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Background: COVID-19 is a multi-systemic infectious disease. Nearly 20%-30% of hospitalized patients have evidence of acute myocardial involvement, portending a poorer prognosis. However, information about the long-term effects of the disease on cardiac functions is sparse. As a result, there is a growing concern about the cardiac sequelae of COVID-19 among survivors. This study aimed to investigate the effects of prior mild-moderate COVID-19 infection on cardiac functions, using speckle tracking echocardiography. Methods: Patients who have been diagnosed with COVID-19 within the previous 6 months and age-, sex-, and risk factor-matched healthy adults were included. All patients underwent a comprehensive echocardiographic examination. Both conventional and 2-dimensional speckle tracking echocardiographic measurements were performed. Serum cardiac biomarkers were also obtained on the day of the echocardiographic study. Results: Compared with healthy controls, COVID-19 survivors had similar left and right ventricular longitudinal strain values at 6 months. Also, left and right atrial peak systolic strain values did not differ between the groups. Conclusion: Our study is valuable in putting forth the unaffected ventricular and atrial functions on long term in uncomplicated COVID-19 cases and may decrease the survivors' anxiety and the number of unnecessary applications to cardiology clinics. [ABSTRACT FROM AUTHOR]

The possibility of creating and easily distributing something via a digital platform creates an enormous material volume. With the increase of scientific publications it becomes harder for scholars to choose the most related and significant resources from the others (Henning & Gunn, 2012). Bibliometrics measures the impact of scholars between each other, but the impact of articles across the entire web is not limited by this. On the other hand, altmetrics measures the impact of articles from the web, alongside bibliometric data. The aim of this study is to investigate the probable relationships between traditional metrics and altmetrics by analysing the PLoS Article-Level Metric (ALM) dataset. [ABSTRACT FROM AUTHOR]