Corpus overview


MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

There are no SARS-CoV-2 protein terms in the subcorpus


SARS-CoV-2 Proteins
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    Trends in clinical characteristics and associations of severe non-respiratory events related to SARS-CoV-2

    Authors: Tal El-Hay; Ehud Karavani; Asaf Perez; Matan Ninio; Sivan Ravid; Michal Chorev; Michal Rosen-Zvi; Tal Patalon; Yishai Shimoni; Anil Jain

    doi:10.1101/2021.03.24.21251900 Date: 2021-03-26 Source: medRxiv

    Background: The 2019 novel coronavirus (SARS-CoV-2) is reported to result in both respiratory and non-respiratory severe health outcomes, but quantitative assessment of the risk - while adjusting for underlying risk driven by comorbidities - is not yet established. Methods: A retrospective observational study using electronic health records of 9,344,021 individuals across the U.S. with at-least 1 year of clinical history and followed up throughout 2020. Results: 131,329 individuals were associated with SARS-CoV-2 infection MESHD by January 6, 2021 in three distinct surges. While the age and number of preexisting conditions had decreased throughout the pandemic, the characteristics of those who experienced severe health events did not. During the second surge, between June 7 and November 18, 2020, 425,988 individuals in the base cohort were admitted to emergency rooms or hospitals. Among them, 15,486 were detected with SAR-CoV-2 within few days of admission. Significant adjusted odds ratios were observed between SARS-CoV-2 infection MESHD and the following severe health events: respiratory (4.38, 95% confidence interval 4.16-4.62), bacterial pneumonia MESHD (3.25, 2.76-3.83), sepsis MESHD (1.71, 1.53-1.91), renal (1.69, 1.57-1.83), hematologic/immune (1.32, 1.20-1.45), and neurological (1.23, 1.09-1.38). Conclusions: SARS-CoV-2 infection MESHD among hospitalized patients is associated with non-negligible increased risk of severe events including multiple non-respiratory ones. These associations, which complement recent studies, are persistent even after accounting for sources of selection and confounding bias, increasing the confidence they are not spurious.

    Antimicrobial susceptibility patterns of respiratory Gram-negative bacterial isolates from COVID-19 MESHD patients in Switzerland

    Authors: Sven N. Hobbie

    doi:10.1101/2021.03.10.21253079 Date: 2021-03-12 Source: medRxiv

    Background Bacterial superinfections associated with COVID-19 MESHD are common in ventilated ICU patients and impact morbidity and lethality. However, the contribution of antimicrobial resistance to the manifestation of bacterial infections MESHD in these patients has yet to be elucidated. Methods We collected 70 Gram-negative bacterial strains, isolated from the lower respiratory tract of ventilated COVID-19 MESHD patients in Zurich, Switzerland between March and May 2020. Species identification was performed using MALDI- TOF HGNC; antibiotic susceptibility profiles were determined by EUCAST disk diffusion and CLSI broth microdilution assays. Selected Pseudomonas aeruginosa isolates were analyzed by whole-genome sequencing. Results P. aeruginosa (46%) and Enterobacterales (36%) comprised the two largest etiologic groups. Drug resistance in P. aeruginosa isolates was high for piperacillin/tazobactam (65.6%), cefepime (56.3%), ceftazidime (46.9%) and meropenem (50.0%). Enterobacterales isolates showed slightly lower levels of resistance to piperacillin/tazobactam (32%), ceftriaxone (32%), and ceftazidime (36%). All P. aeruginosa isolates MESHD and 92% of Enterobacterales isolates were susceptible to aminoglycosides, with apramycin found to provide best-in-class coverage. Genotypic analysis of consecutive P. aeruginosa isolates in one patient revealed a frameshift mutation in the transcriptional regulator nalC that coincided with a phenotypic shift in susceptibility to {beta}-lactams and quinolones. Conclusions Considerable levels of antimicrobial resistance may have contributed to the manifestation of bacterial superinfections in ventilated COVID-19 MESHD patients, and may in some cases mandate consecutive adaptation of antibiotic therapy. High susceptibility to amikacin and apramycin suggests that aminoglycosides may remain an effective second-line treatment of ventilator-associated bacterial pneumonia MESHD, provided efficacious drug exposure in lungs can be achieved.

