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    ์ ๋ถ„ ๋ฐ ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฒ• ์œตํ•ฉ์„ ์ด์šฉํ•œ ์Šค๋งˆํŠธํฐ ๋‹ค์ค‘ ๋™์ž‘์—์„œ ๋ณดํ–‰ ํ•ญ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์ฐฌ๊ตญ.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses. First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment. Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur. Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ €๊ฐ€ํ˜• ๊ด€์„ฑ์„ผ์„œ๋ฅผ ์ด์šฉํ•œ ๋ณดํ–‰ํ•ญ๋ฒ•์‹œ์Šคํ…œ (PDR: Pedestrian Dead Reckoning)์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋ณดํ–‰์ž๊ฐ€ ์‹ค๋‚ด์—์„œ ์Šค๋งˆํŠธํฐ์„ ๋“ค๊ณ  ์ด๋™ํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋™์ž‘ ์ƒํ™ฉ์—์„œ๋„ ์šด์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก, ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฐ˜ ์ธก์ •์น˜๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์ ๋ถ„ ๊ธฐ๋ฐ˜์˜ ๋ณดํ–‰์ž ํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌ์„ฑํ•œ๋‹ค. ๋˜ํ•œ ํƒ€์›์ฒด ๊ธฐ๋ฐ˜ ์ž์„ธ ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌ์„ฑํ•˜์—ฌ ์™ธ๋ž€ ์ƒํ™ฉ์—์„œ๋„ ๊ฐ•์ธํ•˜๊ฒŒ ์ž์„ธ๋ฅผ ์ถ”์ •ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋™์ž‘ ์ธ์‹ ์ •๋ณด๋ฅผ ์ด์šฉ, ๋™์ž‘์— ๋”ฐ๋ฅธ ์ธก์ •์น˜ ์—…๋ฐ์ดํŠธ๋ฅผ ๋‹ฌ๋ฆฌํ•จ์œผ๋กœ์จ ์œ„์น˜ ์ถ”์ • ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ๋จผ์ € ์Šค๋งˆํŠธํฐ ๊ธฐ๊ธฐ์˜ ์ด๋™ ๋ฐฉํ–ฅ์„ ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ํƒ€์›์ฒด ๊ธฐ๋ฒ• ๊ธฐ๋ฐ˜ ์ ์‘ ์ž์„ธ ์ถ”์ •์„ ์ œ์•ˆํ•œ๋‹ค. ์ž์„ธ ์ถ”์ • ๊ธฐ๋ฒ• (AHRS: Attitude and Heading Reference System)์€ ์ž์ด๋กœ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž์„ธ๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ์ž์ด๋กœ ์„ผ์„œ์˜ค์ฐจ์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋“œ๋ฆฌํ”„ํŠธ๋ฅผ ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด ์ธก์ •์น˜๋กœ ๊ฐ€์†๋„๊ณ„์™€ ์ง€์ž๊ณ„๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๊ฐ€์† ๋ฐ ์ง€์ž๊ณ„ ์™ธ๋ž€ ์ƒํ™ฉ์—์„œ๋Š” ์ž์„ธ ์ถ”์ • ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง€๋Š”๋ฐ, ์ถ”์ • ์„ฑ๋Šฅ์„ ํšจ๊ณผ์ ์œผ๋กœ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํƒ€์›์ฒด ๊ธฐ๋ฐ˜ ์ ์‘ ์ž์„ธ ์ถ”์ • ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ธก์ •์น˜ ์™ธ๋ž€์ด ๋“ค์–ด์˜ค๋Š” ๊ฒฝ์šฐ, ์™ธ๋ž€์˜ ๋ฐฉํ–ฅ์„ ๊ณ ๋ คํ•˜์—ฌ ํƒ€์›์ฒด ๊ธฐ๋ฒ•์œผ๋กœ ์ธก์ •์น˜ ๊ณต๋ถ„์‚ฐ์„ ์กฐ์ •ํ•ด์คŒ์œผ๋กœ์จ ๋ฐฉํ–ฅ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ์ ์‘ ์ถ”์ • ๊ธฐ๋ฒ•๋ณด๋‹ค ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •์น˜ ์—…๋ฐ์ดํŠธ๋ฅผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ์™ธ๋ž€์ด ํ•œ ์ถ•์œผ๋กœ๋งŒ ๋“ค์–ด์˜ค๋Š” ๊ฒฝ์šฐ, ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ฐฉํ–ฅ์„ ๊ณ ๋ คํ•ด ๋‚˜๋จธ์ง€ ๋‘ ์ถ•์— ๋Œ€ํ•ด์„œ๋Š” ์—…๋ฐ์ดํŠธ ํ•ด์คŒ์œผ๋กœ์จ ์ธก์ •์น˜๋ฅผ ๋ถ€๋ถ„์ ์œผ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ ˆ์ดํŠธ ํ…Œ์ด๋ธ”, ๋ชจ์…˜ ์บก์ณ ์žฅ๋น„๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ž์„ธ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ๋‹ค์–‘ํ•œ ๋™์ž‘์—์„œ๋„ ์šด์šฉ ๊ฐ€๋Šฅํ•œ ์ ๋ถ„ ๋ฐ ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฒ•์„ ์œตํ•ฉํ•˜๋Š” ๋ณดํ–‰ํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์Šค๋งˆํŠธํฐ์„ ์ด์šฉํ•ด ์‹ค๋‚ด๋ฅผ ์ด๋™ํ•  ๋•Œ์—๋Š” ์ž์œ ๋„๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์ „ํ™” ๊ฑธ๊ธฐ, ๋ฌธ์ž, ๋ฐ”์ง€ ์ฃผ๋จธ๋‹ˆ ๋„ฃ๊ธฐ ๋“ฑ ๋‹ค์–‘ํ•œ ๋™์ž‘์ด ๋ฐœ์ƒ ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ธฐ์กด์˜ ์Šค๋งˆํŠธํฐ ๊ธฐ๋ฐ˜ ๋ณดํ–‰ ํ•ญ๋ฒ•์—์„œ๋Š” ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜๋Š”๋ฐ, ์ด๋Š” ๋ณดํ–‰์ž์˜ ์ง„ํ–‰ ๋ฐฉํ–ฅ๊ณผ ๊ธฐ๊ธฐ์˜ ๋ฐฉํ–ฅ์ด ์ผ์น˜ํ•˜๋Š” ๊ฒฝ์šฐ์—๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•˜๋ฉฐ ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ์ž์„ธ ์˜ค์ฐจ๋กœ ์ธํ•œ ์œ„์น˜ ์˜ค์ฐจ๊ฐ€ ํฌ๊ฒŒ ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ ๋ถ„ ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒํƒœ๋ณ€์ˆ˜๋ฅผ ๊ตฌ์„ฑํ•˜๊ณ  ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๋‚˜์˜ค๋Š” ์œ„์น˜ ๋ฒกํ„ฐ๋ฅผ ์ธก์ •์น˜๋กœ ์‚ฌ์šฉํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋งŒ์•ฝ ๊ธฐ๊ณ„ํ•™์Šต์„ ํ†ตํ•ด ์ธ์‹ํ•œ ๋™์ž‘์„ ๋ฐ”ํƒ•์œผ๋กœ ์ง„ํ–‰ ๋ฐฉํ–ฅ๊ณผ ๊ธฐ๊ธฐ ๋ฐฉํ–ฅ์ด ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ, ์ฃผ์„ฑ๋ถ„ ๋ถ„์„์„ ํ†ตํ•ด ๊ณ„์‚ฐํ•œ ์ง„ํ–‰๋ฐฉํ–ฅ์„ ์ด์šฉํ•ด ์ง„ํ–‰ ๋ฐฉํ–ฅ์„, ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์–ป์€ ๋ณดํญ์œผ๋กœ ๊ฑฐ๋ฆฌ๋ฅผ ์—…๋ฐ์ดํŠธํ•ด ์คŒ์œผ๋กœ์จ ๋ณดํ–‰ ์ค‘ ๋ฐœ์ƒํ•˜๋Š” ์—ฌ๋Ÿฌ ๋™์ž‘์—์„œ๋„ ๊ฐ•์ธํ•˜๊ฒŒ ์šด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์–‘ํ•œ ๋™์ž‘ ์ƒํ™ฉ ๋ฐ ๊ฒฝ๋กœ๋ฅผ ๊ณ ๋ คํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์œ„์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์ด ๋‹ค์–‘ํ•œ ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ๋„ ์•ˆ์ •์ ์œผ๋กœ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜๊ณ  ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Objectives and Contribution 5 1.3 Organization of the Dissertation 6 Chapter 2 Pedestrian Dead Reckoning System 8 2.1 Overview of Pedestrian Dead Reckoning 8 2.2 Parametric Approach 9 2.2.1 Step detection algorithm 11 2.2.2 Step length estimation algorithm 13 2.2.3 Heading estimation 14 2.3 Integration Approach 15 2.3.1 Extended Kalman filter 16 2.3.2 INS-EKF-ZUPT 19 2.4 Activity Recognition using Machine Learning 21 2.4.1 Challenges in HAR 21 2.4.2 Activity recognition chain 22 Chapter 3 Attitude Estimation in Smartphone 26 3.1 Adaptive Attitude Estimation in Smartphone 26 3.1.1 Indirect Kalman filter-based attitude estimation 26 3.1.2 Conventional attitude estimation algorithms 29 3.1.3 Adaptive attitude estimation using ellipsoidal methods 30 3.2 Experimental Results 36 3.2.1 Simulation 36 3.2.2 Rate table experiment 44 3.2.3 Handheld rotation experiment 46 3.2.4 Magnetic disturbance experiment 49 3.3 Summary 53 Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54 4.1 System Overview 55 4.2 Machine Learning-based Pose Classification 56 4.2.1 Training dataset 57 4.2.2 Feature extraction and selection 58 4.2.3 Pose classification result using supervised learning in PDR 62 4.3 Fusion of the Integration and Parametric Approaches in PDR 65 4.3.1 System model 67 4.3.2 Measurement model 67 4.3.3 Mode selection 74 4.3.4 Observability analysis 76 4.4 Experimental Results 82 4.4.1 AHRS results 82 4.4.2 PCA results 84 4.4.3 IA-PA results 88 4.5 Summary 100 Chapter 5 Conclusions 103 5.1 Summary of the Contributions 103 5.2 Future Works 105 ๊ตญ๋ฌธ์ดˆ๋ก 125 Acknowledgements 127Docto

    ์•„์„ธ์•ˆ(ASEAN) ์‹œ์žฅ์—์„œ์˜ ํ•œ์ค‘์ผ ๊ฒฝ์Ÿ๊ด€๊ณ„ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œ์ง€์—ญํ•™์ „๊ณต), 2020. 8. ๊น€์ข…์„ญ.As of 2019, South Korea(hereinafter Korea) ranked 9th largest country in total trade and was 7th largest exporter of goods in the world. Korea has a high trade dependency ratio, which recorded 70.4% for merchandise trade in GDP ratio in 2018. Koreas economy depends largely on trade, so that expanding its exporting markets throughout the globe is one of the most important strategies for its future economic growth. However, Koreas export is too much concentrated on the Chinese market, accounting for 26.8% in 2018 among the total export to the world. Therefore, Korea may reduce risks by diversifying its trading partners. Chinas emergence in the world market has been an alert to many developing countries including neighboring Korea. Risks of economic instability are much tied to Chinas performance. Korea, as one of the countries whose economic growth largely depends on export is facing a task for diversification of risks through branching out into the other promising markets such as the ASEAN market. However, China, is aggressively expanding its overseas market and now is turning its eye to the ASEAN countries. This phenomenon is an urgent alert to Korea in a sense that Koreas export market may be threatened by China and be facing increasing ASEAN market losses in future. To ASEAN market, China is the largest exporting country now, followed by Japan, who used to be the largest exporter until the early 2000s. This paper is probing into two main questions. First, whether Chinas threat and Koreas losses are in fact happening in the ASEAN market due to Chinas rise. Secondly it will examine what led to its relative collapse of Japan. Through calculating Chinas threat to Korea by applying KEVIN P.GALLAGHER AND ROBERTO PORZECANSKI(2011)s methodology, it is found out that Koreas ASEAN export market is threatened by China. By applying MAURICIO(2006)s methodology, it is found out that Koreas losses to China is more apparent in low and medium tech products than high-tech products. It implies that Korea should be prepared for a further loss in the market by enlarging the gains of the products which Korea has advantages and reduce the losses of those being threatened industries. The result of threat calculation also showed that Japan was threatened by China even strong than Korea was. There are three main reasons for this phenomenon of Japans collapse in the market : Chinas rise as a strong competitor; Japans strategic transition; Japanese companies localization and new business model. However, RCA of Japan compared with China and Korea shows that Japan is still having comparative advantages in low-tech manufactures excluding textile, garment and footwear, and all medium technology manufactures. China has a higher RCA