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    ๋Œ€๋™๋งฅ์— ๋Œ€ํ•œ ์กฐ์ž‘์ด ์—†๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์—์„œ ๊ธฐ์กด์˜ ์œ„ํ—˜์„ฑ ์˜ˆ์ธก ์‹œ์Šคํ…œ์˜ ์ž„์ƒ์  ์œ ์šฉ์„ฑ ํ‰๊ฐ€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์ž„์ƒ์˜๊ณผํ•™๊ณผ, 2020. 8. ๊น€๊ธฐ๋ด‰.Background: Risk prediction scoring systems are used to measure perioperative risk and identify high-risk patients. Currently, the Society of Thoracic Surgeons (STS) risk model and European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) are widely used for cardiac surgery. Additionally, the Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) score II predicts 4-year mortality after coronary artery bypass grafting (CABG). This study aimed to evaluate the performance of preexisting preoperative risk evaluation systems, such as the STS risk model, EuroSCORE II, and SYNTAX score II, for patients undergoing an-aortic off-pump coronary bypass grafting (OPCAB). Methods: Of 1,140 patients had planned to undergo isolated OPCAB preoperatively between January 2010 and June 2017, 1048 patients (isolated anaortic OPCAB: 1043, on-pump conversion: 5) were enrolled in this study. The STS risk score, EuroSCORE II, and SYNTAX score were retrospectively or prospectively calculated with dedicated online software. Calibration of the STS risk model and EuroSCORE II were performed by the risk-adjusted event ratio that was defined as observed events divided by expected events (O/E ratio) and the Hosmer-Lemeshow test. The discrimination powers of the STS risk model and EuroSCORE II were evaluated by the area under the receiver operating characteristic curve (AUC). Students t-test was used to compare SYNTAX score I and II between patients with and without mortality or morbidity. Results: Operative mortality occurred in 10 patients (0.95%). The predicted mortality rates calculated by the EuroSCORE II and STS risk model were 2.58 ยฑ 4.15% and 1.72 ยฑ 2.92%, respectively. The O/E ratio of the EuroSCORE II was 0.370 (confidence interval(CI): 0.177 โ€“ 0.681), and the EuroSCORE II significantly overpredicted the operative mortality for patients (P = 0.003). EuroSCORE II showed good discrimination power with an AUC of 0.784. The O/E ratio of mortality in the STS risk model was 0.556 (CI: 0.266 โ€“ 1.023), and the STS risk model overpredicted the operative mortality with marginal significance (P = 0.052). However, in the subgroup analysis, the STS risk model significantly overpredicted mortality (O/E ratio: 0.481, CI: 0.193-0.992). Permanent stroke occurred in 6 patients (0.53%). The predicted permanent stroke occurrence rate calculated by the STS risk model was 1.73 ยฑ 1.48%. The O/E ratio was 0.332 (CI: 0.121 โ€“ 0.722), and the STS risk model significantly overpredicted the permanent stroke occurrence rate (P = 0.011). In terms of discrimination power for the STS risk model, the AUC for operative mortality and permanent stroke were 0.876 and 0.740, respectively. There was no significant difference in SYNTAX score I value between patients who did and did not experience mortality or morbidity. However, patients with mortality or morbidity showed a significantly higher SYNTAX score II than those without mortality or morbidity. Conclusions: The preexisting risk prediction scoring systems for CABG, the STS risk model and EuroSCORE II, overpredicted the risk of mortality and stroke rate for anaortic OPCAB. These findings suggest the possibility that anaortic OPCAB can lower the operative mortality and occurrence of postoperative stroke than conventional CABG. In addition, these results show that the characteristics of the surgical method, especially whether anaortic OPCAB is performed, should be considered to predict the operative risk for CABG.