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https://www.selleckchem.com/products/way-309236-a.html The use of social media assists in the distribution of COVID-19 information to the general public and health professionals. Alternative-level metrics (ie, altmetrics) and PlumX metrics are new bibliometrics that can assess how many times a scientific article has been shared and how much a scientific article has spread within social media platforms. Our objective was to characterize and compare the traditional bibliometrics (ie, citation count and impact factors) and new bibliometrics (ie, Altmetric Attention Score [AAS] and PlumX score) of the top 100 COVID-19 articles with the highest AASs. The top 100 articles with highest AASs were identified with Altmetric Explorer in May 2020. The AASs, journal names, and the number of mentions in various social media databases of each article were collected. Citation counts and PlumX Field-Weighted Citation Impact scores were collected from the Scopus database. Additionally, AASs, PlumX scores, and citation counts were log-transformed and adjusted by +1 for linearicles at this point in time. Altmetric and PlumX metrics should be used to complement traditional citation counts when assessing the dissemination and impact of a COVID-19 article.Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the risk of sudden unexpected death in epilepsy (SUDEP) in patients with generalized tonic-clonic seizures (GTCSs). While video-based automated seizure detection systems may be able to provide seizure alarms in both in-hospital and at-home settings, earlier studies have primarily employed hand-designed features for such a task. In contrast, deep learning-based approaches do not rely on prior feature selection and have demonstrated outstanding performance in many data classification tasks. Despite these advantages, neural network-based video classification has rarely been attempted for seizure detection. We here assessed the feasibility and eff

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