in all of low technology manufactures than Japan, and Korea has a higher RCA in all of high-tech products than Japan. Thus, it implies that in general Japan is indeed losing its market in the ASEAN market except for automotive products. Another interesting result accompanied was that Japan, who is known as possessing high advantage in high-tech electronics, has been losing this market to Korea in terms of its export. They are some hypothesis possibly explaining this phenomenon. However, it is remained to be further studied in future. Therefore, Korea has to keep its competitiveness in product groups which already have comparative advantage and compete for low and mid-tech manufactures with China. Besides, the Korean companies should also keep eyes on ASEANs domestic market and prepare for the local competition with the Japanese company especially in the automotive industry.2019๋…„ ๊ธฐ์ค€, ํ•œ๊ตญ์€ ์ „์„ธ๊ณ„ ๋ฌด์—ญ 9์œ„, ์ˆ˜์ถœ 7์œ„ ๊ตญ๊ฐ€์ด๋‹ค. ํ•œ๊ตญ์˜ ๋ฌด์—ญ ์˜์กด๋„๋Š” ์•ฝ 70.4%๋กœ ์ค‘๊ตญ(34%), ์ผ๋ณธ (29.9%) ๋“ฑ ์—ฌํƒ€ ๋™๋ถ์•„ ๊ตญ๊ฐ€์— ๋น„ํ•ด ๋งค์šฐ ๋†’์€ ํŽธ์— ์†ํ•œ๋‹ค. ์ฆ‰, ๋ฌด์—ญ์€ ํ•œ๊ตญ ๊ฒฝ์ œ ๋ฐœ์ „์˜ ์ค‘์š”ํ•œ ์š”์†Œ์ด๋ฉฐ, ์ˆ˜์ถœ ์‹œ์žฅ ํ™•๋Œ€ ๋“ฑ์„ ํ†ตํ•œ ์„ฑ์žฅ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ํ•œํŽธ, ํ˜„์žฌ ํ•œ๊ตญ์€ ๋Œ€์ค‘๊ตญ ์ˆ˜์ถœ ๋น„์ค‘์ด 26.8%์— ๋‹ฌํ•˜๋Š” ๋“ฑ ์ค‘๊ตญ ๋‹จ์ผ ์‹œ์žฅ์— ๋Œ€ํ•œ ์˜์กด๋„๊ฐ€ ๋†’์€ ํŽธ์œผ๋กœ, ํ–ฅํ›„ ์ˆ˜์ถœ์„ ๋‹ค๋ณ€ํ™” ํ•˜๋Š” ๋“ฑ ๋ฏธ๋ž˜ ๋ฆฌ์Šคํฌ๋ฅผ ์ค„์ด๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ์‹ ํฅ๊ฐ•๊ตญ์ธ ์ค‘๊ตญ์˜ ๋ถ€์ƒ์€ ํ•œ๊ตญ์„ ํฌํ•จํ•œ ์ฃผ๋ณ€๊ตญ์—๊ฒŒ ์œ„ํ˜‘์œผ๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ๊ณ , ๊ธ€๋กœ๋ฒŒ ์‹œ์žฅ์—์„œ์˜ ์ค‘๊ตญ์˜ ์œ„์ƒ์ด ์ปค์ ธ๊ฐ์— ๋”ฐ๋ผ ๋™์กฐํ™” ํ˜„์ƒ์ด ์‹ฌํ™”๋˜๋ฉด์„œ ํ•œ๊ตญ์˜ ๊ฒฝ์ œ์˜ ๋ถˆํ™•์‹ค์„ฑ๋„ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด์—, ํ•œ๊ตญ์€ ์•„์„ธ์•ˆ ๋“ฑ ์ž ์žฌ๋ ฅ์ด ๋†’์€ ์ˆ˜์ถœ ์‹œ์žฅ ์ง„์ถœ์„ ํ™•๋Œ€ํ•˜๋Š” ๋“ฑ ๋‹ค๋ณ€ํ™”๊ฐ€ ํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ค‘๊ตญ ๋˜ํ•œ ์„ ์ง„๊ตญ ์ค‘์‹ฌ์˜ ๋ฌด์—ญ๊ตฌ์กฐ๋ฅผ ํƒˆํ”ผํ•˜๊ณ ์ž ์•„์„ธ์•ˆ์„ ๋น„๋กฏํ•œ ์‹ ํฅ๊ตญ๊ฐ€๋กœ ์ˆ˜์ถœ์„ ๋‹ค๋ณ€ํ™”ํ•˜๊ณ  ์žˆ์–ด ํ•œ๊ตญ์—๊ฒŒ ํฐ ์œ„ํ˜‘ ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ํ•œ๊ตญ์˜ ์ƒ๋Œ€์  ์†์‹ค(loss)์ด ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ, ์ค‘๊ตญ์€ ์•„์„ธ์•ˆ ์‹œ์žฅ ์ ์œ ์œจ 1์œ„ ๊ตญ๊ฐ€๋กœ, 2000๋…„๋Œ€ ์ดˆ๋ถ€ํ„ฐ ๊ณต๊ฒฉ์ ์ธ ์ˆ˜์ถœ ์ „๋žต์„ ํ†ตํ•ด ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ์˜ค๋žซ๋™์•ˆ 1์œ„ ์ž๋ฆฌ๋ฅผ ์ฐจ์ง€ํ–ˆ๋˜ ์ผ๋ณธ์„ ์ œ์น˜๊ณ  ์•„์„ธ์•ˆ์˜ ์ตœ๋Œ€ ์ˆ˜์ž…๊ตญ์˜ ์ง€์œ„๋ฅผ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ์ค‘๊ตญ์ด ์–ผ๋งŒํผ ํ•œ๊ตญ๊ณผ ์ผ๋ณธ์—๊ฒŒ ์œ„ํ˜‘์œผ๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ์ค‘๊ตญ์˜ ๋Œ€ ์•„์„ธ์•ˆ ์‹œ์žฅ ์ ์œ ์œจ ํ™•๋Œ€๊ฐ€ ํ•œ๊ตญ์— ์‹ค์ œ๋กœ ์–ผ๋งŒํผ์˜ ์†์‹ค(loss)์ด ๋ฐœ์ƒํ–ˆ๋Š”๊ฐ€๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋˜ํ•œ, ์ค‘๊ตญ์˜ ๋ถ€์ƒ๊ณผ ๋Œ€์กฐ์ ์œผ๋กœ ์•„์„ธ์•ˆ ์ˆ˜์ž…์‹œ์žฅ์—์„œ์˜ ์ผ๋ณธ์˜ ์‡ ํ‡ด์— ๋Œ€ํ•œ ๋ฐฐ๊ฒฝ์— ๋Œ€ํ•ด์„œ ์งš์–ด๋ณธ๋‹ค. ์ฃผ์š” ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ๋Š” KEVIN P.GALLAGHER AND ROBERTO PORZECANSKI(2011)์ด ์ œ์‹œํ•œ ์œ„ํ˜‘์ด๋ก ์„ ์ ์šฉํ•˜์—ฌ ์œ„ํ˜‘์˜ ์ •๋„๋ฅผ ์ธก์ •ํ–ˆ์œผ๋ฉฐ, MAURICIO(2006)์˜ ์ด๋“๊ณผ ์†์‹ค ์ธก์ • ์ˆ˜์‹์„ ์ ์šฉํ•˜์—ฌ ์ค‘๊ตญ์˜ ๋Œ€ ์•„์„ธ์•ˆ ์‹œ์žฅ ํ™•์žฅ์— ๋”ฐ๋ฅธ ํ•œ๊ตญ๊ณผ ์ผ๋ณธ์˜ ์†์‹ค์„ ์ธก์ •ํ–ˆ๋‹ค. ๋˜ํ•œ, UNCTAD์˜ ํ’ˆ๋ชฉ ๊ทธ๋ฃน ๋ถ„๋ฅ˜๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๊ทธ๋ฃน๋ณ„๋กœ ๋ถ„์„์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. 1990๋…„~2018 ์ˆ˜์ถœ์ž… ํ†ต๊ณ„๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ์œ„ํ˜‘๊ณผ ์†์‹ค ์ •๋„ ์ธก์ •์— ์žˆ์–ด์„œ๋Š” 2010๋…„๊ณผ 2018๋…„์„ ๊ธฐ์ค€์œผ๋กœ ๋ถ„์„์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ํ•œ๊ตญ์˜ ๋Œ€ ์ค‘๊ตญ ์†์‹ค์€ ๊ณ ๊ธฐ์ˆ  ํ’ˆ๋ชฉ๋ณด๋‹ค ์ค‘์ € ๊ธฐ์ˆ  ํ’ˆ๋ชฉ์—์„œ ๋” ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ผ๋ณธ์€ ๊ณ ๊ธฐ์ˆ  ํ’ˆ๋ชฉ์„ ํฌํ•จํ•œ ์ „ ํ’ˆ๋ชฉ์—์„œ ์†์‹ค์ด ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์•„์„ธ์•ˆ ์‹œ์žฅ์„ ์ผ์ฐํžˆ ์„ ์ ํ–ˆ๋˜ ์ผ๋ณธ์€ ์˜ˆ์ƒ๊ณผ ๋‹ฌ๋ฆฌ ๊ณ ๊ธฐ์ˆ  ํ’ˆ๋ชฉ์—์„œ๊นŒ์ง€ ์ค‘๊ตญ์—๊ฒŒ ์•„์„ธ์•ˆ ์‹œ์žฅ์„ ๋นผ์•—๊ธฐ๊ณ  ์žˆ๋Š” ๋ชจ์Šต์„ ๋ณด์˜€๊ณ , ์†์‹ค์˜ ์ •๋„์—์„œ๋„ ํ•œ๊ตญ๋ณด๋‹ค ์‹ฌํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ๊ฒฝ์Ÿ์ž์˜ ๋“ฑ์žฅ, ์ผ๋ณธ์˜ ์ „๋žต์  ๋ณ€ํ™”, ์ผ๋ณธ ๊ธฐ์—…์˜ ํ˜„์ง€ํ™” ๋ฐ ๋น„์ฆˆ๋‹ˆ์Šค ๋ชจ๋ธ์˜ ๋ณ€ํ™” ๋“ฑ ์š”์ธ์ด ์ž‘์šฉํ•œ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ์˜ ํ•œ์ค‘์ผ RCA ๋ถ„์„์—์„œ, ์ผ๋ณธ์€ ์ค‘์ € ๊ธฐ์ˆ  ํ’ˆ๋ชฉ, ํŠนํžˆ ์ž๋™์ฐจ ๋ถ„์•ผ(automotive)์—์„œ ์šฐ์œ„๊ฐ€ ์žˆ์—ˆ๋˜ ๋ฐ˜๋ฉด, ๊ณ ๊ธฐ์ˆ  ํ’ˆ๋ชฉ์—์„œ๋Š” ์•ฝ์„ธ๋ฅผ ๋ณด์ด๊ธฐ๋„ ํ–ˆ๋‹ค. ํ•œํŽธ, ํ•œ๊ตญ์ด ์•„์„ธ์•ˆ ์‹œ์žฅ์—์„œ ํ•˜์ดํ…Œํฌ ๊ฐ•๊ตญ์ธ ์ผ๋ณธ์„ ์ œ์น˜๊ณ  ์ƒ๋Œ€์ ์œผ๋กœ ๊ณ ๊ธฐ์ˆ  ๋ถ€ํ’ˆ ๋“ฑ ํ’ˆ๋ชฉ์—์„œ ์„ ์ „ํ•˜๊ณ  ์žˆ๋Š” ์š”์ธ์œผ๋กœ๋Š” ์‹ ์ˆ˜์š” ํŒŒ์•…, ์†Œ๋น„์‹œ์žฅ ์„ ์ , ํ˜„์ง€ํ™” ์ „๋žต ๋“ฑ์„ ๊ผฝ์„ ์ˆ˜ ์žˆ๋‹ค. ํ–ฅํ›„, ํ•œ๊ตญ์ด ๋Œ€ ์•„์„ธ์•ˆ ์ˆ˜์ถœ์„ ํ™•๋Œ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์œ„ํ˜‘ ๋ถ„์„๊ณผ ์†์‹ค ๋ถ„์„์„ ํ†ตํ•ด ๋Œ€ ์ค‘๊ตญ ๊ฒฝ์Ÿ ์šฐ์œ„๊ฐ€ ์žˆ๋Š” ํ’ˆ๋ชฉ๊ตฐ์— ๋Œ€ํ•ด์„œ๋Š” ์‹œ์žฅ ์šฐ์œ„๋ฅผ ๊ฐ•ํ™”ํ•˜๊ณ , ์†์‹ค์ด ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚œ ๋ถ€๋ถ„์— ๋Œ€ํ•ด์„œ ์ „๋žต์  ์ˆ˜์ •์ด ํ•„์š”ํ•˜๋‹ค. ์•„์„ธ์•ˆ์€ ์ˆ˜์ถœ ๋Œ€์ƒ๊ตญ์œผ๋กœ์„œ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ์„ธ๊ณ„ ๊ณต์žฅ๊ณผ ์ƒˆ๋กœ์šด ์†Œ๋น„์‹œ์žฅ์œผ๋กœ๋„ ๋ถ€์ƒํ•˜๊ณ  ์žˆ์–ด ์ˆ˜์ถœ ํ™•๋Œ€ ์ผํŽธ๋„์˜ ๋ฐฉ์‹๋ณด๋‹ค ํ˜„์ง€ํ™”๋ฅผ ๊ณ ๋ คํ•œ ๋‹ค์–‘ํ•œ ์ง„์ถœ ํ™•๋Œ€ ์ „๋žต์˜ ์ˆ˜๋ฆฝ์ด ํ•„์š”ํ•˜๋‹ค. ํŠนํžˆ, ๋กœ์ปฌ ์ž๋™์ฐจ ์‹œ์žฅ์— ์šฐ์œ„๋ฅผ ๊ณ ์ˆ˜ํ•˜๊ณ  ์žˆ๋Š” ์ผ๋ณธ๊ณผ์˜ ๊ฒฝ์Ÿ์— ๋Œ€๋น„ํ•ด ์šฐ๋ฆฌ ๊ธฐ์—…๋“ค์˜ ํ˜„์ง€ํ™” ์ „๋žต, ๋น„์ฆˆ๋‹ˆ์Šค ๋ชจ๋ธ์˜ ํ˜์‹  ๋“ฑ์ด ์š”๊ตฌ๋œ๋‹ค.โ… . Introduction 1 โ…ก. Methodology 3 โ…ข. Competition in the ASEAN Market 8 3.1. Importance of the ASEAN Market 8 3.2 Koreas Export Similarity with Rising China 12 3.2.1 Rising China in the ASEAN Market 12 3.2.2 Koreas Export Similarity with China and Japan 18 3.3. Chinas Threat to Korea 19 3.3.1 Market Share Changes and DCRP 19 3.3.2 Chinas Threat to Korea 22 3.4. Koreas Losses to China 26 โ…ฃ. Chinas Rising and Japans Falling? 30 4.1. Japans Falling in the ASEAN Market 30 4.2. Plausible Reasons for Japans Loss in Export 31 4.2.1 Rising Competition 32 4.2.2 Japans Strategic Transition 34 4.2.3 Localization & New Business Model in ASEAN 38 4.2.4 Others 41 4.3. Japans Still Favorable Comparative Advantage 42 4.4. How Korea Defeated High-tech Intensive Japan? 43 โ…ค. Conclusion 44 iMaste

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ฌผ๋ฌผ๋ฆฌ ๋ฐ ํ™”ํ•™์ƒ๋ฌผํ•™๊ณผ, 2018. 2. ์„์˜์žฌ.To survive in a continuously changing environment, bacteria sense concentration gradients of attractants or repellents, and purposefully migrate until a more favourable habitat is encountered. While glucose is known as the most effective attractant, the flagellar biosynthesis and hence chemotactic motility has been known to be repressed by glucose in some bacteria. To date, the only known regulatory mechanism of the repression of flagellar synthesis by glucose is via downregulation of the cAMP level, as shown in a few members of the family Enterobacteriaceae. In this thesis, it has been shown in Vibrio vulnificus, motile and curved rod-shaped halophilic bacterium with a single polar flagellum, the glucose mediated inhibition of flagellar motility operates by a completely different mechanism. In the presence of glucose, glucose-specific enzyme IIA (EIIAGlc) of the phosphoenolpyruvate: sugar phosphotransferase system is dephosphorylated and inhibits the polar localization of FapA (flagellar assembly protein A) by sequestering it from the flagellated pole. A loss or delocalization of FapA results in a complete failure of flagellation and motility. However, when glucose is depleted, EIIAGlc is phosphorylated and releases FapA such that free FapA can be localized back to the pole and trigger flagellation. The ligand fishing experiment revealed that FapA interacts with not only EIIAGlc but also a polar landmark protein, HubP (hub of the pole), which anchors client proteins to the cell poles and modulates the localization of the chromosome origin, chemotactic signaling proteins and flagellum. In the absence of HubP, FapA is diffused throughout the cytoplasm, indicating that proper polar targeting of FapA depends on HubP. HubP competes with dephosphorylated EIIAGlc for binding to FapA to regulate the early stage of flagellar assembly. Together, these results suggest that dephosphorylated EIIAGlc inhibits flagellation by sequestrating FapA from polar localized HubP in the presence of glucose and thereby enables V. vulnificus cells to adapt to and stay in a glucose-rich environment.Chapter I. Introduction 1 1. Background information on Vibrio vulnificus 2 1.1. Overview of V. vulnificus 2 1.2. Virulence factors of V. vulnificus 3 1.2.1. Capsular polysaccharide (CPS) and Lipopolysaccharide (LPS) 3 1.2.2. Hemolysin and RTX toxin 3 1.2.3. Motility 4 2. Bacterial motility 4 2.1. Bacterial flagella 5 2.1.1. Flagellar gene regulation 6 2.1.2. Flagellar assembly 6 2.1.3. Structural diversity of the hook basal body (HBB) 8 2.1.4. Landmark system for localization of flagella 9 2.1.4.1. Regulation of flagellation patterns by FlhF and FlhG 9 2.1.4.1.1. The role of FlhF and FlhG in monotrichous flagellation 9 2.1.4.1.2. The role of FlhF and FlhG in amphitrichous, lophotrichous and peritrichous flagellation 9 2.1.4.2. HubP, a landmark