์„œ๋ก : ์ˆ˜์ˆ  ํ›„ ์œ„ํ—˜๋„๋ฅผ ์ ์ˆ˜๋กœ ์˜ˆ์ธกํ•˜๋Š” ์‹œ์Šคํ…œ์€ ํ™˜์ž์˜ ์ˆ˜์ˆ  ์œ„ํ—˜๋„๋ฅผ ์ธก์ •ํ•˜๊ณ , ๊ณ ์œ„ํ—˜๊ตฐ ํ™˜์ž๋ฅผ ํ™•์ธํ•˜๋Š”๋ฐ ์ด์šฉ๋œ๋‹ค. ํ˜„์žฌ STS ์œ„ํ—˜๋„ ์˜ˆ์ธก ๋ชจ๋ธ๊ณผ EuroSCORE II๋Š” ์‹ฌ์žฅ ์ˆ˜์ˆ  ํ™˜์ž์—์„œ ๋„๋ฆฌ ์ด์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, SYNTAX score II ๋Š” ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ  ํ›„ 4๋…„ ์‚ฌ๋ง๋ฅ ์„ ์˜ˆ์ธกํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜„์žฌ ์กด์žฌํ•˜๊ณ  ์žˆ๋Š” ์œ„ํ—˜๋„ ์˜ˆ์ธก ์‹œ์Šคํ…œ์ธ STS ์œ„ํ—˜๋„ ์˜ˆ์ธก ๋ชจ๋ธ, EuroSCORE II, SYNTAX score II๊ฐ€ ๋Œ€๋™๋งฅ ์กฐ์ž‘์ด ์—†๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ  ํ™˜์ž์—์„œ ์ž„์ƒ์  ์œ ์˜์„ฑ์„ ๋ณด์ด๋Š”์ง€ ํ‰๊ฐ€ํ•ด ๋ณด๋„๋ก ํ•˜๊ฒ ๋‹ค. ๋ฐฉ๋ฒ•: 2010๋…„ 1์›”๋ถ€ํ„ฐ 2017๋…„ 6์›”๊นŒ์ง€, ์ˆ˜์ˆ  ์ „ ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์„ ๊ณ„ํšํ•œ 1,140๋ช…์˜ ํ™˜์ž ์ค‘, ๋Œ€๋™๋งฅ ์กฐ์ž‘์ด ์—†๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์„ ์‹œํ–‰ํ•œ 1043๋ช…์˜ ํ™˜์ž์™€ ์ˆ˜์ˆ  ์ค‘ ์‹ฌํ๊ธฐ๋ฅผ ๊ฐ€๋™ํ•˜๊ฒŒ ๋œ 5๋ช…์˜ ํ™˜์ž๋ฅผ ํฌํ•จํ•˜์—ฌ ์ด 1048๋ช…์˜ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. STS score, EuroSCORE II, SYNTAX score II๋Š” ์ธํ„ฐ๋„ท ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ํ›„ํ–ฅ์  ๋˜๋Š” ์ „ํ–ฅ์ ์œผ๋กœ ๊ณ„์‚ฐ๋˜์—ˆ๋‹ค. STS ์œ„ํ—˜๋„ ๋ชจ๋ธ๊ณผ EuroSCORE II์˜ calibration์€ ์‹ค์ œ ๋ฐœ์ƒํ•œ ์‚ฌ๊ฑด ์ˆ˜๋ฅผ ์˜ˆ์ธก ๋ฐœ์ƒ ์‚ฌ๊ฑด ์ˆ˜๋กœ ๋‚˜๋ˆˆ risk-adjusted event ratio (O/E ratio) ์™€ Hosmer-Lemeshow ๊ฒ€์‚ฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๊ณ , ๋ถ„๋ณ„๋ ฅ์€ ROC curve์˜ ๋ฉด์  (AUC)์„ ํ†ตํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. Students t-test๋Š” ์ˆ˜์ˆ  ํ›„ ์‚ฌ๋ง ๋˜๋Š” ํ•ฉ๋ณ‘์ฆ์ด ๋ฐœ์ƒํ•œ ํ™˜์ž์™€ ๊ทธ๋ ‡์ง€ ์•Š์€ ํ™˜์ž์—์„œ SYNTAX score I ๊ณผ II๋ฅผ ๋น„๊ตํ•˜๋Š”๋ฐ ์ด์šฉ๋˜์—ˆ๋‹ค. ๊ฒฐ๊ณผ: ์ˆ˜์ˆ  ํ›„ ์‚ฌ๋ง์€ 10๋ช…(0.95%)์—์„œ ๋ฐœ์ƒํ–ˆ๋‹ค. EuroSCORE II์™€ STS ์œ„ํ—˜๋„ ๋ชจ๋ธ๋กœ ๊ณ„์‚ฐํ•œ ์˜ˆ์ธก ์‚ฌ๋ง๋ฅ ์€ ๊ฐ๊ฐ 2.58 ยฑ 4.15%, 1.72 ยฑ 2.92% ์ด์˜€๋‹ค. EuroSCORE II๋Š” ์‚ฌ๋ง๋ฅ ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ๋†’๊ฒŒ ํ‰๊ฐ€ ํ–ˆ์œผ๋ฉฐ (P = 0.003), O/E ratio๋Š” 0.370 (์‹ ๋ขฐ๊ตฌ๊ฐ„: 0.177-0.681) ์ด์—ˆ๋‹ค. EuroSCORE II๋Š” AUC๊ฐ€ 0.784๋กœ ์ข‹์€ ๋ถ„๋ณ„๋ ฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. STS ์œ„ํ—˜๋„ ๋ชจ๋ธ์˜ ์‚ฌ๋ง๋ฅ ์— ๋Œ€ํ•œ O/E ratio๋Š” 0.556 (์‹ ๋ขฐ๊ตฌ๊ฐ„: 0.266 โ€“ 1.023) ์˜€์œผ๋ฉฐ, ์˜ˆ์ธก ์‚ฌ๋ง๋ฅ ์€ ์‹ค์ œ ๋ฐœ์ƒ์— ๋น„ํ•˜์—ฌ ๋†’๊ฒŒ ๊ณ„์‚ฐ๋˜์—ˆ์œผ๋ฉฐ, ํ†ต๊ณ„์ ์œผ๋กœ ๊ฒฝ๊ณ„์„ฑ ์œ ์˜์„ฑ์„ ๋ณด์˜€๋‹ค (P = 0.052). ๊ทธ๋Ÿฌ๋‚˜ ํ•˜์œ„์ง‘๋‹จ ๋ถ„์„์—์„œ STS ์œ„ํ—˜๋„ ๋ชจ๋ธ์€ ์‚ฌ๋ง๋ฅ ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ๋†’๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค. (O/E ratio: 0.481, ์‹ ๋ขฐ๊ตฌ๊ฐ„: 0.193-0.992). ์ˆ˜์ˆ  ํ›„ ์˜๊ตฌ์  ๋‡Œ์กธ์ค‘์€ 6๋ช…(0.53%)์—์„œ ๋ฐœ์ƒํ–ˆ์œผ๋ฉฐ, STS ์œ„ํ—˜๋„ ๋ชจ๋ธ๋กœ ์˜ˆ์ธกํ•œ ์˜๊ตฌ์  ๋‡Œ์กธ์ค‘ ๋ฐœ์ƒ๋ฅ ์€ 1.73 ยฑ 1.48%์ด์—ˆ๋‹ค. O/E ratio๋Š” 0.332 (์‹ ๋ขฐ๊ตฌ๊ฐ„: 0.121 โ€“ 0.722) ์˜€๊ณ , STS ์œ„ํ—˜๋„ ๋ชจ๋ธ์€ ์˜๊ตฌ์ ์ธ ๋‡Œ์กธ์ค‘ ๋ฐœ์ƒ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ๋†’๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค (P = 0.011). STS ์œ„ํ—˜๋„ ๋ชจ๋ธ์˜ ์‚ฌ๋ง๋ฅ ๊ณผ ์˜๊ตฌ์  ๋‡Œ์กธ์ค‘ ๋ฐœ์ƒ๋ฅ ์— ๋Œ€ํ•œ ๋ถ„๋ณ„๋ ฅ์€ AUC๊ฐ€ ๊ฐ๊ฐ 0.876, 0.740์ด์—ˆ๋‹ค. ์ˆ˜์ˆ  ํ›„ ์‚ฌ๋ง ๋˜๋Š” ํ•ฉ๋ณ‘์ฆ์ด ๋ฐœ์ƒํ•œ ํ™˜์ž์™€ ๊ทธ๋ ‡์ง€ ์•Š์€ ํ™˜์ž์˜ SYNTAX score๋ฅผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ, ๋‘ ๊ทธ๋ฃน์—์„œ SYNTAX score I์€ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜์œผ๋ฉฐ (P =0.469), SYNTAX score II๋Š” ์ˆ˜์ˆ  ํ›„ ์‚ฌ๋ง ๋˜๋Š” ํ•ฉ๋ณ‘์ฆ์ด ๋ฐœ์ƒํ•œ ํ™˜์ž์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค (P <0.001). ๊ฒฐ๋ก : ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ์œ„ํ—˜๋„ ์˜ˆ์ธก ์‹œ์Šคํ…œ์ธ STS ์œ„ํ—˜๋„ ๋ชจ๋ธ๊ณผ EuroSCORE II๋Š” ๋Œ€๋™๋งฅ ์กฐ์ž‘์ด ์—†๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์—์„œ ์‚ฌ๋ง๋ฅ ๊ณผ ๋‡Œ์กธ์ค‘ ๋ฐœ์ƒ๋ฅ ์„ ์‹ค์ œ๋ณด๋‹ค ๋†’๊ฒŒ ์˜ˆ์ธกํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์ด ๊ธฐ์กด์˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ๋ณด๋‹ค ์‚ฌ๋ง๋ฅ ๊ณผ ๋‡Œ์กธ์ค‘ ๋ฐœ์ƒ์„ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ, ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ ์˜ ์ˆ˜์ˆ  ์œ„ํ—˜๋„๋ฅผ ์˜ˆ์ธกํ•  ๋•Œ ๋Œ€๋™๋งฅ ์กฐ์ž‘์ด ์—†๋Š” ๋ฌด์‹ฌํ๊ธฐํ•˜ ๊ด€์ƒ๋™๋งฅ ์šฐํšŒ์ˆ  ๊ฐ™์€ ์ˆ˜์ˆ ์˜ ๋ฐฉ๋ฒ•์  ํŠน์„ฑ์ด ๋ฐ˜๋“œ์‹œ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.1. Introduction 1 2. Patients and methods 3 2.1 Patient characteristics and surgical procedure 3 2.2 Calculation of the risk prediction scores 4 2.3 Statistical analysis 7 3. Results 8 3.1 Performance of the EuroSCORE II 8 3.2 Performance of the STS risk model 9 3.3 Performance of SYNTAX score I and II 12 3.4 Subgroup analyses for high-risk patients 12 4. Discussion 12 4.1 Limitations 18 5. Conclusions 18 6. Acknowledgment 19 7. References 20 8. Figures and tables 26 9. ๊ตญ๋ฌธ ์ดˆ๋ก 46Docto

    Experimental Study on Tow Deformation during Impregnation in LCM

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2012. 8. ์ด์šฐ์ผ.์•ก์ƒ์ฃผ์ž…์„ฑํ˜•๊ณต์ •(LCM)์€ ์ œ์กฐ๊ณต์ • ์ƒ์˜ ์ด์ ๊ณผ ๊ฒฝ์ œ์  ์ด์ ์œผ๋กœ ์ธํ•ด ๋ณตํ•ฉ์žฌ๋ฃŒ ์ œ์กฐ ์‚ฐ์—…์—์„œ ๊ฐ๊ด‘์„ ๋ฐ›๊ณ  ์žˆ๊ณ , ๊ธฐ์กด์˜ ๊ณต์ •์„ ๋Œ€์ฒดํ•ด ๋‚˜๊ฐ€๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ˆ˜์ง€ ์ฃผ์ž… ๊ณต์ •์— ์žˆ์–ด ์œ ๋Ÿ‰๊ณผ ํˆฌ๊ณผ์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋” ์ˆ˜ํ–‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ํˆฌ๊ณผ์„ฑ๊ณ„์ˆ˜๋Š” ์ˆ˜์ง€ ์ฃผ์ž… ๊ณต์ •๊ณผ ์ถฉ์ง„ ์‹œ๊ฐ„์„ ์ดํ•ดํ•˜๋Š” ์ค‘์š”ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. Darcys law์— ๋”ฐ๋ฅด๋ฉด ํˆฌ๊ณผ์„ฑ๊ณ„์ˆ˜๋Š” ์œ ๋Ÿ‰๊ณผ ์œ ๋Ÿ‰์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์„ฌ์œ  ๋‹ค๋ฐœ์˜ ํ˜•์ƒ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๋ณ€ํ™”๊ฐ€ ์ผ์–ด๋‚˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ฌ์œ  ๋‹ค๋ฐœ์˜ ํ˜•ํƒœ์™€ ์ˆ˜์ง€ ์ฃผ์ž… ์••๋ ฅ์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์œ ๋Ÿ‰์ด ํˆฌ๊ณผ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ ์˜ˆ์ƒํ•˜๋ฉฐ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹ค์ œLCM ๊ณต์ •๊ณผ ์œ ์‚ฌํ•œ ์กฐ๊ฑด์˜ ์‹คํ—˜๊ธฐ๊ตฌ๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ๊ณต์ • ์ง„ํ–‰ ์ค‘ ์ธก๋ฉด์—์„œ ์„ฌ์œ ๋‹ค๋ฐœ์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€์œผ๋ฉฐ, ์œ ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์„ฌ์œ ๋‹ค๋ฐœ์˜ ํ˜•์ƒ์ด ๋ณ€ํ™”ํ•˜์˜€๊ณ  ๊ฒฐ๊ณผ์ ์œผ๋กœ ํˆฌ๊ณผ์„ฑ๊ณ„์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Liquid Composites Molding(LCM) is one of the most effective manufacturing method in terms of cost and processes so it has