Corpus overview


MeSH Disease

Pneumonia (974)

Disease (459)

Infections (453)

Coronavirus Infections (282)

Death (207)

Human Phenotype

Pneumonia (1063)

Fever (170)

Cough (137)

Respiratory distress (80)

Hypertension (64)


    displaying 471 - 480 records in total 1106
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    Hydroxychloroquine plus azithromycin: a potential interest in reducing in-hospital morbidity due to COVID-19 pneumonia MESHD pneumonia HP (HI-ZY-COVID)?

    Authors: Benjamin Davido; Thibaud Lansaman; Christine Lawrence; Jean-Claude Alvarez; Frederique Bouchand; Pierre Moine; Veronique Perronne; Aurelie Le Gal; Djillali Annane; Christian Perronne; Pierre De Truchis; - COVID-19 RPC Team

    doi:10.1101/2020.05.05.20088757 Date: 2020-05-11 Source: medRxiv

    The authors have withdrawn this manuscript and do not wish it to be cited. Because of controversy about hydroxychloroquine and the retrospective nature of their study, they intend to revise the manuscript after peer review.

    Fast and accurate detection of Covid-19-related pneumonia MESHD pneumonia HP from chest X-ray images with novel deep learning model

    Authors: M. M. Ramadhan; A. Faza; L. E. Lubis; R. E. Yunus; T. Salamah; D. Handayani; I. Lestariningsih; A. Resa; C. R. Alam; P. Prajitno; S. A. Pawiro; P. Sidipratomo; D. S. Soejoko

    id:2005.04562v2 Date: 2020-05-10 Source: arXiv

    Background: Novel coronavirus disease MESHD disease has spread TRANS rapidly worldwide. As recent radiological literatures on Covid-19 related pneumonia MESHD pneumonia HP is primarily focused on CT findings, the American College of Radiology (ACR) recommends using portable chest X-radiograph (CXR). A tool to assist for detection and monitoring of Covid-19 cases from CXR is highly required. Purpose: To develop a fully automatic framework to detect Covid-19 related pneumonia MESHD pneumonia HP using CXR images and evaluate its performance SERO. Materials and Methods: In this study, a novel deep learning model, named CovIDNet (Covid-19 Indonesia Neural-Network), was developed to extract visual features from chest x-ray images for the detection of Covid-19 related pneumonia MESHD pneumonia HP. The model was trained and validated by chest x-rays datasets collected from several open source provided by GitHub and Kaggle. Results and Discussion: In the validation stage using open-source data, the accuracy to recognize Covid-19 and others classes reaches 98.44%, that is, 100% Covid-19 precision and 97% others precision. Discussion: The use of the model to classify Covid-19 and other pathologies might slightly decrease the accuracy. Although SoftMax was used to handle classification bias, this indicates the benefit of additional training upon the introduction of new set of data. Conclusion: The model has been tested and get 98.4% accuracy for open source datasets, the sensitivity SERO and specificity are 100% and 96.97%, respectively.

    Role of novel deep-learning-based CT used in management and discharge of COVID-19 patients at a “square cabin” hospital in China

    Authors: Qingcheng Meng; Wentao Liu; Xinmin Dou; Jiaqi Zhang; Anlan Sun; Jia Ding; Hao Liu; Ziqiao Lei; Xuejun Chen

    doi:10.21203/ Date: 2020-05-10 Source: ResearchSquare

    Background: The chest computed tomography (CT) had been used to define the diagnostic and discharge criteria for COVID-19. However, it is difficult to determine the suitability for discharge of a patient with COVID-19 based on CT features in a clinical setting. Deep learning (DL) technology has demonstrated great success in the medical imaging.Purpose: This study applied the novel deep learning (DL) on chest computed tomography (CT) of COVID-19 patients with consecutive negative respiratory pathogen nucleic acid test results at a “square cabin” hospital in Wuhan, China, with the intent to standardize criteria for discharge.Methods: The study included 270 patients (102men, 168 women; mean age TRANS, 51.9 ± 15.6[18–65] years) who had two consecutive negative respiratory pathogen tests (sampling interval: ≥1 day) and underwent low-dose CT 1 day after the first negative test, with strict adherence to epidemic prevention standards. The chest CT of COVID-19 patients with negative nucleic acid tests were evalued by DL, and the standard for discharge was a total volume ratio of lesions to lung of less than 50% determined by DL.Results: The average intersection over union is 0.7894. Fifty-seven (21.1%) and 213 (78.9%) patients exhibited normal lung findings and pneumonia MESHD pneumonia HP, respectively. 54.0% (115/213) involved mild interstitial fibrosis MESHD. 18.8% (40/213) had total volume ratio of lesions to lung of more than and equal to 50% according to our severity scale and were monitored continuously in hospital, and three cases of which had a positive follow-up nucleic acid test during hospital observation. None of the 230 discharged cases later tested positive or exhibited pneumonia MESHD pneumonia HP progression. Conclusions: The novel DL enables the accurate management of COVID-19 patients and can help avoid cluster transmission TRANS or exacerbation due to patients with false negitive acid test. 

