Corpus overview


MeSH Disease

Human Phenotype


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    Proposal of selective wedge instillation of pulmonary surfactant for COVID-19 pneumonia HP pneumonia MESHD based on computational fluid dynamics simulation

    Authors: Hiroko Kitaoka; Hisato Kobayashi; Takayuki Takimoto; Takashi Kijima

    doi:10.21203/ Date: 2020-09-11 Source: ResearchSquare

    Background: The most important target cell of SARS-CoV-2 is Type II pneumocyte which produces and secretes pulmonary MESHD surfactant (PS) that prevents alveolar MESHD collapse. PS instillation therapy is dramatically effective for infant respiratory distress HP respiratory distress MESHD syndrome but has been clinically ineffective for ARDS. Nowadays, ARDS is regarded as non-cardiogenic pulmonary edema HP pulmonary edema MESHD with vascular hyper-permeability regardless of direct relation to PS dysfunction. However, there is a possibility that the ineffectiveness of PS instillation for ARDS is due to insufficient delivery MESHD. Then, we performed PS instillation simulation with realistic human airway models by the use of computational fluid dynamics, and investigated how instilled PS would move in the liquid layer covering the airway wall and reach to alveolar regions MESHD.Methods: Two types of 3D human airway model were prepared: One was from the trachea to lobular bronchi and the other was from a sub-segmental bronchus to respiratory bronchioles. Thickness of the liquid layer covering the airway was assigned as 14 % of the inner radius of the airway segment. Initially existing liquid layer was assumed to be replaced by instilled PS. Flow rate of instilled PS was assigned a constant value, which was determined by the total amount and instillation time in clinical use. The PS concentration of the liquid layer during instillation was computed by solving advective-diffusion equation.Results: The driving pressure from the trachea to respiratory bronchioles was calculated at 317 cmH2O, which is about 20 times of a standard value in conventional PS instillation method where the driving pressure is given by difference between inspiratory and end-expiratory pressures of a ventilator. It means that almost all PS would not reach alveolar MESHD regions but move to and fro within the airway according to the change of ventilator pressure. On the other hand, the driving pressure from sub-segmental bronchus was calculated at 273 cm H2O, that is clinically possible by wedge instillation under bronchoscopic observation. Conclusions: The simulation study has revealed that selective wedge instillation under bronchoscopic observation should be tried for COVID-19 pneumonia HP pneumonia MESHD even before ARDS. It will be also useful for preventing secondary lung fibrosis MESHD

    Identification of pulmonary comorbid diseases network based repurposing effective drugs for COVID-19

    Authors: Jai Chand Patel; Rajkumar Tulswani; Pankaj Khurana; Yogendra Kumar Sharma; Lilly Ganju; Bhuvnesh Kumar; Ragumani Sugadev

    doi:10.21203/ Date: 2020-05-08 Source: ResearchSquare

    The number of hospitalization of COVID-19 patients with one or more comorbid diseases is highly alarming. Despite the lack of large clinical data and incomplete understanding of virus pathology, identification of the COVID-19 associated diseases with clinical precision are highly limited. In this regard, our text mining of 6238 PubMed abstracts (as on 23 April 2020) successfully identified broad spectrum of COVID-19 comorbid diseases/disorders (54), and their prevalence SERO on the basis of the number of occurrence of disease terms in the abstracts. The disease ontology based semantic similarity network analysis revealed the six highly comorbid diseases of COVID-19 namely Viral Pneumonia HP, Pulmonary Fibrosis HP Pulmonary Fibrosis MESHD, Pulmonary Edema HP Pulmonary Edema MESHD, Acute Respiratory Distress Syndrome MESHD Respiratory Distress HP Syndrome ( ARDS MESHD), Chronic Obstructive Pulmonary Disease HP Chronic Obstructive Pulmonary Disease MESHD ( COPD MESHD) and Asthma HP. The disease gene bipartite network revealed 15 genes that were strongly associated with several viral pathways including the corona viruses may involve in the manifestation (mild to critical) of COVID-19. Our tripartite network- based repurposing of the approved drugs in the world market revealed six promising drugs namely resveratrol, dexamethasone, acetyl cysteine, Tretinoin, simvastatin and aspirin to treat comorbid symptoms of COVID-19 patients. Our animal studies in rats and literatures strongly supported that resveratrol is the most promising drug to possibly reduce several comorbid symptoms associated with COVID-19 including the severe hypoxemia HP hypoxemia MESHD induced vascular leakage. Overall, the anti-viral properties of resveratrol against corona virus could be readily exploited to effectively control the viral load at early stage of COVID-19 infection through nasal administration.

    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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