been substituting existing processes such as autoclave, pultrusion, filament winding. However, we need to study for the relation between volume flow rate and permeability during impregnation further because the permeability is a key parameter to figure out the impregnation process of resin and the impregnation time. According to Darcys law, permeability is proportional to volume flow rate and volume flow rate influences on fiber-tow geometry. Therefore this study has been conducted on the assumption that fiber-tow geometry and volume flow rate influenced by injection pressure would affect permeability. Experimental apparatus similar to an actual LCM process was made and the cross section pictures of the process during impregnation were taken. As volume flow rate increases, fiber-tow geometry is changed. As a result, that permeability is increased is confirmed.ABSTRACT I CONTENTS III LIST OF TABLES AND FIGURES IV LIST OF NOMENCLATURE VI CHAPTER 1 1 1. 1. ๋ณตํ•ฉ์žฌ๋ฃŒ(COMPOSITE MATERIALS) 1 1.2. ์•ก์ƒ์ฃผ์ž…์„ฑํ˜•(LIQUID COMPOSITE MOLDING, LCM) 2 1.3. ์—ฐ๊ตฌ ๋ชฉ์  2 CHAPTER 2 4 2.1 ์žฌ๋ฃŒ 4 2.1.1 ์—”์ง„์˜ค์ผ 4 2.1.2 ์œ ๋ฆฌ์„ฌ์œ  4 2.2 ์‹คํ—˜ ๊ธฐ๊ธฐ 5 2.2.1 LCM ๊ณต์ • ๊ธฐ๊ธฐ 5 2.2.2 ์ดฌ์˜ ๊ธฐ๊ธฐ 6 2.2.3 ์ˆ˜์ง€ ์ฃผ์ž… ๊ธฐ๊ธฐ 6 2.2.4 ์••๋ ฅ ์ธก์ • ๊ธฐ๊ธฐ 6 2.3 ์‹คํ—˜ ์ˆ˜ํ–‰ 7 2.3.1 ์‹คํ—˜ ๊ธฐ๊ตฌ ์„ค์น˜ 7 2.3.2 ์‹คํ—˜ ์ˆ˜ํ–‰ 8 CHAPTER 3 9 3.1 ์œ ๋Ÿ‰์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์„ฌ์œ ๋‹ค๋ฐœ์˜ ๋ณ€ํ˜• 9 3.1.1 ์„ฌ์œ ๋‹ค๋ฐœ์˜ ๋ณ€ํ˜• ๋ฐ ์˜ํ–ฅ 10 CHAPTER 4 13 REFERENCES 14 ์ดˆ ๋ก 26Maste

    ๋ผ์ง€์— ์ด์‹ํ•œ ํฌ๊ด„์ ์ธ ํ•ญ์„ํšŒํ™” ์ฒ˜๋ฆฌ๋ฅผ ์‹œํ–‰ํ•œ ์ƒˆ๋กœ์šด ์†Œ ์‹ฌ๋‚ญ ํŒจ์น˜์˜ ์กฐ๊ธฐ ์„ฑ์ 

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ ํ‰๋ถ€์™ธ๊ณผํ•™ ์ „๊ณต, 2016. 2. ์ž„์ฒญ.Introduction: Bovine pericardial patches have been widely used for various cardiovascular surgeries. However, calcification still remains an important drawback. We evaluated the short term safety and effectiveness of our comprehensive anti-calcification procedure in a comparative study of the novel and commercially available bovine pericardial patch in a swine implantation model. Material and Methods: Our comprehensive anti-calcification procedure consisted of 4 steps, including decellularization with sodium dodecyl sulfate and tritonX-100, space filler treatment with polyethylene glycol, glutaraldehyde cross-linking with organic solvent, and detoxification with glycine. We simultaneously implanted both the commercially available bovine pericardial patch (Supple Peri-Guardยฎ) and novel bovine pericardial patch processed by the comprehensive anti-calcification procedure into the main pulmonary artery in 7 pigs. Every pig underwent a cardiac angiography and was humanely sacrificed on the 28th postoperative day. The extracted patches were stained with hematoxylin and eosin. Results: All pigs survived for 4 weeks without any complication. Cardiac angiography showed the absence of leakage and structural problem. Neointimas were formed evenly without intimal hyperplasia. There were no significant differences in the degree of inflammation, necrosis, and calcification between the novel and commercially available patch (p = 0.450, p = 0.317, p = 0.999). Conclusions: Novel bovine pericardial patch using comprehensive anti-calcification procedure was similar to existing cardiovascular patch in early surgical results in a swine model. The comprehensive