Corpus overview


MeSH Disease

Human Phenotype


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    COVID-19: In the Eye of the Cytokine Storm

    Authors: Roberto de la Rica; Marcio Borges; Marta Gonzalez-Freire

    id:10.20944/preprints202005.0157.v1 Date: 2020-05-09 Source:

    The dysregulated release of cytokines has been identified as one of the key factors behind poorer outcomes in COVID-19. This ‘cytokine storm ‘produces an excessive inflammatory and immune response, especially in the lungs, leading to acute respiratory distress HP respiratory distress MESHD (ARDS), pulmonary edema HP pulmonary edema MESHD and multi-organ failure MESHD. Alleviating this inflammatory state is crucial to improve prognosis. Pro-inflammatory factors play a central role in COVID-19 severity, especially in patients with comorbidities In these situations, an overactive, untreated immune response can be deadly, suggesting that mortality in COVID-19 cases is likely due to this virally driven hyperinflammation. Administering immunomodulators has not yielded conclusive improvements in other pathologies characterized by dysregulated inflammation MESHD such as sepsis HP sepsis MESHD, SARS-CoV-1 and MERS. The success of these drugs at reducing COVID-19-driven inflammation MESHD is still anecdotal and comes with serious risks. It is also imperative to screen the elderly TRANS for risk factors that predispose them to severe COVID-19. Immunosenescence and comorbidities should be taken into consideration. In this review, we summarize the latest data available about the role of the cytokine storm in COVID-19 disease severity as well as potential therapeutic approaches to ameliorate it. We also examine the role of inflammation MESHD in other diseases often comorbid with COVID-19, such as aging, sepsis HP sepsis MESHD, and pulmonary disorders MESHD. Finally, we identify gaps in our knowledge and suggest priorities for future research aimed at stratifying patients according to risk as well as personalizing therapies in the context of COVID19-driven hyperinflammation.

    Automatic Detection of Coronavirus Disease MESHD (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

    Authors: Sara Hosseinzadeh Kassani; Peyman Hosseinzadeh Kassasni; Michal J. Wesolowski; Kevin A. Schneider; Ralph Deters

    id:2004.10641v1 Date: 2020-04-22 Source: arXiv

    The newly identified Coronavirus pneumonia MESHD pneumonia HP, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough HP, sore throat, and fever HP fever MESHD. Symptoms can progress to a severe form of pneumonia HP pneumonia MESHD with critical complications, including septic shock MESHD shock HP, pulmonary edema HP pulmonary edema MESHD, acute respiratory distress syndrome MESHD respiratory distress HP syndrome and multi-organ failure MESHD. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance SERO of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance SERO with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

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MeSH Disease
Human Phenotype

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