220 research outputs found

    Categorization์„ ์ด์šฉํ•œ WiFi ๊ธฐ๋ฐ˜ ์ €๋ณต์žก๋„ ํ–‰๋™ ์ธ์‹ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022.2. ์ „ํ™”์ˆ™.As smart homes and augmented reality (AR) become popular, the convenient human-computer interaction (HCI) methods are also attracting attention. Among them, many researchers have paid attention to gesture recognition that is simple and intuitive for humans. Camera-based and sensor-based gesture recognition have been very successful, but have limitations including privacy issues and inconvenience. On the other hand, WiFi-based gesture recognition using channel state information (CSI) does not have these limitations. However, since the WiFi signal is noisy, Deep learning (DL) models have been commonly utilized to improve the gesture recognition performance. DL models require large training data, large memory, and high computational complexity, resulting in long latencies that disrupt real-time systems. To solve this problem, support vector machines (SVMs) that require less computation and memory than powerful deep learning models can be utilized. However, the SVM shows poor performance when there are many target classes. In this paper, we propose a categorization method that can divide ten gestures into four categories. Since only two or three target gestures belong to each category, a traditional machine learning model like support vector machine (SVM) can achieve high accuracy while requiring less computation and memory consumption than the DL models. According to the experimental results, when using the SVM alone, the accuracy is about 58%. However, when used with categorization, it can improve up to 90%. Furthermore, the gesture recognition performance of the DL models can also be improved by combining the proposed categorization method if the hardware has sufficient memory and computational complexity.์Šค๋งˆํŠธํ™ˆ๊ณผ ์ฆ๊ฐ•ํ˜„์‹ค(AR)์ด ๋ณดํŽธํ™”๋˜๋ฉด์„œ ํŽธ๋ฆฌํ•œ ์ธ๊ฐ„-์ปดํ“จํ„ฐ ์ƒํ˜ธ์ž‘์šฉ ๋ฐฉ์‹๋„ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์ด ์ธ๊ฐ„์—๊ฒŒ ๊ฐ„ํŽธํ•˜๊ณ  ์ง๊ด€์ ์ธ Gesture Recognition์— ์ฃผ๋ชฉํ•ด ์™”๋‹ค. ์นด๋ฉ”๋ผ ๊ธฐ๋ฐ˜ ๋ฐ ์„ผ์„œ ๊ธฐ๋ฐ˜ Gesture Recognition์€ ๋งค์šฐ ์„ฑ๊ณต์ ์ด์—ˆ์ง€๋งŒ ๊ฐœ์ธ ์ •๋ณด ๋ณดํ˜ธ ๋ฌธ์ œ ๋ฐ ๋ถˆํŽธํ•จ ๋“ฑ์˜ ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ฐ˜๋ฉด, ์ฑ„๋„ ์ƒํƒœ ์ •๋ณด(CSI)๋ฅผ ์ด์šฉํ•œ WiFi ๊ธฐ๋ฐ˜ Gesture Recognition์€ ์ด๋Ÿฌํ•œ ์ œํ•œ์ด ์—†๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ WiFi ์‹ ํ˜ธ์— ๋…ธ์ด์ฆˆ๊ฐ€ ๋งŽ๊ธฐ ๋•Œ๋ฌธ์— Gesture Recognition ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์ด ์ผ๋ฐ˜์ ์œผ๋กœ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์€ ๋Œ€๊ทœ๋ชจ ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์™€ ๋Œ€์šฉ๋Ÿ‰ ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๊ณ  ๋†’์€ ๊ณ„์‚ฐ ๋ณต์žก๋„๋กœ ์ธํ•ด ์‹ค์‹œ๊ฐ„ ์‹œ์Šคํ…œ์„ ๋ฐฉํ•ดํ•˜๋Š” ๊ธด ์ง€์—ฐ ์‹œ๊ฐ„์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ•๋ ฅํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ๋ณด๋‹ค ์—ฐ์‚ฐ๊ณผ ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ๋œ ํ•„์š”ํ•œ SVM์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ SVM์€ ๋Œ€์ƒ ํด๋ž˜์Šค๊ฐ€ ๋งŽ์„์ˆ˜๋ก ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ €ํ•˜๋˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ gesture๋ฅผ ์—ฌ๋Ÿฌ ๋ฒ”์ฃผ๋กœ ๋‚˜๋ˆ”์œผ๋กœ์จ ๋Œ€์ƒ ํด๋ž˜์Šค๋ฅผ ์ค„์ด๋Š” ๋ฒ”์ฃผํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. gesture segment๋ผ๊ณ  ํ•˜๋Š” gesture unit์„ ์ฐพ๋Š” ๊ฒƒ์ด ๋ฒ”์ฃผํ™” ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ์ด๋‹ค. ๊ฐ Gesture๋Š” ๊ณ ์œ ํ•œ gesture segment ๊ฐœ์ˆ˜๋ฅผ ๊ฐ€์ง€๋ฏ€๋กœ gesture๋ฅผ ์ˆซ์ž๋กœ ๋ฒ”์ฃผํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ๋ฐ€๊ธฐ ๋ฐ ๋‹น๊ธฐ๊ธฐ์™€ ๊ฐ™์€ ์—ฐ์† gesture์—๋Š” ๋‘ ๊ฐœ์˜ segment๊ฐ€ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ segment๋Š” ๋ฐ€๊ธฐ์ด๊ณ  ๋‘ ๋ฒˆ์งธ segment๋Š” ๋‹น๊ธฐ๊ธฐ์ด๋‹ค. ์‚ฌ๋žŒ๋“ค์ด ํ˜„์žฌ gesture segment๋ฅผ ์ค‘์ง€ํ•˜๊ณ  ๋‹ค์Œ gesture segment๋ฅผ ์ˆ˜ํ–‰ํ•  ๋•Œ segment ์‚ฌ์ด์— short pause๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” CSI ์ง„ํญ์˜ ๋ณ€๋™์„ ๋ถ„์„ํ•˜์—ฌ ์ด๋Ÿฌํ•œ short pause๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์Œ์„ ๊ด€์ฐฐํ–ˆ๋‹ค. CSI์˜ ์ง„ํญ์€ ์‚ฌ๋žŒ์ด ์›€์ง์ผ ๋•Œ ๋” ์ปค์ง€๊ณ  ๊ทธ ๋ฐ˜๋Œ€๋„ ๋งˆ์ฐฌ๊ฐ€์ง€์ด๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ง„ํญ์˜ ๋ณ€ํ™”๋ฅผ ์ด์šฉํ•˜์—ฌ short pause๋ฅผ ์ฐพ๊ณ  gesture segment๋ฅผ ๋‚˜๋ˆ„๋Š” ๋ฒ”์ฃผํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ฒ”์ฃผํ™” ์ดํ›„ ๋ฒ”์ฃผ์— ํ•ด๋‹นํ•˜๋Š” SVM์ด CSI ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฐœ์ƒํ•œ gesture๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฒ”์ฃผํ™” ๋ฐฉ๋ฒ•์€ 98.5%์˜ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๊ณ  ์ตœ์ข…์ ์œผ๋กœ 10๊ฐœ์˜ gesture์— ๋Œ€ํ•ด SVM์˜ ์„ฑ๋Šฅ์„ ์•ฝ 30% ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๊ฐ€ ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์€ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋น„๊ต๋Œ€์ƒ์— ๋น„ํ•ด ํ›จ์”ฌ ์ ์€ ๋ฉ”๋ชจ๋ฆฌ์™€ ์ง€์—ฐ ์‹œ๊ฐ„์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ์ค€์ˆ˜ํ•œ ์ •ํ™•๋„๋ฅผ ๊ฐ€์ง€๋ฉฐ AP ๋ฐ IoT ์žฅ์น˜์™€ ๊ฐ™์€ ์ œํ•œ๋œ ํ•˜๋“œ์›จ์–ด์—๋„ ๋ฐฐํฌํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค.Abstract - i Contents - iii List of Figures - iv List of Tables - v Chapter 1. Introduction - 1 Chapter 2. System Model - 4 Chapter 3. Proposed Scheme - 6 3.1 Overview - 6 3.2 Preprocessing - 8 3.3 Gesture Segmentation and Categorization - 10 3.4 Feature Extraction - 12 3.5 Classification - 13 Chapter 4. Performance Evaluation - 15 4.1 Experimental Setup - 15 4.2 Categorization Performance - 16 4.3 Overall Performance - 18 4.4 Performance comparison with baseline - 19 4.5 Effect of the channel - 21 Chapter 5. Conclusion - 22 Bibliography - 24 Abstract in Korean - 26์„