anti-calcification procedure could facilitate appropriate bioprosthetic properties of the bovine pericardium.1. Introduction 1 2. Material and methods 3 2.1 Material 3 2.1.1 Bovine pericardial patches 3 2.2 Methods 4 2.2.1 Comprehensive anti-calcification procedure 4 2.2.2 Operation 7 2.2.3 Postoperative care 8 2.2.4 Cardiac angiography and histology 9 2.3 Statistical analysis 9 3. Results 11 3.1 Blood sample test 11 3.2 Cardiac angiography and gross findings 11 3.3 Histologic findings 12 4. Discussion 15 5. Conclusions 23 6. References 24 7. Figures and table 31 8. ๊ตญ๋ฌธ์ดˆ๋ก 40Maste

    ์ถฉ๋Œ ํšŒํ”ผ๋ฅผ ์œ„ํ•œ ๊ธด๊ธ‰ ์ฃผํ–‰ ๋ณด์กฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2014. 2. ์ด๊ฒฝ์ˆ˜.In recent years, automakers have been trying to help drivers to avoid or mitigate collision with active safety system. Practical applications have become possible due to recent advances in exterior sensors and electronically controllable actuators. These advances have opened up many possibilities for active safety systems like lane keeping assistance system (LKAS), advanced emergency braking system (AEBS) and blind spot detection (BSD). The further enhancement of these technologies will lead to an automated driving system that requires a collision avoidance function using by automatic braking and even automatic throttle and steering. This dissertation focuses on automated collision avoidance using automated steering control and control of motor-driven power steering (MDPS) torque overlay and differential braking for emergency driving support (EDS). A robust Model Predictive Control (MPC) method is used in order to guarantee safety constraints despite of disturbances and model uncertainty. A minimum Robust Positively Invariant (RPI) set of vehicle state error is calculated and the robust MPC calculates the appropriate collision avoidance steering. The performance of the proposed algorithm has been investigated via computer simulations. The simulation studies show that the controlled vehicle can achieve safe collision avoidance maneuver using small lateral acceleration in a long distance preceding vehicle avoidance scenario, and it can achieve safe collision avoidance maneuver using high lateral acceleration in a sudden appeared vehicle avoidance scenario. Electrically controllable actuators, MDPS and differential brake system are used as actuators instead of automated steering and a radar and camera are used as a sensor system for the EDS algorithm. Using environment and vehicle information obtained from the sensor system, a risk of collision and drivers intention are determined. A trapezoidal acceleration profile (TAP) is generated incorporating the drivers intention and based on the TAP, the MDPS overlay torque is determined in order to assist the drivers speed of response. The differential braking is determined to maximize the minimum vehicle-to-vehicle distance to avoid collision. From the non-linear optimal control problem, the rule-based control algorithm is designed for real-time application. The performance of the proposed algorithm has been investigated via computer simulations and real-time human-in-the-loop simulations. The simulation studies show that the controlled vehicle can secure additional vehicle-to-vehicle distance in severe lane change maneuvering for collision avoidance. The success rate of collision