system in Vibrio 10 2.2. Chemotaxis 11 2.2.1. Chemotaxis in V. cholerae 12 3. The phosphoenolpyruvate: carbohydrate phosphotransferase system 12 3.1. Barcterial PTS 12 3.2. PTS-mediated regulation in E. coli 13 3.2.1. Carbon catabolite repression 13 3.2.1.1. Inducer exclusion 14 3.2.1.2. Induction prevention 14 3.2.2. Interaction between adenylate cyclase and EIIAGlc 15 3.3. PTS in V. vulnificus 15 3.4. PTS-mediated regulation in V. vulnificus 15 3.4.1. Interaction between Vibrio insulin degrading enzyme (vIDE) and EIIAGlc 15 4. The aims of this study 16 Chapter II. Materials and Methods 17 1. Bacterial strains, plasmids, and culture conditions 18 1.1. Construction of the pDM4-fapA plasmid and a fapA deletion mutant 18 1.2. Construction of the pDM4-hubP plasmid and a hubP deletion mutant 19 1.3. Construction of plasmids pSY001 and pSY002 to overexpress EIIAGlc 19 1.4. Construction of plasmids pSY003 and pSY004 to overexpress FapA 19 1.5. Construction of plasmids pSY005 and pSY006 to overexpress GFP and GFP-FapA, respectively 20 1.6. Construction of plasmids pSY007, pSY008, and pSY009 to overexpress RFP (pSY007, 008) and FlhF-RFP (pSY009) 20 1.7. Construction of pRK-based expression vector for EIIAGlc and EIIAGlc(H91A), pSY010 and pSY011, respectively 21 1.8. Construction of pJK1113-based expression vector for EIIAGlc and EIIAGlc(H91A), pSY012 and pSY013, respectively 21 1.9. Construction of plasmids pSY014 to overexpress GFP-FapA 21 1.10. Construction of plasmids pSY015 to overexpress FapA 22 1.11. Construction of the plasmid pRK-P1-FapA 22 1.12. Construction of the plasmid pRK-FapA 22 1.13. Construction of the plasmid pSY016 to overexpress HubP 658 22 1.14. Construction of the plasmid pSY017, pSY018, and pSY019 to overexpress HubP truncation mutants 23 1.15. Construction of the plasmid pSY020 and pSY021 to overexpress HubP 23 1.16. Construction of the plasmid pSY022 and pSY023 to overexpress HubP-RFP and HubP 658-RFP, respectively 23 1.17. Construction of the plasmid pSY024, pSY025 and, pSY026 to overexpress HubP truncation mutants 24 1.18. Construction of the plasmid pSY027, pSY028 and, pSY029 to overexpress HubP truncation mutants 24 2. Purification of overexpressed proteins 32 3. Ligand-fishing experiment 32 3.1. Ligand fishing using metal affinity chromatography 32 3.2. In gel-digestion 33 4. Confirmation of specific binding 33 4.1. Gel filtration chromatography of the protein-protein complex 33 4.1.1. Gel filtration chromatography of the EIIAGlc-FapA complex 33 4.1.2. Gel filtration chromatography of the FapA-HubP complex 34 4.2. Surface plasmon resonance spectroscopy 34 4.3. Native polyacrylamide gel electrophoresis 34 5. Determination of the phosphorylation state of EIIAGlc 35 6. Determination of cAMP concentration 35 7. Motility assay and transmission electron microscopy 36 8. Visualization of fluorescent fusion proteins in live cells 36 9. RNA purification and qRT-PCR 36 10. 5 Rapid amplification of cDNA ends 36 11. Comparison of virulence 37 12. Isolation of flagellar hook basal body 37 Chapter III. Results 39 1. Flagellar motility in V. vulnificus is repressed in the presence of glucose 40 1.1. Effect of glucose on motility and flagellar formation 40 1.2. Effects of various PTS sugars on motility and flagellar formation 40 1.3. Genetic organization of chemotaxis gene clusters and the pts operon 44 2. Dephosphorylated EIIAGlc inhibits flagellar motility in V. vulnificus by a cAMP-independent mechanism 44 2.1. Inhibition of motility by dephosphorylated EIIAGlc 44 2.2. Effect of cAMP on the glucose-mediated inhibition of flagellar motility 46 3. Dephosphorylated EIIAGlc directly interacts with a flagellar assembly protein, FapA 50 3.1. Ligand fishing using EIIAGlc as bait 50 3.2. Confirmation of interaction between EIIAGlc and FapA 52 3.2.1. Confirmation of interaction between EIIAGlc and FapA using TALON metal-affinity resin 52 3.2.2. Determination of the binding stoichiometry between EIIAGlc and FapA using a gel filtration column 52 3.2.3. Measurement of the dissociation constant between EIIAGlc and FapA using surface plasmon resonance spectroscopy 56 3.2.4. Confirmation of interaction between dephosphorylated EIIAGlc and FapA using native polyacrylamide gel electrophoresis 56 4. Dephosphorylated EIIAGlc inhibits flagellation by delocalizing FapA 59 4.1. Phenotypes of the fapA mutant 59 4.2. Localization of FapA to the flagellated pole 59 4.3. Transcriptional activation of class III and IV flagellar genes by FapA 62 4.4. Inhibiton of flagellar genes by dephosphorylated EIIAGlc 65 4.5. Delocalization of FapA from the pole by dephosphorylated EIIAGlc 65 5. The fapA gene should be coordinately expressed with its upstream genes for flagellar motility 68 5.1. Complementation of the fapA mutant by episomal expression of FapA 68 5.2. Coordinate expression of fapA with its upstream genes 72 6. Effect of FapA on pathogenicity of V. vulnificus 75 7. FapA specifically interacts with HubP 75 7.1. Ligand fishing using FapA as bait 75 7.2. Confirmation of interaction between FapA and HubP 80 7.2.1. Confirmation of interaction between FapA and HubP using ligand fishing experiment 80 7.2.2. Confirmation of interaction between FapA and HubP using TALON metal-affinity resin 80 7.2.3. Confirmation of interaction between FapA and HubP truncation mutant using a gel filtration column 84 8. HubP binds directly to FapA to control its subcellular distribution 86 8.1. HubP, a determinant of polar FapA localization 86 8.2. Sequestration of FapA from binding to HubP by dephosphorylated EIIAGlc 89 9. Possible role of FapA in the early stage of flagellar assembly 89 9.1. Effect of FapA mislocalization on flagella production in the hubP mutant 89 9.2. Effect of FapA on flagellar assembly 92 Chapter IV. Discussion 98 References 105 ๊ตญ๋ฌธ์ดˆ๋ก 118Docto

    An Investigation of Athletic Injuries by Age and the Effect of an Injury Prevention Exercise Program for the Elite Basketball Players

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ฒด์œก๊ต์œก๊ณผ, 2023. 2. ๊น€์—ฐ์ˆ˜.Objectives: This study aimed to investigate athletic injuries in elite basketball players and analyze them according to sex, age range, athletic career, position played, and injury site. In addition, I investigated the intervention effect of injury prevention exercise programs through a systematic literature review and meta-analysis. Methods: Dissertation 1 describes the investigation of athletic injuries by age range. Elite basketball players registered with the Korean Sport & Olympic Committee as of 2022 were selected for inclusion. The required sample size was calculated using an assigned sample from quota sampling with a confidence level of 95% and a 15% margin of error. An online questionnaire using Google Forms was used as the measurement tool, and comprised basic questions regarding athletic injury based on the site of injury, injury-related questions, and psychology questions. For each item, a frequency analysis was conducted, and the percentage was determined. The injury incidence rate (IR) per 1,000 AE (athlete-exposure) was calculated, and a 95% confidence interval (CI) was presented accordingly. The between-group difference according to the presence or absence of athletic injury was confirmed using a Chi-square test, and an independent t-test or one-way analysis of variance was performed to determine the difference in IR between the groups. In addition, to confirm the odds ratio (OR) for the presence or absence of athletic injury according to the covariates, the OR and 95% CI were calculated using logistic regression analysis. Data were analyzed using Stata/SE (version 17.0; Stata Corp., College Station, TX, USA), and the statistical significance level was set at p<0.05. Dissertation 2 presents the systematic review and meta-analysis. A literature review was conducted in conformance with the PRISMA guidelines, and key questions were selected according to the PICOTS-SD format. On October 14, 2022, foreign electronic databases (MEDLINE, EMBASE, Cochrane library, SPORTDiscus) and domestic electronic databases (KISS, RISS4U) were searched. Risk of bias (RoB) 2 was used for the RoB assessment of the selected literature, and Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) was used for the systematic literature review. For data analysis and synthesis, Comprehensive Meta-Analysis (version 4; Biostat, Englewood, NJ, USA), was used; the incidence rate ratio (IRR) was calculated for the size of the intervention effect, and the 95% CI was presented. Results: In Dissertation 1, a total of 400 elite basketball players were evaluated for athletic injuries, and 195 (48.75%) experienced injuries. Significant differences regarding the occurrence of injuries were found in the groups based on sex, age range, and athletic career. The IR was highest among college athletes regardless of sex, and a significant difference was confirmed only for age range; this difference was identified in high school and college athletes. The frequency of injury and re-injury to the lower limbs was highest. Skin bruising was defined as the first injury to the head and upper limbs, followed by muscle inflammation of the trunk, and ligament sprains/ruptures of the lower