    ์ค€๋ชจ์ˆ˜์  ๊ฐ€๋ฒ• ๋ชจํ˜•์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋‹ค์ค‘ ์Šฌ๋กฏ ๋จธ์‹  ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ†ต๊ณ„ํ•™๊ณผ, 2019. 2. PaikCho, Myunghee.Contextual multi-armed bandit (MAB) algorithms have been shown promising for maximizing cumulative rewards in sequential decision tasks under uncertainty when contextual information is given. Applications include news article recommendation systems, web page ad placement algorithms, revenue management, and mobile health. However, most of the proposed contextual MAB algorithms rely on strong, linear assumptions between the reward and the context of the action. This thesis proposes a new contextual MAB algorithm for a relaxed, semiparametric reward model that supports nonstationarity. The proposed method is less restrictive, easier to implement and faster than two alternative algorithms that consider the same model. It can be shown that the high-probability upper bound of the regret incurred by the proposed algorithm has the same order as the Thompson sampling algorithm for linear reward models without restricting action choice probabilities. The proposed algorithm and existing algorithms are evaluated via simulation and also applied to Yahoo! news article recommendation log data provided by Yahoo! Webscope.๋‹ค์ค‘ ์Šฌ๋กฏ ๋จธ์‹  (MAB) ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ˆœ์ฐจ ๊ฒฐ์ • ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋Š” ์—ฐ๊ตฌ ๋ถ„์•ผ๋กœ์„œ, ํŠน์ • ํ™˜๊ฒฝ ์•ˆ์—์„œ ํ•™์Šต์ž์—๊ฒŒ ์„ ํƒ ๊ฐ€๋Šฅํ•œ ๋‹ค์ˆ˜์˜ ํ–‰๋™๋“ค์ด ์ฃผ์–ด์กŒ์„ ๋•Œ, ์ด๋“ค ์ค‘ ๋ณด์ƒ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ํ–‰๋™์„ ์„ ํƒํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์ด๋‹ค. ํ•™์Šต์ž๋Š” ํ–‰๋™์„ ์„ ํƒํ•˜๊ณ  ๋ณด์ƒ์„ ๋ฐ›๋Š” ๊ณผ์ •์„ ๋ฐ˜๋ณตํ•˜๋ฉด์„œ ๋ณด์ƒ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ถ•์ ํ•˜๊ณ  ํ•™์Šตํ•˜์—ฌ ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ์ตœ์ ์˜ ํ–‰๋™์— ๊ฐ€๊นŒ์šด ํ–‰๋™์„ ์„ ํƒํ•˜๊ฒŒ ๋œ๋‹ค. ์‚ฌ์ด๋“œ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋‹ค์ค‘ ์Šฌ๋กฏ ๋จธ์‹  (Contextual MAB) ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ˆœ์ฐจ์  ์˜์‚ฌ ๊ฒฐ์ • ์‹œ์— ์‚ฌ์ด๋“œ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” MAB ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋ฉฐ ์ตœ๊ทผ Yahoo!์˜ ๋‰ด์Šค ๊ธฐ์‚ฌ ์ถ”์ฒœ ์‹œ์Šคํ…œ์— ์ ์šฉ๋˜์–ด ๊ธฐ์กด์— ๋น„ํ•ด ๊ธฐ์‚ฌ ํด๋ฆญ์ˆ˜๋ฅผ ํฌ๊ฒŒ ์ฆ๊ฐ€์‹œํ‚ค๋ฉด์„œ ๋งŽ์€ ์„ฑ๊ณผ๋ฅผ ๊ฑฐ๋‘์—ˆ๋‹ค. ์ด์™ธ์—๋„ Contextual MAB ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ฃผ๋กœ ์ด์šฉ๋˜๋Š” ๋ถ„์•ผ๋กœ๋Š” ์›น ํŽ˜์ด์ง€ ๊ด‘๊ณ  ๋ฐฐ์น˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜, ์ˆ˜์ต ๊ด€๋ฆฌ, ๋ชจ๋ฐ”์ผ ํ—ฌ์Šค ์‹œ์Šคํ…œ ๋“ฑ ๋‹ค์–‘ํ•˜๋‹ค. ๋” ์ข‹์€ MAB ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋” ๋งŽ์€ ๋ณด์ƒ๊ณผ ์ˆ˜์ต์„ ์ฐฝ์ถœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ๋ถ„์•ผ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ๊นŒ์ง€ ์ œ์•ˆ๋œ ๋Œ€๋ถ€๋ถ„์˜ Contextual MAB ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณด์ƒ๊ณผ ์‚ฌ์ด๋“œ ์ •๋ณด ์‚ฌ์ด์— ์ œํ•œ์ ์ธ ์„ ํ˜• ๋ชจํ˜•์„ ๊ฐ€์ •ํ•œ๋‹ค. ํŠนํžˆ ๋ณด์ƒ ๊ฐ’์˜ ๋ถ„ํฌ๊ฐ€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ•˜์ง€ ์•Š๋Š” ๋‹ค๋Š” ๊ฐ€์ •์€ ์•ž์„œ ์†Œ๊ฐœํ•œ ์‹ค์ œ ๋ฌธ์ œ๋“ค์— ์ ์šฉํ•˜๊ธฐ์—๋Š” ๋น„ํ˜„์‹ค์ ์ด๋ผ๋Š” ์ง€์ ์„ ๋ฐ›๋Š”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์„ ํ˜• ๊ฐ€์ •๋ณด๋‹ค ์™„ํ™”๋œ ์ค€๋ชจ์ˆ˜์  ๊ฐ€๋ฒ• ๋ชจํ˜• ํ•˜์—์„œ๋„ ์ข‹์€ ์„ฑ๋Šฅ์„ ๊ฐ€์ง€๋Š” ์ƒˆ๋กœ์šด Contextual MAB ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ค€๋ชจ์ˆ˜์  ๋ณด์ƒ ๋ชจํ˜• ํ•˜์—์„œ ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ๋ฐœ์ƒ๋˜๋Š” ๋ˆ„์  ๋ณด์ƒ์ด ์ตœ์  ๋ณด์ƒ์œผ๋กœ ์ˆ˜๋ ดํ•˜๋Š” ์†๋„๋Š” ๋” ์ œํ•œ์ ์ธ ์„ ํ˜• ๋ชจํ˜• ํ•˜์—์„œ ํ†ฐ์Šจ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ๋ฐœ์ƒ๋˜๋Š” ๋ˆ„์  ๋ณด์ƒ์ด ์ˆ˜๋ ดํ•˜๋Š” ์†๋„์™€ ์œ ์‚ฌํ•˜๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋™์ผํ•œ ๋ชจํ˜•์„ ๋‹ค๋ฃจ๋Š” ๋‘๊ฐœ์˜ ์„ ํ–‰ ์—ฐ๊ตฌ์— ๋น„ํ•ด ๋œ ์ œํ•œ์ ์ด๊ณ  ๊ตฌํ˜„ํ•˜๊ธฐ ์‰ฌ์šฐ๋ฉฐ, ๊ตฌํ˜„ ์†๋„๊ฐ€ ๋” ๋น ๋ฅด๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ธฐ์กด ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‘œ๋ณธ ์„ฑ์งˆ์„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค. ๋”๋ถˆ์–ด, Yahoo! ์›น์Šค์ฝ”ํ”„๊ฐ€ ์ œ๊ณตํ•˜๋Š” Yahoo! ๋‰ด์Šค ๊ธฐ์‚ฌ ์ถ”์ฒœ ๋กœ๊ทธ ๋ฐ์ดํ„ฐ์— ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•œ ๊ฒฐ๊ณผ๋„ ์†Œ๊ฐœํ•œ๋‹ค.Abstract i 1 Introduction 1 2 Literature Review 6 2.1 The multi-armed bandit problem . . . . . . . . . . 6 2.2 Linear contextual MAB . . . . . . . . . . . . . . . 7 2.2.1 Upper confidence bound (UCB) algorithm . 8 2.2.2 Thompson sampling (TS) algorithm . . . . 15 2.3 Adversarial MAB . . . . . . . . . . . . . . . . . . . 19 2.3.1 EXP4.P algorithm . . . . . . . . . . . . . . 21 2.4 Semiparametric contextual MAB . . . . . . . . . . 23 2.4.1 Action-centered TS algorithm . . . . . . . . 25 2.4.2 BOSE algorithm . . . . . . . . . . . . . . . 28 3 Proposed method 31 3.1 Proposed algorithm . . . . . . . . . . . . . . . . . . 31 3.2 Proof . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.1 Stage (a) . . . . . . . . . . . . . . . . . . . 34 3.2.2 Stage (b) . . . . . . . . . . . . . . . . . . . 40 3.2.3 Stage (c) . . . . . . . . . . . . . . . . . . . 42 3.2.4 Stage (d) . . . . . . . . . . . . . . . . . . . 42 3.2.5 Stage (e) . . . . . . . . . . . . . . . . . . . 44 3.2.6 Stage (f) . . . . . . . . . . . . . . . . . . . . 44 4 Simulation study 46 5 Real data analysis 50 5.1 Off-policy evaluation method . . . . . . . . . . . . 52 5.1.1 Assumptions . . . . . . . . . . . . . . . . . 52 5.1.2 Algorithm : when L selects each arm uniformly at random. . . . . . . . . . . . . . . 52 5.1.3 Algorithm 2 : when L does not select each arm uniformly at random. . . . . . . . . . . 55 5.2 Application results . . . . . . . . . . . . . . . . . . 57 6 Concluding remarks 59 Abstract (in Korean) 64Docto

    Macrophage polarization in obese diabetic patients and its potential role as a predictive marker of diabetic improvement after bariatric surgery