    Efficiency of Artificial Intelligence in Detecting COVID-19 MESHD Pneumonia and Other Pneumonia Causes by Quantum Fourier Transform Method

    Authors: Erdi ACAR; Bilge Oztoprak; Mustafa Resorlu; Murat Das; Ihsan Yilmaz; Ibrahim Oztoprak

    doi:10.1101/2020.12.29.20248900 Date: 2021-01-04 Source: medRxiv

    The new coronavirus ( COVID-19 MESHD) appeared in Wuhan in December 2019 and has been announced as a pandemic by the World Health Organization (WHO). Currently, this deadly pandemic has caused more than 1 million deaths worldwide. Therefore, it is essential to detect positive cases as early as possible to prevent the further spread of this outbreak. Currently, the most widely used COVID-19 MESHD detection technique is a real-time reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR is time-consuming to confirm infection in the patient. Because RT-PCR is less sensitive, it provides high false-negative results. Computed tomography (CT) is recommended as a solution to this problem by healthcare professionals because of its higher sensitivity for early and rapid diagnosis. In addition, radiation used in CT poses a serious threat to patients. In this study, we propose a CNN-based method to distinguish COVID-19 MESHD pneumonia MESHD from other types of viral and bacterial pneumonia MESHD using low-dose CT images to reduce the radiation dose used in CT. In our study, we used a data set consisting of 7717 CT images of 350 patients that we collected from Canakkale Onsekiz Mart University Research Hospital. We used a CNN-based network that suppresses noise to remove interference from low-dose CT images. In the image preprocessing phase, we provided lung segmentation from CT images and applied quantum Fourier transform. By evaluating all possible variations of local knowledge at the same time with quantum Fourier transformation, the most informative spatial information was extracted. In CNN-based architecture, we used pre-trained ResNet50v2 as a feature extractor and fine-tune by training with our dataset. We visualized the efficiency of the ResNet50v2 network using the t-SNE method. We performed the classification process with a fully connected layer. We created a heat map using the GradCam technique to see where the model focuses on the images while classifying. In this experimental study, the results of 99.5%, 99.2%, 99.0%, 99.7%, and 99.1%, were obtained in the context of performance criteria such as accuracy, precision, sensitivity, specificity, and f1 score, respectively. This study revealed the artificial intelligence-based computer-aided diagnosis (CAD)system as an effective and fast method to accurately diagnose COVID-19 MESHD pneumonia MESHD.

    Prevalence of bacterial pathogens and potential role in COVID-19 MESHD severity in patients admitted to intensive care units in Brazil

    Authors: Fabiola Marques Carvalho; Leandro Nascimento Lemos; Luciane Prioli Ciapina; Rennan Garcias Moreira; Alexandra Gerber; Ana Paula Guimaraes; Tatiani Fereguetti; Virginia Antunes de Andrade Zambelli; Renata Avila; Tailah Bernardo de Almeida; Jheimson Silva Lima; Shana Priscila Coutinho Barroso; Mauro Martins Teixeira; Renan Pedra Souza; Cynthia Chester Cardoso; Renato Santana Aguiar; Ana Tereza Ribeiro Vasconcelos

    doi:10.1101/2020.12.22.20248501 Date: 2020-12-24 Source: medRxiv

    Secondary bacterial and fungal infections MESHD are associated with respiratory viral infections MESHD and invasive mechanical ventilation. In Coronavirus disease 2019 MESHD ( COVID-19 MESHD), lung injury MESHD by SARS-CoV-2 and impaired immune response can provide a favorable environment for microorganism growth and colonization MESHD in hospitalized individuals. Recent studies suggest that secondary bacterial pneumonia MESHD is a risk factor associated with COVID-19 MESHD. In Brazil, knowledge about microbiota present in COVID-19 MESHD patients is incipient. This work describes the microbiota of 21 COVID-19 MESHD patients admitted to intensive care units from two Brazilian centers. We identified respiratory, nosocomial and bacterial pathogens as prevalent microorganisms. Other bacterial opportunistic and commensal species are also represented. Virulence factors of these pathogenic species, metabolic pathways used to evade and modulate immunological processes and the interconnection between bacterial presence and virulence in COVID-19 MESHD progression are discussed.

