ABSTRACT
The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.
ABSTRACT
BACKGROUND: The available information on the echocardiographic features of cardiac injury related to the novel coronavirus disease 2019 (COVID-19) and their prognostic value are scattered in the different literature. Therefore, the aim of this study was to investigate the echocardiographic features of cardiac injury related to COVID-19 and their prognostic value. METHODS: Published studies were identified through searching PubMed, Embase (Elsevier), and Google scholar databases. The search was performed using the different combinations of the keywords "echocard*," "cardiac ultrasound," "TTE," "TEE," "transtho*," or "transeso*" with "COVID-19," "sars-COV-2," "novel corona, or "2019-nCOV." Two researchers independently screened the titles and abstracts and full texts of articles to identify studies that evaluated the echocardiographic features of cardiac injury related to COVID-19 and/or their prognostic values. RESULTS: Of 783 articles retrieved from the initial search, 11 (8 cohort and 3 cross-sectional studies) met our eligibility criteria. Rates of echocardiographic abnormalities in COVID-19 patients varied across different studies as follow: RV dilatation from 15.0% to 48.9%; RV dysfunction from 3.6% to 40%; and LV dysfunction 5.4% to 40.0%. Overall, the RV abnormalities were more common than LV abnormalities. The majority of the studies showed that there was a significant association between RV abnormalities and the severe forms and death of COVID-19. CONCLUSION: The available evidence suggests that RV dilatation and dysfunction may be the most prominent echocardiographic abnormality in symptomatic patients with COVID-19, especially in those with more severe or deteriorating forms of the disease. Also, RV dysfunction should be considered as a poor prognostic factor in COVID-19 patients.