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์ž„์ƒ์˜๊ณผํ•™๊ณผ, 2021. 2. ์ดํ˜์ค€.Background: In the adipose tissue of obese patients, infiltration of macrophages is commonly observed, and among them, polarization of M1 macrophages, which have proinflammatory characteristics, is more prominent than that of anti-inflammatory M2 macrophages. Therefore, the purpose of this study was to analyze the polarization pattern of M1 macrophages and inflammation in visceral fat obtained during bariatric surgery in obese patients with diabetes in comparison to that of obese patients without diabetes, and to explore the possibility of M1 polarization as a preoperative predictor. Methods: The study subjects were the experimental group (obese type patients with diabetes with a body mass index of 30 kg/m2 or higher), control group 1 (obese type patients without diabetes with a body mass index of 30 kg/m2 or higher), control group 2 (normal 45 body weight early gastric cancer patients with diabetes), and control group 3 (normal body weight early gastric cancer patients without diabetes). Stromal vascular fraction cells were isolated from visceral adipose tissue obtained during bariatric surgery (in control groups 2 and 3, during gastric cancer surgery), and M1 and M2 polarization was analyzed using flow cytometry. The number of macrophages and crown like structures (CLS) was confirmed by immunohistochemistry. Expression levels of M1 macrophage-related genes (NOS2, PELI1), and M2 macrophage-related genes (ARG1) were measured. Blood tests were performed in each group to measure inflammation indices (white blood cell count, neutrophil count, lymphocyte count, and C-reactive protein), and visceral fat area and subcutaneous fat area were measured usingl computed tomography. In the experimental group, the difference was determined by measuring glycosylated hemoglobin (HbA1c) levels before and 3 months after surgery, and the existing predictive clinical scores of surgical results, and the association of visceral fat with M1 polarization before surgery was confirmed. Results: In total, 12 experimental groups, 8 control groups 1, 3 control groups 2, and 11 control groups 3 were enrolled. Flow cytometry results indicated that macrophages were the highest in the experimental group (5.77ยฑ2.49, P=0.0032). Immunohistochemistry analysis showed that the number of CLS was the significantly high in the experimental group (2.71ยฑ1.7, P<0.0001). In the experimental 46 group, a significant lowexpression level of the M2 gene (ARG1) was observed in the qRT-PCR (P=0.0085), and in the blood test, the leukocyte (9.51ยฑ1.82, P=0.0009), neutrophil (603.21ยฑ154.52, P=0.0047), lymphocyte (258.14ยฑ69.13, P=0.0020 counts were significantly the highest in the experimental group in comparing to the other groups. Computed tomography showed that the visceral fat mass (23.39ยฑ7.25, P<0.0001) was significantly the highest in the experimental group. There was a positive correlation between M1 polarization and HbA1c reduction at 3 months postoperatively (R=0.9048, P=0.0020). Conclusion: In the visceral fat of obese type patients with diabetes, inflammatory response and M1 macrophage polarization were significantly higher than those observed in obese type patients without diabetes. Thus, it was confirmed that M1 polarization and inflammatory response of adipose tissue owing to obesity were related to diabetes in obese patients, and the possibility of M1 polarization is suggested as aapredictor of the outcome of bariatric surgery.๋ฐฐ๊ฒฝ: ๋น„๋งŒ ํ™˜์ž์˜ ์ง€๋ฐฉ์กฐ์ง์—์„œ๋Š” ํ”ํžˆ ๋Œ€์‹์„ธํฌ์˜ ์นจ์œค์ด ๊ด€์ฐฐ๋˜๋ฉฐ, ๊ทธ ์ค‘ ์ „์—ผ์ฆ์„ฑ ํŠน์ง•์„ ๊ฐ€์ง„ M1 ๋Œ€์‹์„ธํฌ ๋ถ„๊ทนํ™”๊ฐ€ ํ•ญ์—ผ์ฆ์„ฑ M2 ๋Œ€์‹์„ธํฌ์˜ ๋ถ„๊ทนํ™”๋ณด๋‹ค ๋‘๋“œ๋Ÿฌ์ง์ด ์ตœ๊ทผ ๋ณด๊ณ ๋˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋น„๋งŒํ˜• ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์—์„œ ๋น„๋งŒ์ˆ˜์ˆ  ์ค‘ ์–ป์–ด์ง„ ๋‚ด์žฅ์ง€๋ฐฉ์„ ํ†ตํ•˜์—ฌ M1 ๋Œ€์‹์„ธํฌ์˜ ๋ถ„๊ทนํ™” ์–‘์ƒ๊ณผ ์—ผ์ฆ๋ฐ˜์‘์„ ๋น„๋งŒ์ด๋ฉด์„œ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•˜์ง€ ์•Š์€ ํ™˜์ž์™€ ๋น„๊ตํ•ด๋ถ„์„ํ•ด๋ณด๊ณ  ์ˆ˜์ˆ  ์ „ ์˜ˆ์ธก์ธ์ž๋กœ์„œ M1 ๋ถ„๊ทนํ™”์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ชจ์ƒ‰ํ•ด ๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ๋ฒ•: ์—ฐ๊ตฌ๋Œ€์ƒ์ž๋Š” ์‹คํ—˜๊ตฐ(์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜ 30 kg/m2 ์ด์ƒ์˜ ๋น„๋งŒ์ด๋ฉด์„œ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•œ ํ™˜์ž), ๋Œ€์กฐ๊ตฐ1 (์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜ 30 kg/m2 ์ด์ƒ์˜ ๋น„๋งŒ์ด๋ฉด์„œ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•˜์ง€ ์•Š์€ ํ™˜์ž), ๋Œ€์กฐ๊ตฐ2 (์ •์ƒ ์ฒด์ค‘์˜ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•œ ์กฐ๊ธฐ์œ„์•” ํ™˜์ž), ๋Œ€์กฐ๊ตฐ3 (์ •์ƒ ์ฒด์ค‘์˜ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•˜์ง€ ์•Š์€ ์กฐ๊ธฐ์œ„์•” ํ™˜์ž)์œผ๋กœ ์ •ํ•˜์˜€๋‹ค. ๋น„๋งŒ๋Œ€์‚ฌ์ˆ˜์ˆ  ๋˜๋Š” ์œ„์•”์ˆ˜์ˆ  ์ค‘ ์–ป์–ด์ง€๋Š” ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง์—์„œ stromal vascular fraction (SVF)์„ธํฌ๋ฅผ ๋ถ„๋ฆฌํ•˜์—ฌ ์œ ์„ธํฌ๋ถ„์„์„ ํ†ตํ•œ M1, M2 ๋ถ„๊ทนํ™” ์–‘์ƒ์„ ๋ถ„์„ํ•˜๊ณ , ๋ฉด์—ญ์กฐ์งํ™”ํ•™๊ฒ€์‚ฌ๋ฅผ ํ†ตํ•œ ๋Œ€์‹์„ธํฌ ์ˆซ์ž ๋ฐ crown like structures (CLS)๋ฅผ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์‹ค์‹œ๊ฐ„ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘๋ฒ•์„ ํ†ตํ•˜์—ฌ M1 ๋Œ€์‹์„ธํฌ ์—ฐ๊ด€ ์œ ์ „์ž(NOS2, PELI1)์™€ M2 ๋Œ€์‹์„ธํฌ ์—ฐ๊ด€ ์œ ์ „์ž(ARG1)๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฐ ๊ตฐ์—์„œ ํ˜ˆ์•ก๊ฒ€์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์—ฌ ์—ผ์ฆ์ง€ํ‘œ(๋ฐฑํ˜ˆ๊ตฌ ์ˆ˜, ์ค‘์„ฑ๊ตฌ ์ˆ˜, ๋ฆผํ”„๊ตฌ ์ˆ˜, C-๋ฐ˜์‘์„ฑ ๋‹จ๋ฐฑ)์„ ์ธก์ •ํ•˜๊ณ , ์ปดํ“จํ„ฐ ๋‹จ์ธต์ดฌ์˜์„ ํ†ตํ•˜์—ฌ ๋‚ด์žฅ์ง€๋ฐฉ๋Ÿ‰, ํ”ผํ•˜์ง€๋ฐฉ๋Ÿ‰์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ตฐ์—์„œ ๊ธฐ์กด์˜ ์ˆ˜์ˆ ์ž„์ƒ์ง€ํ‘œ ๋ฐ ์ˆ˜์ˆ  ์ „, ์ˆ˜์ˆ  3๊ฐœ์›” ํ›„ ๋‹นํ™”ํ˜ˆ์ƒ‰์†Œ๋ฅผ ์ธก์ •ํ•˜์—ฌ ์ฐจ์ด๊ฐ’์„ ๊ตฌํ•˜๊ณ , ์ˆ˜์ˆ  ์ „ ๋‚ด์žฅ์ง€๋ฐฉ์˜ M1 ๋Œ€์‹์„ธํฌ์™€์˜ ์—ฐ๊ด€์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ์‹คํ—˜๊ตฐ 12๋ช…, ๋Œ€์กฐ๊ตฐ1 8๋ช…, ๋Œ€์กฐ๊ตฐ2 3๋ช…, ๋Œ€์กฐ๊ตฐ3 11๋ช…์ด ๋“ฑ๋ก๋˜์—ˆ๋‹ค. ์œ ์„ธํฌ๋ถ„์„ ๊ฒฐ๊ณผ ๋Œ€์‹์„ธํฌ๋Š” ์‹คํ—˜๊ตฐ์—์„œ ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€๋˜์–ด ์žˆ์—ˆ๋‹ค(5.77ยฑ2.49, P=0.0032). ๋ฉด์—ญ์กฐ์งํ™”ํ•™๊ฒ€์‚ฌ ๊ฒฐ๊ณผ, ๋Œ€์‹์„ธํฌ ๋ฐ CLS ๊ฐœ์ˆ˜๋Š” ์‹คํ—˜๊ตฐ์—์„œ ๋Œ€์กฐ๊ตฐ๋“ค์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€๋˜์–ด ์žˆ์—ˆ๋‹ค (2.71ยฑ1.7, P<0.0001). ์‹คํ—˜๊ตฐ์—์„œ ์‹ค์‹œ๊ฐ„ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘์—์„œ M2 gene (ARG1)์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๊ณ (P=0.0085), ํ˜ˆ์•ก๊ฒ€์‚ฌ์—์„œ๋Š” ์‹คํ—˜๊ตฐ์—์„œ ๋ฐฑํ˜ˆ๊ตฌ ์ˆ˜(9.51ยฑ1.82, P=0.0009), ์ค‘์„ฑ๊ตฌ ์ˆ˜(603.21ยฑ154.52, P=0.0047), ๋ฆผํ”„๊ตฌ ์ˆ˜(258.14ยฑ69.13, P=0.0020)๊ฐ€ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ปดํ“จํ„ฐ ๋‹จ์ธต์ดฌ์˜์—์„œ๋Š” ์‹คํ—˜๊ตฐ์—์„œ ๋‚ด์žฅ์ง€๋ฐฉ๋Ÿ‰(23.39ยฑ7.25, P<0.0001)์ด ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. M1 ๋ถ„๊ทนํ™”์™€ ์ˆ˜์ˆ  ํ›„ 3๊ฐœ์›” ๋‹นํ™” ํ˜ˆ์ƒ‰์†Œ์˜ ๊ฐ์†Œ ํญ์€ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค(R=0.9048, P=0.0020). ๊ฒฐ๋ก : ๋น„๋งŒํ˜• ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์˜ ๋‚ด์žฅ์ง€๋ฐฉ์—์„œ ๋น„๋งŒ์ด๋ฉด์„œ ๋‹น๋‡จ๋ณ‘์„ ๋™๋ฐ˜ํ•˜์ง€ ์•Š์€ ํ™˜์ž์— ๋น„ํ•ด์„œ ์—ผ์ฆ๋ฐ˜์‘ ๋ฐ M1 ๋Œ€์‹์„ธํฌ ๋ถ„๊ทนํ™” ์–‘์ƒ์ด ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋น„๋งŒ ํ™˜์ž์—์„œ, ๋น„๋งŒ์œผ๋กœ ์ธํ•œ ์ง€๋ฐฉ์กฐ์ง์˜ M1 ๋ถ„๊ทนํ™” ๋ฐ ์—ผ์ฆ๋ฐ˜์‘์ด ๋‹น๋‡จ๋ณ‘๊ณผ์˜ ๊ด€๋ จ์ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๊ณ , ๋น„๋งŒ์ˆ˜์ˆ ์˜ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์˜ˆ์ธก์ธ์ž๋กœ์„œ M1 ๋ถ„๊ทนํ™”์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜์˜€๋‹ค.ABSTRACT...........................................................................................i CONTENTS..........................................................................................iii LIST OF TABLES AND FIGURES.............................................................iv INTRODUCTION...................................................................................1 MATERIALS AND METHODS................................................................5 RESULTS.............................................................................................11 DISCUSSION.......................................................................................26 REFERENCES......................................................................................35 ABSTRACT IN ENGLISH.......................................................................44Docto

