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MeSH Disease

Human Phenotype

Transmission

gender (1)


Seroprevalence
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    Single-cell analysis reveals the function of lung progenitor cells in COVID-19 patients

    Authors: Wei Zuo; Zixian Zhao; Yu Zhao; Yueqing Zhou; Xiaofan Wang; Ting Zhang

    doi:10.1101/2020.07.13.200188 Date: 2020-07-13 Source: bioRxiv

    The high mortality of severe 2019 novel coronavirus disease MESHD (COVID-19) cases is mainly caused by acute respiratory distress HP respiratory distress MESHD syndrome ( ARDS MESHD), which is characterized by increased permeability of the alveolar MESHD epithelial barriers, pulmonary edema HP pulmonary edema MESHD and consequently inflammatory tissue damage. Some but not all patients showed full functional recovery after the devastating lung damage, and so far there is little knowledge about the lung repair process1. Here by analyzing the bronchoalveolar lavage fluid (BALF) of COVID-19 patients through single cell RNA-sequencing (scRNA-Seq), we found that in severe (or critical) cases, there is remarkable expansion of TM4SF1+ and KRT5+ lung progenitor cells. The two distinct populations of progenitor cells could play crucial roles in alveolar MESHD cell regeneration and epithelial barrier re-establishment, respectively. In order to understand the function of KRT5+ progenitors in vivo, we transplanted a single KRT5+ cell-derived cell population into damaged mouse lung. Time-course single-cell transcriptomic analysis showed that the transplanted KRT5+ progenitors could long-term engrafted into host lung and differentiate into HOPX+ OCLN+ alveolar MESHD barrier cell which restored the epithelial barrier and efficiently prevented inflammatory cell infiltration. Similar barrier cells were also identified in some COVID-19 patients with massive leukocyte infiltration. Altogether this work uncovered the mechanism that how various lung progenitor cells work in concert to prevent and replenish alveoli loss MESHD post severe SARS-CoV-2 infection MESHD.

    The Molecular Basis of Gender TRANS Variations in Mortality Rates Associated with the Novel Coronavirus (COVID-19) Outbreak

    Authors: Ibrahim Y. Hachim; Mahmood Y. Hachim; Iman Mamdouh Talaat; Vanessa M. López-Ozuna; Narjes Saheb Sharif-Askari; Rabih Halwani; Qutayba Hamid

    id:10.20944/preprints202005.0364.v1 Date: 2020-05-23 Source: Preprints.org

    Since the outbreak of the novel coronavirus disease MESHD (COVID-19) at the end of 2019, the clinical presentation of the disease showed a great heterogeneity with a diverse impact between different subpopulations. Emerging evidence from different parts of the world showed significantly poor outcome among males TRANS compared to female TRANS patients. A better understanding of the molecular mechanisms behind this difference might be a fundamental step for a more effective and targeted response to the outbreak. For that reason, here we try to investigate the molecular basis of the gender TRANS variations in mortality rates related to COVID-19 infection MESHD. To achieve this, we used our in-house pipeline to process publicly available lung transcriptomic data from 141 females TRANS compared to 286 males TRANS. After excluding Y specific genes, our results showed a shortlist of 73 genes that are differentially expressed between the two groups. Our results showed downregulation of a group of genes that are involved in the regulation of hydrolase activity including (AGTR1, CHM MESHD, DDX3X, FGFR3, SFRP2, and NLRP2), which is also believed to be essential for lung immune response and antimicrobial activity in the lung tissues in males TRANS compared to females TRANS. In contrast, our results showed an upregulation of angiotensin II receptor type 1 (AGTR1), a member of the renin-angiotensin system (RAS) that plays a role in angiotensin-converting enzyme 2 (ACE2) activity modulation. Interestingly, recent reports and experimental animal models highlight an important role of this receptor in SARS-Coronavirus lung damage MESHD as well as pulmonary edema HP pulmonary edema MESHD, suggesting a possible role of its blockers like losartan and olmesartan as potential therapeutic options for COVID-19 infection MESHD. Finally, our results also showed a differential expression of different genes that are involved in the immune response including the NLRP2 and PTGDR2, further supporting the notion of the sex-based immunological differences. Taken together, our results provide an initial evidence of the molecular mechanisms that might be involved in the differential outcomes observed between both genders TRANS during the COVID-19 outbreak. This might be essential for the discovery of new targets and more precise therapeutic options to treat COVID-19 patients from different clinical and epidemiological characteristics with the aim of improving their outcome.

    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%.

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


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