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    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„

    ๋ฃจ๋ถ€์†Œ์‚ฌ์ด๋“œ์˜ ์ƒˆ๋กœ์šด ํ•ญ์šฐ์‹ ํŠน์„ฑ ์—ฐ๊ตฌ์™€ ๋ฐฐ๋‹น์ฒด ํ•ฉ์„ฑ

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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

    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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