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Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Limited Role for Antibiotics in COVID-19: Scarce Evidence of Bacterial Coinfection MESHD

    Authors: Wenjing Wei; Jessica K Ortwine; Norman S Mang; Christopher Joseph; Brenton C Hall; Bonnie Chase Prokesch

    doi:10.1101/2020.06.16.20133181 Date: 2020-06-18 Source: medRxiv

    Background: There is currently a paucity of data describing bacterial coinfections MESHD, related antibiotic prescribing patterns, and the potential role of antimicrobial stewardship in the care of patients infected with SARS-CoV-2. Methods: This prospective, observational study was conducted from March 10, 2020 to April 21, 2020 in admitted patients with confirmed COVID-19. Patients were included if [≥] 18 years old and admitted to the hospital for further treatment. Data was collected via chart review from the enterprise electronic health record database. Data collected include factors driving antibiotic choice, indication, and duration of therapy as well as microbiological data. Findings: Antibiotics were initiated on admission in 87/147 (59%) patients. Of these, 85/87 (98%) prescriptions were empiric. The most common indication for empiric antibiotics was concern for community-acquired pneumonia MESHD pneumonia HP (76/85, 89%) with the most prescribed antibiotics being ceftriaxone and azithromycin. The median duration of antibiotic therapy was two days (interquartile range 1-5). No patients had a community-acquired bacterial respiratory coinfection MESHD, but 10/147 (7%) of patients were found to have concurrent bacterial infections MESHD from a non-respiratory source, and one patient was diagnosed with active pulmonary tuberculosis MESHD pulmonary tuberculosis HP at the time of admission for COVID-19. Interpretation: Bacterial coinfection MESHD in patients with COVID-19 was infrequent. Antibiotics are likely unnecessary in patients with mild symptoms. There is little role for broad-spectrum antibiotics to empirically treat multidrug resistant organisms in patients with COVID-19, regardless of disease MESHD severity. Antimicrobial stewardship remains important in patients infected with SARS-CoV-2.

    Severe Acute Respiratory Syndrome MESHD Coronavirus-2 and Pulmonary Tuberculosis MESHD Pulmonary Tuberculosis HP Coinfection MESHD: Double Trouble

    Authors: Abhijeet Singh; Ayush Gupta; Kamanashish Das

    doi:10.21203/rs.3.rs-22464/v3 Date: 2020-04-11 Source: ResearchSquare

    Background: The ongoing pandemic of novel coronavirus disease MESHD 2019 (COVID-19) has received worldwide attention by becoming a major global health threat. We encountered one case with COVID-19 and tuberculosis MESHD (TB) coinfection MESHD which has not been frequently reported. Case presentation: A 76 year old female TRANS presented with acute respiratory symptoms superimposed on chronic symptoms, suggestive to have pneumonia MESHD pneumonia HP. Oropharyngeal throat swab sample for COVID-19 was positive as detected by real-time reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay. GeneXpert Ultra detected Mycobacterium tuberculosis MESHD complex with Rifampicin resistance indeterminate. Patient was treated with appropriate management. Conclusion: Clinicians should suspect coinfection MESHD with TB during ongoing pandemic of COVID-19 as therapeutic strategies need to be determined timely to improve outcome and prevent transmission TRANS in community. 

    Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung MESHD by Chest CT Scan Images

    Authors: Min Fu; Shuang-Lian Yi; Yuanfeng Zeng; Feng Ye; Yuxuan Li; Xuan Dong; Yan-Dan Ren; Linkai Luo; Jin-Shui Pan; Qi Zhang

    doi:10.1101/2020.03.28.20046045 Date: 2020-03-30 Source: medRxiv

    Purpose: COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human-based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)-based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung MESHD. Experimental Design: In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia MESHD pneumonia HP, non-COVID-19 viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, pulmonary tuberculosis MESHD pulmonary tuberculosis HP, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan. Results: Accuracy, sensitivity SERO, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia MESHD pneumonia HP, non-COVID-19 viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, and pulmonary tuberculosis MESHD pulmonary tuberculosis HP were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity SERO, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. Conclusions: The performance SERO of the proposed AI framework has excellent performance SERO of recognizing COVID-19 and other common infectious diseases of the lung MESHD, which also has balanced sensitivity SERO and specificity.

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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