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    ์ž๊ฐ€ ๋ฉด์—ญ ๋‡Œ์ฒ™์ˆ˜์—ผ ๋งˆ์šฐ์Šค ๋ชจ๋ธ์—์„œ deferoxamine์„ ์ „์ฒ˜๋ฆฌํ•œ ๊ฐœ์˜ ์ง€๋ฐฉ์œ ๋ž˜ ์ค‘๊ฐ„์—ฝ์ค„๊ธฐ์„ธํฌ๋กœ๋ถ€ํ„ฐ ์œ ๋ž˜ํ•œ ์„ธํฌ์™ธ์†Œํฌ์ฒด์˜ ๋ฉด์—ญ ์กฐ์ ˆ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2023. 2. ์œคํ™”์˜.Mesenchymal stem/stromal cells (MSCs) are effective therapeutic agents that ameliorate inflammation through paracrine effect. In particular, several studies have been tried to apply MSC to autoimmune neurological diseases such as multiple sclerosis. Multiple sclerosis is a disease in which nerve damage occurs when inflammatory cells infiltrate nerve tissue due to loosing self-regulated immune system. Also in dogs, there is a similar disease, which is a meningoencephalitis of unknown etiology (MUE). The cause of MUE is not yet clear, but it is assumed to be caused by immune problem, and in this regard, main treatment is administrating non-specific immunosuppressants. Although about 25% of dogs which has neurological disease struggle with MUE, treatment has not been developed significantly. Non-specific immunosuppressants, including steroids, has several problems such as gastrointestinal disorders and hormonal secretion disorders, on the other hand, immunosuppressants treatment effect is not guaranteed, one of the difficulties in treating MUE. In this respect, extracellular vesicles (EVs) derived from MSCs have been investigated as a treatment option for autoimmune diseases. However, further study is needed on clinical efficacy of EV. To improve the secretion of anti-inflammatory factors from MSCs, preconditioning with hypoxia or hypoxia-mimetic agents has been attempted. Moreover, the molecular changes in preconditioned MSC-derived EVs have been explored and its clinical efficacy has not been proven. This study aimed to evaluate the therapeutic effect of EVs derived from deferoxamine (DFO)-preconditioned canine adipose tissue-derived (cAT)-MSCs (EVDFO) in an experimental autoimmune encephalomyelitis (EAE) mouse model and explore the mechanism underlying immunomodulation function of EV. This dissertation is composed of three parts. The first part of dissertation revealed that cAT-MSCs preconditioned with DFO (MSCDFO) can more effectively direct and reprogram macrophage polarization into the M2 anti-inflammation state by paracrine effect. MSCDFO exhibited enhanced secretion of anti-inflammatory factors such as prostaglandin E2 and tumor necrosis factor-ฮฑ-stimulated gene-6. To evaluate the interaction between MSCDFO and macrophages, RAW 264.7 cells were co-cultured with cAT-MSCs using the transwell system, and changes in the expression of factors related to macrophage polarization were analyzed using the quantitative real-time PCR and western blot assays. When RAW 264.7 cells were co-cultured with MSCDFO, the expression of M1 and M2 markers decreased (iNOS, 1.32 fold, p<0.01; IL-6, 3.46 fold, p<0.05) and increased (CD206, 2.61 fold, p<0.001; Ym1, 4.92 fold, p<0.01), respectively, compared to co-culturing with non-preconditioned cAT-MSCs. Thus, cAT-MSCs preconditioned with DFO can more effectively direct and reprogram macrophage polarization into the M2 phase, an anti-inflammatory state. The second part of dissertation is designed to evaluate that EVDFO regulated macrophage through activating signal transducer and transcription3 (STAT3) phosphorylation. In MSCDFO, Hypoxia-inducible factor 1-alpha was found to accumulate and expression of Cyclooxygenase-2 (COX-2) was increased (16.77 fold, p<0.001). Changes in expression of COX-2 were reflected in the derived EVs as well. The canine macrophage cell line, DH82, was treated with EVnon and EVDFO after lipopolysaccharide stimulation and polarization changes were evaluated with quantitative real-time PCR and immunofluorescence analyses. When DH82 was treated with EVDFO, the expression of M1 marker was reduced (IL-1ฮฒ, 2.45 fold, p<0.001; IL-6, 17.26 fold, p<0.001) while that of M2 surface marker was enhanced (CD206, 7.24 fold, p<0.001) compared to that when DH82 was treated with EVnon. Further, phosphorylation of STAT3 expression was increased more when DH82 cells were treated with EVDFO (1.79 fold, p<0.001). EV derived from cAT-MSC treated with si-COX2 showed similar effect with EV and the effect of immunomodulation was decreased than EVDFO (IL-1ฮฒ, 2.21 fold, p<0.001; IL-6, 1.43 fold, p<0.001; CD206, 2.27 fold, p<0.001). Thus, COX-2 in EV may be one of key factor to regulate STAT3 and modulate macrophage. The last part of dissertation demonstrates that EVDFO treatment has a relatively higher efficacy in reducing inflammation than non-preconditioned EV treatment and could modulate immune system through regulating STAT3 in EAE model. EAE mice were divided into different groups based on intranasal administration of EVs or EVDFO (C57BL/6, male, control=6, EAE=8, EAE+EV=8, EAE+EVDFO=8, 10 ฮผg/day;14 injections). On day 25 post-EAE induction, the mice were euthanized, and the spleen, brain, and spinal cord were analyzed into histopathologic and expression of RNA and protein level. Histologically, in the EV and EVDFO groups, the infiltration of inflammatory cells decreased significantly (EV, 1.38 fold, p<0.01; EVDFO, 1.72 fold, p<0.01), and demyelination was alleviated (EV, 2.96 fold, p<0.05; EVDFO, 5.28 fold, p<0.05). Immunofluorescence staining showed that the expression of CD206 and Foxp3, markers of M2 macrophages and regulatory T (Treg) cells, respectively, increased significantly in the EVDFO group compared to the EAE and EAE+EV group. In the EAE group, the number of CD4+CD25+Foxp3+ Treg cells in the spleen decreased significantly compared with the naรฏve group (2.74 fold, p<0.001). In contrast, the number of Treg cells showed a greater increase in the EAE+EVDFO group than in the EAE+EV group (1.55 fold, p<0.05). The protein expression of STAT3 and pSTAT3 increased in the spleen in the EAE groups compared to the naรฏve group (STAT3, 2.02 fold, p<0.001; pSTAT3, 2.14 fold, p<0.001). However, following EV treatment, STAT3 expression decreased compared to the EAE group (1.32 fold, p<0.001), especially reduction of STAT3 was evident in EVDFO compared to EV group (1.90 fold, p<0.001). Therefore, EV could regulate STAT3 expression and EVDFO has more effect than EV. In conclusion, that preconditioned with DFO in cAT-MSC is an effective method to improve immunomodulation effect of EVs. Also, EVDFO is potential therapeutic option for multiple sclerosis through regulating STAT3 pathway and modulating immune system. These findings suggest a new approach to cell free therapy with preconditioned EV in other autoimmune diseases as well as multiple sclerosis. Furthermore, this study is a major basis that EVDFO can be applied as a new treatment for MUE in dogs and the cornerstone to the development of autoimmune disease treatment in veterinary medicine.์ค‘๊ฐ„์—ฝ์ค„๊ธฐ์„ธํฌ(Mesenchymal stem cell; MSC)์˜ ๋ถ„๋น„ ๋Šฅ๋ ฅ์€ ์—ผ์ฆ์„ ๊ฐœ์„ ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ํšจ๊ณผ์ ์ด๋ผ ๋ณด๊ณ ๋˜์–ด, ์ด๋ฅผ ์—ผ์ฆ ์น˜๋ฃŒ์ œ๋กœ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•ด ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ๋‚˜ ๋‹ค๋ฐœ์„ฑ ๊ฒฝํ™”์ฆ๊ณผ ๊ฐ™์ด ์ž๊ฐ€๋ฉด์—ญ ์‹ ๊ฒฝ์งˆํ™˜์—์„œ๋„ ์ค„๊ธฐ์„ธํฌ ์น˜๋ฃŒ๋ฅผ ์ ์šฉํ•˜๋ ค๋Š” ๋…ธ๋ ฅ๋“ค์ด ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋‹ค๋ฐœ์„ฑ ๊ฒฝํ™”์ฆ์€ ์‚ฌ๋žŒ์˜ ์ž๊ฐ€๋ฉด์—ญ ์‹ ๊ฒฝ์งˆํ™˜ ์ค‘ ํ•˜๋‚˜๋กœ, ์ž๊ธฐ ์กฐ์ ˆ ๋ฉด์—ญ์„ธํฌ์˜ ์ด์ƒ์œผ๋กœ ์ธํ•ด ์‹ ๊ฒฝ์กฐ์ง์„ ์™ธ๋ถ€์š”์ธ์œผ๋กœ ์˜ค์ธํ•˜๊ณ  ์—ผ์ฆ ์„ธํฌ๋“ค์ด ์นจ์œค๋˜๋ฉด์„œ ์‹ ๊ฒฝ์†์ƒ์ด ์ƒ๊ธฐ๋Š” ์งˆํ™˜์ด๋‹ค. ๊ฐœ์—์„œ๋Š” ์œ ์‚ฌํ•œ ์งˆํ™˜์œผ๋กœ ์›์ธ ๋ถˆ๋ช…์˜ ๋น„๊ฐ์—ผ์„ฑ ๋‡Œ์ˆ˜๋ง‰์—ผ์ด ์žˆ๋‹ค. ํ•ด๋‹น ์งˆํ™˜์˜ ์›์ธ์€ ์•„์ง ๋ช…ํ™•ํ•˜๊ฒŒ ๋ฐํ˜€์ง€์ง€ ์•Š์•˜์œผ๋‚˜ ๋ฉด์—ญ ์ด์ƒ์œผ๋กœ ์ธํ•ด ์ƒ๊ธฐ๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜๋ฉฐ, ์ด์™€ ๊ด€๋ จํ•ด ๋น„ํŠน์ด์  ๋ฉด์—ญ์–ต์ œ์ œ๋กœ ์น˜๋ฃŒํ•œ๋‹ค. ๊ฐœ์˜ ์‹ ๊ฒฝ์งˆํ™˜ ์ค‘ ์•ฝ 25%๋ฅผ ์ฐจ์ง€ํ•  ์ •๋„๋กœ ๋งŽ์€ ์ˆ˜์˜ ๊ฐœ๊ฐ€ ํ•ด๋‹น ์งˆํ™˜์œผ๋กœ ํˆฌ๋ณ‘์„ ํ•˜๋‚˜ ์•„์ง๊นŒ์ง€ ์น˜๋ฃŒ๋ฒ•์ด ํฌ๊ฒŒ ๋ฐœ๋‹ฌ๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค. ์Šคํ…Œ๋กœ์ด๋“œ๋ฅผ ํฌํ•จํ•œ ๋น„ํŠน์ด์  ๋ฉด์—ญ์–ต์ œ์ œ๋Š” ์œ„์žฅ๊ด€ ์žฅ์• , ํ˜ธ๋ฅด๋ชฌ ๋ถ„๋น„ ์žฅ์•  ๋“ฑ์˜ ๋ฌธ์ œ๋ฅผ ์œ ๋ฐœํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ด๋ฉฐ, ๊ทธ์— ๋ฐ˜ํ•ด ์น˜๋ฃŒ ํšจ๊ณผ๊ฐ€ ํ™•์‹คํ•˜๊ฒŒ ๋ณด์žฅ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฒƒ์ด ๋‡Œ์ˆ˜๋ง‰์—ผ ์น˜๋ฃŒ์˜ ์–ด๋ ค์šด ์ ์ด๋‹ค. ์ž๊ฐ€๋ฉด์—ญ ์‹ ๊ฒฝ์งˆํ™˜์—์„œ ๋น„ํŠน์ด์  ๋ฉด์—ญ์–ต์ œ์ œ ์น˜๋ฃŒ์˜ ๋‹จ์ ์„ ๋ณด์™„ํ•  ๋‹ค๋ฅธ ์น˜๋ฃŒ ์š”๋ฒ•๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๊ฐœ๋ฐœ๋“ค์ด ์ด์–ด์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ์ค‘ ํ•˜๋‚˜๊ฐ€ MSC์—์„œ ์œ ๋ž˜๋œ ์„ธํฌ์™ธ์†Œํฌ์ฒด(extracellular vesicle; EV)์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•„์ง๊นŒ์ง€๋Š” ์„ธํฌ์™ธ์†Œํฌ์ฒด์˜ ์ž„์ƒ์  ํšจ๋Šฅ์ด ์ถฉ๋ถ„ํžˆ ์ž…์ฆ๋˜์ง€ ์•Š์•„ ์‹ค์ œ ์ž„์ƒ์—์„œ ์ ์šฉ๋˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด MSC์˜ ํ•ญ์—ผ์ฆ ์ธ์ž ๋ถ„๋น„๋ฅผ ์ด‰์ง„์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋“ค์ด ๊ณ ์•ˆ๋˜์—ˆ๊ณ , ๊ทธ์ค‘ ํ•œ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์ด ์ €์‚ฐ์†Œ ๋ฐฐ์–‘ ํ˜น์€ ์ €์‚ฐ์†Œ์ฆ ๋ชจ๋ฐฉ์ œ๋ฅผ ์‚ฌ์šฉํ•œ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์ด๋‹ค. ์ „์ฒ˜๋ฆฌํ•œ MSC์—์„œ ์œ ๋ž˜ํ•œ ์„ธํฌ์™ธ์†Œํฌ์ฒด ๋‚ด์˜ ๋ถ„์ž ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋‚˜, ์•„์ง๊นŒ์ง€ ์ด๊ฒƒ์˜ ์ž„์ƒ์  ํšจ๋Šฅ์ด ์ถฉ๋ถ„ํžˆ ๋ฐํ˜€์ง€์ง€ ์•Š์•„ ์ถ”๊ฐ€์ ์ธ ์ „์ž„์ƒ ๋ฐ ์ž„์ƒ ์ ์šฉ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹คํ—˜์  ์ž๊ฐ€๋ฉด์—ญ์„ฑ ๋‡Œ์ฒ™์ˆ˜์—ผ(experimental autoimmune encephalomyelitis; EAE) ๋งˆ์šฐ์Šค ๋ชจ๋ธ์—์„œ deferoxamine (DFO)๋กœ ์ „์ฒ˜๋ฆฌํ•œ ๊ฐœ ์ง€๋ฐฉ์กฐ์ง์œ ๋ž˜(canine adipose tissue derived; cAT)-MSCs์—์„œ ์œ ๋ž˜๋œ ์„ธํฌ์™ธ์†Œํฌ์ฒด(EVDFO)์˜ ์น˜๋ฃŒ ํšจ๊ณผ๋ฅผ ํ™•์ธํ•˜๊ณ  ์„ธํฌ์™ธ์†Œํฌ์ฒด์˜ ๋ฉด์—ญ์กฐ์ ˆ๊ธฐ๋Šฅ์˜ ์ž‘์šฉ์›๋ฆฌ๋ฅผ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ, DFO๊ฐ€ ์ „์ฒ˜๋ฆฌ๋œ cAT-MSC (MSCDFO)๊ฐ€ ๋ถ„๋น„ ํšจ๊ณผ๋ฅผ ํ†ตํ•ด ๋Œ€์‹์„ธํฌ๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ M2 ํ•ญ์—ผ์ฆ ์ƒํƒœ๋กœ ์œ ๋„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค. MSCDFO์™€ ๋Œ€์‹์„ธํฌ์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๊ณต๋ฐฐ์–‘ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ RAW 264.7 ์„ธํฌ์™€ MSCDFO๋ฅผ ๊ฐ™์ด ๋ฐฐ์–‘ํ•˜์˜€์œผ๋ฉฐ, ์ค‘ํ•ฉํšจ์†Œ ์—ฐ์‡„๋ฐ˜์‘ ๊ธฐ๋ฒ•๊ณผ western blot ๋ถ„์„ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋Œ€์‹์„ธํฌ ๋ถ„๊ทน๊ณผ ๊ด€๋ จ๋œ ์ธ์ž์˜ ๋ฐœํ˜„ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. RAW 264.7 ์„ธํฌ๋ฅผ MSCDFO์™€ ๊ณต๋ฐฐ์–‘ํ•œ ๊ฒฝ์šฐ, ์ „์ฒ˜๋ฆฌ ํ•˜์ง€ ์•Š์€ cAT-MSC์™€ ๊ณต๋ฐฐ์–‘ํ•œ ๊ฒฝ์šฐ์— ๋น„ํ•ด M1 ๋ฐ M2 ๋งˆ์ปค์˜ ๋ฐœํ˜„์ด ๊ฐ๊ฐ ๊ฐ์†Œ(iNOS, 1.32๋ฐฐ, p<0.01; IL-6, 3.46๋ฐฐ, p<0.05) ๋ฐ ์ฆ๊ฐ€(CD206, 2.61๋ฐฐ, p<0.001; Ym1, 4.92๋ฐฐ, p<0.01)ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ DFO๋กœ ์ „์ฒ˜๋ฆฌํ•œ cAT-MSC๋Š” ๋Œ€์‹์„ธํฌ ๋ถ„๊ทน์„ ๋” ํšจ๊ณผ์ ์œผ๋กœ ํ•ญ์—ผ์ฆ ์ƒํƒœ์ธ M2 ๋‹จ๊ณ„๋กœ ์œ ๋„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘ ๋ฒˆ์งธ, EVDFO๊ฐ€ STAT3์˜ ์ธ์‚ฐํ™” ํ™œ์„ฑํ™”๋ฅผ ํ†ตํ•ด ๋Œ€์‹์„ธํฌ๋ฅผ ์กฐ์ ˆํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. MSCDFO์—์„œ๋Š” HIF-1ฮฑ๊ฐ€ ์ถ•์ ๋˜๊ณ  COX-2์˜ ๋ฐœํ˜„์ด ์ฆ๊ฐ€(16.77๋ฐฐ, p<0.001)ํ•˜์˜€๋‹ค. COX-2์˜ ๋ฐœํ˜„ ๋ณ€ํ™”๋Š” MSCDFO์—์„œ ์œ ๋ž˜ํ•œ ์„ธํฌ์™ธ์†Œํฌ์ฒด์—๋„ ๋ฐ˜์˜๋˜์—ˆ๋‹ค. ๊ฐœ ๋Œ€์‹์„ธํฌ์ฃผ DH82๋ฅผ LPS๋กœ ์ž๊ทนํ•œ ๋’ค EVnon ๋ฐ EVDFO๋ฅผ ์ฒ˜๋ฆฌํ•˜์—ฌ ๊ทธ ๋ณ€ํ™”๋ฅผ ์ค‘ํ•ฉํšจ์†Œ ์—ฐ์‡„๋ฐ˜์‘ ๊ธฐ๋ฒ• ๋ฐ ๋ฉด์—ญ ํ˜•๊ด‘ ์—ผ์ƒ‰ ๋ถ„์„์„ ํ†ตํ•ด ํ‰๊ฐ€ํ•˜์˜€๋‹ค. DH82๋ฅผ EVDFO๋กœ ์ฒ˜๋ฆฌํ•œ ๊ฒฝ์šฐ, M1 ๊ด€๋ จ๋œ ์ง€ํ‘œ์˜ ๋ฐœํ˜„์€ ๊ฐ์†Œ(IL-1ฮฒ, 2.45๋ฐฐ, p<0.001; IL-6, 17.26๋ฐฐ, p<0.001)ํ•˜์˜€์œผ๋‚˜, M2 ๊ด€๋ จ๋œ ์ง€ํ‘œ์˜ ๋ฐœํ˜„์€ EVnon์œผ๋กœ ์ฒ˜๋ฆฌํ•œ ๊ฒฝ์šฐ๋ณด๋‹ค ํ–ฅ์ƒ(CD206, 7.24๋ฐฐ, p<0.001) ๋˜์—ˆ๋‹ค. ๋˜ํ•œ DH82 ์„ธํฌ๋ฅผ EVDFO๋กœ ์ฒ˜๋ฆฌํ–ˆ์„ ๋•Œ STAT3 ๋ฐœํ˜„์˜ ์ธ์‚ฐํ™”๋Š” ๋” ์ฆ๊ฐ€ํ•˜์˜€๋‹ค(1.79๋ฐฐ, p<0.001). si-COX2๋กœ ์ฒ˜๋ฆฌ๋œ cAT-MSC์—์„œ ์œ ๋ž˜ํ•œ EV๋Š” ์•„๋ฌด ์ฒ˜๋ฆฌํ•˜์ง€ ์•Š์€ EV์™€ ์œ ์‚ฌํ•œ ํšจ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๋Œ€์‹์„ธํฌ ์กฐ์ ˆ ํšจ๋Šฅ์€ EVDFO๋ณด๋‹ค ๊ฐ์†Œํ•˜์˜€๋‹ค(IL-1ฮฒ, 2.21๋ฐฐ, p<0.001; IL-6, 1.43๋ฐฐ, p<0.001; CD206, 2.27๋ฐฐ, p<0.001). ๋”ฐ๋ผ์„œ EV ๋‚ด์— ์žˆ๋Š” COX-2๋Š” STAT3๋ฅผ ์กฐ์ ˆํ•˜์—ฌ ๋Œ€์‹์„ธํฌ๋ฅผ ๋ณ€ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํ•ต์‹ฌ ์ธ์ž ์ค‘ ํ•˜๋‚˜๋กœ ์ถ”์ •๋œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, EVDFO ์น˜๋ฃŒ๊ฐ€ ์ „์ฒ˜๋ฆฌ ํ•˜์ง€ ์•Š์€ EV๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ๋” ๋†’์€ ํ•ญ์—ผ์ฆ ํšจ๋Šฅ์„ ๊ฐ€์ง€๋ฉฐ EAE ๋ชจ๋ธ์—์„œ STAT3 ์กฐ์ ˆ์„ ํ†ตํ•ด ๋ฉด์—ญ ์ฒด๊ณ„๋ฅผ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค. ์‹คํ—˜ ๋น„๊ต๋ฅผ ์œ„ํ•ด EAE ๊ทธ๋ฃน๊ณผ EV ๋˜๋Š” EVDFO๋ฅผ ๋น„๊ฐ• ํˆฌ์—ฌํ•œ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค(C57BL/6, male, control=6, EAE=8, EAE+EV=8, EAE+EVDFO=8, 10 ฮผg/์ผ/14ํšŒ). ์งˆํ™˜์„ ์œ ๋„ํ•œ์ง€ 25์ผ ์ฐจ์— ์ฅ๋ฅผ ์•ˆ๋ฝ์‚ฌ ์‹œํ‚ค๊ณ  ๋น„์žฅ, ๋‡Œ, ์ฒ™์ˆ˜๋ฅผ ์กฐ์ง๋ณ‘๋ฆฌํ•™์ , RNA ๋ฐ ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„ ์ •๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์กฐ์งํ•™์ ์œผ๋กœ EV ๋ฐ EVDFO๊ตฐ์—์„œ๋Š” ์ฒ™์ˆ˜์—์„œ์˜ ์—ผ์ฆ ์„ธํฌ์˜ ์นจ์œค์ด ํ˜„์ €ํžˆ ๊ฐ์†Œํ•˜์˜€๊ณ (EV, 1.38๋ฐฐ, p<0.01; EVDFO, 1.72๋ฐฐ, p<0.01) ํƒˆ์ˆ˜์ดˆํ™” ํ˜„์ƒ์ด ์™„ํ™”๋˜์—ˆ๋‹ค (EV, 2.96๋ฐฐ, p<0.05; EVDFO, 5.28๋ฐฐ, p<0.05). ๋ฉด์—ญ ํ˜•๊ด‘ ์—ผ์ƒ‰์„ ํ†ตํ•ด M2 ๋Œ€์‹์„ธํฌ์™€ ์กฐ์ ˆ T ์„ธํฌ(Treg)์˜ ์ง€ํ‘œ์ธ CD206๊ณผ Foxp3์˜ ๋ฐœํ˜„์ด EAE์™€ EAE+EV ๊ทธ๋ฃน์— ๋น„ํ•ด EVDFO ๊ทธ๋ฃน์—์„œ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. EAE ๊ทธ๋ฃน์—์„œ ๋น„์žฅ์˜ CD4+CD25+Foxp3+ Treg ์„ธํฌ์˜ ์ˆ˜๋Š” naรฏve ๊ทธ๋ฃน์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ–ˆ๋‹ค(2.74๋ฐฐ, p<0.001). ๋ฐ˜๋ฉด, Treg ์„ธํฌ์˜ ์ˆ˜๋Š” EAE+EV ๊ทธ๋ฃน๋ณด๋‹ค EAE+EVDFO ๊ทธ๋ฃน์—์„œ ๋” ํฐ ์ฆ๊ฐ€๋ฅผ ๋ณด์˜€๋‹ค(1.55๋ฐฐ, p<0.05). STAT3์™€ pSTAT3์˜ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์€ naรฏve ๊ทธ๋ฃน๊ณผ ๋น„๊ตํ•˜์˜€์„ ์‹œ EAE ๊ทธ๋ฃน์˜ ๋น„์žฅ์—์„œ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค(STAT3, 2.02๋ฐฐ, p<0.001; pSTAT3, 2.14๋ฐฐ, p<0.001). ๊ทธ๋Ÿฌ๋‚˜ EV ์ฒ˜๋ฆฌํ•œ ๊ทธ๋ฃน์—์„œ๋Š” EAE ๊ทธ๋ฃน์— ๋น„ํ•ด STAT3 ๋ฐœํ˜„์ด ๊ฐ์†Œํ•˜์˜€์œผ๋ฉฐ(1.32๋ฐฐ, p<0.001), ํŠนํžˆ EV ๊ทธ๋ฃน์— ๋น„ํ•ด EVDFO๊ฐ€ ์ฃผ์ž…๋œ ๊ทธ๋ฃน์—์„œ STAT3์˜ ๊ฐ์†Œ๊ฐ€ ๋šœ๋ ทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค(1.90๋ฐฐ, p<0.001). ๋”ฐ๋ผ์„œ EV๋Š” STAT3 ๋ฐœํ˜„์„ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜๋ฉฐ, EVDFO๋Š” EV๋ณด๋‹ค ๊ทธ ํšจ๊ณผ๊ฐ€ ํฌ๋‹ค๊ณ  ์ข…ํ•ฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, cAT-MSC๋ฅผ DFO๋กœ ์ „์ฒ˜๋ฆฌ ํ•˜๋Š” ๊ฒƒ์€ MSC์™€ ์„ธํฌ์™ธ์†Œํฌ์ฒด์˜ ๋ฉด์—ญ์กฐ์ ˆ ํšจ๊ณผ๋ฅผ ํ–ฅ์ƒ์‹œ์ผœ ์น˜๋ฃŒ๋Šฅ์„ ํšจ์œจ์ ์œผ๋กœ ์˜ฌ๋ฆด ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋˜ํ•œ, EVDFO๊ฐ€ ๋ฉด์—ญ์„ธํฌ ๋‚ด์˜ STAT3๋ฅผ ์œ ๋™์ ์œผ๋กœ ๋ณ€ํ™”์‹œํ‚ค๊ณ  ์ด๋ฅผ ํ†ตํ•ด์„œ ๋ฉด์—ญ ์ฒด๊ณ„๋ฅผ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ, STAT3๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์ด ๋‹ค๋ฐœ์„ฑ ๊ฒฝํ™”์ฆ์˜ ์น˜๋ฃŒ๋ฒ•์˜ ์ค‘์š” ์›๋ฆฌ๋กœ ์ œ์‹œ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ๋‹ค๋ฐœ์„ฑ ๊ฒฝํ™”์ฆ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค๋ฅธ ์ž๊ฐ€๋ฉด์—ญ ์งˆํ™˜์—์„œ์˜ ์ƒˆ๋กœ์šด ๋ฌด์„ธํฌ ์น˜๋ฃŒ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ๊ฐœ์˜ ์งˆํ™˜ ์ค‘ ์ด์™€ ์œ ์‚ฌํ•œ ์งˆํ™˜์ธ ์›์ธ ๋ถˆ๋ช…์˜ ๋น„๊ฐ์—ผ์„ฑ ๋‡Œ์ฒ™์ˆ˜๋ง‰์—ผ์—๋„ EVDFO๊ฐ€ ์ƒˆ๋กœ์šด ์น˜๋ฃŒ์ œ๋กœ ์ ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋Š” ์ฃผ์š”ํ•œ ์ฒซ ๊ทผ๊ฑฐ์ด๋ฉฐ, ์ˆ˜์˜ํ•™์—์„œ์˜ ์ž๊ฐ€ ๋ฉด์—ญ ์งˆํ™˜ ์น˜๋ฃŒ ๋ฐœ์ „์˜ ํ† ๋Œ€๊ฐ€ ๋˜๋Š” ์—ฐ๊ตฌ์ด๋‹ค.LITERATURE REVIEW 1 1. Generalities of preconditioned mesenchymal stem cell (MSCs) 1 2. Properties of extracellular vesicles (EVs) derived from MSCs 2 3. Immunomodulation function of EVs derived from MSCs 3 4. Preclinical and clinical application of MSC derived EVs in immune disorders 5 Chapter โ… . Preconditioning of canine adipose tissue-derived mesenchymal stem cells with deferoxamine potentiates anti-inflammatory effects by directing/reprogramming M2 macrophage polarization 12 1. Introduction 12 2. Material and methods 14 2.1. Isolation and characterization of canine adipose tissue-derived (cAT)-MSCs 14 2.2. Cell culture and expansion 15 2.3. Cell viability analysis 15 2.4. RNA extraction, cDNA synthesis, and the quantitative real-time polymerase chain reaction (qRT-PCR) 16 2.5. Protein extraction, cell fractionation, and western blot analysis 16 2.6. ELISA 17 2.7. Co-culture of macrophages with preconditioned cAT-MSCs 18 2.8. Statistical analyses 18 3. Results 18 3.1. Viability of DFO preconditioning in cAT-MSC 18 3.2. DFO induces hypoxic response in cAT-MSCs 19 3.3. DFO preconditioning increases the expression and secretion of anti-inflammatory factors 19 3.4. MSCDFO direct macrophage polarization in vitro 20 4. Discussion 21 5. Table and Figures 27 Chapter โ…ก. Extracellular vesicles derived from DFO-preconditioned canine AT-MSCs reprogram macrophages into M2 phase 40 1. Introduction 40 2. Materials and methods 42 2.1. Cell preparation and culture 42 2.2. Transfection of cAT-MSCs with siRNA 43 2.3. Isolation and characterization of EVs derived from cAT-MSCs 44 2.4. RNA extraction, cDNA synthesis, and quantitative real-time polymerase chain reaction (qRT-PCR) 45 2.5. Protein extraction, cell fractionation, and western blotting 45 2.6. Immunofluorescence analyses 46 2.7. Statistical analyses 47 3. Results 47 3.1. Characterization of cAT-MSC derived EVs 47 3.2. Elevation of HIF-1ฮฑ/COX-2 expression in MSCDFO 48 3.3. cAT-MSC-derived EVs transport COX-2 to DH82 and activate the phosphorylation of STAT3 48 3.4. Change of polarization of DH82 when treated with preconditioned EVs 49 4. Discussion 50 5. Table and Figures 54 Chapter โ…ข. Deferoxamine preconditioned cAT-MSC derived EV alleviate inflammation in EAE mouse model through regulating STAT3 66 1. Introduction 66 2. Material and Methods 68 2.1 Cell isolation and culture 68 2.2. Isolation and characterization of EVs from cAT-MSCs 69 2.3. EAE induction and therapy 70 2.4. Histological analysis 71 2.5. Immunohistochemistry analysis 72 2.6. RNA extraction, cDNA synthesis, and real-time PCR 73 2.7. Protein extraction and western blotting 73 2.8. Isolated splenocytes and activation 74 2.9. Cytokine assay 74 2.10. Flow cytometry analysis of the Treg cell population 75 2.11. Obtaining PBMCs and treatment with EVs 75 2.12. Statistical analyses 76 3. Results 76 3.1. Characterization of cAT-MSC-derived EVs and elevation of protein levels in MSCDFO 76 3.2. cAT-MSC-derived EVs and EVDFO alleviated clinical signs and histological changes in the EAE mouse model 77 3.3. Cytokine and protein level changes in the spinal cord and brain of EV-treated mice 79 3.4. EVs altered the Treg cell population in the EAE mouse model 80 3.5. Cytokine and protein level changes in the spleen in EV-treated mice 80 3.6. Evaluation of the effect of EVs in canine PBMCs through RNA and protein expression analyses 81 4. Discussion 82 5. Table and Figures 89 GENERAL CONCLUSION 107 REFERENCES 111๋ฐ•

    ์„ ํƒ์  ์•„๋กœ๋งˆํƒ€์ œ ์กฐ์ ˆ์ œ์˜ ๋Œ€์ฒด์žฌ๋กœ์„œ ์•ฝ์ฝฉ๊ป์งˆ์ถ”์ถœ๋ฌผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€(๋ฐ”์ด์˜ค๋ชจ๋“ˆ๋ ˆ์ด์…˜์ „๊ณต),2019. 8. ์ด๊ธฐ์›.์—์ŠคํŠธ๋กœ๊ฒ์€ ์—ฌ์„ฑ์˜ ์ƒ์‹๊ณผ ์„ฑ์  ๋ฐœ๋‹ฌ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ์Šคํ…Œ๋กœ์ด๋“œ ํ˜ธ๋ฅด๋ชฌ์ด๋‹ค. ์—ฌ์„ฑ์€ ๋…ธํ™”๊ฐ€ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ๋‚œ์†Œ์˜ ๊ธฐ๋Šฅ์ด ์ €ํ•˜๋˜๊ณ  ์—์ŠคํŠธ๋กœ๊ฒ์˜ ๋ถ„๋น„๊ฐ€ ๊ฐ์†Œํ•˜๋ฏ€๋กœ, ์ •์ƒ์ ์ธ ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์ค€์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์ด ์—ฌ์„ฑ ๊ฑด๊ฐ•์— ์ค‘์š”ํ•˜๋‹ค. ์•„๋กœ๋งˆํƒ€์ œ๋Š” ์—์ŠคํŠธ๋กœ๊ฒ ํ•ฉ์„ฑ์— ํ•„์ˆ˜์  ํšจ์†Œ์ด๋ฉฐ, ์กฐ์ง ํŠน์ด์ ์œผ๋กœ ๋ฐœํ˜„ํ•œ๋‹ค. ์„ ํƒ์  ์•„๋กœ๋งˆํƒ€์ œ ์กฐ์ ˆ์ œ (selective aromatase modulator; SAM)๋Š” ์—์ŠคํŠธ๋กœ๊ฒ ํ•ฉ์„ฑ์„ ํ•„์š”๋กœ ํ•˜๋Š” ๋ถ€์œ„์—์„œ ์•„๋กœ๋งˆํƒ€์ œ๋ฅผ ๋ฐœํ˜„ํ•  ์ˆ˜ ์žˆ๊ณ , ์—์ŠคํŠธ๋กœ๊ฒ ์˜์กด์„ฑ ์•” ์กฐ์ง์—์„œ ์•„๋กœ๋งˆํƒ€์ œ ๊ณผ๋ฐœํ˜„์„ ์–ต์ œํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ฐฑ๋…„๊ธฐ ๋ฐ ํ๊ฒฝ๊ธฐ ์—ฌ์„ฑ์˜ ์—์ŠคํŠธ๋กœ๊ฒ ์ €ํ•˜ ์ฆ์ƒ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์ฒด๋‚ด์—์„œ ์—์ŠคํŠธ๋กœ๊ฒ์„ ๊ทผ๋ณธ์ ์œผ๋กœ ์ƒ์„ฑํ•˜๊ณ , ์—์ŠคํŠธ๋กœ๊ฒ ์ƒ์„ฑ์ด ํ•„์ˆ˜์ ์ธ ์กฐ์ง์—์„œ ์•„๋กœ๋งˆํƒ€์ œ์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์ฒœ์—ฐ๋ฌผ์„ ๋ฐœ๊ฒฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋”๋ถˆ์–ด ์•„๋กœ๋งˆํƒ€์ œ ๋ฐœํ˜„์„ ์กฐ์ง ํŠน์ด์ ์œผ๋กœ ์กฐ์ ˆํ•˜๋Š” ์„ ํƒ์  ์•„๋กœ๋งˆํƒ€์ œ ์กฐ์ ˆ์ œ๋กœ์„œ์˜ ์ž‘์šฉ์„ ํ™•์ธํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด, ์•ฝ์ฝฉ๊ป์งˆ์ถ”์ถœ๋ฌผ์ด ์‚ฌ๋žŒ์˜ ๋ถ€์‹  ํ”ผ์งˆ ์•”์„ธํฌ์ฃผ์ธ NCI H295R ์„ธํฌ์™€ ์ฅ์˜ ๋‚œ์†Œ์—์„œ ์œ ๋ž˜ํ•œ ๋‚œํฌ์—์„œ 17 ๋ฒ ํƒ€-์—์ŠคํŠธ๋ผ๋””์˜ฌ ์ƒํ•ฉ์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์•ฝ์ฝฉ๊ป์งˆ์ถ”์ถœ๋ฌผ์€ ์Šคํ…Œ๋กœ์ด๋“œ ํ˜ธ๋ฅด๋ชฌ ์ƒํ•ฉ์„ฑ ๊ฒฝ๋กœ์—์„œ ์Šคํ…Œ๋กœ์ด๋“œ ํ˜ธ๋ฅด๋ชฌ ์ƒ์„ฑ ํšจ์†Œ์ธ 3ฮฒ-HSD ์™€ CYP19A1 ์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ 17 ๋ฒ ํƒ€-์—์ŠคํŠธ๋ผ๋””์˜ฌ ์ƒํ•ฉ์„ฑ์„ ์ด‰์ง„ํ•˜์˜€๋‹ค. ์•ฝ์ฝฉ๊ป์งˆ์ถ”์ถœ๋ฌผ์€ ๋ถ€์‹  ๋ฐ ๋‚œ์†Œ์—์„œ๋Š” ์—์ŠคํŠธ๋กœ๊ฒ์„ ์ƒํ•ฉ์„ฑ ํ•˜์˜€์ง€๋งŒ, ์—์ŠคํŠธ๋กœ๊ฒ ์˜์กด์„ฑ ์•”์„ธํฌ๋กœ ์•Œ๋ ค์ง„ ์œ ๋ฐฉ์•” ๋ฐ ๋‚œ์†Œ์•”์„ธํฌ์—์„œ๋Š” ์„ธํฌ ์ฆ์‹์„ ์–ต์ œํ•˜์˜€๋‹ค. ๋”๋ถˆ์–ด H295R ์„ธํฌ์—์„œ CYP19A1 ์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œ์ผฐ์ง€๋งŒ ๋‹จ์ผ ๋ฐฐ์–‘๋œ ์ง€๋ฐฉ์„ธํฌ ๋ฐ ์œ ๋ฐฉ์•”์„ธํฌ์™€ ๊ณต๋™ ๋ฐฐ์–‘๋œ ์ง€๋ฐฉ์„ธํฌ์—์„œ CYP19A1 ์˜ ๋ฐœํ˜„์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์•ฝ์ฝฉ๊ป์งˆ์ถ”์ถœ๋ฌผ์ด ์กฐ์ง ํŠน์ด์ ์œผ๋กœ ์—์ŠคํŠธ๋กœ๊ฒ ์ƒํ•ฉ์„ฑ ๋ฐ ์•„๋กœ๋งˆํƒ€์ œ ๋ฐœํ˜„์„ ์ด‰์ง„ํ•˜๋Š” ์—์ŠคํŠธ๋กœ๊ฒ ์ €ํ•˜ ์ฆ์ƒ ๊ฐœ์„ ์šฉ ์ฒœ์—ฐ๋ฌผ ์˜์•ฝํ’ˆ ๊ธฐ๋Šฅ์„ฑ ์†Œ์žฌ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.Estrogen is a steroid hormone that plays an important role in female reproductive function and sexual development. As women get older, the secretion of estrogen decreases and estrogen supplementation is crucial to alleviate symptoms related to estrogen deficiency. CYP19 (also called aromatase) is the key enzyme responsible for estrogen production and it is expressed in a tissue-specific manner. Aromatase should be expressed in sites that require estrogen synthesis and inhibited overexpression in estrogen-dependent cancer tissues. Therefore, it is necessary to investigate natural products that can supplement estrogen and regulate aromatase expression tissue specifically for menopausal womens health. In this study, we found that Yak-Kong seed coat extract increases 17ฮฒ-estradiol production in human adrenocortical carcinoma NCI-H295R cells and mouse antral follicles. It promotes 17ฮฒ-estradiol biosynthesis by increasing the protein and gene expression of CYP19A1 and 3ฮฒ-HSD in steroidogenesis pathway. Furthermore, it suppresses proliferation of breast and ovarian cancer cells known as estrogen dependent cancer cells and regulates aromatase expression of adipocytes followed a different pattern from that of H295R cells. These results suggest that the extract of Yak Kong seed coat could be a potential alternative to selective aromatase modulator for menopausal women.CONTENTS ABSTRACT .................................................................................................................... โ…ฐ CONTENTS .................................................................................................................... โ…ฒ โ… . INTRODUCTION........................................................................................................ 1 โ…ก. MATERIALS AND METHODS .......................................................................... 7 1. Soybean materials .................................................................................................... 7 2. Animals ....................................................................................................................... 8 3. Cell culture ................................................................................................................. 8 4. Antral follicle culture.............................................................................................. 11 5. Hormone measurement ........................................................................................ 12 6. Real-time quantitative PCR ................................................................................. 12 7. Western blotting ...................................................................................................... 14 8. Cell viability assay .................................................................................................. 15 9. 3T3-L1 and MCF-7 coculture .............................................................................. 16 10. Statistical analysis ................................................................................................. 18 โ…ข. RESULTS .................................................................................................................. 19 1. Yak-Kong seed coat extract has the most estradiol-stimulating effect in H295R cells among soybean extracts ..................................................................... 19 2. Yak-Kong seed coat extract promotes production of 17ฮฒ-estradiol in a dosedependent manner in H295R cells and mouse antral follicles ........................... 22 3. Yak-Kong seed coat extract increases protein and mRNA levels of CYP19A1 in H295R cells ........................................................................................ 26 4. Yak-Kong seed coat extract increases protein and mRNA levels of 3ฮฒ-HSD in H295R cells .............................................................................................................. 29 5. Yak-Kong seed coat extract does not increase protein levels of CYP11A1, CYP17A1, or 17ฮฒ-HSD in H295R cells .................................................................. 32 6. Yak-Kong seed coat extract decreases viability of human breast and ovarian cancer cells .................................................................................................... 34 7. Yak-Kong seed coat extract does not stimulate CYP19A1 expression in 3T3-L1 adipocytes, both mono- and co-cultured with MCF-7 cells ................ 37 โ…ฃ. DISCUSSION ......................................................................................................... 40 โ…ค. REFERENCES ....................................................................................................... 46 ๊ตญ๋ฌธ ์ดˆ๋ก .......................................................................................................................... 51Maste

    ๊ฐ€๋ณ€ ํ† ํด๋กœ์ง€ ํŠธ๋Ÿฌ์Šค ๋กœ๋ด‡์˜ ์•ˆ์ •์ ์ธ ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๊ณผ, 2020. 8. ๊น€์ข…์›.Variable Topology Truss (VTT) is truss structured modular robot that can self-reconfigure its topology and geometric configuration, which can be usefully applied to rescuing work in disaster site. In this thesis, design of VTT is introduced and stable rolling locomotion algorithm for VTT is proposed. To achieve self-reconfiguration feature, VTT are composed specially designed members and nodes. VTTs members consist of Spiral Zippers which are novel linear actuators that has high extension ratio, light weight and high strength. VTTs nodes consist of Passive Member-Ends and Master Member-Ends. Passive Member-Ends are linkage type spherical joint with large angle range that can accommodate many members. Master Member-Ends are spherical manipulators that built in Sphere and it move member to change topology of VTT. Rolling locomotion of VTT is achieved by controlling the center of mass by geometric reconfiguration. However, the locomotion planning is complex problem, because VTT is parallel mechanism with high degree of freedom and many constraints, which makes it difficult to predict and avoid constraints for feasible planning. Thus, it needs stable algorithm that can find locomotion trajectory even in complicated and large environment. In addition, since VTT has many sophisticated components, the algorithm must prevent VTT being damaged from ground by tumbling. To meet the requirements, proposed locomotion algorithm is composed of 3 steps; support polygon planning, center of mass planning and node position planning. In support polygon planning, support polygon path is planned by newly proposed random search algorithm, Polygon-Based Random Tree (PRT). In center of mass planning, trajectory of desired projected center of mass is planned by maximizing stability feature. Planned support polygon path and center of mass trajectory guide VTT to have good-conditioned shape which configuration is far from constraints and makes locomotion planning success even in complex and large environment. In node position planning, Non-Impact Rolling locomotion algorithm was developed to plan position of VTTs nodes that prevent damage from the ground while following planned support polygon path and center of mass trajectory. The algorithm was verified by two case study. In case study 1, locomotion planning and simulation was performed considering actual constraints of VTT. To avoid collision between VTT and obstacle, safety space was defined and considered in support polygon planning. The result shows that VTT successfully reaches the goal while avoiding obstacles and satisfying constraints. In case study 2, locomotion planning and simulation was performed in the environment having wide space and narrow passage. Nominal length of VTT was set to be large in wide space to move efficiently, and set to be small in narrow passage to pass through it. The result shows that VTT successfully reaches the goal while changing its nominal length in different terrain.๊ฐ€๋ณ€ ํ† ํด๋กœ์ง€ ํŠธ๋Ÿฌ์Šค (Variable Topology Truss, VTT)๋Š” ํ† ํด๋กœ์ง€์™€ ๊ธฐํ•˜ํ•™์  ํ˜•์ƒ์˜ ์žฌ๊ตฌ์„ฑ์ด ๊ฐ€๋Šฅํ•œ ํŠธ๋Ÿฌ์Šค ๊ตฌ์กฐ์˜ ๋ชจ๋“ˆ ๋กœ๋ด‡์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” VTT์˜ ์„ค๊ณ„ ๊ตฌ์กฐ๋ฅผ ์†Œ๊ฐœํ•˜๊ณ  VTT์˜ ์•ˆ์ •์ ์ธ ์ฃผํ–‰์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. VTT๋Š” ํ† ํด๋กœ์ง€์™€ ๊ธฐํ•˜ํ•™์  ํ˜•์ƒ์˜ ์žฌ๊ตฌ์„ฑ์„ ์œ„ํ•ด ํŠน์ˆ˜ํ•œ ๊ตฌ์กฐ์˜ ๋ฉค๋ฒ„์™€ ๋…ธ๋“œ๋ฅผ ๊ฐ€์ง„๋‹ค. VTT์˜ ๋ฉค๋ฒ„๋Š” ๋†’์€ ์••์ถ•๋น„, ๊ฐ€๋ฒผ์šด ์ค‘๋Ÿ‰, ๋†’์€ ๊ฐ•๋„๋ฅผ ๊ฐ€์ง„ ์‹ ๊ฐœ๋… ์„ ํ˜• ๊ตฌ๋™๊ธฐ์ธ ์ŠคํŒŒ์ด๋Ÿด ์ง€ํผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. VTT์˜ ๋…ธ๋“œ๋Š” ํŒจ์‹œ๋ธŒ ๋ฉค๋ฒ„ ์—”๋“œ์™€ ๋งˆ์Šคํ„ฐ ์—”๋“œ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ํŒจ์‹œ๋ธŒ ๋ฉค๋ฒ„๋Š” ๋งํ‚ค์ง€ ๊ตฌ์กฐ์˜ 3 ์ž์œ ๋„ ๊ด€์ ˆ๋กœ, ๋„“์€ ๊ฐ๋„ ๊ตฌ๋™ ๋ฒ”์œ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๊ณ  ๋งŽ์€ ์ˆ˜์˜ ๋ฉค๋ฒ„๋ฅผ ์—ฐ๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์Šคํ„ฐ ๋ฉค๋ฒ„ ์—”๋“œ๋Š” ๋…ธ๋“œ ๋ถ€์˜ ๋‚ด์žฅ๋œ ๊ตฌํ˜• ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋กœ, ํ† ํด๋กœ์ง€ ์žฌ๊ตฌ์„ฑ ์‹œ ๋ฉค๋ฒ„๋ฅผ ์ด๋™์‹œํ‚ค๋Š”๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. VTT๋Š” ๊ธฐํ•˜ํ•™์  ํ˜•์ƒ์„ ๋ณ€ํ™”ํ•˜์—ฌ ๊ตฌ๋ฅด๋Š” ์›€์ง์ž„์„ ํ†ตํ•ด ์ฃผํ–‰ํ•œ๋‹ค. VTT์˜ ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์„œํฌํŠธ ํด๋ฆฌ๊ณค ๊ณ„ํš ๋‹จ๊ณ„, ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ณ„ํš ๋‹จ๊ณ„, ๋…ธ๋“œ ์œ„์น˜ ๊ณ„ํš ๋‹จ๊ณ„๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ์„œํฌํŠธ ํด๋ฆฌ๊ณค ๊ณ„ํš ๋‹จ๊ณ„์—์„œ๋Š” ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆ๋œ ๋ฌด์ž‘์œ„ ํƒ์ƒ‰ (random search) ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ Polygon-Based Random Tree (PRT)์„ ์ ์šฉํ•ด ์„œํฌํŠธ ํด๋ฆฌ๊ณค์˜ ๊ฒฝ๋กœ๋ฅผ ๊ณ„ํšํ•œ๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ณ„ํš ๋‹จ๊ณ„์—์„œ๋Š” ์•ˆ์ •์„ฑ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” VTT์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ๊ณ„ํšํ•œ๋‹ค. ๊ณ„ํš๋œ ์„œํฌํŠธ ํด๋ฆฌ๊ณค ๊ฒฝ๋กœ์™€ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ VTT๊ฐ€ ์ œํ•œ ์กฐ๊ฑด์œผ๋กœ๋ถ€ํ„ฐ ๋จผ ์ข‹์€ ์ƒํƒœ์˜ ํ˜•์ƒ์„ ์œ ์ง€ํ•˜๊ฒŒ ํ•˜์—ฌ ๋ณต์žกํ•œ ํ™˜๊ฒฝ์— ๋Œ€ํ•ด์„œ๋„ ๊ฒฝ๋กœ ๊ณ„ํš์ด ์‹คํŒจํ•˜์ง€ ์•Š๊ณ  ์•ˆ์ •์ ์œผ๋กœ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋…ธ๋“œ ์œ„์น˜ ๊ณ„ํš ๋‹จ๊ณ„์—์„œ๋Š” ์„œํฌํŠธ ํด๋ฆฌ๊ณค ๊ฒฝ๋กœ์™€ ๋…ธ๋“œ ์œ„์น˜์˜ ๊ถค์ ์„ ์ถ”์ข…ํ•˜๋Š” ๋…ธ๋“œ ์œ„์น˜ ๊ถค์ ์„ ๊ณ„ํšํ•œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋น„์ถฉ๊ฒฉ ๋กค๋ง ์ด๋™ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (Non-Impact Rolling locomotion algorithm)์„ ์ ์šฉํ•˜์—ฌ ์ง€๋ฉด๊ณผ์˜ ์ถฉ๋Œ๋กœ ์ธํ•œ ์ถฉ๊ฒฉ์ด ์ผ์–ด๋‚˜์ง€ ์•Š๋Š” ๊ถค์ ์„ ๊ณ„ํšํ•œ๋‹ค. ์‹ค์ œ VTT์˜ ์ œํ•œ ์กฐ๊ฑด์„ ๋ฐ˜์˜ํ•œ ๋ชจ๋ธ์— ๋ณธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์—ฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ, VTT๊ฐ€ ๋ชจ๋“  ์ œํ•œ ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๊ณ  ์žฅ์• ๋ฌผ์„ ํšŒํ”ผํ•˜๋ฉด์„œ ๋ชฉํ‘œ ์ง€์ ์— ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Previous Truss Type Modular Robot 4 1.3 Previous Research on VTTs Locomotion 8 1.3.1 Heuristic Based Methods 9 1.3.2 Optimization Based Method 10 1.4 Objectives of Locomotion Algorithm 12 1.5 Contribution of Thesis 13 1.5.1 Introduction to Hardware Design of VTT 13 1.5.2 Stable Rolling Locomotion of VTT 15 Chapter 2. Design of Variable Topology Truss 17 2.1 Member Design 18 2.1.1 Spiral Zipper 20 2.1.2 Tensioner 26 2.2 Node Design 28 2.2.1 Passive Member-End and Sphere 29 2.2.2 Master Member-End 36 2.3 Control System 40 2.4 Node Position Control Experiment 44 Chapter 3. Mathematical Model of Variable Topology Truss 47 3.1 Configuration and Terminology 47 3.2 Inverse Kinematics 50 3.3 Constraints 51 3.4 Stability Criteria 64 Chapter 4. Locomotion Algorithm 66 4.1 Concept of Locomotion Algorithm 67 4.1.1 Method for Successful Planning and Obstacle Avoidance 67 4.1.2 Method to Prevent Damage from the Ground 71 4.1.3 Step of Locomotion Algorithm 72 4.2 Support Polygon Planning 73 4.2.1 Polygon-Based Random Tree (PRT) Algorithm 73 4.2.2 Probabilistic Completeness of PRT Algorithm 79 4.3 Center of Mass Planning 85 4.4 Node Position Planning 86 4.4.1 Concept of Non-Impact Rolling Locomotion 86 4.4.2 Planning Algorithm for Non-Impact Rolling Locomotion 89 4.4.3 Optimization Problem of Moving Phase 94 4.4.4 Optimization Problem of Landing Phase 98 4.4.5 Optimization Problem of Transient Phase 99 Chapter 5. Experimental Verification 100 5.1 Case Study 1: Actual VTT Prototype 101 5.1.1 Simulation Condition 101 5.1.2 Obstacle Avoidance Method 103 5.1.3 Simulation Result 104 5.2 Case Study 2: Environment with Narrow Passage 111 5.2.1 Simulation Condition 111 5.2.2 Support Polygon Planning with Varying Nominal Length 114 5.2.3 Simulation Result 117 Chapter 6. Conclusion 126 Bibliography 129 Abstract in Korean 134Docto

    ๊ทธ๋ž˜ํ•€๊ธฐ๋ฐ˜ ๋ฌผ์งˆ์„ ์ด์šฉํ•œ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™” ํšจ๊ณผ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€(๋ฐ”์ด์˜ค๋ชจ๋“ˆ๋ ˆ์ด์…˜์ „๊ณต),2019. 8. ์ž„์ •๋ฌต.Cartilage is a tissue consisting of chondrocytes and extracellular matrix (ECM), which is highly elastic, buffers against a given force, and the friction coefficient of joint cartilage is very low, helping the joints to move in a state of little friction. But cartilage is a kind of expendable body part that wears out as much as it is used, and it is also damaged by inflammation, trauma, and aging. Cartilage has very limited self-renewal because there are no blood vessels, nerves, or lymphatic vessels. Thus, cartilage cannot easily stop when it starts to damage, and damaged cartilage has many limitations about regenerating into cartilage with normal function and structure. Also, the damaged cartilage area becomes more vulnerable to mechanical pressure, so it is easily broken and worn, resulting in larger defects. As such, a number of cartilage-related diseases develop and progresses faster than other tissues. Some typical diseases include degenerative arthritis, which causes pain due to cartilage wear, and others include rheumatoid arthritis, achondroplasia, pyogenic arthritis, and chondrosarcoma. Furthermore, these cartilage-related diseases can lead to bone-related complications if they persist for a long time. These diseases can lead to a reduction in the quality of life and life expectancy of patients, and the loss of substantial medical costs. Therefore, it is very important to regenerate damaged cartilage early, but there are many deficiencies in current cartilage treatment. Although many researchers continue to suggest ways to treat cartilage-related diseases, they are still far from normal and perfect cartilage regeneration. Up until now, studies have been actively conducted to regenerate damaged cartilage tissue using an ideal biomaterial that can mimic and replace cartilage tissue with various cells. Among them, cells that are widely used in the field of regenerative medicine are human adipose-derived stromal cells (hASCs). This cell has a great advantage that it can be easily obtained from any tissue of the human body, and it is suitable as a material for a cell therapy agent because it does not take much time for cell proliferation and can be repeatedly collected. And recently, graphene (G), a carbon-based material, is emerging as a biomaterial in tissue engineering and regenerative medicine. In addition, a variety of graphene-based materials (GBMs) have also been recognized for their research value as biomaterials. GBMs are derivatives of G. Most GBMs are made through the process of oxidation. GBMs that have the advantage of oxidation are more applied in various fields than G. In particular, GBMs play a role as biomaterials due to their various functional groups, biocompatibility, mechanical stability, and other characteristics. And GBMs have been extensively studied in the field of tissue regeneration and repair. However, since G was discovered in 2004 and is a new material only about 20 years old, there is a limit to the lack of prior research related to cartilage. Therefore, cartilage regeneration studies related to GBMs are essential. And because accurate standard indicators for GBMs have not yet been created, characterization and comparison of various GBMs is critical. Based on an understanding of the characteristics of GBMs, this study was conducted to apply to cartilage regeneration studies. The GBMs used in this study were graphene oxide (GO), nano-graphene oxide (nGO) and graphene quantum dot (GQD). All are oxidized GBMs. These are the forms in which parts of the surface have been replaced with oxygen as graphene is oxidized, and the great advantage is that the binding force of each layer of graphite is reduced and distributed well in the solution. Since it can be synthesized in the solution phase, it can be mass-produced and overcome the disadvantages of graphene, which is a very expensive material and has a very high production cost. And because it is easier to attach new functional groups to oxygen than to carbon, it is possible to make more functionalized graphene derivatives. As hydrophobic graphene turns into hydrophilic, oxidized GBMs, affinity with cells increases, and it has the advantage of being easily mixed into culture medium. Therefore, since these advantages allow me to study GBMs as biomaterials, I analyzed the characteristics of each GBMs using Fourier Transform Infra-Red (FT-IR), X-ray Photoelectron Spectroscopy (XPS), and Electrophoretic Light Scattering (ELS). Functional group analysis confirmed that all GBMs were oxidized by showing binding to carboxyl groups, hydroxyl groups and epoxy groups common to surface of GBMs. Through C1s spectra analysis, the degree of oxidation can be determined by the binding and binding ratio of carbon and oxygen. It was found that oxidation is high in the order of nGO, GO, and GQD. Hydrodynamic radius analysis was used to confirm the hydrodynamic radius of each material. This means the radius of the particles in the solution. Therefore, the size of each particle in the solution can be known, and the order in which it is large was confirmed to be GO, nGO and GQD. Finally, Zeta potential analysis was used to confirm the dispersion stability of the particles in solution. The larger the negative value, the higher the dispersion stability. Generally, over โ€“30 means having good stability, so I found that GO and nGO had better dispersion stability than GQD. Therefore, the dispersion stability is high in the order of nGO, GO, and GQD. These data show that all three GBMs are oxidized and help to understand and compare the physical and chemical properties of each particle. Based on the unique properties of these GBMs, I applied them to chondrogenic differentiation of hASCs. Although studies on cartilage regeneration using various cells and biomaterials have been actively studied, there are still no standardized therapies related to cartilage as various problems such as potential side effects on regenerative therapy and verification of effects. So the study was conducted to investigate the effect on the cartilage differentiation of hASCs by applying GBMs, which have recently emerged as biomaterials in the field of regenerative medicine, to cartilage research. First, I conducted a study on the characterization of hASCs. Microscopic photographs showed that hASCs had fibroblastic morphology, and the ability to differentiate into osteocyte, adipocyte and chondrocyte was confirmed through three different dyeing reagents. And based on the GO with the largest particle size, I confirmed the concentration suitable for chondrogenic differentiation of hASCs. As a result, it was confirmed that a concentration of 10 ฮผg / ml or less was suitable. Thus, GO, nGO, and GQD at concentrations of 1 and 10 ฮผg / ml were applied to hASCs. As a result, the size of the chondrocyte pellet was not different from the induction group. However, it was confirmed that Glycosaminoglycans (GAGs) were significantly increased in the 1 ฮผg / ml group of GO and 1 ฮผg / ml of nGO group compared with the induction group through alcian blue staining and toluidine blue staining. These results suggest that GO and nGO, the oxidized graphene, support Chondrogenic differentiation of hASCs. The main purpose of this study was to determine the effect of GBMs on chondrogenic differentiation of hASCs. Particularly, it was confirmed that the effect of chondrogenic differentiation is different according to the particle size and degree of oxidation of GBMs. These studies will help to understand GBMs, which are bio-new materials, and will greatly contribute to the development of new cartilage therapies using these materials.