limbs. The severe injury types based on the period of interruption of competition and training, were bone/skin bruises to the head, spondylopathy of the trunk, bone fractures and skin bleeds of the upper limbs, and ligament sprains/ruptures to the lower limbs. In the case of the head and upper limbs, the intrinsic cause of the athletic injury was due to 'excessive technique/movement attempt', and in the case of the trunk and lower limbs, it was due to 'overuse/lack of rest'. Extrinsic causes were all 'problems caused by other players' regardless of the injury site. The OR for the presence or absence of athletic injury according to the covariate was higher in high school and university athletes than in elementary school athletes, and the OR in the third quartile was higher than that in the first quartile of competition AE. The systematic literature review and meta-analysis presented in Dissertation 2 included 11 studies. As a result of adjusting the IRR, the size of the intervention effect of the injury prevention exercise program was statistically significant. Regardless of sex, the average age, amount, and type of intervention exercise program, were significant factors, whereas the site of injury was only significant in the lower limbs. Analysis of the statistically significant injury prevention exercise programs confirmed that they were performed 3.5ยฑ0.7 times per week for 20.0ยฑ8.2 min per session, for an average of 27.4ยฑ8.8 weeks. Conclusions: I confirmed that the actual condition of athletic injuries varies according to sex, age range, and injury site, which suggests that preventive measures for injury should be customized according to each variable. Therefore, effective injury prevention measures that can be applied to the field could be formulated by developing a sports injury database (Injury Surveillance System; ISS) suitable for the domestic situation, and by conducting a long-term investigation. Additionally, this systematic literature review and meta-analysis confirmed that injury prevention exercise program interventions are effective in reducing athletic injuries in elite basketball players. Therefore, implementation of an injury prevention exercise program could actively reduce the occurrence of injuries in the field, and should be performed at least 3.5 times per week, for 20.0 min per session, for a total of 27.4 weeks.์—ฐ๊ตฌ ๋ชฉ์  ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์—˜๋ฆฌํŠธ ๋†๊ตฌ์„ ์ˆ˜์˜ ์Šคํฌ์ธ  ์†์ƒ ์‹คํƒœ๋ฅผ ์—ฐ๋ น๋ณ„๋กœ ์กฐ์‚ฌํ•˜๊ณ , ์„ฑ, ์—ฐ๋ น, ์„ ์ˆ˜ ๊ฒฝ๋ ฅ, ํฌ์ง€์…˜, ์†์ƒ ๋ถ€์œ„์— ๋”ฐ๋ผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ๊ณผ ๋ฉ”ํƒ€๋ถ„์„์„ ํ†ตํ•ด ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ์ค‘์žฌ ํšจ๊ณผํฌ๊ธฐ๋ฅผ ํ™•์ธํ•˜๊ณ ์ž ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ์†Œ๋…ผ๋ฌธ 1์—์„œ๋Š” 2022๋…„ ๊ธฐ์ค€์œผ๋กœ ๋Œ€ํ•œ์ฒด์œกํšŒ์— ๋“ฑ๋ก๋œ ์—˜๋ฆฌํŠธ ๋†๊ตฌ์„ ์ˆ˜๋ฅผ ๋ชจ์ง‘๋‹จ์œผ๋กœ ํ•˜๊ณ , ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‹ ๋ขฐ์ˆ˜์ค€ 95%, ํ‘œ๋ณธ์˜ค์ฐจ 15%๋กœ ์„ค์ •ํ•˜์—ฌ ๋น„ํ™•๋ฅ ํ‘œ์ง‘์˜ ํ• ๋‹นํ‘œ๋ณธ์„ ํ†ตํ•ด ๋ชฉํ‘œ ํ‘œ๋ณธ ์ˆ˜๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ธก์ •๋„๊ตฌ๋Š” Google Form์„ ํ™œ์šฉํ•œ ์˜จ๋ผ์ธ ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋ฌธํ•ญ์€ ๊ธฐ๋ณธ๋ฌธํ•ญ, ์†์ƒ ๋ถ€์œ„๋ณ„ ์†์ƒ ์—ฌ๋ถ€ ์งˆ๋ฌธ ๋ฌธํ•ญ, ์†์ƒ ๊ด€๋ จ ๋ฌธํ•ญ, ์‹ฌ๋ฆฌ ๊ด€๋ จ ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๊ฐ ๋ฌธํ•ญ์€ ๋นˆ๋„๋ถ„์„์„ ์‹ค์‹œํ•˜๊ณ  ๋ฐฑ๋ถ„์œจ์„ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, 1,000AE(Athlete-Exposure) ๋‹น ์†์ƒ ๋ฐœ์ƒ๋ฅ (Injury incidence rate; IR)์„ ์‚ฐ์ถœํ•˜๊ณ  ์ด์— ๋”ฐ๋ฅธ 95% ์‹ ๋ขฐ๊ตฌ๊ฐ„(Confidence Interval; CI)์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์†์ƒ ๋ฐœ์ƒ ์œ ๋ฌด์— ๋”ฐ๋ฅธ ๊ทธ๋ฃน ๊ฐ„ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ต์ฐจ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๊ณ , IR์˜ ๊ทธ๋ฃน ๊ฐ„ ์ฐจ์ด๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•˜์—ฌ ๋…๋ฆฝํ‘œ๋ณธ t-๊ฒ€์ • ๋˜๋Š” ์ผ์›๋ฐฐ์น˜ ๋ถ„์‚ฐ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ณต๋ณ€์ธ์— ๋”ฐ๋ฅธ ์†์ƒ ์œ ๋ฌด์˜ ์Šน์‚ฐ๋น„๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์˜ค์ฆˆ๋น„(Odds Ratio; OR)์™€ 95% CI๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ž๋ฃŒ๋Š” Stata/SE(version 17.0; StataCorp., College Station, TX, USA) ํ”„๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ํ†ต๊ณ„์  ์œ ์˜์ˆ˜์ค€์€ p<0.05๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์†Œ๋…ผ๋ฌธ 2์—์„œ๋Š” PRISMA์˜ ์ง€์นจ์— ์ค€๊ฑฐํ•˜์—ฌ ๋ฌธํ—Œ๊ณ ์ฐฐ ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , PICOTS-SD ๊ธฐ์ค€์— ์˜ํ•ด ํ•ต์‹ฌ ์งˆ๋ฌธ์„ ์„ ์ •ํ•˜์˜€๋‹ค. ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ 2022๋…„ 10์›” 14์ผ์— ๊ตญ์™ธ ๊ฒ€์ƒ‰์›(MEDLINE, EMBASE, Cochrane library, SPORTDiscus)๊ณผ ๊ตญ๋‚ด ๊ฒ€์ƒ‰์›(KISS, RISS4U)์„ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ƒ‰ํ•˜์˜€๋‹ค. ์„ ์ •๋œ ๋ฌธํ—Œ์˜ ๋น„๋šค๋ฆผ ์œ„ํ—˜ ํ‰๊ฐ€๋Š” RoB 2๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ์€ Covidence systematic review software(Veritas Health Innovation, Melbourne, Australia) ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„ ๋ฐ ํ•ฉ์„ฑ์€ ๋ฉ”ํƒ€๋ถ„์„ ์ „์šฉ ์†Œํ”„ํŠธ์›จ์–ด์ธ Comprehensive Meta-Analysis (version 4; Biostat, Englewood, NJ, USA) ํ”„๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ค‘์žฌ ํšจ๊ณผํฌ๊ธฐ๋Š” IRR์„ ์‚ฐ์ถœํ•˜๊ณ  95% CI๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ์†Œ๋…ผ๋ฌธ 1์—์„œ ์ด 400๋ช…์˜ ์—˜๋ฆฌํŠธ ๋†๊ตฌ์„ ์ˆ˜๋ฅผ ์Šคํฌ์ธ  ์†์ƒ ์‹คํƒœ๋ฅผ ์กฐ์‚ฌํ•œ ๊ฒฐ๊ณผ, 195๋ช…(48.75%)์ด ์†์ƒ์„ ๊ฒฝํ—˜ํ•˜์˜€๊ณ  ์†์ƒ ๋ฐœ์ƒ ์—ฌ๋ถ€์— ๋”ฐ๋ฅธ ๊ทธ๋ฃน ๊ฐ„ ์ฐจ์ด๋Š” ์„ฑ๋ณ„, ์—ฐ๋ น๋ณ„, ์„ ์ˆ˜ ๊ฒฝ๋ ฅ๋ณ„ ๊ทธ๋ฃน์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. IR์€ ์„ฑ๋ณ„์— ๊ด€๊ณ„์—†์ด ๋ชจ๋‘ ๋Œ€ํ•™๋ถ€๊ฐ€ ๊ฐ€์žฅ ๋†’์•˜๊ณ , ์—ฐ๋ น๋ณ„ ๊ทธ๋ฃน์—์„œ๋งŒ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ ๊ทธ ์ฐจ์ด๋Š” ๊ณ ๋“ฑ๋ถ€์™€ ๋Œ€ํ•™๋ถ€์—์„œ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜์ง€ ๋ถ€์œ„์˜ ์†์ƒ ๋ฐ ์žฌ์†์ƒ ๋นˆ๋„๊ฐ€ ๊ฐ€์žฅ ๋งŽ์•˜๊ณ , ์†์ƒ ์ข…๋ฅ˜ 1์ˆœ์œ„๋Š” ๋จธ๋ฆฌ, ์ƒ์ง€ ๋ถ€์œ„๋Š” ํ”ผ๋ถ€-๋ฉ, ๋ชธํ†ต ๋ถ€์œ„๋Š” ๊ทผ์œก-์—ผ์ฆ, ํ•˜์ง€ ๋ถ€์œ„๋Š” ์ธ๋Œ€-์—ผ์ขŒ/ํŒŒ์—ด์ด์—ˆ๋‹ค. ์‹œํ•ฉ ๋ฐ ํ›ˆ๋ จ์˜ ์ค‘๋‹จ ๊ธฐ๊ฐ„์— ๋”ฐ๋ฅธ Severe ์†์ƒ ์ข…๋ฅ˜ 1์ˆœ์œ„๋Š” ๋จธ๋ฆฌ ๋ถ€์œ„๋Š” ๋ผˆ/ํ”ผ๋ถ€-๋ฉ, ๋ชธํ†ต ๋ถ€์œ„๋Š” ์ฒ™์ถ”๋ณ‘์ฆ, ์ƒ์ง€ ๋ถ€์œ„๋Š” ๋ผˆ-๊ณจ์ ˆ, ํ”ผ๋ถ€-์ถœํ˜ˆ, ํ•˜์ง€ ๋ถ€์œ„๋Š” ์ธ๋Œ€-์—ผ์ขŒ/ํŒŒ์—ด์ด์—ˆ๋‹ค. ์†์ƒ ๋ฐœ์ƒ์˜ ๋‚ด์  ์›์ธ์€ ๋จธ๋ฆฌ, ์ƒ์ง€ ๋ถ€์œ„์˜ ๊ฒฝ์šฐ ๋ฌด๋ฆฌํ•œ ๊ธฐ์ˆ /๋™์ž‘ ์‹œ๋„๋กœ ์ธํ•˜์—ฌ, ๋ชธํ†ต, ํ•˜์ง€ ๋ถ€์œ„๋Š” ๊ณผ์‚ฌ์šฉ/ํœด์‹ ๋ถ€์กฑ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์™ธ์  ์›์ธ์€ ์†์ƒ ๋ถ€์œ„์™€ ๊ด€๊ณ„์—†์ด ๋ชจ๋‘ ๋‹ค๋ฅธ ์„ ์ˆ˜๋กœ ์ธํ•œ ๋ฌธ์ œ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต๋ณ€์ธ์— ๋”ฐ๋ฅธ ์†์ƒ ์œ ๋ฌด์˜ ์Šน์‚ฐ๋น„๋Š” ์—ฐ๋ น๋ณ„ ๊ทธ๋ฃน์—์„œ ์ดˆ๋“ฑ๋ถ€์— ๋น„ํ•˜์—ฌ ๊ณ ๋“ฑ๋ถ€์™€ ๋Œ€ํ•™๋ถ€์˜ ์Šน์‚ฐ๋น„๊ฐ€ ๊ฐ๊ฐ ๋†’์•˜๊ณ , ์‹œํ•ฉ AE 1๋ถ„์œ„ ์ˆ˜์— ๋น„ํ•ด 3๋ถ„์œ„ ์ˆ˜์˜ ์Šน์‚ฐ๋น„๊ฐ€ ๋†’์•˜๋‹ค. ์†Œ๋…ผ๋ฌธ 2์—์„œ ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ตœ์ข…์ ์œผ๋กœ 11ํŽธ์˜ ๋ฌธํ—Œ์ด ๋ฉ”ํƒ€๋ถ„์„์— ํฌํ•จ๋˜์—ˆ๊ณ , IRR์„ ๋ณด์ •ํ•œ ๊ฒฐ๊ณผ ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ์ค‘์žฌ ํšจ๊ณผํฌ๊ธฐ๊ฐ€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž์˜ ์„ฑ๋ณ„, ํ‰๊ท  ์—ฐ๋ น, ์ค‘์žฌ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ์šด๋™๋Ÿ‰ ๋ฐ ์ข…๋ฅ˜์™€ ๊ด€๊ณ„์—†์ด ๋ชจ๋‘ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์†์ƒ ๋ฐœ์ƒ ๋ถ€์œ„๋Š” ํ•˜์ง€ ๋ถ€์œ„ ๊ทธ๋ฃน์—์„œ๋งŒ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚œ ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์„ ์ •๋ฆฌํ•˜๋ฉด ํ‰๊ท ์ ์œผ๋กœ 27.4์ฃผยฑ8.8์ฃผ, 1ํšŒ ์ˆ˜ํ–‰ ์‹œ 20.0ยฑ8.2๋ถ„, ์ผ์ฃผ์ผ์— 3.5ยฑ0.7ํšŒ ์ˆ˜ํ–‰๋œ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ฒฐ๋ก  ์„ฑ, ์—ฐ๋ น, ์†์ƒ ๋ถ€์œ„์— ๋”ฐ๋ผ ์Šคํฌ์ธ  ์†์ƒ ์‹คํƒœ๊ฐ€ ๋‹ฌ๋ผ์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๊ฐ ๋ณ€์ธ์— ๋”ฐ๋ผ ์†์ƒ์˜ ์˜ˆ๋ฐฉ ๋Œ€์ฑ…์ด ๋‹ฌ๋ผ์ ธ์•ผํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ตญ๋‚ด์˜ ์‹ค์ •์— ๋งž๋Š” ์Šคํฌ์ธ  ์†์ƒ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค(Injury Surveillance System; ISS)๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ ์žฅ๊ธฐ๊ฐ„ ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค๋ฉด ํ˜„์žฅ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ์†์ƒ ์˜ˆ๋ฐฉ ๋Œ€์ฑ…์ด ๋งˆ๋ จ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ํŒ๋‹จ๋œ๋‹ค. ๋˜ํ•œ ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ๊ณผ ๋ฉ”ํƒ€๋ถ„์„์„ ํ†ตํ•ด ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ์ค‘์žฌ๊ฐ€ ์—˜๋ฆฌํŠธ ๋†๊ตฌ์„ ์ˆ˜์˜ ์†์ƒ ๋ฐœ์ƒ ๊ฐ์†Œ์— ํšจ๊ณผ์ ์ธ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์— ํ˜„์žฅ์—์„œ ์ ๊ทน์ ์œผ๋กœ ์†์ƒ ๋ฐœ์ƒ ๊ฐ์†Œ๋ฅผ ์œ„ํ•ด ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์„ ์ค‘์žฌํ•ด์•ผํ•˜๋ฉฐ, ์ตœ์†Œํ•œ ์ผ์ฃผ์ผ์— 3.5ํšŒ, 1ํšŒ ์ˆ˜ํ–‰ ์‹œ 20.0๋ถ„ ์ด์ƒ, ์ด 27.4์ฃผ๊ฐ„ ์‹ค์‹œํ•˜๋Š” ๊ฒƒ์ด ์†์ƒ ์˜ˆ๋ฐฉ์— ํšจ๊ณผ์ ์ผ ๊ฒƒ์ด๋ผ ํŒ๋‹จ๋œ๋‹ค.โ… . ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  6 3. ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค 6 4. ์šฉ์–ด์˜ ์ •์˜ 7 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 8 1. ์Šคํฌ์ธ  ์†์ƒ 8 1) ์Šคํฌ์ธ  ์†์ƒ์˜ ์ •์˜ 8 2) ์Šคํฌ์ธ  ์†์ƒ ๋ฐœ์ƒ ์œ„ํ—˜ ์š”์ธ(risk factor) ๋ฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜(mechanism) 9 3) IR 11 2. ๋†๊ตฌ์™€ ์Šคํฌ์ธ  ์†์ƒ 13 1) ๋†๊ตฌ 13 2) ๋†๊ตฌ์„ ์ˆ˜์—๊ฒŒ ํ•„์š”ํ•œ ์ƒ๋ฆฌํ•™์  ์š”์ธ ๋ฐ ์†์ƒ๊ณผ์˜ ๊ด€๊ณ„ 14 3) ๋†๊ตฌ์„ ์ˆ˜์˜ ์Šคํฌ์ธ  ์†์ƒ ์‹คํƒœ 16 3. ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ 18 1) ์†์ƒ ์˜ˆ๋ฐฉ 18 2) ๋†๊ตฌ์„ ์ˆ˜๋ฅผ ์œ„ํ•œ ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ 19 โ…ข. ์†Œ๋…ผ๋ฌธ 1 21 1. ์„œ ๋ก  22 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 25 1) ์—ฐ๊ตฌ ๋Œ€์ƒ 25 2) ์—ฐ๊ตฌ ์„ค๊ณ„ 25 3) ์ธก์ • ๋„๊ตฌ 28 4) ์—ฐ๊ตฌ ์ ˆ์ฐจ 29 5) ํ†ต๊ณ„ ๋ถ„์„ 30 3. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 31 1) ์—ฐ๊ตฌ ๋Œ€์ƒ์˜ ํŠน์„ฑ 31 2) ์Šคํฌ์ธ  ์†์ƒ ๋ฐœ์ƒ ์—ฌ๋ถ€ 32 3) ์Šคํฌ์ธ  ์†์ƒ ๋ฐœ์ƒ ์ˆ˜์ค€ ๋ฐ IR 35 4) ์‹ ์ฒด ๋ถ€์œ„์— ๋”ฐ๋ฅธ ์†์ƒ ๋นˆ๋„, ์ข…๋ฅ˜, ์žฌ์†์ƒ, ์ค‘๋‹จ ๊ธฐ๊ฐ„ 39 5) ์Šคํฌ์ธ  ์†์ƒ ๋ฐœ์ƒ์›์ธ 51 6) ๊ณต๋ณ€์ธ์— ๋”ฐ๋ฅธ ์†์ƒ ์œ ๋ฌด์˜ ์Šน์‚ฐ๋น„ 53 7) ์Šคํฌ์ธ  ์†์ƒ ๋ฐœ์ƒ ์งํ›„์˜ ์ตœ์ดˆ ์ฒ˜์น˜ ๋ฐ ์ดํ›„ ์น˜๋ฃŒ ๋ฐฉ๋ฒ• 54 8) ์‹ฌ๋ฆฌ์„ค๋ฌธ 56 4. ๋…ผ ์˜ 61 โ…ฃ. ์†Œ๋…ผ๋ฌธ 2 69 1. ์„œ ๋ก  70 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 73 1) ์—ฐ๊ตฌ ์„ค๊ณ„ 73 2) ํ•ต์‹ฌ ์งˆ๋ฌธ 73 3) ๊ฒ€์ƒ‰ ์šฉ์–ด์™€ ๋ฐฉ๋ฒ• 75 4) ์ž๋ฃŒ ์„ ์ • 75 5) ์ž๋ฃŒ ์ถ”์ถœ 76 6) ๋น„๋šค๋ฆผ ์œ„ํ—˜ ํ‰๊ฐ€ 77 7) ์ž๋ฃŒ ๋ถ„์„ ๋ฐ ํ•ฉ์„ฑ 77 3. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 80 1) ์ž๋ฃŒ ์„ ์ • 80 2) ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ์— ํฌํ•จ๋œ ๋ฌธํ—Œ์˜ ํŠน์„ฑ 81 3) ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ์ค‘์žฌ ํšจ๊ณผ 88 4) ์ถœํŒ ํŽธํ–ฅ (Publication bias) 89 5) ํ•˜์œ„๊ทธ๋ฃน ๋ถ„์„ (Subgroup analysis) 90 6) ์Šคํฌ์ธ  ์†์ƒ ์˜ˆ๋ฐฉ ์šด๋™ ํ”„๋กœ๊ทธ๋žจ์˜ ํŠน์„ฑ 95 7) ๋ฉ”ํƒ€๋ถ„์„์— ํฌํ•จ๋œ ๋ฌธํ—Œ์˜ ๋น„๋šค๋ฆผ ์œ„ํ—˜ ํ‰๊ฐ€ 96 4. ๋…ผ ์˜ 99 5. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 104 ์ฐธ๊ณ ๋ฌธํ—Œ 108 ๋ถ€ ๋ก 139 Abstract 168๋ฐ•

    DNA ๋ฉ”ํ‹ธํ™”๊ฐ€ ์ œํ•œํšจ์†Œ์™€ DNA ๊ฒฐํ•ฉ ๋ฐ ๋ถ„ํ•ด ๋™์—ญํ•™์— ์ฃผ๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๋‹จ๋ถ„์ž ํ˜•๊ด‘ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™๋ถ€(๋ฌผ๋ฆฌํ™”ํ•™์ „๊ณต), 2014. 2. ๊น€์„ฑ๊ทผ.DNA methylation plays a great role both in eukaryotic and prokaryotic cells. In eukaryotes, it suppresses gene regulation, whereas in prokaryotes, specifically in bacteria, it protects the cell from invasion of foreign genes. Many studies have been carried out for the effect of DNA methylation at the ensemble level, but in this study, we investigated the effect of DNA methylation on DNA-protein interaction by single-molecule fluorescence to understand the interaction at the molecular level. We measured the association and dissociation rates of native as well as methylated DNA and found that the values converge to the corresponding ensemble rates. We were able to differentiate the kinetics of association, and dissociation and found that our result was consistent with the fact that DNA methylation interrupts the DNA- protein interaction, especially at a specific kinetic step.Abstract 1. Introduction 2. Basic principles 2.1 Total internal reflection fluorescence microscopy 2.2 Protein binding induced fluorescence 2.3 Restriction endonuclease, HindIII 2.4 Sample preparation 3. A novel way of detecting protein association and dissociation dynamics and dimer binding of protein. 3.1 Introduction 3.2 Experimental 3.3 Results and discussion 3.3.1 Reaction time in ensemble3.3.2 Enzyme concentration 3.3.3 Specific vs. Nonspecific binding 3.3.4 Single-molecule test 3.4 Conclusion 4. Effects of methylation on dsDNA to dissociation protein from dsDNA. 4.1 Introduction 4.2 Results and discussion 4.2.1 Ensemble test 4.2.2 Single-molecule test 4.2.3 Arrhenius plot 4.2.4 Simulation fitted with single-molecule date 4.3 Conclusion 5. References 6. Appendix 6.1 Ion concentration 6.2 Reaction in imaging buffer 6.3 Enzyme activity on Wild type vs. fluorophore labeled dsDNA 6.4 Time trajectory of a single molecule ๊ตญ๋ฌธ์ดˆ๋กMaste

    ์ฃผ๋ณ€๋ถ€ ์—ฌ์„ฑ์˜ ์ด์ฃผ์™€ ๋Œ๋ด„๋ ˆ์ง์˜ ๋ฐœ์ „ - ์„œ์šธ์‹œ ์‹๋ชจ์™€ ์กฐ์„ ์กฑ ๋Œ๋ด„๋…ธ๋™์ž๋ฅผ ์‚ฌ๋ก€๋กœ -

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ง€๋ฆฌํ•™๊ณผ, 2016. 8. ์ด์ •๋งŒ.๋ณธ ๋…ผ๋ฌธ์€ ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง(regime of carework migration)์ด๋ผ๋Š” ์šฉ์–ด๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ๋„์‹œ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์ด ๋Œ๋ด„ ์˜์—ญ์„ ์™ธ์ฃผํ•œ๋‹ค๋Š” ๊ณต๋™์˜ ๋ชฉํ‘œ๋ฅผ ์œ„ํ•ด ์–ด๋–ป๊ฒŒ ํ†ต์น˜์—ฐํ•ฉ์„ ํ˜•์„ฑํ•ด ์™”๋Š”์ง€ ๋ถ„์„ํ•œ๋‹ค. ์ฆ‰, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฃผ๋ณ€๋ถ€ ์ง€์—ญ ์—ฌ์„ฑ๋“ค์ด ์„ธ๊ณ„ ์ค‘์‹ฌ๋„์‹œ์— ์žˆ๋Š” ๋Œ๋ด„์‹œ์žฅ์˜ ์ตœํ•˜์ธต ๋…ธ๋™๊ณ„๊ธ‰์œผ๋กœ ์œ ์ž…๋˜๋Š” ํ˜„์ƒ์ด ์ดํ•ด๊ด€๊ณ„์ž๋“ค์˜ ์ •์น˜๊ฒฝ์ œํ•™์  ์ด์ต ์ถ”๊ตฌ ๊ณผ์ •์—์„œ ๋ฒŒ์–ด์ง„ ๊ณต๊ฐ„์ •์น˜์˜ ์‚ฐ๋ฌผ์ž„์„ ๋ฐํžŒ๋‹ค. ์„ ์ง„๊ตญ์˜ ๋Œ๋ด„ ์œ„๊ธฐ์™€ ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์—ฌ์„ฑ ์ด์ฃผ๋…ธ๋™์ž์˜ ๋„์ž…์— ๊ด€ํ•œ ์ด์•ผ๊ธฐ๋Š” ์ด์ฃผ ์—ฐ๊ตฌ์ž๋“ค ์‚ฌ์ด์—์„œ ํ’๋ถ€ํžˆ ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๊ฑฐ์‹œ์ ์ธ ๊ด€์ ์—์„œ ๊ตญ๊ฐ€๋“ค์˜ ์ •์ฑ…์„ ๋ถ„์„ํ•˜๊ฑฐ๋‚˜ ๋ฏธ์‹œ์ ์ธ ๊ด€์ ์—์„œ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž๋“ค์˜ ๋…ธ๋™ ๊ฒฝํ—˜์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌํ•œ ํ•™๋ฌธ ์กฐ๋ฅ˜์—์„œ ์ค‘๋ฒ”์œ„์  ์ ‘๊ทผ์€ ๋ฐฐ์ œ๋˜์—ˆ๊ณ  ์—ญ๋™์ ์ธ ์œ ๊ธฐ์ฒด๋กœ์„œ์˜ ๋„์‹œ์˜ ์„ฑ๊ฒฉ์„ ํŒŒ์•…ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ์˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์— ๊ธฐ์—ฌํ•˜๊ธฐ ์œ„ํ•ด ์ด์ฃผ ๋ ˆ์ง ์ด๋ก ์„ ์ ์šฉํ•˜์—ฌ ์ •๋ถ€, ๊ฐ€์ •, ๋ฏผ๊ฐ„ ๋“ฑ์˜ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์ด ๋Œ๋ด„๋…ธ๋™์‹œ์žฅ์„ ์ž์‹ ๋“ค์ด ์›ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ์–ด๋–ป๊ฒŒ ์ƒ์ƒํ•˜์—ฌ ์™”๋Š”์ง€๋ฅผ ๊ณ ์ฐฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ ๋‘ ๊ฐ€์ง€์˜ ๊ฒฐ๋ก ์„ ์ œ์‹œํ•œ๋‹ค: ์ฒซ์งธ, ์ด์ฃผ์˜ ์—ฌ์„ฑํ™”์˜ ํ๋ฆ„์„ ํƒ€๊ณ  ์œ ์ž…๋˜๋Š” ์—ฌ์„ฑ๋“ค์„ ๋Œ๋ด„์‹œ์žฅ์˜ ์ตœํ•˜์ธต์— ๋ฐฐ์น˜์‹œํ‚ค๋Š” ๊ณผ์ •์—๋Š” ๋‹ค์–‘ํ•œ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์ด ๊ฐœ์ž…ํ•ด ์™”๋‹ค. ๊ฐ๊ฐ์˜ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์ด ์ˆ˜ํ–‰ํ•œ ์—ญํ• ์€ ์‹๋ชจ๋ฅผ ๊ณ ์šฉํ•˜๋˜ 1970๋…„๋Œ€๊นŒ์ง€์˜ ์‹œ๊ธฐ์™€ ์กฐ์„ ์กฑ์„ ์ฃผ๋กœ ์ฑ„์šฉํ•˜๋Š” 1990๋…„๋Œ€ ์ดํ›„์˜ ์‹œ๊ธฐ๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅด๋‹ค. ์‹๋ชจ ๊ณ ์šฉ ์‹œ๊ธฐ์˜ ์ •๋ถ€ ๊ธฐ๊ด€๋“ค์€ ๋ฌธ์ œ์‹œ๋˜๋Š” ์ด์ฃผ์—ฌ์„ฑ์„ ๋ฐฐ์ถœ์‹œํ‚ค๊ณ  ์‹ ์›์ด ํ™•์‹คํ•œ ์‹๋ชจ๋“ค์„ ๋‚จ๊ฒจ๋‘๋Š” ๊ฐ์‹œ์ž์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๋ฐ˜๋ฉด 1990๋…„๋Œ€์— ์ด๋ฅด๋Ÿฌ ์ •๋ถ€ ๊ธฐ๊ด€์€ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž๋ฅผ ์ถ”๋ฐฉํ•˜๋Š” ์†Œ๊ทน์ ์ธ ์—ญํ• ์—์„œ ๋ฒ—์–ด๋‚˜ ๋ฒ•๊ณผ ์ œ๋„์˜ ๊ฐœ์„ ์„ ํ†ตํ•ด ์ด๋“ค์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๋Š” ์—ญํ• ๋กœ ๋‚˜์•„๊ฐ”๋‹ค. ์‹๋ชจ์™€ ์กฐ์„ ์กฑ ๊ณ ์šฉ์ฃผ ๊ฐ€์ •๋“ค์€ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž์˜ ๋ณดํ˜ธ์ž์ด์ž ๊ด€๋ฆฌ์ž ์—ญํ• ์„ ๋Œ€ํ–‰ํ•˜์˜€๋‹ค. ๊ณ ์šฉ์ฃผ์˜ ๊ฐ€์ •๋“ค์€ ์ด์ฃผ์—ฌ์„ฑ๋“ค์ด ๋„์‹œ์—์„œ ์‚ด์•„๋‚จ๋Š” ๋ฐฉ๋ฒ•์„ ์•Œ๋ ค์ฃผ๋Š” ๋ฌธํ™”์  ์ค‘๊ฐœ์ž์„ ์—ญํ• ์„ ํ–ˆ๋‹ค. ๋”๋ถˆ์–ด ์ง์—…์†Œ๊ฐœ์†Œ์™€ ์ž์„ ๋‹จ์ฒด, ์ด์ฃผ์ž ๊ตํšŒ๋“ค์€ ์ •๋ถ€ ๊ธฐ๊ด€๊ณผ ์ด์ฃผ์—ฌ์„ฑ, ๊ทธ๋ฆฌ๊ณ  ๊ฐœ๋ณ„ ๊ฐ€์ •์„ ์ด์–ด์ฃผ๋Š” ์„œ๋น„์Šค ๋ถ„๋ฐฐ์ž๋กœ ๊ธฐ๋Šฅํ–ˆ๋‹ค. ๋‘˜์งธ, ๋„์‹œ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์€ ์•ˆ์ •์ ์ธ ์ด์ฃผ๋…ธ๋™๋ ฅ์˜ ์ˆ˜๊ธ‰์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•ด ์„œ๋กœ๊ฐ„์— ๋น„๊ณต์‹์  ์—ฐํ•ฉ์„ ๊ฒฐ์„ฑํ•˜๊ณ  ๊ฐ์ž๊ฐ€ ์›ํ•˜๋Š” ๋ฐฉํ–ฅ์˜ ๋Œ๋ด„๋…ธ๋™์‹œ์žฅ์„ ์ฃผ๋„ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ํŠนํžˆ ์ดํ•ด๊ด€๊ณ„์ž๋“ค๊ฐ„์˜ ๊ถŒ๋ ฅ์œ„๊ณ„๋Š” ์ •์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ์—ˆ์œผ๋ฉฐ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž๋ฅผ ๊ด€๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ถŒ๋ ฅ ์ˆ˜๋‹จ์ด ์‹œ๊ธฐ๋ณ„๋กœ ๋‹ฌ๋ผ์ง€๋ฉด์„œ ์—ญ๋™์ ์œผ๋กœ ๋ณ€ํ™”ํ–ˆ๋‹ค. ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง์˜ ์ดˆ๊ธฐ ํ˜•์„ฑ ๋‹จ๊ณ„์—๋Š” ๋น„๊ณต์‹์  ์‹œ๋ฏผ๊ถŒ์— ๋Œ€ํ•œ ํ†ต์ œ ๊ถŒ๋ ฅ์„ ๊ฐ€์ง„ ๊ฐ€์ • ํ–‰์œ„์ž๋“ค์ด, ํ›„๋ฐ˜์—๋Š” ๊ณต์‹์  ์‹œ๋ฏผ๊ถŒ์— ๋Œ€ํ•œ ํ†ต์ œ ๊ถŒ๋ ฅ์„ ๊ฐ€์ง„ ๊ณต๊ณต ํ–‰์œ„์ž๋“ค์ด ๋ ˆ์ง ์œ„๊ณ„์˜ ์ƒ์œ„๊ถŒ์— ์˜ค๋ฅผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ณผ๊ฑฐ์—๋Š” ์‹๋ชจ ๊ณ ์šฉ์ฃผ๋“ค์ด ๊ตญ๊ฐ€ ๋ฐ ๋„์‹œ ์ œ๋„์˜ ๊ณต๋ฐฑ์„ ๋ฉ”์›€์œผ๋กœ์จ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž๋“ค์ด ๋„์‹œ์—์„œ ์‚ด์•„๊ฐˆ ์ˆ˜ ์žˆ๋Š” ์ž์›๋“ค์„ ๋น„๊ณต์‹์ ์œผ๋กœ ์ œ๊ณตํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ ์ •๋ถ€๋Š” ์ทจ์—…๊ด€๋ฆฌ์ œ, ๋ฐฉ๋ฌธ์ทจ์—…์ œ ๋“ฑ์˜ ์ œ๋„๋ฅผ ์ฒด๊ณ„ํ™”ํ•จ์— ๋”ฐ๋ผ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž์˜ ๊ณ ์šฉ, ๊ด€๋ฆฌ, ์ถ”๋ฐฉ์„ ๊ด€์žฅํ•˜๊ณ  ์ด๋“ค์ด ๋„์‹œ์—์„œ ์‚ด์•„๊ฐˆ ์—ญ๋Ÿ‰์„ ํ†ต์ œํ•ด ์™”๋‹ค. ํ–ฅํ›„ ์‹œ๋ฏผ๋‹จ์ฒด๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ด์ฃผ์—ฌ์„ฑ ๊ธฐ๋ฐ˜์˜ ๋Œ๋ด„์‹œ์Šคํ…œ์ด ๊ฐ€์ง„ ์ง€์†๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ๋น„ํŒ์ด ์ œ๊ธฐ๋˜๋ฉด์„œ ๋Œ๋ด„๋ ˆ์ง์— ๋Œ€ํ•ญํ•œ ์„ธ๋ ฅ์ด ๋ฐœ๋‹ฌํ•˜๊ฒŒ ๋œ๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ฌ์„ฑ ์ด์ฃผ๋…ธ๋™์ž๋ผ๋Š” ๊ณตํ†ต์ ์„ ์ง€๋‹Œ ์‹๋ชจ์™€ ์กฐ์„ ์กฑ ์—ฌ์„ฑ์„ ์‚ฌ๋ก€๋กœ ์„ ์ •ํ•˜์—ฌ ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง ๋ฐœ๋‹ฌ์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๊ณผ ๊ด€๋ จํ•ด ์„œ์šธ์— ๊ฑฐ์ฃผํ•˜๋Š” 10๋ช…์˜ ์กฐ์„ ์กฑ ์ž…์ฃผ ๋Œ๋ด„๋…ธ๋™์ž, 14๋ช…์˜ ์กฐ์„ ์กฑ ๊ณ ์šฉ์ฃผ, 6๋ช…์˜ ์‹๋ชจ ๊ณ ์šฉ์ฃผ, 7๋ช…์˜ ๋„์‹œ ์ดํ•ด๊ด€๊ณ„์ž๋ฅผ ํฌํ•จํ•˜์—ฌ ์ด 37๋ช…์˜ ์ฃผ๋ฏผ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์‹ฌ์ธต ๋ฐ ๋น„๊ณต์‹์  ์ธํ„ฐ๋ทฐ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ์ธํ„ฐ๋ทฐ๋กœ ๋ถ„์„์ด ๋ถˆ๊ฐ€๋Šฅํ•œ ๋ถ€๋ถ„์€ ์•„์นด์ด๋ธŒ ๋ถ„์„, ์ฐธ์—ฌ๊ด€์ฐฐ, ๊ณผ๊ฑฐ ์‹ ๋ฌธ๊ธฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ณด์ถฉํ•˜๊ณ ์ž ํ–ˆ์œผ๋ฉฐ ์ž๋ฃŒ ์ˆ˜์ง‘์€ 2014๋…„ 3์›” 16์ผ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•˜์—ฌ 2016๋…„ 6์›” 16์ผ์— ์ข…๊ฒฐ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„ ๊ณผ์ •์„ ํ† ๋Œ€๋กœ ๋ณธ ๋…ผ์˜๋Š” ๊ถ๊ทน์ ์œผ๋กœ ์ด์ฃผ ๋ ˆ์ง ์ด๋ก ๊ณผ ์ด์ฃผ๋…ธ๋™์ž์˜ ๊ณต๊ฐ„์ •์น˜์— ๊ด€ํ•œ ๊ธฐ์กด ์ง€๋ฆฌํ•™ ๋…ผ์˜์— ๊ธฐ์—ฌํ•˜๊ณ ์ž ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 3 ์ œ 3 ์ ˆ ์šฉ์–ด ์ •์˜ 5 ์ œ 2 ์žฅ ๋ฌธํ—Œ ์—ฐ๊ตฌ 8 ์ œ 1 ์ ˆ ๋Œ๋ด„์„œ๋น„์Šค์˜ ์ „์ง€๊ตฌ์  ๋ถ„์—…ํ™” 8 ์ œ 2 ์ ˆ ์ด์ฃผ๋ ˆ์ง์ด๋ก ์„ ํ†ตํ•œ ๋Œ๋ด„์„œ๋น„์Šค์˜ ์žฌํ•ด์„ 11 ์ œ 3 ์žฅ ์„œ์šธ์‹œ ๋Œ๋ด„์‹œ์Šคํ…œ์˜ ๋ณ€์ฒœ ๋ฐ ํ˜„ํ™ฉ 15 ์ œ 1 ์ ˆ ์„œ์šธ์‹œ ๋Œ๋ด„์ •์ฑ…์˜ ์—ญ์‚ฌ ๋ฐ ํ˜„ํ™ฉ 15 1. ์„œ์šธ์‹œ ๋Œ๋ด„์ •์ฑ…์˜ ์—ญ์‚ฌ 15 2. ์„œ์šธ์‹œ ๋Œ๋ด„์ •์ฑ…์˜ ํ˜„ํ™ฉ 18 ์ œ 2 ์ ˆ ์„œ์šธ์‹œ ์ด์ฃผ ๋Œ๋ด„๋…ธ๋™์ž์˜ ์—ญ์‚ฌ 20 1. 