC) 0.768; 95%CI: 0.717-0.819). Kaplan-Meier curves for all-cause mortality outcomes showed that patients with above normal range of BCR had worse prognosis (p<0.0001). Logistic regression analysis revealed that BCR above the normal range was independently associated with death MESHD in COVID-19 patients (Odds ratio 7.54; 95%CI: 1.55-36.66; P=0.012). ROC curves showed that the nomogram had good discrimination in the training cohort (AUC 0.838; 95%CI 0.795–0.880) and the validation cohort (AUC 0.929; 95%CI 0.869-0.989). Using maximum Youden index, the cutoff values of 59.8 points, the sensitivity SERO and specificity were 75.4% and 81%. The calibration curves showed good agreement between nomogram prediction and actual observation. DCA and CICA indicated the clinical usefulness of the prediction nomogram. Conclusion: BCR was a useful prognostic factor for COVID-19 patients. Development of an individualized prediction nomogram BCR-based, which can effectively predict the risk of mortality, and then, help clinicians to improve individual treatment, make clinical decisions timely and early.

    Forecasting COVID-19 Pandemic: A Data-Driven Analysis

    Authors: Khondoker Nazmoon Nabi

    doi:10.21203/ Date: 2020-05-19 Source: ResearchSquare

    In this paper, a new Susceptible-Exposed-Symptomatic Infectious- Asymptomatic TRANS Infectious-Quarantined-Hospitalized-Recovered-Dead (SEIDIUQHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission TRANS dynamics of the novel coronavirus disease MESHD (COVID-19). The purpose of this study is to give a tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a Trust-region-reflective (TRR) algorithm which one of the well-known real data fitting techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the beginning of June with the peak size of 15, 774 (95% CI, 13,814-17,734) symptomatic infectious cases in Russia, 26, 449 (95% CI, 23,489-29,409) cases in Brazil, 9, 504 (95% CI, 8,378-10,630) cases in India and 2,209 (95% CI, 1,878-2,540) cases in Bangladesh. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic TRANS carriers TRANS, our analysis estimates the value of the basic reproduction number TRANS ( R0 TRANS) as of May 11, 2020 was found to be 4.234 (95% CI, 3.764-4.7) in Russia, 5.347 (95% CI, 4.737-5.95) in Brazil, 5.218 (95% CI, 4.56-5.81)in India, 4.649 (95% CI, 4.17-5.12) in the United Kingdom and 3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coeffcient (LHS-PRCC) which is a global sensitivity SERO analysis (GSA) method is applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic TRANS carriers TRANS, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period TRANS are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could signicantly affect the transmission TRANS dynamics of the novel coronavirus. Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.

    Stepping out of lockdown should start with school re-openings while maintaining distancing measures. Insights from mixing matrices and mathematical models.

    Authors: Emma Sue McBryde; James M Trauer; Adeshina Adekunle; Romain Ragonnet; Michael T Meehan

    doi:10.1101/2020.05.12.20099036 Date: 2020-05-19 Source: medRxiv

    Australia is one of a few countries which has managed to control COVID-19 epidemic before a major epidemic took place. Currently with just under 7000 cases and 100 deaths MESHD, Australia is seeing less than 20 new cases per day. This is a positive outcome, but makes estimation of current effective reproduction numbers TRANS difficult to estimate. Australia, like much of the world is poised to step out of lockdown and looking at which measures to relax first. We use age TRANS- based contact TRANS matrices, calibrated to Chinese data on reproduction numbers TRANS and difference in infectiousness and susceptibility of children TRANS to generate next generation matrices (NGMs) for Australia. These matrices have a spectral radius of 2.49, which is hence our estimated basic reproduction number TRANS for Australia. The effective reproduction number TRANS (Reff) for Australia during the April/May lockdown period is estimated by other means to be around 0.8. We simulate the impact of lockdown on the NGM by first applying observations through Google Mobility Report for Australia at 3 locations: home (increased contacts by 18%), work (reduced contacts by 34%) and other (reduced contacts by 40%), and we reduce schools to 3% reflecting attendance rates during lockdown. Applying macro-distancing to the NGM leads to a spectral radius of 1.76. We estimate that the further reduction of the reproduction number TRANS to current levels of Reff = 0.8 is achieved by a micro-distancing factor of 0.26. That is, in a given location, people are 26% as likely as usual to have an effective contact with another person. We apply both macro and micro-distancing to the NGMs to examine the impact of different exit strategies. We find that reopening schools is estimated to reduce Reff from 0.8 to 0.78. This is because increase in school contact is offset by decrease in home contact. The NGMs all estimate that adults TRANS aged TRANS 30-50 are responsible for the majority of transmission TRANS. We also find that micro-distancing is critically important to maintain Reff <1. There is considerable uncertainty in these estimates and a sensitivity SERO and uncertainty analysis is presented.

    Experience of Massive Distance Online Education for Medical Colleges and Universities in China to Counter the COVID-19 Pandemic

    Authors: Jiyang Zhao; Hai Xiao; Yong Li; Da Wen; Peixiang Xu; Yao Fu; Jie Piao; Jiangheng Liu; Depin Cao; Zhaohua Zhong; Guang Zhao

    doi:10.21203/ Date: 2020-05-19 Source: ResearchSquare

    Background As the COVID-19 outbreak influenced teaching in China, Chinese medical colleges and universities carried out massive online education with the participants of more than one million students on the for-profit or non-profit big–data teaching platforms, based on existing online teaching resources and other ways such as MOOC, Micro-course, Live-course and interactive discussion.Methods A questionnaire survey was conducted via two rounds of 56-in-total questions on overall situations, platforms, teaching methods, training, teachers and students’ experiences, and encountered problems for 1747 teachers and 7223 students, which involved 741 courses. Comparative analysis was done on the survey results.Results A majority of teachers and students were satisfied with teaching platforms and effects; large commercial teaching platforms could provide better services; 67.21% of teachers and 64.67% of students preferred live teaching; only 76.84% of teachers and 58.69% of students were systematically trained; weak network, lack of training, overcrowded access to teaching platforms, lack of interaction, lack of efficient real-time evaluation and fatigue MESHD fatigue HP were current difficulties.Conclusions Good results of massive distance online education and teachers and students’ satisfaction with teaching platform performance SERO show that online education can be used as a main means to replace traditional classroom education in emergency MESHD situations.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from and is updated on a daily basis (7am CET/CEST).



MeSH Disease
Human Phenotype

Export subcorpus as Endnote

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.