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 3๊ฐ€์ง€์˜ ๋‹ค๋ฅธ ์ข…๋ฅ˜์˜ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์˜ ๋ฌผ๋ฆฌ์ , ํ™”ํ•™์  ํŠน์„ฑ์„ ๋ถ„์„ํ•˜๊ณ  ๋น„๊ต๋ฅผ ํ†ตํ•ด ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๊ทธ๋ž˜ํ•€ ์–‘์ž์ ์— ๋Œ€ํ•œ ์ดํ•ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์™€ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์„ ์ƒ์ฒด ์†Œ์žฌ๋กœ ํ™œ์šฉํ•œ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์กฐ์ง ๊ณตํ•™ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. Chapter 3์—์„œ๋Š” ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์˜ ํŠน์„ฑ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๋ฌผ๋ฆฌ์ , ํ™”ํ•™์  ๋ถ„์„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•œ ํ›„, ๋ถ„์„๋œ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์„ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™”์— ๋Œ€ํ•œ ํšจ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ž˜ํ•€์€ ๋ฐœ๊ฒฌ๋œ ์ง€ 20๋…„์ด ์ฑ„ ๋˜์ง€ ์•Š์€ ๋ฐ”์ด์˜ค์‹ ์†Œ์žฌ๋กœ, ์ฐธ๊ณ ํ•  ๋งŒํ•œ ์ด์ „ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์—†๋‹ค. ๊ทธ๋ž˜์„œ ๊ทธ๋ž˜ํ•€ ์œ ๋„์ฒด๋“ค์˜ ๋ฌผ๋ฆฌ, ํ™”ํ•™์  ํ‘œ์ค€ ์ง€ํ‘œ๊ฐ€ ๋งˆ๋ จ๋˜์–ด ์žˆ์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ ๊ฐ์˜ ํŠน์„ฑ๋“ค์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ถ„์„ํ•˜๊ณ  ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋จผ์ €, Fourier Transform Infra-Red (FT-IR)๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ ๋ฌผ์งˆ๋“ค์˜ ํ‘œ๋ฉด์— ์–ด๋–ค ์ž‘์šฉ๊ธฐ๋“ค๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์˜€๋‹ค. 3 ์ข…๋ฅ˜์˜ ๊ทธ๋ž˜ํ•€์˜ ํ‘œ๋ฉด์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ์นด๋ฅด๋ณต์‹ค๊ธฐ, ํ•˜์ด๋“œ๋ก์‹ค๊ธฐ, ์—ํญ์‹œ๊ธฐ๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” 3์ข…๋ฅ˜์˜ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์ด ๋ชจ๋‘ ์‚ฐํ™”๋˜์—ˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. X-ray Photoelectron Spectroscopy (XPS)๋ฅผ ์ด์šฉํ•ด์„œ๋Š” C1s spectra ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ํƒ„์†Œ์™€ ์‚ฐ์†Œ์˜ ๊ฒฐํ•ฉ๊ณผ ๋น„์œจ๋กœ ์‚ฐํ™” ์ •๋„๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ๊ทธ ๊ฒฐ๊ณผ, ์‚ฐํ™” ์ •๋„๋Š” ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๊ทธ๋ž˜ํ•€ ์–‘์ž์  ์ˆœ์„œ๋กœ ํฐ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. Electrophoretic light scattering (ELS)๋ฅผ ์ด์šฉํ•ด์„œ๋Š” ๊ฐ ๋ฌผ์งˆ๋“ค์˜ ์œ ์ฒด์—ญํ•™์  ๋ฐ˜๊ฒฝ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์ด๋Š” ์šฉ์•ก ์†์—์„œ ์ž…์ž๊ฐ€ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๋ฐ˜๊ฒฝ์„ ์˜๋ฏธํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ž…์ž์˜ ํฌ๊ธฐ๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ž…์ž์˜ ํฌ๊ธฐ๋Š” ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๊ทธ๋ž˜ํ•€ ์–‘์ž์  ์ˆœ์„œ๋กœ ํฐ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ELS๋ฅผ ์ด์šฉํ•˜์—ฌ zeta potential ๊ฐ’์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์ œํƒ€ ํฌํ…์…œ ๊ฐ’์ด ๋†’์€ ์Œ์˜ ๊ฐ’์„ ๊ฐ€์งˆ์ˆ˜๋ก ์šฉ์•ก์—์„œ์˜ ๋ถ„์‚ฐ ์•ˆ์ •์„ฑ์ด ๋†’์œผ๋ฉฐ, ์ผ๋ฐ˜์ ์œผ๋กœ โ€“40์ด ๋„˜์–ด๊ฐ€๋ฉด ์•ˆ์ •์„ฑ์ด ์ข‹๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋ถ„์‚ฐ ์•ˆ์ •์„ฑ์ด ๋†’์€ ์ˆœ์„œ๋Š” ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๊ทธ๋ž˜ํ•€ ์–‘์ž์  ์ˆœ์„œ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์˜ ํ‘œ๋ฉด ์ž‘์šฉ๊ธฐ, ์‚ฐํ™” ์ •๋„, ์ž…์ž์˜ ํฌ๊ธฐ, ๋ถ„์‚ฐ์•ˆ์ •์„ฑ์— ๋Œ€ํ•ด ์ดํ•ดํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ์ด๋Š” ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์„ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์ ์šฉ์‹œํ‚ฌ ๋•Œ ๋งŽ์€ ๋„์›€์„ ์ค„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ถ„์„ํ•œ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์„ ๋ฐ”์ด์˜ค์†Œ์žฌ๋กœ์„œ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๊ด€ํ•œ ์—ฐ๊ณจ ์žฌ์ƒ ์—ฐ๊ตฌ๋ฅผ ํ•˜์˜€๋‹ค. ์†Œ์ˆ˜์„ฑ์ธ ๊ทธ๋ž˜ํ•€์„ ์‚ฐํ™”์‹œ์ผœ ๋งŒ๋“  ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์€ ์นœ์ˆ˜์„ฑ์œผ๋กœ ์„ฑ์งˆ์ด ๋ฐ”๋€Œ๋ฉด์„œ ์„ธํฌ์™€์˜ ์นœํ™”์„ฑ์ด๋‚˜ ๋ฐฐ์–‘ ๋ฐฐ์ง€์— ์‰ฝ๊ฒŒ ๋ถ„์‚ฐ๋  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์„ ๊ฐ€์ ธ ์กฐ์ง๊ณตํ•™ ๋ถ„์•ผ์— ์ ‘๋ชฉ์‹œํ‚ค๊ธฐ ์œ ๋ฆฌํ•˜๋‹ค. ๊ทธ๋ž˜์„œ ๋‚˜๋Š” ๋จผ์ €, ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ํ˜•ํƒœํ•™์ ์ธ ๋ถ„์„๊ณผ ๊ณจ, ์ง€๋ฐฉ, ์—ฐ๊ณจ๋กœ์˜ ๋ถ„ํ™”๋Šฅ์— ๋Œ€ํ•œ ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ๋Š” ์„ฌ์œ ์•„์„ธํฌ์™€ ๊ฐ™์€ ๋ชจ์–‘์„ ๊ฐ€์กŒ์œผ๋ฉฐ, ๊ณจ, ์ง€๋ฐฉ, ์—ฐ๊ณจ๋กœ์˜ ๋ถ„ํ™”๋Šฅ์„ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‚˜์„œ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™”์— ์ ํ•ฉํ•œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์˜ ๋†๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์„ ๊ธฐ์ค€์œผ๋กœ ํ•œ ์ด์œ ๋Š” ์ž…์ž์˜ ํฌ๊ธฐ๊ฐ€ ๊ฐ€์žฅ ํฌ๊ณ , ์กฐ์ง ๊ณตํ•™๊ณผ ๊ด€๋ จ๋œ ์ฐธ๊ณ ๋ฌธํ—Œ์ด ๊ฐ€์žฅ ๋งŽ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, 10 ใŽ/ใŽ–์˜ ์ดํ•˜์˜ ๋†๋„์—์„œ ์ ํ•ฉํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ž˜์„œ 1๊ณผ 10 ใŽ/ใŽ–์˜ ๋†๋„์˜ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€, ๊ทธ๋ž˜ํ•€ ์–‘์ž์ ์„ ์ด์šฉํ•ด ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ ๋ถ„ํ™” ์˜ํ–ฅ์„ ํ™•์ธํ•˜์˜€์„ ๋•Œ, ์ฝ˜๋“œ๋กœ์‚ฌ์ดํŠธ์˜ ํŽ ๋ › ํฌ๊ธฐ์—๋Š” ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜๋‹ค. ํ•˜์ง€๋งŒ alcian blue staining๊ณผ toluidine blue staining์œผ๋กœ ์กฐ์งํ•™์  ๋ถ„์„์„ ํ•˜์˜€์„ ๋•Œ, 1 ใŽ/ใŽ–์˜ ๋†๋„์˜ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€๊ณผ ๋‚˜๋…ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ๊ตฐ์—์„œ ์–‘์„ฑ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด GAGs์˜ ํ•จ๋Ÿ‰์ด ์œ ์˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, 100 nm ์ด์ƒ์˜ ํฌ๊ธฐ๋ฅผ ๊ฐ€์ง„ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์ด ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ๊ธฐ์งˆ์„ธํฌ์˜ ์—ฐ๊ณจ๋ถ„ํ™”์— ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์žฌ์ƒ ์˜ํ•™์ด๋‚˜ ์กฐ์ง๊ณตํ•™ ๋ถ„์•ผ์—์„œ ๋– ์˜ค๋ฅด๋Š” ๋ฐ”์ด์˜ค ์‹ ์†Œ์žฌ๋กœ ์ž๋ฆฌ๋งค๊น€์„ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์•ž์œผ๋กœ ๊ทธ๋ž˜ํ•€ ๊ธฐ๋ฐ˜ ๋ฌผ์งˆ๋“ค์ด ์„ธํฌ์™€ ์„ธํฌ ์™ธ ๊ธฐ์งˆ ๋“ฑ ์ฃผ๋ณ€ ํ™˜๊ฒฝ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด๋‚˜ ์„ธํฌ ๋…์„ฑ, ์ ํ•ฉํ•œ ๋ฐฐ์–‘ ์กฐ๊ฑด์— ๊ด€ํ•œ ๊นŠ์ด ์žˆ๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋” ์ง„ํ–‰๋œ๋‹ค๋ฉด ์ถฉ๋ถ„ํžˆ ์กฐ์ง ๊ณตํ•™์  ์‘์šฉ์ด ๊ฐ€๋Šฅํ•ด ์งˆ ๊ฒƒ์ด๋‹คPART I : GENERAL INTRODUCTION & LITERATURE REVIEW 1 CHAPTER 1 : General Introduction 2 CHAPTER 2 : Literature Review 9 1. Cartilage 10 1.1. Definition 10 1.2. Composition 10 1.3. Cartilage-related diseases 15 2. Tissue engineering 22 2.1. Definition 22 2.2. Materials . 22 2.2.1 Cells . 23 2.2.2 Biomaterials . 26 3. Cartilage regeneration 29 PART II : EFFECT OF GRAPHENE-BASED MATERIALS ON CHONDROGENIC DIFFERENTIATION OF HUMAN ADIPOSE-DERIVED STROMAL CELLS 31 CHAPTER 1 : Effect of graphene-based materials on chondrogenic differentiation of human adipose-derived stromal cells 32 1. Introduction 33 2. Materials and Methods 39 3. Results 44 4. Discussion 58 PART III : GENERAL DISCUSSION & CONCLUSION 61 1. General discussion and conclusion 62 REFERENCES 66 SUMMARY IN KOREAN 95Maste

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2020. 8. ๋ฐ•์žฌํฅ.๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์€ ๋ณดํ–‰์˜ ์•ˆ์ •์„ฑ์„ ๋–จ์–ดํŠธ๋ฆฌ๋Š” ์š”์ธ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณดํ–‰ ์ค‘ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ์ด ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ ๋งˆ์ฐฐ๋ ฅ๋ณด๋‹ค ์ปค์ง€๋ฉด, ๋ฐœ์€ ์ ‘์ด‰์„ ์ƒ์‹คํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์ง€๊ฒŒ ๋œ๋‹ค. ์—ฌ๊ธฐ์„œ, ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ ๋งˆ์ฐฐ๋ ฅ์€ ๋ฐœ์— ์ž‘์šฉํ•˜๋Š” ์ˆ˜์ง๋ ฅ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๊ฒŒ ๋œ๋‹ค. ์ฆ‰, ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด ์ƒ์„ฑ์˜ ์ธก๋ฉด์—์„œ ๋ณด์ž๋ฉด, ๋กœ๋ด‡ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜ํ‰๋ ฅ๊ณผ ์ˆ˜์ง๋ ฅ์„ ์–ด๋–ป๊ฒŒ ์„ค๊ณ„ํ•˜๋Š”์ง€์— ๋”ฐ๋ผ ๋ณดํ–‰ ์ค‘ ๋ฏธ๋„๋Ÿฌ์ง์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๋ฐ”๋€๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ์„ ์œ„ํ•ด ์ž์ฃผ ์‚ฌ์šฉ๋˜์–ด์™”๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์€ ๋กœ๋ด‡์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๋†’์ด๋ฅผ ์ผ์ •ํ•˜๊ฒŒ ์œ ์ง€ํ•˜๋„๋ก ์ œํ•œํ•œ๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ๋†’์ด ์ œํ•œ ๋•Œ๋ฌธ์— ๋กœ๋ด‡์˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๊ฐ€์†๋„๋Š” ๋ณดํ–‰ ์†๋„์™€ ๊ด€๋ จ ์—†์ด ํ•ญ์ƒ ์ค‘๋ ฅ ๊ฐ€์†๋„๊ฐ€ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜ํ‰ ๋ฐฉํ–ฅ์˜ ๊ฐ€์†๋„๋Š” ๋ณดํ–‰ ์†๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉด ๋น„๋ก€ํ•˜์—ฌ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋น ๋ฅธ ๋ณดํ–‰ ์†๋„์—์„œ๋Š” ์ˆ˜์ง๋ ฅ์— ๋น„๋ก€ํ•˜๋Š” ๋งˆ์ฐฐ๋ ฅ์— ๋น„ํ•ด ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ์ด ์ปค์ง€๋ฉด์„œ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์— ์˜ํ•œ ์ผ์ •ํ•œ ์ˆ˜์ง ๋†’์ด ๊ตฌ์† ์กฐ๊ฑด์ด ๋กœ๋ด‡ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์ ์ ˆํ•œ ์ˆ˜์ง ์›€์ง์ž„์„ ์ƒ์„ฑํ•จ์œผ๋กœ์จ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ์ค‘ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ์ธ๊ฐ„๊ณตํ•™ ๋ถ„์•ผ์—์„œ๋Š” Available Coefficient of Friction(aCOF)๊ณผ Utilized Coefficient of Friction(uCOF)์„ ์ด์šฉํ•˜์—ฌ ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง ๊ฐ€๋Šฅ์„ฑ์„ ์˜ˆ์ธกํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ˆ˜ํ–‰๋๋‹ค. ์—ฌ๊ธฐ์„œ, aCOF๋Š” ๋‘ ๋ฌผ์ฒด์˜ ์žฌ์งˆ์ด๋‚˜ ์ƒํƒœ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๋Š” ๋งˆ์ฐฐ ๊ณ„์ˆ˜์ด๋‹ค. ๋ฐ˜๋ฉด, uCOF๋Š” ๋ณดํ–‰ ์ค‘ ์ง€์ง€ํ•˜๋Š” ๋ฐœ์— ๊ฐ€ํ•ด์ง€๋Š” ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ๊ณผ ์ˆ˜์ง๋ ฅ์˜ ๋น„์ด๋‹ค. ์ธ๊ฐ„๊ณตํ•™ ์—ฐ๊ตฌ๋“ค์— ๋”ฐ๋ฅด๋ฉด, uCOF๊ฐ€ aCOF๋ฅผ ์ดˆ๊ณผํ•  ๋•Œ ๋ฐœ์€ ์ ‘์ด‰์„ ์ƒ์‹คํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์ง€๊ฒŒ ๋œ๋‹ค. ๋กœ๋ด‡ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง ๊ฐ์†Œ๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋กœ๋ด‡ ๋ณดํ–‰ ์ค‘ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” uCOF๊ฐ€ ๋กœ๋ด‡ ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ aCOF ๋ณด๋‹ค ์ž‘์•„์ง€๋„๋ก ์ ์ ˆํ•œ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ์ด ๊ฐ€๋Šฅํ•œ๋ฐ, ๊ฐ„๋‹จํ•˜๋ฉด์„œ๋„ ํšจ์œจ์ ์ธ ๋ฐฉ๋ฒ•์€ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์—๋„ˆ์ง€๊ฐ€ ๋ณด์กด๋˜๋„๋ก ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ธฐ์กด ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์ˆ˜ํ‰ ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๊ณ , ์šด๋™ ์—๋„ˆ์ง€์™€ ์œ„์น˜ ์—๋„ˆ์ง€๊ฐ€ ๊ตํ™˜๋˜๋ฉด์„œ ์ „์ฒด ์—๋„ˆ์ง€๊ฐ€ ๋ณด์กด๋˜๋Š” ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ถ”๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์—๋„ˆ์ง€ ๋ณด์กด ์›๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์–‘์˜ ์ผ(Mechanical Work) ์ƒ์„ฑ์„ ์ตœ์†Œํ™”ํ•จ์œผ๋กœ์จ ๊ด€์ ˆ์˜ ์–‘์˜ ์ผ ์ƒ์„ฑ์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋ณดํ–‰ ์ค‘ ์—๋„ˆ์ง€ ํšจ์œจ์„ ๋†’์ด๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ aCOF ๋ณด๋‹ค ์ž‘๋„๋ก ๋ณดํ–‰ ์ค‘ uCOF๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์–‘์˜ ์ผ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ์ ์ ˆํ•œ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์ด ๊ฐ์†Œํ•˜๋ฉด์„œ ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด ์ƒ์„ฑ์„ ์œ„ํ•ด, ๋จผ์ € ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ uCOF์— ๊ด€ํ•œ ์—ฐ๊ตฌ์™€ ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ ๊ด€์ ˆ์˜ ์ผ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์„ ํ–‰ํ•œ๋‹ค. ์‚ฌ๋žŒ ๋ณดํ–‰์— ๊ด€ํ•œ ๋ถ„์„ ์—ฐ๊ตฌ์™€ ์‚ฌ๋žŒ ๋ณดํ–‰์˜ ์›๋ฆฌ ์ดํ•ด๋ฅผ ํ†ตํ•ด ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ ๋ฐฉ๋ฒ•์ด ์ œ์‹œ๋œ๋‹ค. ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๊ตฌํ•ด์ง„ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ์‹คํ—˜์— ์ ์šฉํ•œ๋‹ค. ๊ถ๊ทน์ ์œผ๋กœ ์ด ๋…ผ๋ฌธ์€, ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ถ”๊ฐ€ํ•จ์œผ๋กœ์จ ๊ธฐ์กด ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜์—ฌ, ๋ฏธ๋„๋Ÿฌ์ง์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๊ฐ์†Œํ•˜๊ณ  ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด์„ ์ƒ์„ฑํ•œ๋‹ค.Foot slippage is one of the factors responsible for the increasing instability during human walking. A slip occurs when the horizontal shear force acting on the foot becomes greater than the frictional force between the foot and the ground, which is proportional to the vertical force. For humanoid robot walking, the possibility of a slip depends upon how the horizontal shear force and vertical force both acting on the foot are designed. In the linear inverted pendulum model (LIPM), which is commonly used to generate the center of mass (COM) trajectory of humanoid robots, the vertical height of the COM is kept constant. The constant height of the COM restricts that the vertical force is always equal to the gravitational force at any walking speed. However, upon increasing the walking speed, the horizontal ground reaction force increases in proportion with the forward and lateral accelerations of the COM. This increase in the horizontal ground reaction force, while the vertical ground force is being constant, suggests that the robot-foot slippage can occur because of the restriction of the vertical motion by the LIPM constraint. By generating the appropriate vertical motion, the robot-foot slippage can be reduced during humanoid robot walking. Researchers in the field of ergonomics have been conducted studies on the relationship between the available coefficient of friction (aCOF) and the utilized coefficient of friction (uCOF) to predict the potential for a slip during human walking. The aCOF is both the static and dynamic coefficient of friction between two objects in contact, and it depends on the properties of the objects. The uCOF is the ratio of the horizontal shear force to the vertical force applied by the supporting foot. Foot slippage occurs when the uCOF exceeds the aCOF. Various types of vertical motion can set the maximum value of the uCOF to be less than the aCOF between the foot and floor for humanoid robot walking. One of the simple and energy-efficient methods is to minimize the mechanical work of the COM by introducing added vertical motion. Therefore, the COM pattern would become more energy efficient by exchanging kinetic energy and potential energy. This thesis aims to generate the appropriate vertical motion of the COM to maintain the utilized coefficient of friction (uCOF) less than the available coefficient of friction between the foot and the ground, and to minimize the mechanical work during humanoid robot walking. Before generating a slip-safe and energy-efficient COM trajectory for humanoid robot walking, studies on analyzing the COM patterns, mechanical work, and uCOF during human walking are conducted to understand the principle of walking. Vertical motions at various speeds are generated using an optimization method. Subsequently, the generated COM motion patterns are used as reference trajectories of the COM for humanoid robot walking. This thesis suggests a way to generate slip-safe and energy-efficient COM patterns, which, in turn, overcome the limitations of the LIPM by adding vertical COM motion.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Contributions of Thesis 3 1.3 Overviews of Thesis 4 Chapter 2 Dynamics of Walking 5 2.1 Walking Model 5 2.1.1 Linear Inverted Pendulum Model 5 2.1.2 Spring-Loaded Inverted Pendulum Model 6 2.1.3 Extrapolated Center of Mass Dynamics 9 2.2 Walking Theory 11 2.2.1 Step-to-Step Transition 11 Chapter 3 HumanWalking Analysis 13 3.1 Motion Capture for Walking 13 3.1.1 Motion Capture Technology 13 3.1.2 Joint Kinematics and Kinetics 15 3.2 Joint and COM During Human Walking 17 3.2.1 Introduction 17 3.2.2 Methods 19 3.2.3 Change of Joint Angle and the COM 20 3.2.4 Discussion 26 3.3 Slipping During Human Walking 27 3.3.1 Introduction 27 3.3.2 Methods 31 3.3.3 Change of uCOF and GRF 34 3.3.4 Interaction Effect Between Heel Area and Speed 36 3.3.5 Discussion 39 3.4 Mechanical Work During Human Walking 44 3.4.1 Introduction 44 3.4.2 Methods 46 3.4.3 Calculation for Joint Mechanical Work 48 3.4.4 Change of Joint Mechanical Work 51 3.4.5 Change of Stride Parameters 53 3.4.6 Discussion 54 Chapter 4 Robot Walking Pattern Generation 59 4.1 Introduction 59 4.2 Forward and Lateral COM 61 4.2.1 XcoM Method 61 4.2.2 Preview Control Method 63 4.3 Vertical COM 64 4.3.1 Calculation for uCOF 64 4.3.2 Calculation for ZMP 65 4.3.3 Calculation for COM Mechanical Work 66 4.3.4 Optimization for Vertical COM Generation 68 4.3.5 Results of Optimization for Vertical COM 73 4.4 Slipping During Robot Walking 75 4.4.1 Robot Simulation 75 4.4.2 Robot Experiments 77 4.5 Mechanical Work During Robot Walking 81 4.5.1 Robot Simulation 81 4.5.2 Robot Experiments 82 4.6 Discussion 87 4.6.1 Tracking Errors in Robot Experiments 87 4.6.2 Effect of Vertical Motions on Real Net Power 91 4.6.3 Trade-Off Between Efficiency and Stability 92 4.6.4 Difference Between Human and Robot 93 Chapter 5 Conclusions 95 Bibliography 97 Abstract (Korean) 111Docto

    Calcium silicate ๊ทผ๊ด€ ์ถฉ์ „ ์žฌ๋ฃŒ์˜ ์‹ค์‹œ๊ฐ„ ๋‚˜๋…ธ ๋ˆ„์ถœ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๋Œ€ํ•™์› ์น˜์˜๊ณผํ•™๊ณผ, 2021. 2. ์ด์šฐ์ฒ .1. Objectives This study aimed to investigate the real-time nanoleakage of calcium silicate (Ca-Si) based root canal filling materials. 2. Materials and Methods Extracted human teeth (n = 30) were decoronated and enlarged apically to size #40 (taper 0.06) using Protaper Next X4 instruments (Dentsply, USA). The teeth were randomly divided into 3 experimental groups and obturated with respective materials; Gutta Percha (GP) with AH 26 sealer using the continuous wave of condensation technique, GP with EndoSeal MTA sealer using the continuous wave of condensation technique, and solely obturated with orthograde Biodentine. The roots were embedded into acrylic resin except the apical 2 mm to prevent leakage through lateral surface and a metal tube was fixed on the coronal orifice by using flowable composite resin. The prepared specimen was connected to a nanoFlow device (IB Systems, Seoul, Korea) under hydrostatic pressure (40 cmโˆ™H2O) and fluid flow was traced through the filled roots. Data were detected at the nanoscale twice per second and automatically recorded in units of nL/s. Leakage was quantified as the mean slope after the curve stabilized over time. Data were statistically analyzed using the Kruskal-Wallis test. The level of significance was set at 5%. 3. Results The calculated leakage values were 0.0670 ยฑ 0.0516 nL/s for GP/AH26, 0.1397 ยฑ 0.1579 nL/s for GP/EndoSeal MTA, and 0.0358 ยฑ 0.0538 nL/s for Biodentine, with no statistically significant differences among the root filling materials (P > 0.05). 4. Conclusion Real-time measurements under hydrostatic pressure with the nanoFlow device enabled precise fluid flow tracing through the root canal filling material. In terms of nanoleakage, the tested root canal filling materials showed no significant differences.1. ๋ชฉ์  ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ Calcium silicate (Ca-Si) ๊ธฐ๋ฐ˜ ๊ทผ๊ด€ ์ถฉ์ „์žฌ์˜ ์‹ค์‹œ๊ฐ„ ๋‚˜๋…ธ ๋ˆ„์ถœ์„ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 2. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 30๊ฐœ์˜ ๋ฐœ์น˜๋œ ์‚ฌ๋žŒ์˜ ๋‹จ๊ทผ์น˜์˜ ์น˜๊ด€์„ ์ œ๊ฑฐํ•˜๊ณ  Protaper Next X4๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ทผ๊ด€์„ ํ™•๋Œ€ํ•œ ํ›„ ์„ธ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด ๊ฐ๊ฐ Gutta percha (GP) ์™€ AH26 ์‹ค๋Ÿฌ, GP ์™€ EndoSeal MTA ์‹ค๋Ÿฌ, Biodentine์œผ๋กœ ์ถฉ์ „ํ•˜์˜€๋‹ค. ๋ถˆํ•„์š”ํ•œ ๋ˆ„์ถœ์„ ๋ง‰๊ธฐ ์œ„ํ•ด ์น˜๊ทผ๋‹จ 2mm ๋ฅผ ์ œ์™ธํ•œ ๋ถ€๋ถ„์„ ์•„ํฌ๋ฆด๋ฆญ ๋ ˆ์ง„์— ๋งค๋ชฐํ•˜๊ณ  ๊ทผ๊ด€ ์ž…๊ตฌ์— ์œ ๋™์„ฑ ๋ณตํ•ฉ๋ ˆ์ง„์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ธˆ์† ๊ด€์„ ๊ณ ์ •ํ•œ ๋’ค, 40cmโˆ™H2O์˜ ์ •์ˆ˜์•• ํ•˜์—์„œ nanoFlow ์žฅ์น˜(IB syetems, Seoul, Korea) ์— ์—ฐ๊ฒฐํ•˜์—ฌ ๊ทผ๊ด€์„ ํ†ตํ•œ ๋ฏธ์„ธ ๋ˆ„์ถœ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๋ฐ์ดํ„ฐ๋Š” ์ดˆ๋‹น 2ํšŒ, ๋‚˜๋…ธ ๋‹จ์œ„๋กœ ๊ฐ์ง€๋˜์—ˆ์œผ๋ฉฐ nL/s ๋‹จ์œ„๋กœ ์ž๋™ ๊ธฐ๋ก๋˜์—ˆ๋‹ค. ๋ˆ„์ถœ ์–‘์€ ๋ˆ„์ถœ ๊ณก์„ ์ด ํ‰ํƒ„ํ™” ๋œ ์ดํ›„์˜ ํ‰๊ท  ๊ธฐ์šธ๊ธฐ๋กœ ๊ณ„์‚ฐ๋˜์—ˆ๋‹ค. ๋ฐ์ดํ„ฐ๋Š” Kruskal-Wallis ํ…Œ์ŠคํŠธ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ†ต๊ณ„์ ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์œ ์˜ ์ˆ˜์ค€์€ 5%๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. 3. ๊ฒฐ๊ณผ ๊ณ„์‚ฐ๋œ ๋‚˜๋…ธ ๋ˆ„์ถœ ๊ฐ’์€ GP/AH26์˜ ๊ฒฝ์šฐ 0.0670 ยฑ 0.0516 nL/s, GP/EndoSeal MTA์˜ ๊ฒฝ์šฐ 0.1397 ยฑ 0.1579 nL/s, Biodentine์˜ ๊ฒฝ์šฐ 0.0358 ยฑ 0.0538 nL/s ๋กœ, ์žฌ๋ฃŒ ์‚ฌ์ด์— ์œ ์˜ํ•œ ์ฐจ์ด๋Š” ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค(P > 0.05). 4. ๊ฒฐ๋ก  nanoFlow ์žฅ์น˜๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ์ •ํ™•ํ•œ ๊ทผ๊ด€ ์ถฉ์ „ ์žฌ๋ฃŒ์˜ ์‹ค์‹œ๊ฐ„ ๋ฏธ์„ธ ๋ˆ„์ถœ์˜ ์ธก์ •์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋‚˜๋…ธ ๋ˆ„์ถœ ์ธก๋ฉด์—์„œ ๊ฐ ๊ทผ๊ด€ ์ถฉ์ „์žฌ ์‚ฌ์ด์—๋Š” ํฐ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค.I. Introduction 1 II. Materials and Methods 7 1. Ethics 7 2. Sample size calculation 7 3. Sample preparation 7 4. Nanoleakage measurement . 9 5. Statistical analysis 10 III. Result 11 IV. Discussion 12 V. Conclusion 18 Reference 19 Tables & Figures 27 Korean Abstract 31Docto