1950๋…„๋Œ€~1980๋…„๋Œ€: ์‹๋ชจ์˜ ๋“ฑ์žฅ๊ณผ ์†Œ๋ฉธ 20 2. 1990๋…„๋Œ€~ํ˜„์žฌ: ์กฐ์„ ์กฑ ๋Œ๋ด„๋…ธ๋™์ž์˜ ๋“ฑ์žฅ 23 ์ œ 4 ์žฅ ๋Œ๋ด„ ์™ธ์ฃผํ™”์— ๊ฐœ์ž…ํ•˜๋Š” ํ–‰์œ„์ž๋“ค์˜ ์—ญํ•  26 ์ œ 1 ์ ˆ ์‹๋ชจ๋ฅผ ๋‘˜๋Ÿฌ ์‹ผ ์ดํ•ด๊ด€๊ณ„์ž๋“ค 26 1. ์ •๋ถ€ ๊ธฐ๊ด€: ๊ฐ์‹œ์ž์˜ ์—ญํ•  26 2. ์‹๋ชจ ๊ณ ์šฉ ๊ฐ€์ •: ๊ด€๋ฆฌ์ž์˜ ์—ญํ•  30 3. ์ง์—…์†Œ๊ฐœ์†Œ ๋ฐ ์ž์„ ๋‹จ์ฒด: ๋ถ„๋ฐฐ์ž์˜ ์—ญํ•  34 ์ œ 2 ์ ˆ ์กฐ์„ ์กฑ ๋Œ๋ด„๋…ธ๋™์ž๋ฅผ ๋‘˜๋Ÿฌ ์‹ผ ์ดํ•ด๊ด€๊ณ„์ž๋“ค 37 1. ์ •๋ถ€ ๊ธฐ๊ด€: ํ†ตํ•ฉ์  ๊ด€๋ฆฌ์ž์˜ ์—ญํ•  37 2. ์กฐ์„ ์กฑ ๊ณ ์šฉ ๊ฐ€์ •: ๋™์กฐ์ž์˜ ์—ญํ•  42 3. ์ง์—…์†Œ๊ฐœ์†Œ ๋ฐ ์ด์ฃผ์ž ๊ตํšŒ: ๋ถ„๋ฐฐ์ž์˜ ์—ญํ•  46 ์ œ 5 ์žฅ ํ–‰์œ„์ž๋“ค๋กœ ๊ตฌ์„ฑ๋œ ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง์˜ ์ถœํ˜„ 51 ์ œ 1 ์ ˆ ์ด์ฃผ์˜ ์—ฌ์„ฑํ™”์— ๋”ฐ๋ฅธ ๋ ˆ์ง ์ถœํ˜„ 51 1. ์‹๋ชจ์™€ ์กฐ์„ ์กฑ ๋Œ๋ด„๋…ธ๋™์ž์˜ ์  ๋”ํ™”๋œ ์ด๋™ 51 2. ์  ๋”ํ™”๋œ ์ด๋™์„ ๋‘˜๋Ÿฌ ์‹ผ ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง์˜ ํƒ„์ƒ 56 ์ œ 2 ์ ˆ ์ด์ฃผ ๋Œ๋ด„๋ ˆ์ง์˜ ๋ณ€์ฒœ๊ณผ ๊ฐˆ๋“ฑ 61 1. ๋ ˆ์ง ํ–‰์œ„์ž๊ฐ„ ๊ถŒ๋ ฅ ์œ„๊ณ„์˜ ๋ณ€ํ™” 61 2. ์‹œ๋ฏผ๋‹จ์ฒด๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ๋Œ€ํ•ญ ์„ธ๋ ฅ์˜ ๋ฐœ๋‹ฌ 67 ์ œ 6 ์žฅ ๊ฒฐ๋ก  72 ์ฐธ๊ณ  ๋ฌธํ—Œ 76 Abstract 83Maste

    The effect of pine nut oil on the development of hepatic steatosis in high-fat diet-induced obese mice

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‹ํ’ˆ์˜์–‘ํ•™๊ณผ, 2013. 2. ํ•œ์„ฑ๋ฆผ.๋น„๋งŒ ์ธ๊ตฌ์˜ ์ฆ๊ฐ€์™€ ํ•จ๊ป˜ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์˜ ์œ ๋ณ‘๋ฅ ์ด ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๋†’์€ ์ˆ˜์น˜๋ฅผ ๊ธฐ๋กํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ผ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์˜ ์น˜๋ฃŒ ๋ฐ ์˜ˆ๋ฐฉ์„ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์‹์ด์ค‘์žฌ ๋ฐฉ๋ฒ•์˜ ๊ฐœ๋ฐœ์ด ์š”๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์žฃ๊ธฐ๋ฆ„์€ ์‹์š• ์กฐ์ ˆ, ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๊ฐ•ํ•˜ ๋“ฑ์˜ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ์žฃ๊ธฐ๋ฆ„์ด ์ฒด์ค‘ ์กฐ์ ˆ ๋ฐ ๊ฐ„ ์ง€๋ฐฉ ์ถ•์  ์–ต์ œ์—๋„ ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค๋Š” ๋ณด๊ณ ๊ฐ€ ์žˆ์œผ๋‚˜ ๊ทธ ์ˆ˜๊ฐ€ ์ œํ•œ์ ์ด๋ฉฐ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ์ง€๋ฐฉ ์‹์ด ์ค‘ ์ผ๋ถ€๋ฅผ ์žฃ๊ธฐ๋ฆ„์œผ๋กœ ๋Œ€์ฒดํ•˜์˜€์„ ๋•Œ ์žฃ๊ธฐ๋ฆ„์ด ๊ณ ์ง€๋ฐฉ ์‹์ด๋กœ ์œ ๋„ํ•œ ๊ฐ„ ์ง€๋ฐฉ์ฆ์„ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. 5์ฃผ๋ น C57BL ๋งˆ์šฐ์Šค์—๊ฒŒ ์ฝฉ๊ธฐ๋ฆ„ ๋˜๋Š” ์žฃ๊ธฐ๋ฆ„์œผ๋กœ ์ง€๋ฐฉ ๊ธ‰์›์„ ๋‹ฌ๋ฆฌํ•œ ๊ณ ์ง€๋ฐฉ ์‹์ด ๋˜๋Š” ์ผ๋ฐ˜ ์‹์ด๋ฅผ 12์ฃผ๊ฐ„ ๊ณต๊ธ‰ํ•˜์˜€๋‹ค. ๊ณ ์ง€๋ฐฉ ์‹์ด๋Š” 45% ์นผ๋กœ๋ฆฌ๋ฅผ ์ง€๋ฐฉ์—์„œ ๊ณต๊ธ‰ํ•˜๋ฉฐ ์ด์ค‘ ์ด ์ค‘ 10% (S10, P10), 20% (S20, P20), 30% (S30, P30)๋ฅผ ์ฝฉ๊ธฐ๋ฆ„ ๋˜๋Š” ์žฃ๊ธฐ๋ฆ„์œผ๋กœ ๋Œ€์ฒดํ•˜๊ณ  ๋‚˜๋จธ์ง€๋Š” ๋ผ์•„๋“œ๋กœ ๊ณต๊ธ‰ํ•˜์˜€๋‹ค. ์ผ๋ฐ˜ ์‹์ด๊ตฐ์€ 10% ์นผ๋กœ๋ฆฌ๋ฅผ ์ฝฉ๊ธฐ๋ฆ„ (SC) ๋˜๋Š” ์žฃ๊ธฐ๋ฆ„ (PC)์œผ๋กœ ๊ณต๊ธ‰ํ•˜์˜€๋‹ค. ์ฒด์ค‘, ์‹์ด ์„ญ์ทจ๋Ÿ‰, ๊ฐ„ ์ง€์งˆ ๋†๋„, ๊ฐ„์—์„œ ์ง€๋ฐฉํ•ฉ์„ฑ ๋ฐ ์‚ฐํ™” ๊ด€๋ จ ์œ ์ „์ž์˜ mRNA ์ˆ˜์ค€, ๋ฐฑ์ƒ‰์ง€๋ฐฉ์—์„œ SIRT3 ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„๋Ÿ‰์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ๊ตฐ ์ค‘ P10, P20, P30 ๊ตฐ์€ ๊ฐ๊ฐ S10, S20, S30 ๊ตฐ์— ๋น„ํ•ด ๊ฐ๊ฐ ์ฒด์ค‘ ์ฆ๊ฐ€๋Ÿ‰ ๋ฐ ๋ฐฑ์ƒ‰ ์ง€๋ฐฉ ๋ฌด๊ฒŒ๊ฐ€ ์ ์–ด, ๊ณ ์ง€๋ฐฉ ์‹์ด๋กœ ์œ ๋„ํ•œ ๋น„๋งŒ ๋งˆ์šฐ์Šค์—์„œ ์žฃ๊ธฐ๋ฆ„์ด ์ฝฉ๊ธฐ๋ฆ„์— ๋น„ํ•ด ์ฒด์ค‘ ์ฆ๊ฐ€ ๋ฐ ๋ฐฑ์ƒ‰ ์ง€๋ฐฉ ์ถ•์ ์„ ์–ต์ œํ•˜์˜€์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ์—์„œ๋„ PC ๊ตฐ์ด SC ๊ตฐ์— ๋น„ํ•ด ๋ฐฑ์ƒ‰ ์ง€๋ฐฉ ๋ฌด๊ฒŒ๊ฐ€ ์ ์—ˆ๋‹ค. ํ•œํŽธ, ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ๊ตฐ ์ค‘ P30 ๊ตฐ์€ S30 ๊ตฐ๋ณด๋‹ค ์นผ๋กœ๋ฆฌ ์„ญ์ทจ๋Ÿ‰์ด ์œ ์˜์ ์œผ๋กœ ์ ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜ ๊ทธ ์™ธ P10, P20๊ตฐ๊ณผ ์žฃ๊ธฐ๋ฆ„ ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ์ธ PC๊ตฐ์€ ๊ฐ ๋Œ€์กฐ๊ตฐ๊ณผ ์นผ๋กœ๋ฆฌ ์„ญ์ทจ๋Ÿ‰์— ์œ ์˜์ ์ด ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ๊ฐ„ ์ค‘์„ฑ์ง€๋ฐฉ ๋†๋„๋Š” ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ๊ตฐ ์ค‘ P10 ๊ตฐ์—์„œ S10 ๊ตฐ์— ๋น„ํ•ด ์œ ์˜์ ์œผ๋กœ ๋‚ฎ์•˜์œผ๋ฉฐ, ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ๊ณผ ๋น„์Šทํ•œ ์ˆ˜์ค€์ด์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์ถ”ํ›„ ์žฃ๊ธฐ๋ฆ„์˜ ๊ฐ„ ์ง€๋ฐฉ์ฆ ์™„ํ™” ํšจ๊ณผ์— ๊ธฐ์—ฌํ•œ ์„ธ๋ถ€ ๊ธฐ์ „์— ๋Œ€ํ•œ ๋ถ„์„์€ ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ์˜ SC, PC ๊ตฐ๊ณผ ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ๊ตฐ์˜ S10, P10 ๊ตฐ์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์žฃ๊ธฐ๋ฆ„ ์„ญ์ทจ๊ตฐ์€ ์ „๋ฐ˜์ ์œผ๋กœ ๊ฐ„ ์กฐ์ง์—์„œ Acadl (long-chain acyl-CoA dehydrogenase) mRNA ์ˆ˜์ค€์ด ๋†’์•˜๋‹ค. ๋”ฐ๋ผ์„œ, P10 ๊ตฐ์—์„œ ๊ฐ„ ์ค‘์„ฑ์ง€๋ฐฉ ์ถ•์ ์ด ๋‚ฎ์€ ๊ฒƒ์€ ์ง€๋ฐฉ์‚ฐํ™”์˜ ์ฆ๊ฐ€์— ์ผ๋ถ€ ๊ธฐ์ธํ•˜์˜€์„ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. ๊ฐ„ ์กฐ์ง์—์„œ Pparg (peroxisome proliferator activated receptor gamma) mRNA์˜ ์ˆ˜์ค€์€ PC ๊ตฐ์—์„œ SC ๊ตฐ์— ๋น„ํ•ด ์œ ์˜์ ์œผ๋กœ ๋‚ฎ์•˜๋‹ค. ๋˜ํ•œ, ์นผ๋กœ๋ฆฌ์ œํ•œ ์‹์ด ์„ญ์ทจ ์‹œ ๋ฐœํ˜„์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ SIRT (sirtuin)3 ๋‹จ๋ฐฑ์งˆ์ด S10 ๊ตฐ์˜ ๋ฐฑ์ƒ‰์ง€๋ฐฉ ์กฐ์ง์—์„œ๋Š” ํ˜„์ €ํ•˜๊ฒŒ ์ €ํ•˜๋˜์–ด ์žˆ์—ˆ๋˜ ๋ฐ˜๋ฉด, P10 ๊ตฐ์—์„œ๋Š” ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ์—์„œ์™€ ๋น„์Šทํ•œ ์ˆ˜์ค€์„ ์œ ์ง€ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ์žฃ๊ธฐ๋ฆ„์ด ๋ฐฑ์ƒ‰์ง€๋ฐฉ ์กฐ์ง์—์„œ ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ์— ๋”ฐ๋ฅธ SIRT3 ์˜ ๋ฐœํ˜„ ๊ฐ์†Œ๋กœ ์ธํ•ด ์ดˆ๋ž˜๋˜๋Š” ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ ์ €ํ•˜ ๋ฐ ์†์ƒ์„ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์žฃ๊ธฐ๋ฆ„์˜ ์„ญ์ทจ๊ฐ€ ๊ณ ์ง€๋ฐฉ ์‹์ด ๋ฐ ์ผ๋ฐ˜ ์‹์ด ์„ญ์ทจ๊ตฐ์—์„œ ์ฒด์ง€๋ฐฉ ์ถ•์ ์„ ์–ต์ œํ•˜๊ณ , ๊ณ ์ง€๋ฐฉ ์‹์ด๋กœ ์œ ๋„ํ•œ ๋น„๋งŒ์—์„œ ๊ฐ„ ์ง€๋ฐฉ์ฆ์„ ์™„ํ™”ํ•˜์˜€์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์žฃ๊ธฐ๋ฆ„ ๋Œ€์ฒด๊ฐ€ ์นผ๋กœ๋ฆฌ์ œํ•œ๊ณผ ๋น„์Šทํ•œ ํšจ๊ณผ๋ฅผ ์œ ๋ฐœํ•จ์œผ๋กœ์จ ๊ณ ์ง€๋ฐฉ ์‹์ด ์„ญ์ทจ์— ๋”ฐ๋ฅธ ์ฒด์ง€๋ฐฉ ์ถ•์ ์„ ์–ต์ œํ•˜๊ณ  ์ „๋ฐ˜์ ์ธ ์ฒด๋‚ด ์—๋„ˆ์ง€ ๋Œ€์‚ฌ๋ฅผ ๊ฐœ์„ ํ•œ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.๊ตญ๋ฌธ์ดˆ๋ก ----------------------------------------------------------------------------- โ…ฐ ๋ชฉ์ฐจ ------------------------------------------------------------------------------------ โ…ณ ํ‘œ ๋ชฉ์ฐจ ------------------------------------------------------------------------------- โ…ต ๊ทธ๋ฆผ ๋ชฉ์ฐจ ---------------------------------------------------------------------------- โ…ถ ๋ถ€๋ก ๋ชฉ์ฐจ ---------------------------------------------------------------------------- โ…ธ โ… . ์„œ๋ก  1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ------------------------------------------------------------------- 1 2. ์—ฐ๊ตฌ ๋ชฉ์  -------------------------------------------------------------------- 3 โ…ก. ๋ฌธํ—Œ ๊ณ ์ฐฐ 1. ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์˜ ๋ณ‘๋ฆฌ ------------------------------------------------ 4 2. ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์˜ ๋ฐœ์ƒ๊ณผ ๊ด€๋ จ๋œ ์ง€ํ‘œ -------------------------- 12 3. ์žฃ๊ธฐ๋ฆ„์˜ ํŠน์„ฑ ๋ฐ ๊ตฌ์„ฑ ์„ฑ๋ถ„ ----------------------------------------- 17 4. ์žฃ๊ธฐ๋ฆ„์˜ ํšจ๋Šฅ ------------------------------------------------------------- 20 โ…ข. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 1. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ์‹คํ—˜ ๋™๋ฌผ ์‚ฌ์œก --------------------------------------- 24 2. ์‹คํ—˜ ์‹์ด ------------------------------------------------------------------ 26 3. ์‹œ๋ฃŒ ์ฑ„์ทจ ------------------------------------------------------------------ 29 4. ์‹คํ—˜ ๋ฐฉ๋ฒ• ------------------------------------------------------------------ 30 5. ํ†ต๊ณ„ ๋ถ„์„ ------------------------------------------------------------------ 42 โ…ฃ. ์‹คํ—˜ ๊ฒฐ๊ณผ 1. ์ฒด์ค‘, ๋ฐฑ์ƒ‰ ์ง€๋ฐฉ ๋ฐ ๊ฐ„ ๋ฌด๊ฒŒ, ์‹์ด ์„ญ์ทจ๋Ÿ‰ ----------------------- 43 2. ํ˜ˆ์ฒญ leptin ๋†๋„ --------------------------------------------------------- 47 3. ํ˜ˆ์ค‘ ์ง€์งˆ ๋†๋„ ----------------------------------------------------------- 49 4. ๊ฐ„ ์ง€์งˆ ๋†๋„ -------------------------------------------------------------- 51 5. ํ˜ˆ์ค‘ fetuin-A ๋†๋„ ๋ฐ ๊ฐ„ ์กฐ์ง์—์„œ fetuin-A mRNA ์ˆ˜์ค€- 53 6. ๊ฐ„ ์กฐ์ง์—์„œ ์ง€๋ฐฉ์‚ฐํ™” ๊ด€๋ จ ์ง€ํ‘œ๋“ค์˜ mRNA ์ˆ˜์ค€ ----------- 55 7. ๊ฐ„ ์กฐ์ง์—์„œ ์ง€๋ฐฉํ•ฉ์„ฑ ๊ด€๋ จ ์ง€ํ‘œ๋“ค์˜ mRNA ์ˆ˜์ค€ ----------- 57 8. ๋ฐฑ์ƒ‰ ์ง€๋ฐฉ ์กฐ์ง์—์„œ SIRT3์˜ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„ -------------------- 59 โ…ค. ๊ณ ์ฐฐ ------------------------------------------------------------------------------- 61 โ…ฅ. ์š”์•ฝ --------------------------------------------------------------------------------- 68 ์ฐธ๊ณ  ๋ฌธํ—Œ ----------------------------------------------------------------------------- 70 ์•ฝ์–ด ๋ชฉ๋ก ----------------------------------------------------------------------------- 77 ๋ถ€๋ก ------------------------------------------------------------------------------------ 79 ์˜๋ฌธ์ดˆ๋ก ---------------------------------------------------------------------------- 100Maste