    Transfer Learning for COVID-19 MESHD Pneumonia Detection and Classification in Chest X-ray Images

    Authors: Iason Katsamenis; Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis

    doi:10.1101/2020.12.14.20248158 Date: 2020-12-16 Source: medRxiv

    We introduce a deep learning framework that can detect COVID-19 MESHD pneumonia MESHD in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection MESHD. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 MESHD is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia MESHD radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 MESHD classification task in chest X-ray images.

    Artificial Intelligence for COVID-19 MESHD Detection -- A state-of-the-art review

    Authors: Parsa Sarosh; Shabir A. Parah; Romany F Mansur; G. M. Bhat

    id:2012.06310v1 Date: 2020-11-25 Source: arXiv

    The emergence of COVID-19 MESHD has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival prediction. The use of Deep Learning ( DL MESHD) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. This paper aims to review the role of Deep Learning MESHD and Artificial intelligence in various aspects of the overall COVID-19 MESHD management and particularly for COVID-19 MESHD detection and classification. The DL MESHD models are developed to analyze clinical modalities like CT scans and X-Ray images of patients and predict their pathological condition. A DL MESHD model aims to detect the COVID-19 MESHD pneumonia MESHD, classify and distinguish between COVID-19 MESHD, Community-Acquired Pneumonia (CAP), Viral and Bacterial pneumonia MESHD, and normal conditions. Furthermore, sophisticated models can be built to segment the affected area in the lungs and quantify the infection volume for a better understanding of the extent of damage. Many models have been developed either independently or with the help of pre-trained models like VGG19, ResNet50, and AlexNet leveraging the concept of transfer learning. Apart from model development, data preprocessing and augmentation are also performed to cope with the challenge of insufficient data samples often encountered in medical applications. It can be evaluated that DL MESHD and AI can be effectively implemented to withstand the challenges posed by the global emergency

    Quantitative Assessment of Chest CT Patterns in COVID-19 MESHD and Bacterial Pneumonia patients: A Deep Learning Perspective

    Authors: Myeongkyun Kang; Philip Chikontwe; Miguel Luna; Kyung Soo Hong; Jong Geol Jang; Jongsoo Park; Kyeong-Cheol Shin; June Hong Ahn; Sang Hyun Park

    doi:10.1101/2020.11.13.20231118 Date: 2020-11-16 Source: medRxiv

    As the number of COVID-19 MESHD patients has increased worldwide, many efforts have been made to find common patterns in CT images of COVID-19 MESHD patients and to confirm the relevance of these patterns against other clinical information. The aim of this paper is to propose a new method that allowed us to find patterns which observed on CTs of patients, and further we use these patterns for disease and severity diagnosis. For the experiment, we performed a retrospective cohort study of 170 confirmed patients with COVID-19 MESHD and bacterial pneumonia MESHD acquired at Yeungnam University hospital in Daegu, Korea. We extracted lesions inside the lungs from the CT images and classified whether these lesions were from COVID-19 MESHD patients or bacterial pneumonia MESHD patients by applying a deep learning model. From our experiments, we found 20 patterns that have a major effect on the classification performance of the deep learning model. Crazy-paving was extracted as a major pattern of bacterial pneumonia MESHD, while Ground-glass opacities (GGOs) in the peripheral lungs as that of COVID-19 MESHD. Diffuse GGOs in the central and peripheral lungs was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 MESHD and bacterial pneumonia MESHD with 95% reported for severity classification. Chest CT analysis with constructed lesion clusters revealed well-known COVID-19 MESHD CT manifestations comparable to manual CT analysis. Moreover, the constructed patient level histogram with/without radiomics features showed feasibility and improved accuracy for both disease and severity classification with key clinical implications.

    FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 MESHD Detection

    Authors: Zhi Qiao; Austin Bae; Lucas M. Glass; Cao Xiao; Jimeng Sun

    id:2010.16039v1 Date: 2020-10-30 Source: arXiv

    To test the possibility of differentiating chest x-ray images of COVID-19 MESHD against other pneumonia MESHD and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia MESHD, non- COVID-19 MESHD viral pneumonia MESHD, and COVID-19 MESHD. To identify COVID-19 MESHD, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), a flexible module to ensemble several convolutional neural network (CNN) models and fuse with a focal loss for accurate COVID-19 MESHD detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 MESHD identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score with 6% relative increase on Covid-19 MESHD identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03) in Recall, and 0.8168(0.03) F1 score.