avoidance has been investigated for 8 test drivers using the human-in-the-loop simulations. It has been shown that the most of the test drivers can benefit from the proposed support system.Abstract i List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Backgraound and Motivation 1 1.2 Previous Researches 5 1.3 Thesis Objectives 8 1.4 Thesis Outline 10 Chapter 2 Automated Collision Avoidance using Robust Model Predictive Control 11 2.1 Model Predictive Control Problem 12 2.1.1 Vehicle model 13 2.1.2 Constraint design 17 2.1.3 Model predictive control formulation 21 2.2 Robustness Analysis 24 2.2.1 Disturbance analysis 24 2.2.1.1 Tire force disturbance 24 2.2.1.2 Vehicle parameter uncertainties 29 2.2.2 Linear state feedback 30 2.2.3 Robust Positively Invariant set computation 31 2.3 Simulation 33 Chapter 3 Emergency Driving Support 41 3.1 Overview of Emergency Driving Support 43 3.1.1 Danger area estimation 43 3.1.2 Index module 46 3.1.2.1 Lane change intention index 46 3.1.2.2 Collision risk index 48 3.1.2 State manager 50 3.2 Motor Driven Power Steering Overlay Torque Control 52 3.2.1 Trapezoidal Acceleration Profile 53 3.2.2 Motor Driven Power Steering overlay torque control 58 3.2.2.1 Linear model analysis 59 3.2.2.2 Non-linear vehicle simulation 63 3.2.2.3 Map-based overlay torque control 65 3.3 Differential Braking Control 66 3.3.1 Optimal control problem 67 3.3.2 Rule-based differential braking control 71 3.3.3 Non-linear vehicle simulation 77 3.4 Evaluation 80 3.4.1 Simulation 80 3.4.2 Best case scenario evaluation 82 3.4.2 Evaluation on a Virtual Test Track 84 Chapter 4 Conclusions and Future Works 89 Bibliography 92 ๊ตญ๋ฌธ์ดˆ๋ก 100Docto

    Clinical effect of sarcopenia and subcutaneous adipose tissue on mortality and prognosis of liver cirrhosis

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    Increasing evidence suggests that decreased skeletal muscle mass (sarcopenia) or adipose tissue assessed predicts negative outcomes in patients with cancer. In liver cirrhosis, while there is consensus that sarcopenia has been associated with higher mortality, the prognostic value of adipose tissue is not clear. We investigated the independent prognostic significance of visceral and subcutaneous adiposity in predicting mortality in liver cirrhosis. and further more we devided group by gender, MELD score to find out ther diffence. The cross-sectional areas (cm2) of skeletal muscles (SM) and subcutaneous and visceral adipose tissues were measured on CT imaging at the level of the third lumbar (L3) vertebra. Adipose tissue markers, including the visceral adipose tissue index (VATI, cm2/m2) and subcutaneous adipose tissue index (SATI, cm2/m2), were estimated for 550 patients diagnosed with cirrhosis. Sarcopenia was defined using established cut-offs in patients with cirrhosis as SM index (SMI) <39 cm2/m2 in females and 50< cm2/m2 in males. The cutoff values for low subcutaneous or visceral adipose tissue were adopted if SATI or VATI were lower than the mean for men and women. Among the patients, most were male (79.2%) with a mean age of 53.8 ยฑ 10 years and model for end-stage liver disease (MELD) score of 10.4 ยฑ 3. Body composition differed according to sex, with male having greater SMI (mean, 53.2 ยฑ 8.6 vs. 47.3 ยฑ 10.5) and VATI (37.76 ยฑ 19.30 vs. 35.95 ยฑ 20.62) whereas SATI (35.98 ยฑ 17.20 vs. 68.01 ยฑ 