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์–ธ๋ก ์ •๋ณดํ•™๊ณผ, 2015. 2. ์œค์„๋ฏผ.์ด ๋…ผ๋ฌธ์€ ๋ฏธ๋””์–ด๊ฐ€ ๊ณต์ค‘์— ๋ฏธ์น˜๋Š” ์˜์ œ์„ค์ •ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•œ ์—ฐ๊ตฌ ๋ฐ ๋ฏธ๋””์–ด๊ฐ€ ์ •์ฑ…๊ณผ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋ ฅ์„ ํƒ๊ตฌํ•œ ์ •์ฑ…์˜์ œ์„ค์ • ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ, ๋ณ€ํ™”ํ•˜๋Š” ๋ฏธ๋””์–ด ํ™˜๊ฒฝ ์†์—์„œ ํฌํ„ธ ์˜์ œ, ์‹ ๋ฌธ๊ณผ TV ์˜์ œ๋ฅผ ํฌํ•จํ•˜๋Š” ์ „ํ†ต์ ์ธ ๋ฏธ๋””์–ด ์˜์ œ, ๋Œ€ํ†ต๋ น์˜ ์ •์ฑ…์˜์ œ๊ฐ€ ์„œ๋กœ ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๋Š” ๊ตญ๋ฉด์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•ด๋ณด์•˜๋‹ค. ์ด๋Š” ๋‰ด๋ฏธ๋””์–ด์˜ ๋“ฑ์žฅ์œผ๋กœ ์ธํ•ด ๊ณต๊ณต ์ด์Šˆ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์Šต๋“ํ•˜๊ฑฐ๋‚˜ ์ƒ์‚ฐ ๋ฐ ์ „ํŒŒํ•˜๋Š” ๊ณผ์ •์—์„œ ์ „ํ†ต ๋ฏธ๋””์–ด์— ๋Œ€ํ•œ ์˜์กด๋„๊ฐ€ ๊ฐ์†Œํ•˜๋ฉฐ ์‚ฌํšŒ์˜ ์˜์ œ์ฃผ์ฒด๊ฐ€ ๋‹ค์–‘ํ™”๋˜๊ณ  ์žˆ๋Š” ๊ฐ€์šด๋ฐ ํฌํ„ธ ๋ฏธ๋””์–ด์˜ ๋“ฑ์žฅ๊ณผ, ํฌํ„ธ์ด ๊ฒฐ์ง‘ํ•˜๋Š” ์˜จ๋ผ์ธ ์—ฌ๋ก ๊ณต์ค‘์˜ ์—ญํ• ์ด ์ •์ฑ…์˜์ œ ํ˜•์„ฑ ๊ณผ์ •์—์„œ ์ƒˆ๋กœ์šด ์˜์ œ๊ถŒ๋ ฅ์œผ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ๋‹ค๋Š” ๊ฐ€์ •์— ์ดˆ์ ์„ ๋‘” ๊ฒƒ์ด๋‹ค. ํŠนํžˆ ์ด๋Ÿฌํ•œ ์ƒํ˜ธ์ž‘์šฉ์„ฑ์ด ํ†ต์ผ, ํ™˜๊ฒฝ, ๋ณต์ง€ ๋“ฑ ๊ณ ์œ ํ•œ ์ •์ฑ…์˜์ œ ์ž์ฒด์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ๊ฒƒ์ด๋ผ๊ณ  ํŒ๋‹จํ•˜๊ณ , ํ†ต์ผยท์™ธ๊ตยท์•ˆ๋ณด, ์ •์น˜(๋Œ€ํ†ต๋ น ๋ฐ ์ •๋ถ€์กฐ์ง ์ฐจ์›/ ๊ณต๊ธฐ์—…ยท๊ณต๊ณต๊ธฐ๊ด€ ๋“ฑ ๊ณต์ ์˜์—ญ/ ๊ตญํšŒ ์ฐจ์›/ ์ „์‚ฌํšŒ์  ์ฐจ์›), ๊ฒฝ์ œ(๊ฑฐ์‹œ์ ์ธ ์ •๋ถ€๊ฒฝ์ œ์ •์ฑ…/ ์ •๋ถ€ ๊ฒฝ์ œ๊ธฐ์กฐ/ ์ •๋ถ€ ์™ธ์  ๊ฒฝ์ œ์š”์†Œ), ๊ต์œก, ๋ณด๊ฑดยท๋ณต์ง€ยท๊ณ ์šฉ, ๋ฌธํ™”ยท์ฒด์œกยท๊ด€๊ด‘, ํ™˜๊ฒฝ, ๊ณต๊ณต์งˆ์„œยท์•ˆ์ „์ด๋ผ๋Š” ์ด 13๊ฐœ์˜ ์ •์ฑ…์œ ํ˜•์„ ์„ค์ •ํ–ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ฐ์˜ ๋ฒ”์ฃผ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์˜์ œ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ฑ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ์‹ ๋ฌธ์˜์ œ, ๋ฐฉ์†ก์˜์ œ, ๋Œ€ํ†ต๋ น์˜์ œ, ํฌํ„ธ๋ฏธ๋””์–ด์˜์ œ, ํฌํ„ธ๊ณต์ค‘์˜์ œ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์˜์ œ ํ˜„์ €์„ฑ์„ ์ธก์ •ํ•œ ํ›„, ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ํ† ๋Œ€๋กœ VAR ๋ถ„์„ ๋ฐ ๊ทธ๋žœ์ € ์ธ๊ณผ๊ด€๊ณ„ ๊ฒ€์ •์„ ์‹ค์‹œํ•˜์—ฌ ์˜์ œ์ฃผ์ฒด ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์˜ ์—ญ๋™์„ฑ์„ ์‹ค์ฆ์ ์œผ๋กœ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. (์—ฐ๊ตฌ๋ฌธ์ œ 1) ์ •์ฑ…์˜์ œ์„ค์ • ๊ณผ์ •์—์„œ ์ „ํ†ต์  ๋ฏธ๋””์–ด์˜์ œ, ๋Œ€ํ†ต๋ น ์ •์ฑ…์˜์ œ, ํฌํ„ธ ํŽธ์ง‘์ž์˜์ œ, ํฌํ„ธ ๊ณต์ค‘์˜์ œ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ฑ์€ ๊ฐ ์ •์ฑ…์˜์ œ ๋ฒ”์ฃผ์˜ ์ฐจ์ด์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”๊ฐ€? (์—ฐ๊ตฌ๋ฌธ์ œ 2) ์ „ํ†ต์  ๋ฏธ๋””์–ด(์‹ ๋ฌธ๊ณผ ๋ฐฉ์†ก), ๋Œ€ํ†ต๋ น, ๋‰ด๋ฏธ๋””์–ด(ํฌํ„ธ ๋ฏธ๋””์–ด, ํฌํ„ธ ๊ณต์ค‘) ์ค‘์—์„œ ์ •์ฑ…์˜์ œ์„ค์ •๊ณผ์ •์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๋Š” ์˜์ œ์ฃผ์ฒด๋Š” ๋ฌด์—‡์ธ๊ฐ€? ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์šฐ์„  ์ •์ฑ… ๋ฒ”์ฃผ์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๊ฐ ์˜์ œ์ฃผ์ฒด ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์˜ ์—ญ๋™์„ฑ์ด ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐ€์žฅ ํ™œ๋ฐœํ•œ ์ƒํ˜ธ์˜ํ–ฅ๋ ฅ์ด ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ ์ •์ฑ… ์œ ํ˜•์€ ๊ฒฝ์ œ์˜์ œ์˜ ํ•˜์œ„์œ ํ˜•์ธ ์ •๋ถ€์˜ ๊ฒฝ์ œ๊ธฐ์กฐ, ์ •์น˜์˜์ œ์˜ ํ•˜์œ„์œ ํ˜•์ธ ์ „์‚ฌํšŒ์  ์ฐจ์›์˜ ์ •์Ÿ, ๊ทธ๋ฆฌ๊ณ  ํ™˜๊ฒฝ ๋ฐ ๊ณต๊ณต์งˆ์„œยท์•ˆ์ „ ๊ด€๋ จ ์ •์ฑ…์˜์—ญ์ด์—ˆ๋‹ค. ๋ฐ˜๋ฉด ํ†ต์ผยท์™ธ๊ตยท์•ˆ๋ณด ์ •์ฑ…์œ ํ˜•๊ณผ ๊ต์œก, ๋ณด๊ฑดยท๋ณต์ง€ยท๊ณ ์šฉ ๊ด€๋ จ ์ •์ฑ…์˜์ œ๋Š” ์˜์ œ์ฃผ์ฒด ๊ฐ„ ํ™œ๋ฐœํ•œ ์ƒํ˜ธ์ž‘์šฉ์ด ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๋”๋ถˆ์–ด ์ „๋ฐ˜์ ์ธ ์˜์ œ ์ƒํ˜ธ์ž‘์šฉ ๊ณผ์ •์—์„œ ์ „ํ†ต์  ๋ฏธ๋””์–ด์˜์ œ๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ์‹ ๋ฌธ์˜์ œ์™€ ๋ฐฉ์†ก์˜์ œ๋Š” ์„œ๋กœ์—๊ฒŒ์„œ๋ณด๋‹ค ํฌํ„ธ ๋ฏธ๋””์–ด ๋ฐ ํฌํ„ธ ๊ณต์ค‘์˜์ œ์™€ ๋” ํฐ ์ƒํ˜ธ ์˜ํ–ฅ๋ ฅ์„ ์ฃผ๊ณ ๋ฐ›๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์˜จ๋ผ์ธ๋ฏธ๋””์–ด์˜์ œ์™€ ์˜จ๋ผ์ธ๊ณต์ค‘์˜์ œ๊ฐ€ ์ •์ฑ…์˜์ œํ˜•์„ฑ๊ณผ์ •์— ๋” ํฐ ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐœํŒ์ด ๋งˆ๋ จ๋˜์—ˆ๋‹ค๋Š” ์ถ”์ •์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ๋‘˜์งธ๋กœ, ์ •์ฑ…์˜์ œ ํ˜•์„ฑ ๊ณผ์ •์—์„œ ์‹ ๊ตฌ ๋ฏธ๋””์–ด ์ง€ํ˜•์—์„œ ์˜์ œ๊ถŒ๋ ฅ์˜ ๋ฐฐ๋ถ„์ด ๊ฐ€์†ํ™”๋˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ ๋ฌธ๊ณผ ๋ฐฉ์†ก, ๋Œ€ํ†ต๋ น, ํฌํ„ธ ๋ฏธ๋””์–ด, ํฌํ„ธ ๊ณต์ค‘ ์ค‘ ์–ด๋–ค ์˜์ œ์ฃผ์ฒด๊ฐ€ ๊ฐ๊ฐ์˜ ์ •์ฑ…์˜์ œํ˜•์„ฑ๊ณผ์ •์—์„œ ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๋Š”์ง€ ๋ถ„์„์„ ํ†ตํ•ด ๋ฐํžˆ๊ณ ์ž ํ–ˆ๋Š”๋ฐ, ์‚ฌ์ „์— ๋Œ€ํ†ต๋ น์ด ์ฃผ๋„ํ•˜๋ฆฌ๋ผ๊ณ  ๊ฐ€์ •๋๋˜ ํ†ต์ผยท์™ธ๊ตยท๊ตญ๋ฐฉ, ๋Œ€ํ†ต๋ น ๋ฐ ์ •๋ถ€์กฐ์ง ๊ด€๋ จ ์ •์น˜ ์˜์ œ, ์ •๋ถ€๊ฒฝ์ œ๊ธฐ์กฐ ๊ด€๋ จ ๊ฒฝ์ œ ์˜์ œ ์œ ํ˜•์—์„œ ๋Œ€ํ†ต๋ น์˜ ์ฃผ๋„์ ์ธ ์˜ํ–ฅ๋ ฅ์ด ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜๋‹ค. ์˜คํžˆ๋ ค ์˜์ œ์„ค์ • ๊ณผ์ •์—์„œ ์˜จ๋ผ์ธ ์˜์—ญ์˜ ์•ฝ์ง„์ด ์ฃผ๋ชฉํ•  ๋งŒํ•œ ๊ฒฐ๊ณผ์˜€๋Š”๋ฐ, ํฌํ„ธ๋ฏธ๋””์–ด์˜์ œ์™€ ํฌํ„ธ๊ณต์ค‘์˜์ œ๋Š” ์ด 13๊ฐœ๋กœ ์„ค์ •ํ•œ ์ •์ฑ…์œ ํ˜• ์ค‘ ํ†ต์ผยท์™ธ๊ตยท์•ˆ๋ณด, ๋ณด๊ฑดยท๋ณต์ง€ยท๊ณ ์šฉ, ๋ฌธํ™”ยท์ฒด์œกยท๊ด€๊ด‘, ์ •์น˜์˜์ œ(์ „ ์‚ฌํšŒ์  ์ •์Ÿ ์ฐจ์›)์˜ 4๊ฐœ ์˜์ œ๋ฅผ ์ฃผ๋„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ๊ฐ€์žฅ ์˜ํ–ฅ๋ ฅ ์žˆ๋Š” ์˜์ œ์„ค์ •์ž๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ํƒ์ƒ‰์ ์œผ๋กœ๋‚˜๋งˆ ์ •์ฑ…์œ ํ˜• ์ž์ฒด์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์ด ์˜์ œ์„ค์ •๊ณผ์ •์— ๊ด€์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ์š”์†Œ๋ผ๋Š” ์ ์„ ์—ผ๋‘์— ๋‘๊ณ  ๊ฑฐ์‹œ์ ์ธ ์ˆ˜์ค€์—์„œ ์ •์ฑ…์˜์ œ์„ค์ •๊ณผ์ •์˜ ์—ญ๋™์„ฑ์„ ์‚ดํŽด๋ณด๊ณ ์ž ํ–ˆ๋‹ค. ๋˜ํ•œ ํ•œ๊ตญ์‚ฌํšŒ์—์„œ ๊ฑฐ๋Œ€ํ•œ ์—ฌ๋ก  ๊ถŒ๋ ฅ์œผ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋Š” ํฌํ„ธ ๋ฏธ๋””์–ด๋ฅผ ์—ฐ๊ตฌ๋Œ€์ƒ์œผ๋กœ ์„ค์ •ํ•ด ํฌํ„ธ์ด ์˜์ œ์„ค์ •๊ณผ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋ ฅ์„ ์‹ค์ฆํ•˜๋Š” ๊ณผ์ •์—์„œ ํ˜„ ์‚ฌํšŒ์˜ ๊ฑฐ์‹œ์ ์ธ ์˜์ œ๊ถŒ๋ ฅ์ด ๋Œ€ํ†ต๋ น๊ณผ ์ „ํ†ต์  ๋ฏธ๋””์–ด๋ฟ ์•„๋‹ˆ๋ผ ๋‰ด๋ฏธ๋””์–ด์—๋„ ๋ถ„๋ฐฐ๋˜๊ณ  ์žˆ๋Š” ๋งฅ๋ฝ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค.์ œ 1์žฅ ์„œ๋ก  1 ์ œ 1์ ˆ ๋ฌธ์ œ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ๋ชฉ์  1 ์ œ 2์žฅ ๊ธฐ์กด ์—ฐ๊ตฌ ๊ฒ€ํ†  ๋ฐ ์ด๋ก ์  ๋…ผ์˜ 4 ์ œ 1์ ˆ ์ •์ฑ…์˜์ œ์„ค์ • ์—ฐ๊ตฌ์˜ ์ „๊ฐœ ๋ฐ ๋ฐœ์ „ 4 1. ์ •์ฑ…์˜์ œ์„ค์ • ๊ณผ์ •์— ์ˆ˜๋ฐ˜๋˜๋Š” ์กฐ๊ฑด๋ณ€์ธ์˜ ํƒ๊ตฌ 7 2. ์ •์ฑ…์˜์ œ์„ค์ • ๊ณผ์ •์˜ ์—ญ๋™์„ฑ ๊ทœ๋ช… 10 3. ์ •์ฑ…์˜์ œ์„ค์ • ๊ณผ์ •์—์„œ ๊ณต์ค‘์˜ ์ฃผ์ฒด์  ์—ญํ•  ์กฐ๋ช… 12 ์ œ 2์ ˆ ๋‰ด๋ฏธ๋””์–ด์˜ ๋“ฑ์žฅ ๋ฐ ์ •์ฑ…์˜์ œ ์„ค์ •๊ณผ ์ •์˜์˜ ๋ณ€ํ™” 17 1. ๋‰ด๋ฏธ๋””์–ด์˜ ๋“ฑ์žฅ: ์ •์น˜์˜ ๋ฏธ๋””์–ดํ™”, ๊ทธ๋ฆฌ๊ณ  ์˜์ œ์„ค์ •๊ณผ์ •์—์„œ ๊ณต์ค‘ ์—ญํ• ์˜ ๋ณ€ํ™” 17 2. ํฌํ„ธ ์˜์ œ์„ค์ •: ๋ฏธ๋””์–ด ํ”Œ๋žซํผ ๋‹จ์œ„์—์„œ์˜ ์˜์ œ์„ค์ • ์˜ํ–ฅ๋ ฅ ๋…ผ์˜์™€ ์˜จ๋ผ์ธ ๊ณต์ค‘์˜ ์ผ์ƒ์  ์˜์ œํ˜•์„ฑ ํ™œ๋™ 21 ์ œ 3์ ˆ ์ •์ฑ… ์ด์Šˆ ์œ ํ˜•์— ๋”ฐ๋ฅธ ์˜์ œ ์˜ํ–ฅ๋ ฅ์˜ ๋ณ€ํ™” 27 ์ œ 3์žฅ ์—ฐ๊ตฌ ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ ๊ฐ€์„ค 33 ์ œ 4์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 36 ์ œ 1์ ˆ ์—ฐ๊ตฌ ๋Œ€์ƒ ์„ ์ • ๋ฐ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 36 1. ๋ถ„์„ ๋Œ€์ƒ ์„ ์ • ๋ฐ ๋ถ„์„ ๊ธฐ๊ฐ„ 36 2. ๋ถ„์„ ๋Œ€์ƒ ๋ณ€์ธ์˜ ์˜์ œ ํ˜„์ €์„ฑ ์ธก์ • 39 ์ œ 2์ ˆ ์—ฐ๊ตฌ ์ ˆ์ฐจ 41 ์ œ 5์žฅ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 46 ์ œ 1์ ˆ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 46 ์ œ 2์ ˆ ์ •์ฑ… ์œ ํ˜•๋ณ„ ์˜์ œ๋ณ€์ˆ˜ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ฑ 48 1. ์ •์ฑ… ์œ ํ˜•๋ณ„ ์˜์ œ๋ณ€์ˆ˜ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ฑ ๋ถ„์„ 48 2. ์†Œ๊ฒฐ 56 ์ œ 3์ ˆ ์ •์ฑ…์˜์ œ์„ค์ • ๊ณผ์ •์— ๋ฏธ์น˜๋Š” ๊ฐ ์˜์ œ์ฃผ์ฒด์˜ ์˜ํ–ฅ๋ ฅ ๋ถ„์„(์—ฐ๊ตฌ๋ฌธ์ œ 2) 58 1. ์ •์ฑ…์˜์ œ์„ค์ •๊ณผ์ •์„ ์ฃผ๋„ํ•˜๋Š” ์˜์ œ์ฃผ์ฒด ๋ถ„์„ 58 2. ์˜์ œ์˜ํ–ฅ๋ ฅ์˜ ๊ฐ•๋„, ์†๋„ ๋ฐ ์ง€์†๋„ ๋น„๊ต 66 1) ์ •์ฑ…๊ฒฐ์ •์ž์˜์ œ์˜ ์˜ํ–ฅ๋ ฅ 66 2) ์ „ํ†ต์  ๋ฏธ๋””์–ด์˜์ œ์˜ ์˜ํ–ฅ๋ ฅ 67 3) ๋‰ด๋ฏธ๋””์–ด์˜์ œ์˜ ์˜ํ–ฅ๋ ฅ 70 4) ์˜์ œ ์˜ํ–ฅ๋ ฅ์˜ ๋ณตํ•ฉ์  ์ž‘์šฉ 74 3. ์†Œ๊ฒฐ 81 ์ œ 6์žฅ ๊ฒฐ๋ก  ๋ฐ ๋…ผ์˜ 83 ์ œ 1์ ˆ ์—ฐ๊ตฌ ์š”์•ฝ ๋ฐ ํ•จ์˜ 83 ์ œ 2์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ 88 89 99Maste

    ๊ฐ„ํ˜ธ ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์™€ ์ง๋ฌด์„ฑ๊ณผ์˜ ๊ด€๊ณ„ : ์‚ฌํšŒ์  ์ง€์›์˜ ์กฐ์ ˆํšจ๊ณผ