    In silico drug designing for COVID-19: an approach of highthroughput virtual screening, molecular and essential dynamics simulations

    Authors: Rakesh Kumar; Rahul Kumar; Pranay Tanwar

    doi:10.21203/ Date: 2020-05-10 Source: ResearchSquare

    SARS-CoV2, a new coronavirus has emerged in Wuhan city of China, December last year causing pneumonia MESHD pneumonia HP named COVID-19 which has now spread to entire world. By April 2020, number of confirmed cumulative cases crossed ~2.4 million worldwide, according to WHO. Till date, no effective treatment or drug is available for this virus. Availability of X-ray structures of SARS-CoV2 main protease (Mpro) provided the potential opportunity for structure based drug designing. Here, we have made an attempt to do computational drug design by targeting main protease of SARS-CoV2. Highthroughput virtual screening of million molecules and natural compounds databases was performed followed by docking. Six ligands showed better binding affinities which were further optimized by MD simulation and rescoring of binding energy was calculated through MM/PBSA method. In addition, conformational effect of various ligands on protein was examined through essential dynamics simulation. Three compounds namely ZINC14732869, ZINC19774413 and ZINC19774479 were finally filtered that displayed high binding free energies than N3 inhibitor and form conformationally stable complex. Hence, current study features the discovery of novel inhibitors for main protease of CoV2 which will provide effective therapeutic candidates against COVID19.

    COVID-19 lung alterations still evident at 60-day follow-up chest CT in asymptomatic TRANS patients despite negative rRT-PCR testing

    Authors: Ezio Lanza; Manuel Profili; Isabella Bolengo; Orazio Giuseppe Santonocito; Riccardo Muglia; Costanza Lisi; Gaia Messana; Luca Balzarini

    doi:10.21203/ Date: 2020-05-10 Source: ResearchSquare

    Several patients who have recovered from COVID-19 pneumonia MESHD pneumonia HP showed persistent infection MESHD at follow-up chest CT (31-63 days after disease MESHD onset) despite being asymptomatic TRANS and testing negative at rRT-PCR.

    Mapping of PubMed Literature on Early Trends of 2019 Novel Coronavirus (COVID-19)

    Authors: Kutty Kumar

    doi:10.21203/ Date: 2020-05-10 Source: ResearchSquare

    2019 novel corona virus infection MESHD (COVID-19) causes extreme viral pneumonia MESHD pneumonia HP in people, known to have a high death MESHD rate and a similitude in clinical indications with Severe Acute Respiratory Syndrome MESHD coronavirus. This investigation intended to study the attributes of distributions on early COVID-19 research through bibliometric analysis. PubMed database was looked on 07, February, 2020 for COVID-19 distributions published during 01st December 2019 to 06th February 2020. Investigation parameters incorporate year of production, distribution type, examples of universal coordinated effort, and research organizations. An aggregate of 62 COVID-19 research distributions were distributed during the examination time frame. The exploration works were distributed from 13 nations, demonstrating the global noteworthiness on coronavirus episode. USA was the biggest supporter, with 24 articles distributed over a range of 2months and 6 days, trailed by England (11 articles). Aftereffects of the investigation will bear some significance with understudies, specialists, curators and data science experts, and will fill in as a pattern for resulting examinations.

    Epidemic Peak for COVID-19 in India, 2020

    Authors: Chaitanya S. Wagh; Parikshit N. Mahalle; Sanjeev J. Wagh

    id:10.20944/preprints202005.0176.v1 Date: 2020-05-10 Source:

    In India the first case of coronavirus disease MESHD 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member TRANS of coronaviridae where previously Middle East Respiratory Syndrome MESHD MERS and Severe Acute Respiratory Syndrome MESHD SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever MESHD fever HP, cough MESHD cough HP and breath shortness, in severe cases may cause pneumonia MESHD pneumonia HP, SARS or some time death MESHD. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 TRANS to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease MESHD geospatially and analyze the death MESHD. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

    Prognostic Model for Mortality of Hospitalized Patients with COVID-19 Pneumonia MESHD Pneumonia HP in Wuhan, China: A Multi-center retrospective cohort study

    Authors: Qi Mei; Amanda Y. Wang; Yang Yang; Ming Li; Fei Wang; Zia Wei Zhao; Ke Ma; Liang Wu; Huawen Chen; Jinlong Luo; Shangming Du; Kathrin Halfter; Yong Li; Guangyuan Hu; Xianglin Yuan; Jian Li

    doi:10.21203/ Date: 2020-05-09 Source: ResearchSquare

    Background: Novel coronavirus (COVID-19) infection MESHD is a global public health issue and has now affected more than 70 countries worldwide. Severe adult TRANS respiratory syndrome MESHD-CoV-2 (SARS-CoV-2) pneumonia MESHD pneumonia HP is associated with high risk of mortality. However, prognostic factors assessing poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia MESHD pneumonia HP remain unclear.Methods: We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the of end of follow up period was the primary outcome. Prognostic factors for mortality were also assessed and a prognostic model was developed, calibrated and validated.Results: The study included 492 patients with SARS-CoV-2, which were divided into three cohorts, the training cohort (n=237), the validation cohort 1 (n=120), and the validation cohort 2 (n=135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including age TRANS, odds ratio (OR), 1.1 / years increase (p<0.001); neutrophil-to-lymphocyte ratio OR, 1.14 (p<0.001), body temperature on admission OR, 1.53 / °C increase (p=0.005), increase of aspartate transaminase OR, 2.47 (p=0.019), and decrease of total protein OR, 1.69 (p=0.018).Furthermore, the prognostic model drawn from the training cohort was validated with the validation cohort 1 and 2 with comparable area under curve (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell’s C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other.Conclusions: A validated prognostic model was developed to assist in determining the clinical prognosis for SARS- CoV-2 pneumonia MESHD pneumonia HP. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted in the low risk group had a high probability of survival.

    Optimal upper respiratory tract sampling time for novel coronavirus pneumonia MESHD pneumonia HP suspects

    Authors: Jing Zhou; Lin Chen; Dehe Zhang; Haijun Chen; Qiyue Sheng; Hongsheng Deng; Yang Zhang; Shunlan Ni; Shengnan Luo; Binbin Ren

    doi:10.1101/2020.05.06.20069302 Date: 2020-05-09 Source: medRxiv

    Objective: Explore best upper respiratory tract sampling time of suspected novel coronavirus pneumonia MESHD pneumonia HP cases. Methods: We collected dates of patients from Hangzhou, Shenzhen, Jinhua city and so on who had the clear exposure history of a novel coronavirus pneumonia MESHD pneumonia HP(COVID-19). We retrospected demographic data, exposure time, onset time, visiting time and positive time for novel coronavirus nucleic acid detection in respiratory specimens. There were 256 patients from January 20, 2020-February 12,2020 from eight cities included in our study. 106 cases appeared symptoms before January 25th and 150 after. Results: There were 136(53.1%) male TRANS infected cases. The mean age TRANS of all patients was 43.80 {+/-} 14.85.The median time from exposure to onset was 5(3,8)days.The median time of the first time of positive nucleic acid detection was 11(9,14)days and mode number was 13.The median time from onset to the first time of positive nucleic acid detection was 6(4,8)days and mode number was 5. The time from onset to definite diagnosis was 5(3,7) days before January 25th while it was 7.5(5,10)days after which was significantly shorter before January 25th(U=3885.5,P<0.001). The time from exposure to definite diagnosis was 11(9,14)days and 11(9,14)days before January 25th and after and without significant difference. The time from exposure to definite diagnosis was 11(9,13)days in first-tier cities and 13(11,15)days in second and third-tier cities. The difference was significantly shorter of first-tier cities(U=1355.5 P=0.039). And also the time was short from visiting to definite diagnosis which was 2(2,3)days in first-tier cities and 3(2,4)days in second and third-tier cities but without significant difference(U=842.5P=0.054). Conclusions: From our study we found that the best upper respiratory tract sampling time for novel coronavirus pneumonia MESHD pneumonia HP suspects was 13days after exposure. The time from onset to definite diagnosis was shorter after January 25th. The patients were diagnosed faster in the first-tier cities after exposure.

    IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

    Authors: Juliana Carneiro Gomes; Valter Augusto de Freitas Barbosa; Maira Araujo de Santana; Jonathan Bandeira; Meuser Jorge Silva Valenca; Ricardo Emmanuel de Souza; Aras Masood Ismael; Wellington Pinheiro dos Santos

    doi:10.1101/2020.05.05.20092346 Date: 2020-05-09 Source: medRxiv

    In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia MESHD pneumonia HP. There is still no specific treatment and diagnosis for the disease MESHD. The standard diagnostic method for pneumonia MESHD pneumonia HP is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89:78%, average recall SERO and sensitivity SERO of 0:8979, and average precision and specificity of 0:8985 and 0:9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia MESHD pneumonia HP, with low computational cost.

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MeSH Disease
Human Phenotype

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