Journal of Korean medical science
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J. Korean Med. Sci. · Feb 2019
Prevalence of Fabry Disease in Korean Men with Left Ventricular Hypertrophy.
Fabry disease is an X-linked recessive disorder caused by deficiency of the lysosomal enzyme α-galactosidase A (α-Gal A). Previous studies identified many cases of Fabry disease among men with left ventricular hypertrophy (LVH). The purpose of this study was to define the frequency of Fabry disease among Korean men with LVH. ⋯ We identified three patients (0.3%) with Fabry disease among unselected Korean men with LVH. Although the prevalence of Fabry disease was low in our study, early treatment of Fabry disease can result in a good prognosis. Therefore, in men with unexplained LVH, differential diagnosis of Fabry disease should be considered.
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J. Korean Med. Sci. · Feb 2019
Aggressive Contact Investigation of In-Hospital Exposure to Active Pulmonary Tuberculosis.
In-hospital detection of newly diagnosed active pulmonary tuberculosis (TB) is important for prevention of potential outbreaks. Here, we report our experience of the aggressive contact investigation strategy in a university hospital in the Republic of Korea after healthcare workers (HCWs), patients, and visitors experience an in-hospital exposure to active pulmonary TB. ⋯ An aggressive contact investigation after an unexpected in-hospital diagnosis of active pulmonary TB revealed a high incidence of LTBI among TB-naïve HCWs who had contact with the index patients.
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J. Korean Med. Sci. · Feb 2019
What are the Barriers to Antenatal Care Utilization in Rufisque District, Senegal?: a Bottleneck Analysis.
This study aimed to analyze the barriers affecting the utilization of antenatal care (ANC) among Senegalese mothers. ⋯ To promote the utilization of ANC services among pregnant women in Senegal, it is important to alleviate the social stigma towards miscarriages and unmarried mothers, and to provide greater social support for pregnancies and newborn deliveries within family.
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J. Korean Med. Sci. · Feb 2019
Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.
In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. ⋯ The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.