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Park, Kyeong Seon,Kwak, SooHeon,Cho, Young Min,Park, Kyong Soo,Jang, Hak C,Kim, Seong Yeon,Jung, Hye Seung John Wiley and Sons Inc. 2017 Journal of diabetes investigation Vol.8 No.2
<P><B>Abstract</B></P><P><B>Aims/Introduction</B></P><P>Dipeptidyl peptidase‐4 inhibitors might have pleiotropic protective effects on cardiovascular disease (CVD), in contrast to sulfonylureas. Therefore, we compared various CVD risk factors between vildagliptin and glimepiride.</P><P><B>Materials and Methods</B></P><P>We carried out a randomized, prospective and crossover trial. A total of 16 patients with type 2 diabetes whose glycated hemoglobin was >7% were randomized to add vildagliptin or glimepiride. After 12‐week treatment, each drug was replaced with the other for another 12 weeks. Before and after each treatment, glucose homeostasis and CVD risk factors were assessed, and the continuous glucose monitoring system was applied to calculate glycemic variability.</P><P><B>Results</B></P><P>The mean age of the participants was 60 years, 31% were men, body mass index 25.5 kg/m<SUP>2</SUP> and HbA1c 8.41%. Both vildagliptin and glimepiride significantly decreased glycated hemoglobin and glycemic variability indices. Despite the improved glucose homeostasis, favorable change of CVD markers was not prominent in both the arms, along with significant weight gain. Only plasma stromal cell‐derived factor (SDF)‐1α decreased by 30% in the vildagliptin arm. According to regression analyses, the reduction of SDF‐1α was independently associated with vildagliptin usage and serum interleukin‐6 changes, but white blood cells were not related with the SDF‐1α changes.</P><P><B>Conclusion</B></P><P>Compared with glimepiride, vildagliptin arrestingly decreased plasma SDF‐1α, and its clinical implications should be further investigated.</P>
( Youngin You ),( Junhyoung Oh ),( Sooheon Kim ),( Kyungho Lee ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.10
As the area covered by the CPS grows wider, agencies such as public institutions and critical infrastructure are collectively measuring and evaluating information security capabilities. Currently, these methods of measuring information security are a concrete method of recommendation in related standards. However, the security controls used in these methods are lacking in connectivity, causing silo effect. In order to solve this problem, there has been an attempt to study the information security management system in terms of maturity. However, to the best of our knowledge, no research has considered the specific definitions of each level that measures organizational security maturity or specific methods and criteria for constructing such levels. This study developed an information security maturity model that can measure and manage the information security capability of critical infrastructure based on information provided by an expert critical infrastructure information protection group. The proposed model is simulated using the thermal power sector in critical infrastructure of the Republic of Korea to confirm the possibility of its application to the field and derive core security processes and goals that constitute infrastructure security maturity. The findings will be useful for future research or practical application of infrastructure ISMSs.
EFA-DTI: Edge Feature Attention을 활용한 약물-표적 상호작용 예측
에르햄바야르 자담바(Erkhembayar Jadamba),김수헌(Sooheon Kim),이현수(Hyeonsu Lee),김화종(Hwajong Kim) 한국정보과학회 2021 정보과학회논문지 Vol.48 No.7
신약개발은 의약 화학, 시스템 및 구조 생물학, 더 나아가 인공지능에 이르기까지 다양한 학문을 필요로 하기 때문에 난이도가 높은 분야라고 할 수 있다. 특히, 약물-표적 상호작용(DTI) 예측은 방대한 양의 화합물로부터 질병을 치료할 수 있는 후보 물질을 도출해내는 과정으로, 신약 개발 과정에 있어 핵심 요소다. 최근에는 컴퓨터 성능이 비약적으로 발전함에 따라, DTI 예측에 소요되는 여러 측면의 비용을 줄이고자 인공지능 신경망을 활용하는 연구가 활발히 진행되고 있다. 따라서, 본 논문에서는 Edge Feature Attention을 적용한 Graph Net Embedding 및 Fingerprint를 활용한 약물 표현 생성과 ProtTrans를 활용한 단백질 표현 생성을 통해 약물과 표적 단백질 간의 상호작용 수치를 예측하는 모델을 제안한다. 해당 모델은 기존의 DTI 연구에서 가장 좋은 성능을 보였던 DeepDTA, GraphDTA보다 높은 성능을 달성하였으며, 이에 대한 실험 및 결과를 기술하였다. Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.