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์ œ๋†์—…๊ธฐ์ˆ ๋Œ€ํ•™์› ๊ตญ์ œ๋†์—…๊ธฐ์ˆ ํ•™๊ณผ, 2018. 8. ๊น€๋„๋งŒ.Rubusoside (Ru, 13-O-ฮฒ-glucosyl-19-O-ฮฒ-D-glucosyl-steviol) is the main component of Rubus suavissmimus S. Lee (Roasaceae), which is known as Chinese sweet leaf. In this study, Ru was characterized as anti-cariogenic materials. Ru was produced from stevioside (Ste) using ฮฒ-galactosidase from Thermus thermophilus, which was expressed in E. coli BL21 (DE3) pLysS through lactose induction. The enzyme was purified by heat-treatment at 70โ„ƒ for 15 min. The 73.3% of mesophilic proteins was eliminated and it showed 85.3% activity yield. Enzyme reaction was carried out with immobilized ฮฒ-galactosidase and Ru was purified with medium performance liquid chromatography (MPLC) equipped with ESLD detector. Ru at 50 mM showed 97.1 ยฑ 0.2% inhibition activity against 0.1 U/mL mutanscrase from Streptococcus mutans. It was shown competitive inhibition activity with IC50 of 2.3 mM and Ki value of 1.1 ยฑ 0.2 mM. MIC and MBC of Ru against S. mutans growth were 7 mM and 10 mM, respectively. MBC was higher than MIC, that is, Ru inhibits S. mutans as a bacteriostatic agent. Additionally, fructosyl-rubusoside (Ru-Frcs) was synthesized using levansucrase from Leuconostoc mesenteroides to improve the taste of rubusoside. Optimal condition for synthesizing Ru-Frcs was 217.8 mM Ru, 723.2 mM sucrose and 22.8 U/mL levanuscrase with 33.5% conversion. Purified Ru-Frc was prepared with high-performance liquid chromatography (HPLC) equipped with NH2 column at flow rate of 4 mL/min. The structure of Ru-Frc 1 and Ru- Frc 2 were confirmed with nuclear magnetic resonance (NMR) Spectrometer 850 MHz as 13-O-[ฮฒ-fructofuranosyl-(2โ†’6)-ฮฒ-D-glucosyl]-19-O-ฮฒ-D-glucosyl-steviol), 13-O-ฮฒ-D-glucosyl-19-O-[ฮฒ-fructofuranosyl-(2โ†’6)-ฮฒ-D-glucosyl]-steviol, respectively.Review of Literature 1 1. Steviol glycosides 1 1.1. Stevioside 3 1.2. Rubusoside 4 2. ฮฒ-galactosidase from Thermus thermophilus 7 3. Mutansucrase from Streptococcus mutans 8 4. Levansucrase from Leuconostoc mesenteroides 9 5. Hypothesis and objectives 10 Materials and Methods 11 1. Preparation of rubusoside 11 1.1. Expression of ฮฒ-galactosidase (ฮฒ-glypi gene) in E.coli 11 1.2. ฮฒ-galactosidase hydrolytic activity assay 12 1.3. Immobilization of ฮฒ-galactosidase 12 1.4. Production and purification of rubusoside 13 2. Study for anti-cariogenicity of rubusoside 14 2.1. Preparation of mutansucrase from Streptococcus mutans 14 2.2. Purification of mutansucrase 14 2.3. Characterization of mutansucrase 15 2.4. Inhibition activity of rubusoside against mutansucrase 15 2.5. Antimicrobial susceptibility test for S. mutans 17 2.6. Minimum inhibition concentration (MIC) and minimum bactericidal concentration (MBC) test for S. mutans 17 3. Synthesis and characterization of fructosyl-rubusoside (Ru-Frcs) 19 3.1. Expression of levansucrase (m1ft gene) in E. coli 19 3.2. Levansucrase hydrolytic activity assay 19 3.3. Synthesis of Ru-Frcs using levansucrase 20 3.4. Optimization for acceptor reaction using response surface methodology (RSM) 21 3.5. Purification of Ru-Frcs 22 3.6. Structural elucidation of Ru-Frcs 23 Results and Discussion 24 1. Preparation of rubusoside 24 1.1. Expression and partial purification of ฮฒ-galactosidase 24 1.2. Production of rubusoside 28 2. Study for anti-cariogenicity of rubusoside 30 2.1. Characterization of mutansucrase from S. mutans 30 2.2. Inhibition activity of rubusoside against mutansucrase 30 2.3. Antimicrobial susceptibility test for S. mutans 34 3. Synthesis and characterization of fructosyl-rubusoside (Ru-Frcs) 37 3.1. Synthesis and optimization of Ru-Frcs using levansucrase 37 3.2. Purification and structural elucidation of Ru-Frcs 44 Conclusion 50 References 52 Abstract in Korean 59Maste

    ๋ฉ”์‹œ์ง€์˜ ์ƒ์ƒํ•จ๊ณผ ๊ตฌ์ฒด์„ฑ์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์–ธ๋ก ์ •๋ณดํ•™๊ณผ, 2021. 2. ๊น€ํ˜„์„.์ด ์—ฐ๊ตฌ๋Š” ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์„ ๋„์ถœํ•˜๊ณ , ๊ทธ๋Ÿฌํ•œ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฒฝํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ–ˆ๋‹ค. ์†Œ์…œ๋ฏธ๋””์–ด๋ฅผ ํ™œ์šฉํ•œ ๊ฒฝ์ฐฐ์˜ ํ™๋ณดํ™œ๋™์€ ๊ด‘์˜์˜ ๋ฒ”์ฃ„์˜ˆ๋ฐฉ์  ์„ฑ๊ฒฉ์„ ์ง€๋‹Œ ์ค‘์š”ํ•œ ์น˜์•ˆํ™œ๋™์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ ๊ทธ ์ž์ฒด์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์— ๊ธฐ์ธํ•œ ํ™•์‚ฐ๊ณผ ์„ค๋“ ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋‹ค๋ฅธ ์ •๋ถ€ ํ™๋ณด๋ฌผ๊ณผ ์ฐจ๋ณ„ํ™”๋˜๋Š” ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋Œ€ํ‘œ์ ์ธ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์œผ๋กœ ์ƒ์ƒํ•จ(vividness)๊ณผ ๊ถŒ๊ณ ์˜ ๊ตฌ์ฒด์„ฑ(recommendation specificity)์„ ๋„์ถœํ–ˆ๋‹ค. ๋‚˜์•„๊ฐ€ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์ด ์ด์•ผ๊ธฐ(narrative)์˜ ํ˜•์‹์„ ์ง€๋‹Œ ์†Œ์…œ๋ฏธ๋””์–ด ๋ฉ”์‹œ์ง€์ž„์„ ๊ทœ๋ช…ํ•˜๊ณ , ์ด์•ผ๊ธฐ์˜ ํšจ๊ณผ๋Š” ์ˆ˜์šฉ์ž์˜ ๋ชฐ์ž…(transportation) ๊ณผ์ •์„ ํ†ตํ•ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ๊ธฐ์กด์˜ ์ด๋ก ์ ยท๊ฒฝํ—˜์  ์—ฐ๊ตฌ์— ์ฃผ๋ชฉํ•˜์—ฌ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ํ™•์‚ฐ ๋ฐ ์„ค๋“ ํšจ๊ณผ๋Š” ์ˆ˜์šฉ์ž์˜ ๋ชฐ์ž…์„ ํ†ตํ•ด ๋‚˜ํƒ€๋‚  ๊ฒƒ์ด๋ผ๊ณ  ์˜ˆ์ธกํ–ˆ๋‹ค. ๋˜ํ•œ ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ํƒœ๋„ ๋ฐ ๋ชฐ์ž…์„ฑ(transportability)๊ณผ ๊ฐ™์€ ์ด์•ผ๊ธฐ ์ˆ˜์šฉ์ž์˜ ๊ฐœ์ธ์  ํŠน์„ฑ์ด ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ์–ด๋– ํ•œ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๊ฐ–๋Š”์ง€ ํƒ์ƒ‰ํ–ˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์กฐ์ ˆ๋œ ๋งค๊ฐœํšจ๊ณผ(moderated mediation)๋ฅผ ํ†ตํ•ด ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ํ™•์‚ฐ ํšจ๊ณผ๋Š” ์ˆ˜์šฉ์ž๋“ค์ด ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์— ๋Œ€ํ•ด ๊ฐ–๊ฒŒ ๋˜๋Š” ๋Œ€ํ™” ์˜๋„ ๋ฐ ๊ณต์œ  ์˜๋„๋ฅผ ํ†ตํ•ด, ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ์„ค๋“ ํšจ๊ณผ๋Š” ์‚ฌ๋žŒ๋“ค์˜ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์— ๋Œ€ํ•ด ์ธ์ง€๋œ ํšจ๊ณผ์„ฑ ๋ฐ ๊ฒฝ์ฐฐ์‹ ๋ขฐ๋„๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” 2(์ƒ์ƒํ•จ[์‹คํ—˜์ฐธ์—ฌ์ž ๊ฐ„]: ๋†’์Œ vs. ๋‚ฎ์Œ)ร—2(๊ถŒ๊ณ ์˜ ๊ตฌ์ฒด์„ฑ[์‹คํ—˜์ฐธ์—ฌ์ž ๊ฐ„]: ๋†’์Œ vs. ๋‚ฎ์Œ)ร—2(๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ ๋‚ด์šฉ[์‹คํ—˜์ฐธ์—ฌ์ž ๋‚ด]: ๊ฒ€๊ฑฐ vs. ๊ตฌ์กฐ)์˜ ํ˜ผํ•ฉ ์„ค๊ณ„(mixed design) ์‹คํ—˜์„ ์‹ค์‹œํ–ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ ์ค‘ ์ƒ์ƒํ•จ์ด ๋ชฐ์ž…์„ ๊ฑฐ์ณ ๋Œ€ํ™” ์˜๋„, ๊ณต์œ  ์˜๋„, ์ธ์ง€๋œ ํšจ๊ณผ์„ฑ, ๊ฒฝ์ฐฐ์‹ ๋ขฐ๋„์— ์ •์ ์ธ ํšจ๊ณผ๋ฅผ ๊ฐ€์กŒ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋Š” ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ํƒœ๋„๊ฐ€ ๋œ ์šฐํ˜ธ์ ์ธ ์‚ฌ๋žŒ์—๊ฒŒ์„œ๋งŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๊ฐ€ ํ™•์‚ฐ ๋ฐ ์„ค๋“ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์—์„œ์˜ ๋ฉ”์‹œ์ง€ ํšจ๊ณผ ์ด๋ก , ๊ทธ๋ฆฌ๊ณ  ์†Œ์…œ๋ฏธ๋””์–ด๋ฅผ ํ™œ์šฉํ•œ ์ „๋žต์  ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ๊ฐ–๋Š” ์ด๋ก ์ ยท์‹ค์ฒœ์  ํ•จ์˜๋ฅผ ๋…ผ์˜ํ–ˆ๋‹ค.This study examined how message features affect the virality and persuasiveness of online police promotional videos that feature police officers arresting criminals or rescuing citizens and present their testimonials. Specifically, the study tested how (a) the vividness of the testimonials of police officers and (b) the specificity of recommendations to call 112 shape the extent to which such narrative-based police videos trigger social sharing and facilitate persuasion by prompting transportation into the videos. The study also explored how individual differences, such as prior attitudes toward police and transportability, moderate the indirect effects of message vividness and recommendation specificity on diffusion and persuasion via narrative transportation. An online experiment (N = 538) was conducted with a 2 (vividness [between-subjects]: high vs. low [audiovisual vs. text testimonial]) ร— 2 (recommendation specificity [between-subjects]: high vs. low [detailed vs. brief information about how to call 112]) ร— 2 (video topic[within-subjects]: arrest vs. rescue) mixed design. The results showed that prior attitudes toward police moderated the indirect effect of message vividness on diffusion and persuasion via narrative transportation. Specifically, individuals who watched more vivid police promotional videos, as compared to those exposed to less vivid videos, experienced greater narrative transportation, which in turn led them to (a) report higher intention to talk about and share the videos with their social networks and (b) perceive the videos more persuasive and show greater trust in police, but only among those who had unfavorable prior attitudes toward police. Such moderated mediation effects were not found among individuals who had more favorable police attitudes. The findings are discussed in light of their theoretical and practical implications.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 12 ์ œ 1 ์ ˆ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ 12 1. ์ƒ์ƒํ•จ 12 2. ๊ถŒ๊ณ ์˜ ๊ตฌ์ฒด์„ฑ 14 ์ œ 2 ์ ˆ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํšจ๊ณผ 17 1. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ํ™•์‚ฐ ํšจ๊ณผ: ๋Œ€ํ™”, ๊ณต์œ  17 2. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ์„ค๋“ ํšจ๊ณผ: ์ธ์ง€๋œ ํšจ๊ณผ์„ฑ, ๊ฒฝ์ฐฐ์‹ ๋ขฐ๋„ 19 ์ œ 3 ์ ˆ ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 21 1. ์ƒ์ƒํ•จ์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 21 2. ๊ถŒ๊ณ ์˜ ๊ตฌ์ฒด์„ฑ์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 23 ์ œ 4 ์ ˆ ๋ชฐ์ž…์˜ ๋งค๊ฐœํšจ๊ณผ 25 1. ๋ชฐ์ž… 25 2. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ชฐ์ž…์ด ๊ฐ–๋Š” ๋งค๊ฐœํšจ๊ณผ 26 ์ œ 5 ์ ˆ ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ํƒœ๋„์™€ ๋ชฐ์ž…์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 30 1. ๋ชฐ์ž…์— ๋Œ€ํ•ด ๊ธฐ์กด์˜ ํƒœ๋„๊ฐ€ ๊ฐ–๋Š” ์กฐ์ ˆํšจ๊ณผ 30 2. ๋ชฐ์ž…์— ๋Œ€ํ•ด ๋ชฐ์ž…์„ฑ์ด ๊ฐ–๋Š” ์กฐ์ ˆํšจ๊ณผ 33 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ๊ฐ€์„ค 36 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ๊ฐ€์„ค 36 1. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 36 2. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ํƒœ๋„์™€ ๋ชฐ์ž…์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 37 3. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์— ๋Œ€ํ•œ ๋ชฐ์ž…์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 38 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• 39 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 40 ์ œ 1 ์ ˆ ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ์ ˆ์ฐจ 40 1. ์‹คํ—˜ ์„ค๊ณ„ 40 2. ์‹คํ—˜ ์ฐธ๊ฐ€์ž ๋ฐ ์‹คํ—˜ ์ง„ํ–‰ ์ ˆ์ฐจ 41 ์ œ 2 ์ ˆ ์‹คํ—˜ ์ž๊ทน 42 ์ œ 3 ์ ˆ ์ฃผ์š” ๋ณ€์ธ์˜ ์ธก์ • 51 1. ์ข…์†๋ณ€์ธ 51 2. ๋งค๊ฐœ๋ณ€์ธ: ๋ชฐ์ž… 54 3. ์กฐ์ ˆ๋ณ€์ธ 55 4. ๊ณต๋ณ€์ธ(covariates) 56 ์ œ 4 ์ ˆ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 59 ์ œ 5 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 61 ์ œ 1 ์ ˆ ์ƒ๊ด€๊ด€๊ณ„ ๊ฒ€์ฆ 61 ์ œ 2 ์ ˆ ๊ฐ€์„ค ๊ฒ€์ฆ ๊ฒฐ๊ณผ 63 1. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 66 2. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ํƒœ๋„์™€ ๋ชฐ์ž…์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 67 3. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์— ๋Œ€ํ•œ ๋ชฐ์ž…์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 69 4. ๊ฐ€์„ค ๊ฒ€์ฆ ๊ฒฐ๊ณผ ์ข…ํ•ฉ 69 ์ œ 6 ์žฅ ๊ฒฐ๋ก  ๋ฐ ๋…ผ์˜ 73 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋‚ด์šฉ์˜ ์š”์•ฝ 73 ์ œ 2 ์ ˆ ๊ฐ€์„ค ๊ฒ€์ฆ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 74 1. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 74 2. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ์ด ๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ํƒœ๋„์™€ ๋ชฐ์ž…์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 76 3. ๊ฒฝ์ฐฐ ํ™๋ณด๋ฌผ์— ๋Œ€ํ•œ ๋ชฐ์ž…์ด ํ™•์‚ฐ๊ณผ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 78 ์ œ 3 ์ ˆ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ 79 ๋ถ€๋ก 1: ์‹คํ—˜์ž๊ทน๋ฌผ์˜ ์ƒ์ƒํ•จ ์ฒ˜์น˜ 82 ๋ถ€๋ก 2: ์‹คํ—˜์ž๊ทน๋ฌผ์˜ ๋ฉ”์‹œ์ง€ ํŠน์„ฑ 85 ์ฐธ๊ณ  ๋ฌธํ—Œ 94 Abstract 104Maste

