Purpose : There has been no exact answer to the question of when to discontinue antiepileptic drugs(AEDs) in children with well-controlled epilepsy for a long period. This study is about the risk factors of relapse after withdrawal of AEDs in seizure(Sz)-free patients to show a guideline for discontinuation of AEDs. Methods : One hundred and sixty-nine children were diagnosed as epileptic at the Pediatric Dept. of Kyung-Hee Univ. between 1993 to 1998, in whom AEDs had been withdrawn after at least two years of Sz-free period. Univariate analysis using Kaplan-Meier survival analysis and multivariate analysis using Cox-proportional hazard model were performed for sixteen risk factors. Results : Forty-nine of the 169 patients(28.9%) had recurrence of Szs. The mean follow-up after withdrawal of AEDs was 4.1 years, mean treatment period was 4.1 years, and the mean Sz-free period was 3.3 years. Factors associated with an increased risk of relapse were young age at onset, symptomatic Sz, Sz type in West and Lennox-Gastaut syndrome, neurologic deficit, longer Sz-controlling period, shorter total treatment period, number of AEDs used(more than one drug), age at withdrawal of AEDs, and Sz-free period less than two years in univariate analysis using Kaplan-Meier mothod. From multivariate analysis, the factors indicating a significantly higher relapse risk were pre-treatment period after first Sz attack, Sz-controlling period, Sz-free period, number of AEDs used, neurologic abnormalities. Conclusion : For epileptic children who were Sz-free for more than two years, and were more than six-years-old, the discontinuation of AEDs should be considered positively, according to age of onset, Sz type, age at withdrawal of AEDs, total treatment period, Sz-controlling period, number of AEDs used, etiology, neurologic deficit, and the wishes of the patients and the their parents.
Our previous studies have demonstrated the biological effects of alginic acid as a brown algae (Undaria pinnatifida) component on inhibitory action of obesity using animal model. Sprague-Dawely (SD) male rats were fed experimental diets (
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (