• The Korean Journal of Physiology and Pharmacology
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• v.25 no.5
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• pp.489-494
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• 2021

Oxaliplatin, a third-generation platinum derivative, is the mainstay of current antineoplastic medications for advanced colorectal cancer therapy. However, peripheral neuropathic complications, especially cold allodynia, undermine the life-prolonging outcome of this anti-cancer agent. Rosavin, a phenylpropanoid derived originally from Rhodiola rosea, exhibits a wide range of therapeutic properties. The present study explored whether and how rosavin alleviates oxaliplatin-induced cold hypersensitivity in mice. In the acetone drop test, cold allodynia behavior was observed from days 3 to 5 after a single injection of oxaliplatin (6 mg/kg, i.p.). Cold allodynia was significantly attenuated following rosavin treatment (10 mg/kg, i.p.). Specific endogenous 5-HT depletion by three consecutive pretreatments with parachlorophenylalanine (150 mg/kg/day, i.p.) abolished the analgesic action of rosavin; this effect was not observed following pretreatment with naloxone (opioid receptor antagonist, 10 mg/kg, i.p.). Furthermore, 5-HT_1A receptor antagonist WAY-100635 (0.16 mg/kg, i.p.), but not 5-HT₃ receptor antagonist MDL-72222 (1 mg/kg, i.p.), blocked rosavin-induced analgesia. These results suggest that rosavin may provide a novel approach to alleviate oxaliplatin-induced cold allodynia by recruiting the activity of 5-HT_1A receptors.

• Journal of Korean Medical classics
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• v.32 no.3
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• pp.1-15
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• 2019

Objectives : The purpose of this study is to find the pronunciations suitable for the meanings of Chinese characters because the pronunciations of Chinese characters with multi-pronunciations in the "Huangdineijing" are written differently in Chinese character dictionaries(字典) and in Korean Medical dictionaries(韓醫學辭典) respectively. Methods : First, the annotations for pronunciations in the "Leijing" were searched, upon which the frequently mispronounced 16 Chinese characters in the "Huangdineijing" were selected. Then these selected Chinese characters were compared and considered with reference to Chinese character dictionaries(published in China and Korea), and Korean Medical dictionaries. Results & Conclusions : It was discovered that in "Leijing" there were 1,537 annotations that used 'FanQie(反切)', 'Pronunciation(音)', 'Replaceable character(通用)', 'Same character (同字)', 'Four intonations(四聲)' etc. In some places, there were additional meanings of the Chinese characters. It was found that 16 Chinese characters were susceptible to wrong pronunciations in the "Huangdineijing", for which the appropriate pronunciations in regards to the meaning of the sentences were determined.

• Journal of Software Engineering Society
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• v.26 no.3
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• pp.63-76
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• 2013

We need to measure the quality of each module to assess software quality. Especially, in industry, it is important to measure quality of modules quantitatively in order to improve bad modules and reuse good modules. In this paper, we define moduleness as characteristic that modules should be possessed and propose a method to measure software moduleness quantitatively. A case study conducted in this paper shows the usefulness of the method by measuring and evaluating moduleness of modules based on open source software developed by C, C++ and Java languages.

• Journal of the Korean Magnetic Resonance Society
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• v.23 no.1
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• pp.12-19
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• 2019

A former study has shown that the spin-lattice relaxation time ($T_1$) in cancerous prostate tissue had enhanced contrast at an ultra-low magnetic field, $132{\mu}T$. To study the field dependence and the origin of the contrast we measured $T_1$ in pairs of ex-vivo prostate tissues at the Earth's magnetic field. A portable and coil-based nuclear magnetic resonance (NMR) system was adopted for $T_1$ measurements at $40{\mu}T$. The $T_1$ contrast, ${\delta}=1-T_1$ (more cancer)/$T_1$(less cancer), was calculated from each pair. Additionally, we performed pathological examinations such as Gleason's score, cell proliferation index, and micro-vessel density (MVD), to quantify correlations between the pathological parameters and $T_1$ of the cancerous prostate tissues.