    4๋Œ€ ์ค‘์ฆ์งˆํ™˜ ๋ณด์žฅ์„ฑ ๊ฐ•ํ™”์ •์ฑ… ์ดํ›„ ์˜๋ฃŒ์ด์šฉ ํ˜•ํ‰์„ฑ ๋ฐ ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ๋ฅ  ๋ณ€ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต),2019. 8. ์ดํƒœ์ง„.ํ•œ๊ตญ ๊ฑด๊ฐ•๋ณดํ—˜์˜ ๋‚ฎ์€ ๋ณด์žฅ์„ฑ์€ ์ง€์†์ ์œผ๋กœ ๋ฌธ์ œ๋กœ ์ œ๊ธฐ๋˜์–ด ์™”๋‹ค. ์ •๋ถ€๋Š” ๊ฑด๊ฐ•๋ณดํ—˜์˜ ๋‚ฎ์€ ๋ณด์žฅ์„ฑ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์˜๋ฃŒ๋น„ ์ง€์ถœ์ด ํฐ 4๋Œ€ ์ค‘์ฆ์งˆํ™˜(์•”, ์‹ฌ์žฅ์งˆํ™˜, ๋‡Œํ˜ˆ๊ด€์งˆํ™˜, ํฌ๊ท€๋‚œ์น˜์งˆํ™˜)์„ ์ค‘์‹ฌ์œผ๋กœ 2013๋…„์—์„œ 2016๋…„๊นŒ์ง€ 4๋Œ€ ์ค‘์ฆ์งˆํ™˜ ๋ณด์žฅ์„ฑ ๊ฐ•ํ™”์ •์ฑ…์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 4๋Œ€ ์ค‘์ฆ์งˆํ™˜ ๋ณด์žฅ์„ฑ ๊ฐ•ํ™”์ •์ฑ… ์‹œํ–‰ ์ดํ›„ ์ •์ฑ… ๋Œ€์ƒ์ž์™€ ๋น„ ๋Œ€์ƒ์ž์˜ ์˜๋ฃŒ์ด์šฉ์˜ ์ˆ˜ํ‰์  ํ˜•ํ‰์„ฑ ๋ฐ ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ๋ฅ ์˜ ๋ณ€ํ™” ์ถ”์ด๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ์ดํ›„์˜ ๋ณด์žฅ์„ฑ ๊ฐ•ํ™”์ •์ฑ… ๋Œ€์•ˆ ์ˆ˜๋ฆฝ์˜ ๊ธฐ์ดˆ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ •์ฑ… ๋Œ€์ƒ์ž์˜ ์™ธ๋ž˜ ์ด์šฉ ํšŸ์ˆ˜๋Š” ๊พธ์ค€ํžˆ ์ €์†Œ๋“์ธต์— ์œ ๋ฆฌํ•œ ๋ถˆํ˜•ํ‰์ด ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ •์ฑ… ๋น„๋Œ€์ƒ์ž์˜ ์™ธ๋ž˜ ์ด์šฉ ํšŸ์ˆ˜๋Š” ๋Œ€์ฒด๋กœ ๊ณ ์†Œ๋“์ธต์— ์œ ๋ฆฌํ•œ ๋ถˆํ˜•ํ‰์ด ๋‚˜ํƒ€๋‚ฌ์ง€๋งŒ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ๋˜ํ•œ ์ƒํ™œ๋น„ ๋Œ€๋น„ ์˜๋ฃŒ๋น„ ์ง€์ถœ์ด 40% ์ด์ƒ์ธ ๊ฒฝ์šฐ ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ์œผ๋กœ ์ •์˜ํ•˜์˜€์„ ๋•Œ, ์ •์ฑ… ๋น„๋Œ€์ƒ์ž ๊ฐ€๊ตฌ์˜ ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ๋ฅ ์ด ์ •์ฑ… ๋Œ€์ƒ์ž ๊ฐ€๊ตฌ์— ๋น„ํ•ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ €์†Œ๋“์ธต์— ๋”์šฑ ์ง‘์ค‘๋œ ์–‘์ƒ์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ํ–ฅํ›„ ์งˆํ™˜ ์ค‘์‹ฌ๋ณด๋‹ค๋Š” ํ™˜์ž์˜ ๋ถ€๋‹ด์ด ํฐ ์„œ๋น„์Šค๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๊ฑด๊ฐ•๋ณดํ—˜์˜ ๋ณด์žฅ์„ฑ ๊ฐ•ํ™”๊ฐ€ ํ•„์š”ํ•˜๊ณ  ์†Œ๋“ ์ˆ˜์ค€์ด ๋‚ฎ์€ ๊ณ„์ธต์˜ ์˜๋ฃŒ๋น„ ๋ถ€๋‹ด์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ์ •์ฑ… ๋ฐฉ์•ˆ์ด ๋งˆ๋ จ๋˜์–ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ํ–ฅํ›„ ์ „์ฒด ๋Œ€์ƒ์— ๋Œ€ํ•œ ์˜๋ฃŒ์ ‘๊ทผ์„ฑ ๋ฐ ๊ฐ€๊ตฌ๊ฐ€ ๊ฒฝํ—˜ํ•˜๋Š” ๊ฒฝ์ œ์  ์œ„ํ—˜์— ๋Œ€ํ•œ ์ง€์†์ ์ธ ๊ด€์‹ฌ๊ณผ ์ด์— ๊ธฐ๋ฐ˜ํ•œ ์ •์ฑ… ์ˆ˜๋ฆฝ์ด ํ•„์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค.In South Korea, low coverage of National health insurance has been a constant problem. In order to improve the coverage of national health insurance, Ministry of health and welfare has implemented the 'Plan to strengthen the four major diseases' and enhanced the coverage of the four major diseases with high medical expenditure from 2013 to 2016. The purpose of this study is to analyze the horizontal equity of healthcare utilization and incidence of the household catastrophic health expenditure 2013 to 2016 using the Korean Health Panel (KHP). The results of this study showed that the outpatient and inpatient utilization equity of non-policy beneficiaries have not been improved, the incidence of catastrophic health expenditure was higher than that of the beneficiaries, and concentrated in the low income groups. In addition, healthcare utilization equity and the incidence of catastrophic health expenditure of policy beneficiaries were not improved. These results suggest that policy improvement should be taken to reduce the burden of medical expenses for those with lower income levels, to strengthen the financial protection function of national health insurance. Further, it suggests that there is a need for continuous monitoring of health service accessibility and financial protection.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  4 II. ๊ด€๋ จ๋ฌธํ—Œ๊ณ ์ฐฐ 6 1. ์˜๋ฃŒ์ด์šฉ์˜ ํ˜•ํ‰์„ฑ 6 1.1. ์ด๋ก ์  ๊ณ ์ฐฐ 6 1.2. ๊ตญ๋‚ด ์„ ํ–‰์—ฐ๊ตฌ 8 2. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ 11 2.1. ์ด๋ก ์  ๊ณ ์ฐฐ 11 2.2. ๊ตญ๋‚ด ์„ ํ–‰์—ฐ๊ตฌ 12 III. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 14 1. ์ž๋ฃŒ์› ๋ฐ ์—ฐ๊ตฌ๋Œ€์ƒ 14 1.1. ์ž๋ฃŒ์› 14 1.2. ์—ฐ๊ตฌ๋Œ€์ƒ 14 2. ๋ถ„์„๋ฐฉ๋ฒ• 16 2.1. ์˜๋ฃŒ์ด์šฉ์˜ ์ˆ˜ํ‰์  ํ˜•ํ‰์„ฑ 16 2.2. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ 18 3. ๋ณ€์ˆ˜์˜ ์ •์˜ 20 3.1. ์˜๋ฃŒ์ด์šฉ์˜ ํ˜•ํ‰์„ฑ 20 3.2. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ 22 IV. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 23 1. ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 23 1.1. ๊ฐœ์ธ ๋‹จ์œ„ 23 1.2. ๊ฐ€๊ตฌ ๋‹จ์œ„ 27 2. ์˜๋ฃŒ์ด์šฉ์˜ ํ˜•ํ‰์„ฑ ๋ถ„์„ 29 2.1. ์™ธ๋ž˜ ์ด์šฉ ํšŸ์ˆ˜ 30 2.2. ์ž…์› ์ด์šฉ ํšŸ์ˆ˜ 33 3. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ถ„์„ 36 3.1. ์˜๋ฃŒ๋น„ ์ง€์ถœ ๋ถ„์„ 36 3.2. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ ์ถ”์ด 37 3.3. ๊ฑด๊ฐ•๋ฌธ์ œ ํŠน์„ฑ๋ณ„ ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ ๋ฐœ์ƒ ์ง‘์ค‘๋„ 41 V. ๊ณ ์ฐฐ ๋ฐ ๊ฒฐ๋ก  45 1. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 45 1.1. ์˜๋ฃŒ์ด์šฉ์˜ ์ˆ˜ํ‰์  ํ˜•ํ‰์„ฑ 45 1.2. ๊ณผ๋ถ€๋‹ด์˜๋ฃŒ๋น„ 47 2. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  ๋ฐ ์˜์˜ 49 3. ๊ฒฐ๋ก  51 VI. ์ฐธ๊ณ ๋ฌธํ—Œ 52 VII. ๋ถ€๋ก 56 Abstract 58Maste