    ํšจ์œจ์ ์ด๊ณ  ์•ˆ์ „ํ•œ ์—๋„ˆ์ง€ ์ €์žฅ์„ ์œ„ํ•œ ๋ฆฌํŠฌ์ด์˜จ์ „์ง€ ๋ฐ ์Šˆํผ์ปคํŒจ์‹œํ„ฐ ์šฉ ์ „๊ทน๋ฌผ์งˆ์˜ ๊ฐœ๋ฐœ๊ณผ ์•„ํ‚คํ…์ณ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2016. 2. ์ด์ข…ํ˜‘.Occasional reports on accidental explosions that appear in the news, increases the safety concerns of using Li ion batteries (LIBs). The scope of LIB is expanding toward large-scale energy storage systems such as electrified transportation and smart grids. Considering these facts, manufacturers and consumers now have concerns regarding the safe use of such devices. The high voltage anode materials have greatly relieved the unstable state of LIB during operation because they can exclude the uneven plating of lithium and reductive electrolyte decomposition. Although various titanium based oxides have been developed as high voltage anode, low capacity or poor potential flatness still severely hinder their industrial usage. In this dissertation, two types of titanium-based crystals are proposed, and they exhibited enhanced Li storage performance as high voltage anode materials as follows. A c-channel that is formed inside stacked (001) planes in rutile TiO2 exhibits the lowest energy barrier for Li migration. In this regard, the rational design of a TiO2 architecture for stacked (001) planes is needed in order to maximize Li storage. Here, a three-dimensional and dendritic TiO2 sphere comprised of c-channel specialized nanorods is proposed, which can be prepared via the specific adsorption of chlorine ion on the (110) plane. Along with a confined Li pathway, such radially assembled TiO2 nanorods show a low intercrystalline resistance. When fabricated into an electrode, the three-dimensional and dendritic TiO2 sphere is capable of delivering an almost 100% Coulombic efficiency in conjunction with long cycle charge/discharge stability (300 cycles). This approach, which is based on theoretical studies and experimental validation, provides guidance for tailoring electrode materials for use in Li storage systems. H2Ti12O25 (HTO), a recently discovered anode material for LIB, has outstanding electrochemical performance compared to other high voltage materials. However, its thermodynamic/kinetic properties as a Li host have not been thoroughly investigated yet. In this study, the Li storing behavior of HTO was intensively characterized by a combined theoretical experimental study. In addition, the strong dependence of electrochemical performance on Li diffusion kinetics stimulated us to develop a nanostructured HTO which provides incorporated Li with a short diffusion length inside a nano-crystal. As a result, the nanostructured HTO showed upgraded Li storage performance. This work suggests that the HTO is one of the most competitive material found to date for the construction of advanced LIB with excellent safety and stability. Electrochemical capacitors (so-called supercapacitors), with high power density and superior cycling ability, are considered to be one of the most stable and safe energy storage systems. High power as well as reasonably high energy density are provided, supercapacitors are likely to be regarded as a representative energy storage system along with batteries. The common challenge in supercapacitor design is the resistive behavior originated from sluggish ion transport, poor electrical conductivity, and high charge transfer resistance, which increases equivalent series resistance of overall cell. In addition, a variety of activation process for the increase of surface area not only are involved in harmful chemicals or toxic metals but also lower its cost-effectiveness. In this dissertation, two strategies for resolving above-mentioned challenging points are proposed3D bicontinuous metal-carbon hybrid and organic gel-wrapped MnO2 thin fim electrode. The three dimensionally aligned bicontinuous carbon and metal hybrids are synthesized. The resulting material shows a significantly high rate capability up to 1,000 V s-1. The proposed strategy exploits an agarose gel as a template for the simultaneous construction of three dimensional (3D) carbon structures filled with a metal-lined architecture in supercapacitors. The carbon framework with three dimensional and interconnected metal lines inside minimizes both empty inner pores and electron transfer barriers, which results in a substantial reduction of the overall electrode resistance. In addition, it can be used for the filteration of voltage ripples due to the ultrafas7t response with high efficiency. A robust hybrid film containing MnO2 was prepared for achieving large areal capacitances. An agarose gel, as an ion-permeable and elastic layer coated on a current collector, plays a key role in stabilizing the deposited pseudocapacitive MnO2. Cyclic voltammetry and electrochemical impedance spectroscopy data indicate that the hybrid electrode is capable of exhibiting a high areal capacitance up to 52.55 mF cm-2, with its superior structural integrity and adhesiveness to the current collector being maintained, even at a high MnO2 loading. The emergence of body-centric power generators such as piezoelectric/solar cell and the functional/morphological evolution of smart phones have created a need for advanced devices for energy storage. In order to meet the power demand of such devices, the construction of a series of unconventional energy storage systems have been developed in the past few years, which enable one to achieve flexible, foldable, and stretchable characteristics. In order to secure their stability and safety of energy storage platform under mechanically stressed conditions, the primary requirements of electrode materials include robust electrical connectivity and mechanical endurance. In the last part of this dissertation, a lint taping method is described for the fabrication of thin layer of conducting network using graphite felt. An all-solid-state, completely foldable and washable energy storage platform was fabricated. By adopting it as a supercapacitor electrode, the physical characteristics and electrochemical properties of such a GFCN are identified. A constructed graphitic fiber network derived from conventional graphite felt was readily assembled into a full-cell by its self-adhering architecture. The as-prepared system exhibits high mechanical properties under various folding motifs and washable characteristics without capacitance fading by virtue of the robust electrical connectivity of the fibrous graphite network and intimate contact between the polymeric gel electrolyte and the electrodes. The collected results suggest that this supercapacitor system is a promising candidate for practically available and wearable energy storage systems with high cost-effectiveness and scalability.Chapter 1. Introduction 1 1.1 Toward safe and efficient Li-ion batteries: Development of high voltage anode materials 1 1.1.1 Development of Li-related batteries from a safety standpoint 1 1.1.2 Challenges in development of high voltage anode materials for use in LIBs 2 1.1.3 Strategy 1: c-channel specialized TiO2 sphere as high voltage anode materials 4 1.1.4 Strategy 2: H2Ti12O25 nanostructures as high voltage anode materials 6 1.2 Toward safe and efficient supercapacitors: Development of hybrid electrode materials 14 1.2.1 Development of supercapacitors for safe and reliable energy storage systems 14 1.2.2 Challenges facing supercapacitors 15 1.2.3 Strategy 1: Bicontinuous metal/carbon hybrid as high power supercapacitor electrodes 15 1.2.4 Strategy 2: Hybrid MnO2 film for enhancing the structural integrity of thin film supercapacitor electrodes 17 1.3 A system level approach to construct robust energy storage platform enduring mechanical stress 23 1.4 Objectives 26 Chapter 2. Radial Alignment of c-Channel Nanorods in 3D Porous TiO2 for Eliciting Enhanced Li Storage Performance 28 2.1 Experimental 28 2.1.1 Preparation of TiO2 materials 28 2.1.2 Characterizations 29 2.1.3 Electrochemical measurements 29 2.1.4 Computational details 30 2.2 Results and discussion 32 2.2.1 Li diffusion dynamics inside rutile TiO2 32 2.2.2 Synthesis of 3D-TS 33 2.2.3 Characterization of 3D-TS 35 2.2.4 Anisotropic growth of TiO2 nanorods 37 2.2.5 Electrochemical analyses 38 Chapter 3. Nanostructured H2Ti12O25 as a Superior High Voltage Anode Materials for Li-Ion Batteries 67 3.1 Experimental 67 3.1.1 Synthesis of H2Ti12O25 67 3.1.2 Characterizations 68 3.1.3 Electrochemical measurements 68 3.1.4 Computational details 69 3.2 Results and discussion 70 3.2.1 Structural evolution during synthesis of HTO 70 3.2.2 Characterizations of HTO made from phase-pure TiO2 70 3.2.3 Diffusion properties of Li inside HTO 72 3.2.4 Effect of operating temperature on Li storage performance 74 3.2.5 Kinetic gap of structural transformation from TiO2 to Na2Ti3O7 75 3.2.6 Synthesis and Li storage performance of nanostructured HTO 78 Chapter 4. 3D Bicontinuous Metal/Carbon Hybrid using an Agarose Gel for Ultra-Fast Charge/Dischargeable Supercapacitor Electrodes 105 4.1 Experimental 105 4.1.1 Fabrication of bicontinuous carbon and 3D Au composites 105 4.1.2 Physicochemical characterizations 105 4.1.3 Electrochemical characterizations 106 4.1.3 Calculations 106 4.2 Results and discussion 107 4.2.1 Preparation of the 3D metal/carbon composite 107 4.2.2 Electrochemical properties of the 3D metal/carbon composite 109 Chapter 5. Hybrid MnO2 Film with Agarose Gel for Enhancing the Structural Integrity of Thin Film Supercapacitor Electrodes 125 5.1 Experimental 125 5.1.1 Fabrication of hybrid MnO2 electrodes 125 5.1.2 Characterizations 125 5.1.3 Calculations 126 5.1.4 3D finite elemental method modelling 127 5.1.5 Density functional theory calculations 128 5.2 Results and discussion 129 5.2.1 Synthesis and characterization of agarose gel-wrapped MnO2 electrode 129 5.2.2 Electrochemical measurements 131 Chapter 6. Robust Energy Storage Platform with Foldability and Washability 147 6.1 Experimental 147 6.1.1 Preparation of graphit felt-based conducting network (GFCN) electrode 147 6.1.2 Assembly of a full-cell system 147 6.1.3 Oxidation of graphite felt 148 6.1.4 Characterizations 149 6.1.5 Calculations 149 6.2 Results and discussion 151 6.2.1 Selection of optimal adhesive substrate 151 6.2.2 Assembly of full-cell 154 6.2.3 Electrochemical measurements 155 6.2.4 Folding tests 156 6.2.5 Washing tests 157 6.2.6 Improvement of capacitance via surface treatments 159 Chapter 7. Summary and Conclusions 179 Chapter 8. Recommendation for Further Research 182 Bibliography 183 ์š”์•ฝ (๊ตญ๋ฌธ์ดˆ๋ก) 198 List of publications 204Docto

    Perception and Inner Struggle Experienced by Nursing Students in Relation with Infection Management through Observation and Performance of Infection Control Activities