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    ๊ฐ„ํ˜ธ๊ด€๋ฆฌ์™€๊ต์œก/์„์‚ฌ[ํ•œ๊ธ€]๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค(์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ˆ˜์ค€๊ณผ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ฆ์ƒ)๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์™€ ์ง๋ฌด์„ฑ๊ณผ์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์‚ฌํšŒ์  ์ง€์›์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ฐ„ํ˜ธ์กฐ์ง์˜ ํšจ๊ณผ์ ์ธ ์ธ์ ์ž์›๊ด€๋ฆฌ์— ๋„์›€์ด ๋˜๋Š” ์ž๋ฃŒ๋ฅผ ์ œ์‹œํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์‹œ๋„๋œ ์„œ์ˆ ์  ์กฐ์‚ฌ์—ฐ๊ตฌ์ด๋‹ค. ์—ฐ๊ตฌ ์ž๋ฃŒ๋Š” 2008๋…„ 11์›”1์ผ๋ถ€ํ„ฐ 11์›”25์ผ๊นŒ์ง€ ์„œ์šธ ์†Œ์žฌ 4๊ฐœ์˜ ์ข…ํ•ฉ๋ณ‘์›์—์„œ ๊ทผ๋ฌดํ•˜๋Š” ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž 141๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์ž๊ธฐ๊ธฐ์ž…์‹ ์งˆ๋ฌธ์ง€๋ฅผ ํ†ตํ•ด ์ˆ˜์ง‘๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๋„๊ตฌ๋Š” ์žฅ์„ธ์ง„๊ณผ ๊ณ ์ƒ๋ฐฑ(2004)์— ์˜ํ•ด ๊ฐœ๋ฐœ๋œ ํ•œ๊ตญ์ธ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ธก์ •๋„๊ตฌ( Korean Occupational Stress Scale ; KOSS), ํ•œ๊ด‘ํ˜„(1992)์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ฆ์ƒ ์ธก์ •๋„๊ตฌ, ์ผ ๋ณ‘์› ์ค‘๊ฐ„๊ด€๋ฆฌ์ž ์—ญ๋Ÿ‰ ํ‰๊ฐ€ ๋„๊ตฌ(2007)๋ฅผ์ˆ˜์ • ๋ณด์™„ํ•œ ์ง๋ฌด์„ฑ๊ณผ ์ธก์ •๋„๊ตฌ, Karasek ๋“ฑ(1982)์˜ ์‚ฌํšŒ์  ์ง€์› ์ธก์ •๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ์ž๋ฃŒ๋Š” SAS system์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ์€ ํ‰๊ท , ํ‘œ์ค€ํŽธ์ฐจ, ๋นˆ๋„, ๋ฐฑ๋ถ„์œจ ๋“ฑ์˜ ๊ธฐ์ˆ ํ†ต๊ณ„๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์™€ ์ง๋ฌด์„ฑ๊ณผ, ์‚ฌํšŒ์  ์ง€์›๊ฐ„์˜ ๊ด€๊ณ„๋Š” t-test, ANOVA, ํ”ผ์–ด์Šจ ์ƒ๊ด€๋ถ„์„์„ ํ•˜์˜€๊ณ  ์‚ฌํšŒ์  ์ง€์›์˜ ์กฐ์ ˆํšจ๊ณผ๋Š” ๋‹จ์ˆœํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ์ค‘๊ฐ„๊ด€๋ฆฌ์ž๊ฐ€ ์ธ์ง€ํ•˜๋Š” ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ˆ˜์ค€ ์ด์ ์€ 160์  ๋งŒ์ ์— 102.15์ (ยฑ 11.63์ ), ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ฆ์ƒ์€ 100์  ๋งŒ์ ์— 36.37์ (ยฑ11.39์ )์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ง๋ฌด์„ฑ๊ณผ๋Š” ์ด์  130์  ๊ธฐ์ค€์œผ๋กœ 100.36์ (ยฑ10.88์ ), ์‚ฌํšŒ์  ์ง€์›์€ ์ด์  40์  ๊ธฐ์ค€์œผ๋กœ 27.16์ (ยฑ5.22์ )์ด์—ˆ๋‹ค. 2. ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ˆ˜์ค€์€ ๊ทผ๋ฌด๋ณ‘์›์˜ ํ˜•ํƒœ์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๊ณ  ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ฆ์ƒ์€ ์ค‘๊ฐ„๊ด€๋ฆฌ์ž๊ฒฝ๋ ฅ, ๊ด€๋ฆฌ๋ณ‘์ƒ ์ˆ˜, ํ˜„ ๊ทผ๋ฌด๋ถ€์„œ์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์ง๋ฌด์„ฑ๊ณผ๋Š” ์ค‘๊ฐ„๊ด€๋ฆฌ์ž๊ฒฝ๋ ฅ๋งŒ์ด ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. 3. ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ฆ์ƒ๋„ ๋†’์•„์กŒ์œผ๋ฉฐ ์ง๋ฌด์„ฑ๊ณผ๋Š” ๋‚ฎ์•„์กŒ๋‹ค. ๋˜ํ•œ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ฆ์ƒ์ด ๋†’์„์ˆ˜๋ก ์ง๋ฌด์„ฑ๊ณผ๋Š” ๋‚ฎ์•„์กŒ์œผ๋ฉฐ, ์‚ฌํšŒ์  ์ง€์›์ด ๋†’์„์ˆ˜๋ก ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ˆ˜์ค€๊ณผ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ฆ์ƒ์€ ๋‚ฎ์•„์กŒ๊ณ  ์ง๋ฌด์„ฑ๊ณผ๋Š” ๋†’์•„์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌํšŒ์  ์ง€์› ์ค‘ ์ƒ์‚ฌ์˜ ์ง€์›์ด ๋†’์•„์ ธ๋„ ์ง๋ฌด์„ฑ๊ณผ๋Š” ๋†’์•„์ง€์ง€ ์•Š์•˜์œผ๋‚˜ ๋™๋ฃŒ์˜ ์ง€์›์ด ๋†’์œผ๋ฉด ์ง๋ฌด์„ฑ๊ณผ๋„ ๋†’์•„์ง€๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. 4. ์‚ฌํšŒ์ ์ง€์›์€ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์ˆ˜์ค€๊ณผ ์ง๋ฌด์„ฑ๊ณผ ๊ด€๊ณ„๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ ์ƒ์‚ฌ์˜ ์ง€์›๊ณผ ๋™๋ฃŒ์˜ ์ง€์›์ด ๋ชจ๋‘ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋‚˜ ๋™๋ฃŒ์˜ ์ง€์›์ด ๋” ๊ฐ•ํ•œ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ฆ์ƒ๊ณผ ์ง๋ฌด์„ฑ๊ณผ ๊ด€๊ณ„์—์„œ๋„ ์ƒ์‚ฌ์˜ ์ง€์›๋ณด๋‹ค ๋™๋ฃŒ์˜ ์ง€์›์ด ๊ฐ•ํ•œ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด ๋ณด๋ฉด, ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค์™€ ์ง๋ฌด์„ฑ๊ณผ๋Š” ๋ฐ€์ ‘ํ•œ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ์œผ๋ฉฐ ์‚ฌํšŒ์  ์ง€์›์€ ์ด๋“ค ๊ฐ„์— ์กฐ์ ˆ ํšจ๊ณผ๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž๊ฐ€ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ค‘์—์„œ๋„ ๊ฐ€์žฅ ๋†’์€ ์ˆ˜์ค€์„ ๋ณด์ธ ์ง๋ฌด๊ด€๋ จ์š”์ธ์„ ๊ตฌ์ฒด์ ์œผ๋กœ ํŒŒ์•…ํ•˜์—ฌ ์ ์ ˆํ•œ ์ˆ˜์ค€์œผ๋กœ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์•ผ ํ•˜๋ฉฐ, ์‚ฌํšŒ์  ์ง€์› ์ค‘ ๋™๋ฃŒ์˜ ์ง€์›์ด ์ง๋ฌด์„ฑ๊ณผ๋ฅผ ๋†’์ด๋Š”๋ฐ ๋” ๊ฐ•ํ•œ ์˜ํ–ฅ์ด ์žˆ์œผ๋ฏ€๋กœ ๋”์šฑ ๊ธด๋ฐ€ํ•œ ๋™๋ฃŒ ๊ฐ„์˜ ํ˜‘์กฐ ๊ด€๊ณ„๋ฅผ ์ฆ์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์กฐ์ง์ฐจ์›์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๋˜ํ•œ ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ๊ฒฝ๋ ฅ์ด ๊ฐ€์žฅ ๋‚ฎ์€ ๊ตฐ์ด ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค ์ˆ˜์ค€๊ณผ ์ฆ์ƒ์„ ๊ฐ€์žฅ ๋†’๊ฒŒ, ์ง๋ฌด์„ฑ๊ณผ๋Š” ๊ฐ€์žฅ ๋‚ฎ๊ฒŒ ์ธ์‹ํ•˜๊ณ  ์žˆ์œผ๋ฏ€๋กœ ๊ฐ„ํ˜ธ์ค‘๊ฐ„๊ด€๋ฆฌ์ž์˜ ๊ฒฝ๋ ฅ์ด ๋‚ฎ์€ ๊ตฐ์— ๋Œ€ํ•œ ์ƒ์‚ฌ์˜ ์ง€์› ๋ฐ ์กฐ์ง์˜ ๋ฐฐ๋ ค์™€ ๊ด€์‹ฌ์ด ๋”์šฑ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. [์˜๋ฌธ]The purpose of this descriptive study was to survey middle-level manager nurses for their job stress (its level and symptoms), and thereupon, analyze the relationship between their job stress and job performances as well as the moderating effects of the social supports to provide for some basic data useful to effective HR management of the nursing organization. For this purpose, the researcher sampled 141 middle-level manager nurses from 4 general hospitals in Seoul for an open-ended questionnaire survey for the period from November 1 through November 25, 2008. The survey tools used were "Korean Occupational Stress Scale" (KOSS) developed by Jang Se-jin and Ko Sang-baek (2004), "Job Stress Symptom Scale" developed by Han Kwang-hyun (1992), "a job performance scale" modified from "Middle-Level Managers' Capacity Assessment Scale" developed for a hospital and "Social Support Scale" developed by Karasek, et al. (1982). The data collected were processed using the SAS system for means, SDs, frequency and percentages about middle-level manager nurses' demographic variables, T-test, ANOVA and Pearson's correlation coefficients about the relationships among their job stress, job performance and social supports, and for the simple regression analysis about the moderating effects of the social supports. The results of this study can be summarized as follows; 1. The job stress level perceived by the middle-level manager nurses scored 102.15 (ยฑ 11.63) in total on the 160-point scale, and the symptoms of the job stress scored 36.37 (ยฑ11.39) in total on the 100-point scale. The job performance scored 100.36 (ยฑ10.88) in total on the 130-point scale and the social supports perceived by them scored 27.16 (ยฑ5.22) in total on the 40-point scale. 2. Middle-level manager nurse' job stress levels differed significantly depending on the types of their hospitals, while the symptoms of their job stress differed significantly depending on their career length, number of the beds managed by them and their current departments. Their job performance differed only depending on their career length. 3. The higher the middle-level manager nurses' job stress level was, the symptoms of their job stress were higher and their job performance was lower. In addition, the higher the symptoms of their job stress were, their job performance was lower. And the higher the social supports were, their job stress level and symptoms were lower, while their job performance was higher. However, supervisors' support among the social supports was not correlated with their job performance, while colleagues' support was positively correlated with their job performance. 4. It was found that the social supports had some moderating effects on the relationship between job stress level and job performance; both supervisors' and colleagues' supports moderated the relationship, but colleagues' support had stronger moderating effects. Colleagues' support also had stronger moderating effects on the relationship between job stress symptoms and job performance. Summing up, middle-level manager nurses' job stress was closely but negative correlated with their job performance, while the social supports moderated the correlation. Hence, it is deemed necessary to determine the job-related factors affecting middle-level manager nurses' job stress most and thereby, help them maintain their job stress at an appropriate level. Since colleagues' support among the social supports was found to have stronger moderating effect on job performances, it is also required of the hospital organizations to improve the cooperative relationships among nurses. In addition, since it was found that those middle-level manager nurses with the shortest career length showed the highest job stress level and symptoms, perceiving their job performance at the lowest level, it is deemed required of their supervisors and hospital organizations to be more considerate of and concerned about this group.ope

    A Case of Biliary Papillomatosis with Cystic Dilatation of Bile Duct

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    A 61-year-old male who complained of right upper quadrant pain was referred to the authors for evaluation after his computed tomography suggested biliary adenocarcinoma. The lesion consisted of multiple cysts with papillary mass and peri-ampullay mass. The patient underwent an operation due to a clinical suspicion of biliary cystadenocarcinoma, but the pathology confirmed biliary papillomatosis (BP) after diagnosing intrahepatic papillary neoplasm with high-grade dysplasia and invasive adenocarcinoma with papillary neoplasm from the distal common bile duct to the duodenum. BP is a disease characterized by multiple papillary masses. Its cause has yet to be discovered. It commonly manifests as bile duct dilation but rarely as a ductal cystic change. Under computed tomography or magnetic resonance imaging, both the BP and the cystic neoplasm can show bile duct dilation and a papillary mass, which makes their differential diagnosis difficult. A confirmative diagnosis can be made through a pathologic examination. BP is classified as a benign disease that can become malignant and may recur, though rarely. Its treatment of choice is surgical resection. Laser ablation or photodynamic therapy can be used for unresectable lesions. In the case featured in this paper, biliary papillomatosis was difficult to differentiate from cystic adenocarcinoma due to diffusely scattered multiple large cystic lesions in the liver, and it was histologically confirmed to have become malignant with cystic duct dilation after the operation. This case is reported herein with a literature review.ope
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