33.20) was higher in females. In a total of 550 patients, the 5-year overall survival in patients with higher SATI was 76.1%, while it was 65.0% in those with lower SATI (p=0.019). The 5-year overall survival in patients with lower VATI was 67.6%, while it was 60.5% in those with higher VATI (p=0.657). A low VATI was not associated with overall survival in both females and males. The 5-year overall survival in patients with sarcopenia was 44.5%, while it was 74.5% in those without sarcopenia (p<0.001). In gender stratified multivariate analyses, higher SATI was significantly associated with overall survival (adjusted HR [aHR]=0.292, p=0.027) in females whereas sarcopenia independently associated with mortality risk (aHR=1.469, p=0.043) in males after adjusting for MELD score and albumin. Also, in MELD score stratified multivariate analyses, higher SATI was significantly associated with overall survival in female high MELD score group, where as male group had different result in low and high MELD score group. In male low MELD score group, higher SATI was associated with overall survival whereas sarcopenia was associated with mortality in male High MELD score group. Moreover, patients with lower SATI were at a significantly higher risk of variceal bleeding than those with higher SATI (HR=0.421, p<0.001). Lower SATI group showed a trend toward a higher risk of developing of ascites than higher SATI group, but the difference was not statistically significant (HR, 0.715; P=0.070). Subcutaneous adipose tissues, but not visceral adipose tissues, appear to be associated with a reduction in mortality risk, demonstrating the prognostic importance of fat distribution in female patients with cirrhosis. Meanwhile, SMI predicts mortality in male patients with cirrhosis. Moreover, the effect of sarcopenia on survival was more pronounced in male patients with low muscle mass. ์•” ํ™˜์ž์— ์žˆ์–ด์„œ ๊ณจ๊ฒฉ๊ทผ์˜ ์ €ํ•˜ ๋˜๋Š” ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง์˜ ๋ณ€ํ™”๊ฐ€ ๋ถˆ๋Ÿ‰ํ•œ ์˜ˆํ›„์™€ ๊ด€๋ จ ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ฐ„๊ฒฝ๋ณ€์ฆ์— ์žˆ์–ด์„œ๋„ ํ˜„์žฌ ์ด์— ๋Œ€ํ•˜์—ฌ ๊ด€ํ•œ ์—ฐ๊ตฌ ๋˜ํ•œ ํ™œ๋ฐœ์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๊ณจ๊ฒฉ๊ทผ์˜ ๊ฐ์†Œ์˜ ๊ฒฝ์šฐ ๊ฐ„๊ฒฝ๋ณ€์ฆ ํ™˜์ž์˜ ์‚ฌ๋ง๋ฅ ์„ ๋†’์ธ๋‹ค๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋ณด๊ณ ๋˜๊ณ  ์žˆ์œผ๋‚˜1 ํ”ผํ•˜์ง€๋ฐฉ ์กฐ์ง๊ณผ ๊ฐ„๊ฒฝํ™”ํ™˜์ž์˜ ์˜ˆํ›„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ„๊ฒฝํ™” ํ™˜์ž์—์„œ ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง, ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง ๋ฐ ๊ณจ๊ฒฉ๊ทผ์„ ์ธก์ •ํ•˜์—ฌ ์‚ฌ๋ง๋ฅ ๊ณผ์˜ ์—ฐ๊ด€์„ฑ์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ์„ฑ๋ณ„ ๋ฐ ๊ฐ„๊ฒฝ๋ณ€์ฆ์˜ ์ง„ํ–‰ ์ •๋„์— ๋”ฐ๋ฅธ ์‚ฌ๋ง๋ฅ ์˜ ์ฐจ์ด ๋ฐ ๋น„๋งŒ์ง€ํ‘œ๋“ค์— ๋”ฐ๋ฅธ ์‹๋„์ •๋งฅ๋ฅ˜ ๋ฐ ๋ณต์ˆ˜ ๊ฐ™์€ ๊ฐ„๊ฒฝํ™” ํ•ฉ๋ณ‘์ฆ์˜ ๋ฐœ์ƒ์˜ ์ฐจ์ด์  ๋˜ํ•œ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด 550๋ช…์˜ ํ™˜์ž์—์„œ ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง๊ณผ ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง ๋ฐ ๊ณจ๊ฒฉ๊ทผ์˜ ๋‹จ์œ„ ๋ฉด์ ์„ ์š”์ถ” 3๋ฒˆ(L3) ์œ„์น˜์—์„œ ์ปดํ“จํ„ฐ ๋‹จ์ธต์ดฌ์˜์„ ํ†ตํ•˜์—ฌ ์ธก์ •ํ•˜์˜€๊ณ , ์ธก์ •๋œ ๋‹จ์œ„๋ฉด์ ์„ ํ‚ค๋กœ ๋‚˜๋ˆ„์–ด ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง์ง€์ˆ˜(visceral adipose tissue index, VATI, cm2/m2)์™€ ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง์ง€์ˆ˜(subcutaneous adipose tissue