    Island Water Environmental Plan Based on Rainwater Management System : Focusing on Woo-Jeon Village in Jeung-Do

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2014. 8. ์ด์œ ๋ฏธ.3,201๊ฐœ์— ๋‹ฌํ•˜๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๋„์„œ์ง€์—ญ์€ ์˜ˆ๋กœ๋ถ€ํ„ฐ ์ง€๋ฆฌ์  ํŠน์„ฑ์ƒ ๊ณ ์งˆ์ ์ธ ๋ฌผ๋ฌธ์ œ๋กœ ๋ถˆํŽธ์„ ๊ฒช์–ด ์™”๊ณ  ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด 1988๋…„๋ถ€ํ„ฐ ์ข…ํ•ฉ๊ฐœ๋ฐœ๊ณ„ํš์„ ํ†ตํ•ด ๊ธ‰์ˆ˜ยท์ „๊ธฐ์‹œ์„ค ๋“ฑ ์ƒํ™œ ๊ธฐ๋ฐ˜ ์‹œ์„ค์„ ํ™•์ถฉํ•ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ตœ๊ทผ ํ˜„๋Œ€์  ๋ผ์ดํ”„ ์Šคํƒ€์ผ๋กœ์˜ ๋ณ€ํ™”์™€ ๋„์„œ์ง€์—ญ์˜ ๊ด€๊ด‘๊ฐœ๋ฐœํ™”๋กœ ์ธํ•ด ๋ฌผ ๋ถ€์กฑ ๋ฌธ์ œ๊ฐ€ ๋” ๊ทน์‹ฌํ•ด์กŒ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฐœ๋ฐœ๋กœ ์ธํ•œ ๋ถˆํˆฌ์ˆ˜์ธต์˜ ์ฆ๊ฐ€์™€ ๊ธฐ์กด ์ˆ˜ํ™˜๊ฒฝ์˜ ๋ถ€์ •์  ๋ณ€ํ™”๋Š” ์ˆ˜์ˆœํ™˜์„ ๋‹จ์ ˆ์‹œํ‚ค๊ณ  ์ƒํƒœ๊ณ„๋ฅผ ์œ„ํ˜‘ํ•˜๊ณ  ์žˆ๋‹ค. ํ˜„์žฌ ๋„์„œ์ง€์—ญ์—์„œ๋Š” ์ง€ํ‘œ์ˆ˜, ์ง€ํ•˜์ˆ˜, ํ•ด์ˆ˜๋‹ด์ˆ˜ํ™” ๊ธฐ์ˆ , ๋น—๋ฌผ๊ด€๋ฆฌ๋ฅผ ํ†ตํ•œ ์šฉ์ˆ˜๊ณต๊ธ‰๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”๋ฐ ์ €์ˆ˜์ง€์™€ ์ง€ํ•˜ ๊ด€์ • ๊ฐœ๋ฐœ, ํ•ด์ˆ˜๋‹ด์ˆ˜ํ™” ์‹œ์„ค ๋“ฑ์€ ๊ฐœ๋ฐœ์— ๋น„์šฉ์ด ๋งŽ์ด ๋“ค๊ณ , ์ง‘์ค‘์‹ ๋ฌผ ๊ด€๋ฆฌ๋ฒ•์œผ๋กœ ์ค‘์•™ ์‹œ์Šคํ…œ์— ๋ฌธ์ œ๊ฐ€ ์ƒ๊ฒผ์„ ๋•Œ ๋‹จ์ˆ˜๊ฐ€ ๋˜์–ด ๋„์„œ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์˜ ์ƒํ™œํ™˜๊ฒฝ์„ ์œ„ํ˜‘ํ•˜๋Š” ๋“ฑ ์•ˆ์ •์„ฑ์ด ํฌ๊ฒŒ ๋–จ์–ด์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ๋ถ„์‚ฐ์‹ ๋น—๋ฌผ๊ด€๋ฆฌ๋Š” ๋น„๊ต์  ์œ ์ง€๋น„์šฉ์ด ์ ๊ณ  ์ „๋ฌธ๊ฐ€๊ฐ€ ์•„๋‹Œ ์ฃผ๋ฏผ๋„ ๋ฌผ ๊ด€๋ฆฌ์— ์ ๊ทน ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์ด๋ฉฐ, ์ƒํƒœ๊ณ„์™€ ํ™˜๊ฒฝ์„ ๋ณด์กดํ•˜๋Š”๋ฐ๋„ ํฐ ๋„์›€์ด ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น—๋ฌผ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์˜ ์„ธ ๊ฐ€์ง€ ๊ธฐ๋Šฅ์ธ ์ง‘์ˆ˜, ์ •ํ™”, ์นจํˆฌ ๊ธฐ๋Šฅ์„ ํ™œ์šฉํ•œ ์ˆ˜ํ™˜๊ฒฝ ๊ณ„ํš์„ ํ†ตํ•ด ์ฒซ์งธ, ๋ณด์กฐ์šฉ์ˆ˜๊ณต๊ธ‰์›์„ ํ™•๋ณดํ•˜์—ฌ ๋„์„œ์ง€์—ญ์˜ ์šฉ์ˆ˜๊ณต๊ธ‰์˜ ๋ถˆ์•ˆ์ • ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ , ๋‘˜์งธ, ๊ฐœ๋ฐœ๋กœ ์ธํ•œ ์ˆ˜์ˆœํ™˜ ์ฒด๊ณ„์˜ ํŒŒ๊ดด๋ฅผ ๊ฐœ์„ ํ•˜๋ฉฐ, ๋งˆ์ง€๋ง‰์œผ๋กœ ์ง€์—ญ ์ž์‚ฐ์ธ ์ˆ˜ํ™˜๊ฒฝ ์š”์†Œ๋ฅผ ์žฌ์ƒํ•˜์—ฌ ์นœ์ˆ˜ ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ณต๊ฐ„์˜ ํšŒ๋ณต์œผ๋กœ ๋งˆ์„ ์ฃผ๋ฏผ๋“ค์˜ ์ „ํ†ต์ ์ธ ์ƒํ™œ๊ด€์Šต์„ ๋ณด์กดํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋จผ์ € ๊ธฐ์กด์— ๋Œ€์ƒ์ง€์—์„œ ํ™œ์šฉํ•˜์˜€๋˜ ๋น—๋ฌผํƒฑํฌ์™€ ์ฒ˜๋งˆ์˜ ๋น—๋ฌผ๋ฐ›์ด, ํ™ˆํ†ต์„ ์žฌ๋ฐฐ์น˜ํ•˜๊ณ  ๊ด€๋ฆฌํ•˜์—ฌ ๋น—๋ฌผ์„ ์ฐจ์ง‘ ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ์ง€๋ถ•์„ ์ง‘์ˆ˜๋ฉด์œผ๋กœ ํ•˜์—ฌ ์ง‘์ˆ˜๋œ ๋น—๋ฌผ์€ ์ƒ์ˆ˜๋„ ๊ณต๊ธ‰๋Ÿ‰์„ ์ตœ์†Œ 31%์—์„œ ์ตœ๋Œ€ 66%๊นŒ์ง€ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ์–ด ๋ณด์กฐ์šฉ์ˆ˜๊ณต๊ธ‰์›์œผ๋กœ์„œ ์ƒํ™œ์šฉ์ˆ˜์™€ ๊ด€๊ด‘๊ฐ์„ ์œ„ํ•œ ์„œ๋น„์Šค ์šฉ์ˆ˜๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ๊ณต๊ธ‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๊ณ  ๋‚˜์•„๊ฐ€ ์ˆ˜์ž์›์„ ์ ˆ์•ฝํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ๋น—๋ฌผ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์˜ ์ •ํ™”์™€ ์นจํˆฌ ๊ธฐ๋Šฅ์„ ์ ์šฉํ•˜์—ฌ ๋‹จ์ ˆ๋œ ์ˆ˜์ˆœํ™˜ ์ฒด๊ณ„๋ฅผ ํšŒ๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ๋Œ€์ƒ์ง€์—์„œ ๊ฐ€์น˜ ์žˆ๋Š” ์ง€์—ญ ์ž์‚ฐ์ธ ๋ชจ๋ž˜์น˜์™€ ๋‘ ๋ฒ™, ๋„๋ž‘์„ ํšŒ๋ณตํ•˜์—ฌ ๋น—๋ฌผ์ด ์ €๋ฅ˜, ์ •ํ™”, ์นจํˆฌ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๊ณ  ๋ฐฐ์ˆ˜๋กœ๋ฅผ ๊ฐœ์„ ํ•˜์—ฌ ๋น—๋ฌผ์˜ ์˜ค์—ผ๋„๋ฅผ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ๋„๋ก ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ ๊ธฐ์กด ์นจํˆฌ์ง€์—ญ์„ ๋ณด์กดํ•˜๊ณ  ์ž์—ฐ์ ์œผ๋กœ ์นจํˆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฉด์ ์„ ๊ณ„ํšํ•˜์—ฌ ์ง€ํ•˜์ˆ˜์œ„๋ฅผ ํšŒ๋ณตํ•ด ์šฐ๋ฌผ์„ ๋‹ค์‹œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ๋ฅผ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ „ํ†ต์ ์œผ๋กœ ๋ฌผ์„ ๊ณต์œ ํ•˜๋ฉฐ ๊ต๋ฅ˜ํ•˜์˜€๋˜ ์ง€์—ญ์ฃผ๋ฏผ๋“ค์˜ ์ƒํ™œ๊ด€์Šต์„ ๋ณด์กดํ•˜๊ณ  ์ง€์—ญ์˜ ์•„์ด๋ดํ‹ฐํ‹ฐ๋กœ ์ •๋น„ํ•˜๊ธฐ ์œ„ํ•ด ์นœ์ˆ˜ ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ณต๊ฐ„์„ ํšŒ๋ณตํ•˜๋Š” ๊ณ„ํš์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋งˆ์„ ์ž…๊ตฌ์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ณต๊ฐ„๋ถ€ํ„ฐ ๋‘ ๋ฒ™์„ ๋”ฐ๋ผ ๋ชจ๋ž˜์น˜๋กœ ์ด์–ด์ง€๋Š” ์‚ฐ์ฑ…๋กœ์™€ ๊นƒ๋ฐญ ์ปค๋ฎค๋‹ˆํ‹ฐ ์„ผํ„ฐ์˜ ๋ ˆ์ธ ๊ฐ€๋“ ๊ณผ ๋ฌผ ๋†€์ดํ„ฐ๋ฅผ ๊ณ„ํšํ•˜์—ฌ ์ง€์—ญ ์ฃผ๋ฏผ๊ฐ„์˜ ์†Œํ†ต๊ณผ ๊ต๋ฅ˜๊ฐ€ ์ˆ˜๊ณต๊ฐ„์„ ์ค‘์‹ฌ์œผ๋กœ ์ผ์–ด๋‚  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€์ƒ์ง€์˜ ์ „ํ†ต์  ์ˆ˜ํ™˜๊ฒฝ ์š”์†Œ์™€ ํ˜„์žฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์š”์†Œ, ์ง€์—ญ ์ฃผ๋ฏผ์˜ ์ƒํ™œ๊ด€์Šต์„ ๋ฉด๋ฐ€ํžˆ ๊ด€์ฐฐํ•˜์—ฌ ๊ณ„ํš์— ์ ๊ทน ๋ฐ˜์˜ํ–ˆ๋‹ค๋Š” ๋ฐ ์˜์˜๊ฐ€ ์žˆ๊ณ , ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์—ฌ๋Ÿฌ ๋„์„œ์ง€์—ญ์ด ํ˜„์žฌ ๋Œ€์ƒ์ง€์™€ ๊ฐ™์€ ๋ฌผ ๋ถ€์กฑ ๋ฌธ์ œ์™€ ๊ฐœ๋ฐœ๋กœ ์ธํ•œ ํŒŒ๊ดด๋œ ์ˆ˜ํ™˜๊ฒฝ์˜ ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฏ€๋กœ ์ด๋ฅผ ํšŒ๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋งˆ์„ ๋‹จ์œ„์˜ ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ œ์‹œํ•จ์œผ๋กœ์จ ์š”์†Œ๋ณ„๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค.์ œ1์žฅ ์„œ๋ก  1 1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 4 1. ๊ณต๊ฐ„์  ๋ฒ”์œ„ 4 2. ๋‚ด์šฉ์  ๋ฒ”์œ„ 5 3์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 6 4์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 7 1. ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ์˜ ํ๋ฆ„ 7 2. ์‹œ์‚ฌ์  ๋ฐ ์ฐจ๋ณ„์„ฑ 10 ์ œ2์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 12 1์ ˆ ๋น—๋ฌผ๊ด€๋ฆฌ ์‹œ์Šคํ…œ 12 1. ์ง‘์ค‘์‹ยท๋ถ„์‚ฐ์‹ ๋น—๋ฌผ๊ด€๋ฆฌ 12 2. ๋ถ„์‚ฐ์‹ ๋น—๋ฌผ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์˜ ๊ตญ๋‚ด์™ธ ์‚ฌ๋ก€ 14 2์ ˆ ๋น—๋ฌผ์ด์šฉํ˜„ํ™ฉ 16 1. ์ผ๋ณธ์˜ ๋น—๋ฌผ์ด์šฉ 17 2. ๋…์ผ์˜ ๋น—๋ฌผ์ด์šฉ 18 3. ๋ฏธ๊ตญ์˜ ๋น—๋ฌผ์ด์šฉ 19 3์ ˆ ๋„์„œ์ง€์—ญ ์ˆ˜์ž์› ์ด์šฉ 20 1. ๋„์„œ์ง€์—ญ ์šฉ์ˆ˜๊ณต๊ธ‰ํ˜„ํ™ฉ 20 2. ๋„์„œ์ง€์—ญ ์ˆ˜์ž์› ๊ด€๋ฆฌ 22 3. ๊ตญ๋‚ด๋„์„œ์ง€์—ญ ์šฉ์ˆ˜๊ณต๊ธ‰ ๋ฐ ์‚ฌ์šฉ ์‚ฌ๋ก€ 24 4์ ˆ ๋„์„œ์ง€์—ญ ๋น—๋ฌผํ™œ์šฉ 27 1. ์ง€์—ญ ์ฃผ๋ฏผ ์ƒํ™œ์—์„œ์˜ ํ™œ์šฉ 28 2. ์™ธ๋ถ€์ธ ๋ฐ ๊ด€๊ด‘๊ฐ์˜ ํ™œ์šฉ 29 5์ ˆ ๋„์„œ ๊ด€๊ด‘๊ฐœ๋ฐœ ๊ณ„ํš 30 1. ๋„์„œ์ข…ํ•ฉ๊ฐœ๋ฐœ๊ณ„ํš 30 2. ์ง€์†๊ฐ€๋Šฅํ•œ ๋„์„œ๊ด€๊ด‘๊ฐœ๋ฐœ 33 3. ์ง€์†๊ฐ€๋Šฅํ•œ ๋„์„œ๊ด€๊ด‘๊ฐœ๋ฐœ ๊ตญ๋‚ด์™ธ ์‚ฌ๋ก€ 35 ์ œ3์žฅ ๋Œ€์ƒ์ง€์˜ ์ดํ•ด 39 1์ ˆ ๋Œ€์ƒ์ง€์˜ ๊ฐœ์š” 39 1. ๋Œ€์ƒ์ง€ ์œ„์น˜ 39 2. ๋Œ€์ƒ์ง€ ์ผ๋ฐ˜ํ˜„ํ™ฉ 40 3. ๋Œ€์ƒ์ง€ ๊ด€๋ จ ๊ณ„ํš ๋ฐ ๋ฒ•๊ทœ 40 2์ ˆ ๋Œ€์ƒ์ง€์˜ ์ธ๋ฌธยท์‚ฌํšŒ์  ์ดํ•ด 43 1. ๋Œ€์ƒ์ง€ ์ผ๋ฐ˜ ํ†ต๊ณ„ 43 2. ๋Œ€์ƒ์ง€ ์—ญ์‚ฌยท๋ฌธํ™” 45 3. ๋Œ€์ƒ์ง€ ๊ฐœ๋ฐœ์˜ ํ๋ฆ„ 47 3์ ˆ ๋Œ€์ƒ์ง€์˜ ์ƒํƒœยทํ™˜๊ฒฝ์  ์ดํ•ด 48 1. ๋Œ€์ƒ์ง€ ์ƒํƒœ์ž์—ฐ๋„ 48 2. ๋Œ€์ƒ์ง€ ๋งˆ์„ ๊ฒฝ๊ด€ ์š”์†Œ ํ˜„ํ™ฉ 51 4์ ˆ ๋Œ€์ƒ์ง€์˜ ์ˆ˜์ž์› ํ˜„ํ™ฉ 58 1. ์šฐ์ „๋งˆ์„ ์ˆ˜์ž์› ํ˜„ํ™ฉ 58 2. ๋Œ€์ƒ์ง€ ์ˆ˜์ž์› ์ธ์‹๋ถ„์„ 61 3. ๋Œ€์ƒ์ง€ ๊ฐ•์ˆ˜๋Ÿ‰ ๋ถ„์„ 67 4. ์šฐ์ˆ˜ ๊ด€๊ฑฐ ๋ฐ ์œ ์—ญ 69 5. ์ˆ˜์ž์› ํ™œ์šฉ ์š”์†Œ ํ˜„ํ™ฉ๋„ 71 5์ ˆ ์„ค๊ณ„ ๋Œ€์ƒ์ง€ ๋ถ„์„์˜ ์ข…ํ•ฉ 75 ์ œ4์žฅ ์šฐ์ „๋งˆ์„ ์ˆ˜ํ™˜๊ฒฝ ๊ณ„ํš 78 1์ ˆ ๊ณ„ํš์˜ ๊ธฐ๋ณธ๋ฐฉํ–ฅ ๋ฐ ๊ตฌ์ƒ 78 1. ๊ณ„ํš์˜ ๊ธฐ๋ณธ๋ฐฉํ–ฅ 78 2. ๊ณ„ํš์˜ ๊ตฌ์ƒ 80 2์ ˆ ์ „์ฒด ์ˆ˜ํ™˜๊ฒฝ ๊ณ„ํš 83 3์ ˆ ์ง‘์ˆ˜์—ญ๋ณ„ ์ˆ˜ํ™˜๊ฒฝ ๊ณ„ํš 85 1. ์ฃผ๊ฑฐ์ง€์—ญ 86 2. ์„œ๋น„์Šค์ง€์—ญ 88 3. ๋†์—…์ง€์—ญ 89 4. ์ง‘์ˆ˜์—ญ ์™ธ ์ง€์—ญ 92 4์ ˆ ์š”์†Œ๋ณ„ ์ˆ˜ํ™˜๊ฒฝ ๊ณ„ํš 93 1. ๋น—๋ฌผํƒฑํฌ, ๋น—๋ฌผ๋ฐ›์ด, ํ™ˆํ†ต 93 2. ๋ชจ๋ž˜์น˜ 96 3. ๋ฐฐ์ˆ˜๋กœ 98 4. ๋‹ด์žฅ 101 5. ๊นƒ๋ฐญ ์ปค๋ฎค๋‹ˆํ‹ฐ์„ผํ„ฐ 104 ์ œ5์žฅ ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 107 [์ฐธ๊ณ ๋ฌธํ—Œ] 110 [Abstract] 113Maste

