ABSTRACT
The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.
ABSTRACT
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.
ABSTRACT
Objective: To analyze the clinical characteristics of 34 COVID-19 cases and to provide the basis for the prevention and control of the epidemic disease. Methods: Thirty-four COVID-19 patients diagnosed with RT-PCR in the isolation ward of the Fourth People's Hospital of Ningxia Hui autonomous region (infectious diseases hospital) from the January 22 to February 4, 2020 were selected as the research subjects. The clinical data were collected. Excel was used to describe the relationship between clinical classification and age distribution, contact history and date of onset. SPSS 25.0 statistical software was used for analysis. The general information, clinical symptoms, blood test, virus nucleic acid test results, epidemiology, CT imaging, treatment and prognosis were analyzed. Results: There were 5 mild cases (5/34), 24 ordinary cases (24/34), 5 severe cases (5/34). The elderly was more common in severe patients. The main clinical symptoms were fever (27/34)and dry cough(26/34). The peripheral blood showed normal or decreased leukocyte count (33/34), decreased lymphocyte count (12/34). The increase of C-reactive protein (CRP) and D-dimer was related to the severity of the disease. Some patients had mild liver and kidney damage. Six patients were diagnosed through 3 or more times of nucleic acid tests. Sixteen cases had Wuhan related history,13 cases were close contacts, 5 cases had no confirmed route. The clustered infections were found in 6 families. In typical cases, CT showed single or multiple patchy ground glass shadow with thickening of interlobular septum. In severe cases, diffuse lesions of both lungs were found, with ground glass shadow, consolidation shadow and strip shadow coexisting. Thirty-four patients were treated with interferon-α and Lopinavir/Ritonavir with good prognosis. Conclusions: The clinical characteristics of COVID-19 were similar to that of general viral pneumonia but with strong infectivity. Close contact and family aggregation caused disease outbreaks. COVID-19 could not be excluded if two nucleic acid tests were negative and high-resolution CT was helpful for differential diagnosis. Early detection, early isolation, early diagnosis and early treatment are important for good prognosis. The effectiveness of antiviral drugs needs to be further verified.