• Journal of Sasang Constitutional Medicine
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• v.13 no.3
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• pp.1-7
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• 2001

We analyzed 45 types of herbal medicines for Soyangin, clinically applied in Dongyisoosebowon. In order to discover the standard of herbal classification for the Sasang constitutions, four concepts of Sasang Medicine were applied. These included 'Sadangron(theory of four groups)', 'Seungganggaehap(ascending-descending and gathering-dispersing)', 'Pyorihanyoul(exterior-interior and hot-cold)', and 'Hyungchiaekmi(fragrance, smell, bodily fluid and taste)'. According to these analyzing methods of herbal properties, we have reached the following conclusions: Herbal medicines for 'Soyangin' are characterized by descending energetics that reinforce the Yin Qi. The 'Exterior cold disease' is treated with herbs that descend the "Exterior Yin" in five ways: by releasing exterior, by resolving dampness, by clearing and transforming heat phlegm, by clearing heat, and by settling and calming the spirit. The 'Interior heat disease' is treated with herbs that raise the "Interior Yang" in three ways: by tonifying kidney Yin, by clearing heat, and by clearing heat and purging.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1379-1396
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• 2021

Rare neurovascular diseases (RNVDs) have not been well-recognized in Korea. They involve the central nervous system and greatly affect the patients' lives. However, these diseases are difficult to diagnose and treat due to their rarity and incurability. We established a list of RNVDs by referring to the previous literature and databases worldwide to better understand the diseases and their current management status. We categorized 68 RNVDs based on their pathophysiology and clinical manifestations and estimated the prevalence of each disease in Korea. Recent advances in genetic, molecular, and developmental research have enabled further understanding of these RNVDs. Herein, we review each disease, while considering its classification based on updated pathologic mechanisms, and discuss the management status of RNVD in Korea.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Tuberculosis and Respiratory Diseases
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• v.77 no.4
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• pp.155-160
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• 2014

Chronic obstructive pulmonary disease (COPD) is characterized by a diverse array of pulmonary and nonpulmonary manifestations, but our understanding of COPD pathogenesis and the factors that influence its heterogeneity in disease presentation is poor. Despite this heterogeneity, treatment algorithms are primarily driven by a single measurement, forced expiratory volume in 1 second ($FEV_1$) as a percentage of its predicted value ($FEV_1%$). In 2011, a major shift in Global Initiative for Chronic Obstructive Lung Disease (GOLD) treatment recommendations was proposed that stratifies patients with COPD on the basis of symptoms and exacerbation history. This article reviews the work reported in 2013 that enlightens our understanding of COPD with respect to COPD classification systems, phenotype, biomarker, exacerbation, and management for patients with COPD.

• BMB Reports
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• v.40 no.2
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• pp.135-141
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• 2007

Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.

• Journal of The Korean Radiological Technologist Association
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• v.28 no.1
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• pp.61-66
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• 2002

Purpose : This study presents a new classification of gastroesophageal reflux to evaluate the patients with Gastroesophageal reflux disease after barium contrast reflux exam to help making the correct diagnosis and Treatment according to the symptoms and

• 한국초전도학회:학술대회논문집
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• v.15
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• pp.53-53
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• 2005

• Journal of Physiology & Pathology in Korean Medicine
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• v.21 no.3
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• pp.776-780
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• 2007

Recently World Health Organization Western Pacific Regional Office (WHO/WPRO) has developed the WHO International Standard Terminologies on Traditional Medicine in the Western Pacific Region (IST), and is developing WHO International Classification of Traditional Medicine/Western Pacific Regional Office (ICTM/WPRO). Regarding ICTM, WHO/WPRO hoped that it will be incorporated to International Classification of Disease (ICD) 11$^{th}$ edition, published in 2015. The author reports the proceedings of these two standardizations on terminologies and diseases of traditional medicine in East Asia.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.324-330
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• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.223-229
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• 2021

Classification of different blood cell types is an essential task for human's medical treatment. The white blood cells have different types of cells. Counting total White Blood Cells (WBC) and differential of the WBC types are required by the physicians to diagnose the disease correctly. This paper used transfer learning methods to the pre-trained deep learning models to classify different WBCs. The best pre-trained model was Inception ResNetV2 with Adam optimizer that produced classification accuracy of 98.4% for the dataset comprising four types of WBCs.