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    Purpose: To study the internal psychological conflicts among nursing students during an infection control protocol carried out in the hospital by measuring their observation skills and performance during clinical training. Methods: Investigation of both pre- and post- infection control was conducted using questionnaires for clinical infection practices. We identified and evaluated the students observation skills, clinical performance, clinical perception,and internal conflict regarding clinical infection control. We also interviewed the students as part of our study. Results: Among parameters such as clinical performance, observation skills, clinical perception, and internal conflict, the average observation skills (t=5.49, p<.001) were significantly lower, while internal conflict among students (t=-7.23, p<.001) was significantly higher than expected prior to clinical training. Generally, there was a negative correlation between observation skills and internal conflict in every aspect of infection control practice (r=-.281, p=.031). Internal conflict was significantly higher than expected in the context of hand hygiene (t=-2.135, p=.037), personal hygiene (t=-3.48, p=.002), and ventilator management (t=-3.69, p<.001). Clinical performance of students in the context of hand hygiene (t=4.69, p<.001), personal hygiene (t=2.06, p=.044), and ventilator management (t=2.68, p<.001) was significantly lower than expected prior to clinical training. Conclusion: Our findings showed that internal psychological conflict is higher when infection control practices are observed or performed to a lesser degree. Therefore, reinforcing education regarding infection control among students, such as developing a systematic program, or consecutive training and monitoring, is suggested

    ๋งˆ์šฐ์Šค์—์„œ ๊ฒฐํ•ต๊ท  ๊ฐ์—ผ ์ƒํƒœ์— ๋”ฐ๋ฅธ ์œ ์ „์ž ๋ฐœํ˜„์˜ ์ฐจ์ด

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    Dept. of Medical Science/์„์‚ฌTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. One-third of the world's population is estimated to be latently infected with M. tuberculosis; however, only approximately 10% of latently infected people will develop this overt disease, and their aerosol droplets can be transmitted through the air. Current tests available for the diagnosis of TB are limited by their inability to differentiate between latent tuberculosis infection (LTBI) and active TB disease. Therefore, an accurate and effective marker for differential diagnosis should be developed for predicting disease progression. Given the increasing interest in identifying new biomarkers against TB, we examined promising biomarkers that could distinguish between chronic/reactivation TB disease and LTBI using an M. tuberculosis-infected mouse model. The lung tissue and serum of C57BL/6 mice were used to identify the differences of gene expression among healthy, LTBI, chronic TB, spontaneous reactivation, and immunosuppressive drug-treated groups. First, microarray analysis using isolated mRNAs from mice tissues showed several important immune-related genes with different expression levels between the chronic/latent and healthy control groups (p < 0.05). Next, quantitative reverse transcription-polymerase chain reaction to validate the results obtained by the microarray was performed. Based on the cDNA microarray results, 17 candidate genes were selected and clustered into four groups: 1) chemokines excluding monocyte chemoattractant proteins (MCPs) (CXCL9, CXCL10, CXCL11, CCL5, CCL19); 2) MCPs (CCL2, CCL7, CCL8, CCL12); 3) Receptors (IL2Rฮฒ, IL7R, IL12Rฮฒ1, IL12Rฮฒ2, IL21R, IL27Rฮฑ); and 4) TNF and IFN- genes. Results from the cDNA microarray and quantitative RT-PCR analyses revealed that expression of the selected cytokine genes was significantly higher in lung tissues of the chronic stage than of the latent stage. CXCL9, CCL7, CCL12 were noticeably increased in the chronic stage compared with those in the latent stage. Therefore, these three significantly increased cytokines in lung tissue from the mouse TB model might be candidates for biomarkers that distinguish the two disease stages. This information can be combined with already reported potential biomarkers to construct a network of more efficient TB markers. We used Luminex assays to confirm the same tendency of gene expression in mice serum. Second, there is a substantial need for biomarkers to distinguish latent, chronic TB from spontaneous reactivation, for predicting disease progression. To identify the immunological status of the latent, chronic, and reactivation stages, immunological genes were analysed in lung tissues from mice infected with M. tuberculosis. Gene expression was screened using cDNA microarray analysis and confirmed by quantitative RT-PCR using isolated microRNAs from mice tissues. In the result, 10 microRNAs (mmu-miR-1a-3p, mmu-miR-133a-5p, mmu-miR-133a-3p, mmu-miR-206-3p, mmu-miR-133b-3p, mmu-miR-3064-5p, mmu-miR-450b-3p, mmu-miR-26b-3p, mmu-miR-181a-2-3p, mmu-miR-8114) were likely to be the biomarkers that can distinguish between latent and reactivation TB. Specifically, mmu-miR-206-3p was noticeably increased in the reactivation stage compared with the latent and chronic stage. And mmu-miR-1a-3p is expected to be a biomarker candidate that can simultaneously distinguish latent, chronic, and reactivation stages. In conclusion, these findings suggest that the protective mechanisms against TB infection may be related to chemokines, MCPs, chemokine receptors and microRNAs that modulate the activity of immune responses, and some of which are potential biomarkers distinguishing different stages of M. tuberculosis infection. ๊ฒฐํ•ต์˜ ์›์ธ์€ ๋Œ€๋ถ€๋ถ„ Mycobacterium tuberculosis ์ด๋ฉฐ, ๊ฐ์—ผ์„ฑ ์งˆ๋ณ‘ ์ค‘์—์„œ ์‚ฌ๋ง๋ฅ ์ด ์„ธ๊ณ„ 2์œ„์— ๋žญํฌ๋˜์–ด ์žˆ๋Š” ์‹ฌ๊ฐํ•œ ์งˆ๋ณ‘์ด๋‹ค. WHO๋Š” 2013๋…„๊นŒ์ง€ M. tuberculosis ์— ๊ฐ์—ผ๋˜์–ด ๋ฐœ๋ณ‘๋œ ์‚ฌ๋žŒ์€ 900๋งŒ ๋ช…, ์‚ฌ๋ง์ž๋Š” 150๋งŒ ๋ช…์ด๋ผ๊ณ  ๋ฐํ˜”๋‹ค. ํ˜„์žฌ ์ „์ฒด ์ธ๊ตฌ์˜ ์•ฝ 1/3, ์ฆ‰ 20์–ต ๋ช… ์ด์ƒ์ด TB ์ž ๋ณต ๊ฐ์—ผ์ž์ธ ๊ฒƒ์ด๋‹ค. ๊ทธ ์ค‘ 5-10% ๋งŒ์ด ํ™œ๋™์„ฑ ๊ฒฐํ•ต ํ™˜์ž๊ฐ€ ๋˜๋Š”๋ฐ, ๋Œ€๋ถ€๋ถ„ ...ope

    ์ด์ฐฌํ•ด ์ž‘๊ณก ใ€Œํ•ด๊ธˆ ๋…์ฃผ๋ฅผ ์œ„ํ•œ '์†์ฃ„์ œ'ใ€ ๋ถ„์„ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์Œ์•…๊ณผ, 2017. 2. ์–‘๊ฒฝ์ˆ™.ํ˜„์žฌ์— ์ด๋ฅด๋Ÿฌ ์ฐฝ์ž‘๊ณก์€ ํ•„์ˆ˜๋ถˆ๊ฐ€๊ฒฐํ•œ ๊ตญ์•…์˜ ํ•œ ์žฅ๋ฅด๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๋”ฐ๋ผ์„œ ์—ฐ์ฃผ๊ฐ€๋“ค์€ ์˜ฌ๋ฐ”๋ฅธ ์Œ์•… ๋ถ„์„์„ ํ†ตํ•ด ์ž‘ํ’ˆ์˜ ์Œ์•…์  ๊ฐ€์น˜๋ฅผ ๊ฐ๊ด€์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ , ๊ทผ๊ฑฐ ์žˆ๋Š” ์Œ์•…์  ์‚ฌ๊ณ ๊ฐ€ ๋ฐ”ํƒ•์ด ๋œ ์—ฐ์ฃผ๋ฅผ ํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด์ฐฌํ•ด ์ž‘๊ณก ใ€Œํ•ด๊ธˆ ๋…์ฃผ๋ฅผ ์œ„ํ•œ ์†์ฃ„์ œใ€๋ฅผ ์—ฐ๊ตฌ ๋ถ„์„ํ•˜์—ฌ ์ž‘๊ณก๊ฐ€์˜ ์ž‘ํ’ˆ์„ธ๊ณ„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์†์ฃ„์ œ์˜ ์˜๋ฏธ์— ๋”ฐ๋ฅธ ์•…๊ธฐ ํŽธ์„ฑ๊ณผ ํ๋ฆ„์„ ๊ณ ์ฐฐํ•˜์˜€์œผ๋ฉฐ, ์ž‘ํ’ˆ์˜ ์ „์ฒด ๊ตฌ์กฐ๋ฅผ ํŒŒ์•…ํ•œ ๋’ค ํ”„๋ž™ํƒˆ ๋ถ„์„ ์ด๋ก ์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ž‘ํ’ˆ์„ ์ด๋ฃจ๋Š” ์Œ๋“ค์˜ ํ•ต์‹ฌ๊ตฌ์กฐ๋ฅผ ์•Œ์•„๋‚ด์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํšจ๊ณผ์ ์ธ ์šด์ง€๋ฒ•, ์šด๊ถ๋ฒ• ๋ฐ ํŠน์ˆ˜๊ธฐ๋ฒ•์ด ํฌํ•จ๋œ ์—ฐ์ฃผ๋ฒ•์„ ์‚ดํŽด๋ณด์•˜์œผ๋ฉฐ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. ์ฒซ์งธ, ๋‹จ์•…์žฅ ํ˜•์‹์ธ ใ€Œํ•ด๊ธˆ ๋…์ฃผ๋ฅผ ์œ„ํ•œ ์†์ฃ„์ œใ€๋Š” ํ…œํฌ ๋ณ€ํ™”์™€ ์„ ์œจ์„ ๊ตฌ์„ฑํ•˜๋Š” ํ•ต์‹ฌ๊ตฌ์กฐ, ์•…๊ตฌ์˜ ํŠน์ง•์— ๋”ฐ๋ผ Introduction, โ… , โ…ก, Cadenza, โ…ข, Codaโ… , Codaโ…ก ํฌ๊ฒŒ 7๋‹จ๋ฝ์œผ๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค. ๋‘˜์งธ, ใ€Œ์†์ฃ„์ œใ€๋Š” ํ•ด๊ธˆ์œผ๋กœ ์ธ๊ฐ„์˜ ์ฃ„์™€ ํšŒ๊ฐœ๋ฅผ ๋‚˜ํƒ€๋‚ด๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ์ง•์€ ํ•˜๋‚˜๋‹˜์˜ ๋ชฉ์†Œ๋ฆฌ, ์ž„์žฌ(๏งถๅœจ)๋ฅผ ํ‘œํ˜„ํ•˜์˜€๋‹ค. ์ž‘๊ณก๊ฐ€์˜ ๊ธฐ๋…๊ต์  ์„ธ๊ณ„๊ด€์„ ํˆฌ์˜ํ•˜์—ฌ ์ž‘ํ’ˆ์„ ํ•ด์„ํ•˜์˜€์„ ๋•Œ, ์ง•(ํ˜น์€ ๊ณต)์˜ ๋ฐ˜์ฃผ๊ฐ€ ์žˆ๋Š” ์•…๊ตฌ์™€ ์ง• ๋ฐ˜์ฃผ๊ฐ€ ์—†๋Š” ์•…๊ตฌ ๋‘ ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ฒซ ๋ฒˆ์งธ ์•…๊ตฌ๋Š” ํ•˜๋‚˜๋‹˜๊ณผ ์ธ๊ฐ„์˜ ํ™”ํ•ฉ๊ณผ ๋œป ์ผ์น˜์˜ ๋‚ด์šฉ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๊ณ , ๋‘ ๋ฒˆ์งธ ์•…๊ตฌ๋Š” ์ธ๊ฐ„์ด ํ•˜๋‚˜๋‹˜์˜ ํ†ต์ œ์—์„œ ๋ฒ—์–ด๋‚˜ ์ฃ„๋ฅผ ์ง“๋Š” ๋ถ€๋ถ„์œผ๋กœ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์Œ์•…์ž‘ํ’ˆ์˜ ํ˜•์„ฑ๊ณผ์ •์—์„œ ํ•ต์‹ฌ์ด ๋˜๋Š” ๊ตฌ์กฐ๋‚˜ ์„ ์œจ์ด ์ „์ฒด๋ฅผ ์ด๋ฃฐ ๊ฒƒ์ด๋ผ ๋ณด๋Š” ํ”„๋ž™ํƒˆ ๊ตฌ์กฐ์ด๋ก ์— ์˜ํ•ด ใ€Œ์†์ฃ„์ œใ€๋ฅผ ๊ตฌ์กฐ ๋ถ„์„ํ•˜์˜€์„ ๋•Œ, ์ž‘ํ’ˆ์˜ ํ•ต์‹ฌ ๊ตฌ์กฐ๋Š” Aโ™ญ์„ ์ค‘์‹ฌ์Œ์œผ๋กœ ์œ„โ€ค์•„๋ž˜ ์™„์ „ 5๋„์˜ 3๊ฐœ ์Œ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๊ด€๊ณ„์ด๋‹ค. ์ด๋Š” Introduction๋‹จ๋ฝ์—์„œ ํ•ต์‹ฌ ๊ตฌ์กฐ์˜ ์„ ์œจ์ด ๋ช…ํ™•ํ•˜๊ฒŒ ๋“œ๋Ÿฌ๋‚˜๋ฉฐ, ์ „๊ฐœ ๋˜๋Š” ์ž‘ํ’ˆ ์†์— ํ™•์žฅ์ด๋‚˜ ์Œ์˜ฎ๊น€๊ณผ ๊ฐ™์€ ๋ณ€ํ˜• ์†์—์„œ ๋Š์ž„์—†์ด ์กด์žฌํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋Š” ์ž๊ธฐ์œ ์‚ฌ์„ฑ์งˆ์„ ์ง€๋‹Œ ํ•ต์‹ฌ๊ตฌ์กฐ์˜ ๋ฐ˜๋ณต์œผ๋กœ ๋ถ€๋ถ„๊ณผ ์ „์ฒด๊ฐ€ ๊ฐ™๋‹ค๋Š” ํ”„๋ž™ํƒˆ ๊ตฌ์กฐ์˜ ํŠน์ง•๊ณผ ๋‹ฎ์•„์žˆ์œผ๋ฉฐ, ํ†ต์ผ์„ฑ๊ณผ ์‘์ง‘์„ฑ์ด ๋‚ด์žฌ๋˜์–ด ์žˆ๋Š” ์ž‘ํ’ˆ์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋„ท์งธ, ์ด์ฐฌํ•ด๋Š” ใ€Œ์†์ฃ„์ œใ€์—์„œ ์ฃผ๋กœ ๊ธ€๋ฆฌ์‚ฐ๋„(Glissando), ํŠธ๋ ˆ๋ชฐ๋กœ(Tremolo), ์ค‘์Œ์ฃผ๋ฒ•(Double-stop)์„ ์‚ฌ์šฉํ•˜์—ฌ ํ’๋ถ€ํ•œ ์Œํ–ฅ๊ณผ ํ˜„๋Œ€์ ์ธ ์ƒ‰์ฑ„๋ฅผ ์ด๋Œ์–ด๋‚ด๊ณ ์ž ํ•˜์˜€๋‹ค. ํŠนํžˆ ์ค‘์Œ์ฃผ๋ฒ•(Double-stop)์—์„œ๋Š” ์•„์ง ํ•ด๊ธˆ์— ๋ณดํŽธํ™” ๋˜์ง€ ์•Š์€ ๊ธฐ๋ฒ•๋“ค๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์–ด ์—ฐ์ฃผ๊ฐ€๋“ค์—๊ฒŒ ๋†’์€ ์—ญ๋Ÿ‰์„ ์š”๊ตฌํ•˜๋ฉฐ, ์ž‘๊ณก๊ฐ€๊ฐ€ ์˜๋„ํ•œ ์Œํ–ฅ๊ณผ ์Œ์ƒ‰์— ๋งž๊ฒŒ๋” ๋‹ค์–‘ํ•œ ๊ธฐ๋ฒ•์„ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€๋‹ค. ํ•„์ž๋Š” ์ž‘ํ’ˆ์˜ ๊ตฌ์กฐ์™€ ์˜๋ฏธ ๋ถ„์„์„ ํ†ตํ•ด ์•Œ๋งž์€ ์—ฐ์ฃผ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด์™€ ๊ฐ™์ด ใ€Œํ•ด๊ธˆ ๋…์ฃผ๋ฅผ ์œ„ํ•œ ์†์ฃ„์ œใ€ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์ž‘๊ณก๊ฐ€ ์ด์ฐฌํ•ด์˜ ์ฒซ ๋ฒˆ์งธ ํ•ด๊ธˆ ์ž‘ํ’ˆ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ž‘ํ’ˆ์˜ ๊ตฌ์กฐ ๋ถ„์„์ด ์‹ค์งˆ์ ์ธ ์—ฐ์ฃผ๋ฒ•์— ์ ์šฉํ•˜๋Š”๋ฐ ๋„์›€์ด ๋˜๊ธธ ๋ฐ”๋ผ๋ฉฐ, ํ˜•์‹์ด ๋šœ๋ ทํ•˜๊ฒŒ ์ •ํ•ด์ง€์ง€ ์•Š์€ ํ˜„๋Œ€์Œ์•… ์ž‘ํ’ˆ ๋ถ„์„์— ์žˆ์–ด ๋ฐฉ๋ฒ•๋ก ์˜ ๋‹ค์–‘ํ™”๋ฅผ ๋ชจ์ƒ‰ํ•ด๋ณผ ์ˆ˜ ์žˆ๊ธธ ๋ฐ”๋ž€๋‹ค.โ… . ์„œ ๋ก  1 1. ์—ฐ๊ตฌ๋ชฉ์  1 2. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๋ฐ ์—ฐ๊ตฌ๋ฒ”์œ„ 5 โ…ก. ์ด์ฐฌํ•ด์˜ ์ž‘ํ’ˆ์„ธ๊ณ„ 7 โ…ข. ใ€Œํ•ด๊ธˆ ๋…์ฃผ๋ฅผ ์œ„ํ•œ ์†์ฃ„์ œใ€ ๋ถ„์„ 12 1. ํ”„๋ž™ํƒˆ ๋ถ„์„ ์ด๋ก  12 2. ๊ตฌ์กฐ ๋ถ„์„ 18 1. Introduction ๋‹จ๋ฝ ๊ตฌ์กฐ ๋ถ„์„ 20 2. โ… ๋‹จ๋ฝ, โ…ก๋‹จ๋ฝ ๊ตฌ์กฐ ๋ถ„์„ 23 3. Cadenza ๋‹จ๋ฝ ๊ตฌ์กฐ ๋ถ„์„ 30 4. โ…ข๋‹จ๋ฝ ๊ตฌ์กฐ ๋ถ„์„ 33 5. Codaโ… , Codaโ…ก๋‹จ๋ฝ ๊ตฌ์กฐ ๋ถ„์„ 36 โ…ฃ. ์—ฐ์ฃผ๋ฒ• 40 1. ์•…๊ธฐํŽธ์„ฑ 40 2. ์šด์ง€๋ฒ• 54 3. ์šด๊ถ๋ฒ• 64 4. ํŠน์ˆ˜์ฃผ๋ฒ• 71 โ…ค. ๊ฒฐ ๋ก  77 ์ฐธ๊ณ ๋ฌธํ—Œ 79 ์ฒจ๋ถ€์•…๋ณด 81 Abstract 89Maste
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