• Nutrition Research and Practice
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• v.13 no.3
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• pp.196-204
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• 2019

BACKGROUND/OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disease triggered by epigenetic alterations, including lysine acetylation at histone or non-histone proteins, affecting the stability or transcription of lipogenic genes. Although various natural dietary compounds have anti-lipogenic effects, their effects on the acetylation status and lipid metabolism in the liver have not been thoroughly investigated. MATERIALS/METHODS: Following oleic-palmitic acid (OPA)-induced lipid accumulation in HepG2 cells, the acetylation status of histone and non-histone proteins, HAT activity, and mRNA expression of representative lipogenic genes, including $PPAR{\gamma}$, SREBP-1c, ACLY, and FASN, were evaluated. Furthermore, correlations between lipid accumulation and HAT activity for 22 representative natural food extracts (NExs) were evaluated. RESULTS: Non-histone protein acetylation increased following OPA treatment and the acetylation of histones H3K9, H4K8, and H4K16 was accelerated, accompanied by an increase in HAT activity. OPA-induced increases in the mRNA expression of lipogenic genes were down-regulated by C-646, a p300/CBP-specific inhibitor. Finally, we detected a positive correlation between HAT activity and lipid accumulation (Pearson's correlation coefficient = 0.604) using 22 NExs. CONCLUSIONS: Our results suggest that NExs have novel applications as nutraceutical agents with HAT inhibitor activity for the prevention and treatment of NAFLD.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.59-66
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• 2019

The emergence of new IT technologies and convergence industries, such as artificial intelligence, bigdata and the Internet of Things, is another chance for South Korea, which has established itself as one of the world's top IT powerhouses. On the other hand, however, privacy concerns that may arise in the process of using such technologies raise the task of harmonizing the development of new industries and the protection of personal information at the same time. In response, the government clearly presented the criteria for deidentifiable measures of personal information and the scope of use of deidentifiable information needed to ensure that bigdata can be safely utilized within the framework of the current Personal Information Protection Act. It strives to promote corporate investment and industrial development by removing them and to ensure that the protection of the people's personal information and human rights is not neglected. This study discusses the strategy of deidentifying personal information protection based on the analysis of fake news. Using the strategies derived from this study, it is assumed that deidentification information that is appropriate for deidentification measures is not personal information and can therefore be used for analysis of big data. By doing so, deidentification information can be safely utilized and managed through administrative and technical safeguards to prevent re-identification, considering the possibility of re-identification due to technology development and data growth.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.131-142
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• 2021

Recently, there has been an active service that provides customized news to news subscribers. In this study, we intend to design a customized news service system through Deep Learning-based Social Network Service (SNS) activity analysis, applying real news and avoiding fake news. In other words, the core of this study is the study of delivery methods and delivery devices to provide customized news services based on analysis of users, SNS activities. First of all, this research method consists of a total of five steps. In the first stage, social network service site access records are received from user terminals, and in the second stage, SNS sites are searched based on SNS site access records received to obtain user profile information and user SNS activity information. In step 3, the user's propensity is analyzed based on user profile information and SNS activity information, and in step 4, user-tailored news is selected through news search based on user propensity analysis results. Finally, in step 5, custom news is sent to the user terminal. This study will be of great help to news service providers to increase the number of news subscribers.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.5
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• pp.161-168
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• 2019

Construction industry is becoming more advanced, but safety accidents are not decreasing and unsafe act (UA) and human errors (HE) are the main causes of safety accidents. Therefore, this study aims to analyze the relationships between unsafe acts and human errors for construction accident prevents. Specifically, the Correlation Analysis is used to quantify 24 combinations of the relationship between the UA and HE. Then, the Kano Model, and Timko Satisfied Coefficient was utilized to find 6 combinations for construction accident prevention plans. As the result of Timko Satisfied Coefficient, an interview was conducted with three safety managers and 6 safety prevention plan is proposed. Through these results, it is expected that the combination of 24 accidents will be basic data of safety management. Especially, the proposed safety prevention plans considering the characteristics of 6 combinations with high correlation can contribute to prevention of safety accidents at the construction site.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.106-115
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• 2021

Classifying work trades using images can serve an important role in a multitude of advanced applications in construction management and automated progress monitoring. However, images obtained from work sites may not always be clean. Defective images can damage an image classifier's accuracy which gives rise to a needs for a method to enhance a work trade image classifier's performance. We propose a method that uses work dependency information to aid image classifiers. We show that using work dependency can enhance the classifier's performance, especially when a base classifier is not so great in doing its job.