Factors affecting disease severity of multisystem inflammatory syndrome in children

Purpose: Multisystem inflammatory syndrome that occurs after SARS-Cov-2 infection with fever, cardiogenic shock and hyperinflammation in children, can be life threatening. In this study, it was aimed to investigate the effects of the complaint and duration at diagnosis on the severity of multisystem inflammatory syndrome in children. Materials and methods: The medical records of 99 pediatric patients, who were diagnosed multisystem inflammatory syndrome between September 2020 and August 2021 according to Centers for Disease Control and Prevention, were evaluated retrospectively. Demographic features, initial findings, and admission time of patients were noted. Patients were categorized according to intensive care necessity. Results: The median age of the patients was 10 (2-18) and 62 (62.6%) of patients were male. The median duration before admission was 4 (1-10) days. All patients has fever, 81.8% had gastrointestinal and 75.8% had cardiovascular involvement at admission. The patients (56.6%) who were accepted as severe and moderate MIS-C required intensive care. Prolonged fever, delayed admission, cardiovascular involvement, high inflammatory markers, lymphopenia and thrombocytopenia were found to key parameters determining the need for intensive care. Conclusion: Multisystem inflammatory syndrome in children is a new disease characterized by fever, signs of inflammation and organ dysfunction associated with SARS-CoV-2 infection. Delayed admission, high cardiac and inflammatory markers at diagnosis increase the need for intensive care. © 2022, Pamukkale University. All rights reserved.

A Practical and Empirical Comparison of Three Topic Modeling Methods using a COVID-19 Corpus: LSA, LDA, and Top2Vec

This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed since the 1980s namely, latent semantic indexing or analysis (LSI/LSA), probabilistic LSI/LSA (pLSI/pLSA), naïve Bayes, the Author-Recipient-Topic (ART), Latent Dirichlet Allocation (LDA), Topic Over Time (TOT), Dynamic Topic Models (DTM), Word2Vec, Top2Vec and \variation and combination of these techniques. For researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm-Top2Vec- with two of the most conventional and frequently-used methodologies-LSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused s. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers. © 2023 IEEE Computer Society. All rights reserved.