    The spatio-temporal landscape of lung pathology in SARS-CoV-2 infection MESHD

    Authors: Andre F Rendeiro; Hiranmayi Ravichandran; Yaron Bram; Steven Salvatore; Alain Borczuk; Olivier Elemento; Robert Edward Schwartz

    doi:10.1101/2020.10.26.20219584 Date: 2020-10-27 Source: medRxiv

    Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 MESHD ( COVID-19 MESHD). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury MESHD including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia MESHD and COVID-19 MESHD, respectively. We provide evidence that SARS-CoV-2 infects MESHD predominantly alveolar epithelial MESHD cells and induces a localized hyper-inflammatory cell state associated with lung damage MESHD. By leveraging the temporal range of COVID-19 MESHD severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19 MESHD, and lung pathology in general.

    Diagnostic utility of a Ferritin-to-Procalcitonin Ratio to differentiate patients with COVID-19 MESHD from those with Bacterial Pneumonia: A multicenter study

    Authors: Amal A. Gharamti; Fei Mei; Katherine C. Jankousky; Jin Huang; Peter Hyson; Daniel B. Chastain; Jiawei Fan; Sharmon Osae; Wayne W. Zhang; Jose G. Montoya; Kristine M. Erlandson; Sias J. Scherger; Carlos Franco-Paredes; Andres F. Henao-Martinez; Leland Shapiro

    doi:10.1101/2020.10.20.20216309 Date: 2020-10-22 Source: medRxiv

    Importance: There is a need to develop tools to differentiate COVID-19 MESHD from bacterial pneumonia MESHD at the time of clinical presentation before diagnostic testing is available. Objective: To determine if the Ferritin-to-Procalcitonin ratio (F/P) can be used to differentiate COVID-19 MESHD from bacterial pneumonia MESHD. Design: This case-control study compared patients with either COVID-19 MESHD or bacterial pneumonia MESHD, admitted between March 1 HGNC and May 31, 2020. Patients with COVID-19 MESHD and bacterial pneumonia co-infection MESHD were excluded. Setting: A multicenter study conducted at three hospitals that included UCHealth and Phoebe Putney Memorial Hospital in the United States, and Yichang Central People Hospital in China. Participants: A total of 242 cases with COVID-19 MESHD infection and 34 controls with bacterial pneumonia MESHD. Main Outcomes and Measures: The F/P in patients with COVID-19 MESHD or with bacterial pneumonia MESHD were compared. Receiver operating characteristic analysis determined the sensitivity and specificity of various cut-off F/P values for the diagnosis of COVID-19 MESHD versus bacterial pneumonia MESHD. Results: Patients with COVID-19 MESHD pneumonia MESHD had a lower mean age (57.11 vs 64.4 years, p=0.02) and a higher BMI (30.74 vs 27.15 kg/m2, p=0.02) compared to patients with bacterial pneumonia MESHD. Cases and controls had a similar proportion of women (47% vs 53%, p=0.5) and COVID-19 MESHD patients had a higher prevalence of diabetes mellitus MESHD (32.6% vs 12%, p=0.01). The median F/P was significantly higher in patients with COVID-19 MESHD (4037.5) compared to the F/P in bacterial pneumonia MESHD (802, p<0.001). An F/P greater than or equal to 877 used to diagnose COVID-19 MESHD resulted in a sensitivity of 85% and a specificity of 56%, with a positive predictive value of 93.2%, and a likelihood ratio of 1.92. In multivariable analyses, an F/P greater than or equal to 877 was associated with greater odds of identifying a COVID-19 MESHD case (OR: 11.27, CI: 4-31.2, p<0.001). Conclusions and Relevance: An F/P greater than or equal to 877 increases the likelihood of COVID-19 MESHD pneumonia MESHD compared to bacterial pneumonia MESHD. Further research is needed to determine if obtaining ferritin and procalcitonin simultaneously at the time of clinical presentation has improved diagnostic value. Additional questions include whether an increased F/P and/or serial F/P associates with COVID-19 MESHD disease severity or outcomes.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins

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