BACKGROUND There is limited data regarding the electrophysiological abnormalities and arrhythmias in children with COVID-19, including those associated with treatment using potentially proarrhythmic hydroxychloroquine (HCQ) and azithromycin (AZN).OBJECTIVES To describe the electrophysiologic findings and arrhythmias associated with pediatric COVID-19 and its treatment.METHODS A single-center retrospective chart review was undertaken and included all patients with (1) symptoms of COVID-19 and (2) PCR-positive nasopharyngeal swabs for SARS-CoV-2 who were placed on continuous telemetry for the duration of their hospitalization during March through May, 2020.RESULTS Thirty-six patients were included in the study. Significant arrhythmias were found in 6 (nonsustained ventricular tachycardia in 5 and sustained atrial tachycardia in 1). All were self-resolving and half prompted prophylactic antiarrhythmic therapy. Patients with significant arrhythmias were likely to have noncardiac comorbidities (4/ 6), but these were not more common than in patients without arrhythmias (20/30, P 5 1). The use of HCQ was associated with statistically significant QTc prolongation (413 6 19 ms vs 425 6 16 ms, P 5.005). QTc was not statistically different in patients with and without arrhythmias (425 6 15 ms vs 425 6 15 ms, P 5 1).CONCLUSIONS In pediatric patients with PCR-positive active COVID-19 infection, significant arrhythmias are infrequent, but are more common than expected in a general pediatric population. Comorbidities are not more common in patients with arrhythmias than in patients without arrhythmias. COVID-19 treatment using HCQ is associated with QTc prolongation but was not associated with arrhythmias in pediatric patients.

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COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.

The past decade is known as the era of integrations where multiple technologies had integrated, and new research trends were seen. The security of data and information in the digital world has been a challenge to everyone; Blockchain technology has attracted many researchers in these scenarios. This paper focuses on finding the current trends in Blockchain technology to help the researchers select an area to carry future research. The data related to Blockchain Technologies have been collected from IEEE, Springer, ACM, and other digital databases. Then, the formulated corpus is used for topic modelling, and Latent Dirichlet Allocation is deployed. The outcomes of the Latent Dirichlet Allocation model are then analyzed based on various extracted key terms and key documents found for each topic. All the topic solution has been identified from the bag of words. The extracted topics are thereafter semantically mapped. Thus, based on the analysis of more than 900 papers, the most recent research trends have been discussed in this paper, ultimately focusing on the areas that need more attention from the research community. Also, the meta data analysis has been accomplished, evaluating the year wise and publication source wise research growth. More than 15 research directions are elaborated in this paper, which can direct and guide the researchers to pursuit the research in specific trends and also, find the research gaps in various technologies associated with Blockchain Technology.

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