    A study on the link between ROS recovery by HDAC6 inhibition and amyloid beta induced axonal transport impairment

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผํ•™๊ณผ, 2015. 2. ๋ฌต์ธํฌ.์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘์—์„œ๋Š” ์‹ ๊ฒฝ์„ธํฌ ๋‚ด์˜ ๋ฒ ํƒ€ ์•„๋ฐ€๋กœ์ด๋“œ ๋‹จ๋ฐฑ์งˆ (Amyloid ฮฒAฮฒ)์˜ ๊ณผ๋„ํ•œ ์ถ•์ ์œผ๋กœ ์‹ ๊ฒฝ์„ธํฌ ๋‚ด ์ถ•์‚ญ์ˆ˜์†ก์˜ ์ €ํ•ด์™€ ํ™œ์„ฑ์‚ฐ์†Œ์ข…์˜ ์ฆ๊ฐ€ํ˜„์ƒ์ด ๋‚˜ํƒ€๋‚˜๊ฒŒ ๋œ๋‹ค. ์ €ํ•˜๋œ ์ถ•์‚ญ์ˆ˜์†ก์€ ํžˆ์Šคํ†ค ๋””์•„์„ธํ‹ธ๋ผ์•„์ œ 6 (Histone deacetylase 6HDAC6)์˜ ์ €ํ•ด์— ์˜ํ•ด ํšŒ๋ณต๋  ์ˆ˜ ์žˆ์Œ์ด ๋ณด๊ณ ๋˜์–ด์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘ ์ƒํ™ฉ์—์„œ HDAC6์˜ ๊ธฐ์งˆ์ธ ํผ๋ก์‹œ๋ ˆ๋…์‹ ์˜ ํ•ญ์‚ฐํ™”๋Šฅ์ €ํ•˜๊ฐ€ ํ™œ์„ฑ์‚ฐ์†Œ์ข…์˜ ์ฆ๊ฐ€๋ฅผ ์•ผ๊ธฐํ•˜๋ฉฐ, ํ•˜ํ–ฅ์กฐ์ ˆ๋กœ ์„ธํฌ ๋‚ด Ca2+๋†๋„๋ฅผ ๋†’์ธ๋‹ค๋Š” ์‚ฌ์‹ค์„ HT22 ์„ธํฌ์ฃผ์— Aฮฒ์™€ HDAC6 ์ €ํ•ด์ œ์ธ Tubastatin A (TBA)๋ฅผ ์ฒ˜๋ฆฌํ•˜์—ฌ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ์ฆ๊ฐ€๋œ ํ™œ์„ฑ์‚ฐ์†Œ์ข…๊ณผ Ca2+์ด ์ถ•์‚ญ์ˆ˜์†ก์„ ๋ง๊ฐ€ํŠธ๋ฆฌ๋Š”๋ฐ ๊ด€์—ฌ๋˜์–ด์žˆ์Œ์„ live cell imaging์„ ์ด์šฉํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ์šด๋™์„ฑ ๊ด€์ฐฐ์„ ํ†ตํ•ด ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ , Aฮฒ์— ์˜ํ•ด ์ €ํ•ด๋œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ์ถ•์‚ญ์ˆ˜์†ก์ด HDAC6 ์ €ํ•ด์ œ์— ์˜ํ•ด ํšŒ๋ณต๋˜๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ, HDAC6๋ฅผ ๋ชฉํ‘œ ๋‹จ๋ฐฑ์งˆ๋กœ ํ•œ ์ €ํ•ด๋ฌผ์งˆ๋“ค์„ western blot๊ณผ live cell imaging์„ ํ†ตํ•ด ์„ ๋ณ„ํ•˜๋Š” ์Šคํฌ๋ฆฌ๋‹ ๋ฐฉ๋ฒ•์„ ํ™•๋ฆฝํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘์—์„œ ์ถ•์‚ญ์ˆ˜์†ก์˜ ๋ง๊ฐ€์ง์ด HDAC6์˜ ์ฃผ์š”๊ธฐ์งˆ์ธ ฮฑ-tubulin ์™ธ์— peroxiredoxin๋„ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค.์ดˆ๋ก i ๋ชฉ์ฐจ iii ํ‘œ ๋ฐ ๊ทธ๋ฆผ ๋ชฉ๋ก iv ์„œ๋ก  1 ์‹คํ—˜์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 5 ๊ฒฐ๊ณผ 12 ๊ณ ์ฐฐ 38 ์ฐธ๊ณ ๋ฌธํ—Œ 45 ์ดˆ๋ก (์˜๋ฌธ) 49Maste

    Anticancer effect of nucleoline-aptamer-conjugated gemcitabine loaded atellocollagen (IO401) in pancreatic cancer patient-derived orthotropic xenograft model

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    Introduction: We investigated the anticancer effect and systemic effect of the atelocollagen (AC) patch coated nucleoline-aptamer-conjugated Gemcitabine (IO401 patch) by directly implanting to the tumor cell in pancreatic cancer patient-derived xenograft (PDX) model to purpose a future potential adjuvant surgical strategy during curative pancreatic resection for pancreatic cancer. Methods: Pancreatic cancer PDX model was established. Animals were grouped randomly (7 mice per group) into three types of patch transplantation groups: G1 = Null AC patch, G2 = Gemcitabine AC patch, G3 = IO401 patch. Tumor volume (length ร— width2, mm3), Tumor weight (mg), and Tumor inhibition rate [1-(Ti-To)/(average tumor volume of group) ร— 100, Ti = endpoint tumor volume, To = start tumor volume] were calculated. Anticancer therapy-related toxicity was evaluated by hematologic and histological findings. Results: G3 (IO401 patch) showed the most significant reduction of tumor growth and tumor weight comparing with G1 (Null AC patch) and G2 (Gemcitabine AC patch) (p = 0.014, p = 0.018). G3 also showed the most significant tumor inhibition rate comparing with G1 and G2 (p = 0.011). G2 and G3 has the low necrosis proportion in histological finding comparing with G1 (p = 0.005, p < 0.05). Moreover, no leukopenia, no anemia, and no neutropenia were observed in G3. Conclusions: We demonstrated the anticancer effect of the IO401 patch by directly implanting to tumor cell in pancreatic cancer PDX model. This directaly implantable aptaber-drug conjugate system on tumor cell is expected to be a new surgical strategy to further increase the oncological importance of margin negative resection in pancreatic cancer surgery. Further research will be needed.ope

    Safety and feasibility of laparoscopic pancreaticoduodenectomy in octogenarians

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    Introduction: With continued technical advances in surgical instruments and growing surgical expertise, many laparoscopic pancreaticoduodenectomies (LPDs) have been safely performed with favorable outcomes, and this approach is being used more frequently. With an increase in the life expectancy, interest in treatments for elderly patients has increased. In this study, we investigated the safety and feasibility of LPD in octogenarians. Methods: From September 2005 to February 2020, resectable/borderline resectable periampullary tumors (PATs) were diagnosed in 71 octogenarians at Sincheon Severance Hospital and CHA Bundang Medical Center. Patients were divided into two groups: those who underwent surgery (PD, N = 38) and those who did not (NPD, N = 33). The group that underwent surgery was further divided into two groups: those who underwent open PD (OPD, N = 19), and those who underwent LPD (LPD, N = 19). Perioperative outcomes, including long-term survival, were retrospectively compared between these groups. Results: There was no significant difference in age, sex, comorbidities, diagnosis, and chemo-radiotherapy between the surgery and non-surgery groups. The PD group had a better survival rate than the NPD group (p 0.999). There was no significant difference in overall survival and disease-free survival between the OPD and LPD groups (p = 0.816, p = 0.446, respectively). Conclusions: LPD is a good alternative for octogenarians with PAT requiring PD.ope
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