index, SATI, cm2/m2), ๊ณจ๊ฒฉ๊ทผ์ง€์ˆ˜(skeletal muscle index, SMI, cm2/m2)๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ทผ๊ฐ์†Œ์ฆ์€ ์ƒ์šฉํ™”๋œ cut-off value๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„๋ฅ˜ ํ•˜์˜€์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ผ ๋ถ„๋ฅ˜ํ•˜์˜€์„ ๋•Œ ์—ฌ์„ฑ์˜ ๊ฒฝ์šฐ SMI <39 cm2/m2, ๋‚จ์„ฑ์˜ ๊ฒฝ์šฐ <50 cm2/m2 ๋กœ ์ •์˜ํ•˜์˜€๋‹ค.2 ์ง€๋ฐฉ์กฐ์ง์ง€์ˆ˜์˜ cut-off value๋Š” ๋ณธ ์—ฐ๊ตฌ์˜ SATI ๋ฐ VATI์˜ ํ‰๊ท ๊ฐ’์„ ๊ธฐ์ค€์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ํ™˜์ž๋“ค ์ค‘ ๋Œ€๋ถ€๋ถ„์€ ๋‚จ์„ฑ ์ด์—ˆ์œผ๋ฉฐ(n=436, 79.2%) ํ‰๊ท  ์—ฐ๋ น์€ 53.8 ยฑ 10.0 ์„ธ์˜€๊ณ  model for end-stage liver disease (MELD) score ์˜ ํ‰๊ท ์€ 10.4 ยฑ 3.0 ์˜€๋‹ค. ์ธ์ฒด ๊ตฌ์„ฑ์€ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ๋‚จ์„ฑ์—์„œ SMI๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ(p=0.001), SATI๋Š” ์—ฌ์„ฑ์—์„œ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค(p=0.001). ์ด 550๋ช…์˜ ํ™˜์ž ์ค‘, ๋†’์€ SATI๋ฅผ ๊ฐ€์ง„ ํ™˜์ž๋“ค์˜ 5๋…„ ์ƒ์กด์œจ์€ 76.1%์˜€์œผ๋ฉฐ, ๋‚ฎ์€ SATI ํ™˜์ž๋“ค์˜ 5๋…„ ์ƒ์กด์œจ 65%์— ๋น„ํ•˜์—ฌ ์œ ์˜ํ•˜๊ฒŒ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค(log-rank P =0.019). ๋‚ฎ์€ VATI ํ™˜์ž๋“ค์˜ 5๋…„ ์ƒ์กด์œจ์€ 67.6%๋กœ ์ธก์ •๋˜์—ˆ๊ณ , ๋†’์€ VATI ํ™˜์ž์—์„œ์˜ 60.5%์™€ ๋น„๊ต ํ•˜์˜€์„ ๋•Œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค(log-rank P =0.657). ๊ทผ๊ฐ์†Œ์ฆ์ด ์žˆ๋Š” ํ™˜์ž์˜ ๊ฒฝ์šฐ ์ƒ์กด์œจ์ด 44.5%๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ๊ทผ๊ฐ์†Œ์ฆ์ด ์—†๋Š” ๊ฒฝ์šฐ 74.5%๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค(log-rank P <0.026). ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์„ธ๋ถ€ ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€์„ ๊ฒฝ์šฐ ์—ฌ์„ฑ ๊ฐ„๊ฒฝ๋ณ€์ฆ ํ™˜์ž ์ค‘ ๋†’์€ SATI level์„ ๊ฐ€์ง„ ํ™˜์ž๊ตฐ์—์„œ ์‚ฌ๋ง๋ฅ ์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ(adjusted HR= 0.292, 95% CI: 0.098-0.869 p=0.027) ๋‚จ์„ฑ ๊ฐ„๊ฒฝํ™” ํ™˜์ž์—์„œ๋Š” ๊ทผ๊ฐ์†Œ์ฆ์ด ์žˆ์„ ๊ฒฝ์šฐ ์‚ฌ๋ง๋ฅ ์ด ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์˜€๋‹ค(adjusted HR= 1.469, 95% CI: 1.013-2.131 p=0.043). ๋˜ํ•œ MELD score์— ๋”ฐ๋ฅธ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๊ณ  ์—ฌ์„ฑ ๊ฐ„๊ฒฝ๋ณ€์ฆ ํ™˜์ž ์ค‘ MELD score 10 ์ด์ƒ ํ™˜์ž๊ตฐ์—์„œ ๋†’์€ SATI level๊ณผ ์ƒ์กด์œจ์ด ์—ฐ๊ด€์„ ๋ณด์˜€์œผ๋ฉฐ, ๋‚จ์„ฑ ํ™˜์ž์˜ ๊ฒฝ์šฐ MELD score 10๋ฏธ๋งŒ ํ™˜์ž๊ตฐ์—์„œ๋Š” ๋†’์€ SATI level๊ณผ ์ƒ์กด์œจ์ด, MELD score 10 ์ด์ƒ ํ™˜์ž๊ตฐ์—์„œ๋Š” ๊ทผ๊ฐ์†Œ์ฆ๊ณผ ์‚ฌ๋ง๋ฅ ์ด ์—ฐ๊ด€์„ฑ์„ ๋ณด์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ๋†’์€ SATI๋ฅผ ๊ฐ–์€ ํ™˜์ž ๊ตฐ์˜ ๊ฒฝ์šฐ ๋‚ฎ์€ SATI ํ™˜์ž๊ตฐ๋ณด๋‹ค ๋ช…ํ™•ํ•˜๊ฒŒ ์ ์€ ์‹๋„์ •๋งฅ๋ฅ˜ ์ถœํ˜ˆ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค(HR=0.421, p<0.001). ๋ณต์ˆ˜์˜ ๊ฒฝ์šฐ ๋†’์€ SATI ํ™˜์ž ๊ตฐ์—์„œ ๋‚ฎ์€ SATI ํ™˜์ž ๊ตฐ๋ณด๋‹ค ๋” ์ ์€ ๊ฒฝํ–ฅ์„ ๋ณด์ด๊ธด ํ•˜์˜€์œผ๋‚˜ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค(HR, 0.715; P=0.070). ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ์—ฌ์„ฑ์—์„œ ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง์ง€์ˆ˜๊ฐ€ ์•„๋‹Œ ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง์ง€์ˆ˜์˜ ์ฆ๊ฐ€๊ฐ€ ํ™˜์ž์˜ ์ƒ์กด์œจ๊ณผ ์—ฐ๊ด€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ ๊ทผ๊ฐ์†Œ์ฆ์€ ๋‚จ์„ฑ ๊ฐ„๊ฒฝํ™” ํ™˜์ž์˜ ์‚ฌ๋ง๋ฅ ์„ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ„๊ฒฝ๋ณ€์ฆ ํ™˜์ž์—์„œ ๊ณจ๊ฒฉ๊ทผ ์™ธ ํ”ผํ•˜์ง€๋ฐฉ์กฐ์ง, ๋‚ด์žฅ์ง€๋ฐฉ์กฐ์ง์„ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์ด ํ™˜์ž์˜ ์˜ˆํ›„ ๋ฐ ์‚ฌ๋ง๋ฅ ์„ ์˜ˆ์ธกํ•˜๋Š” ๋งค๊ฐœ๋ณ€์ˆ˜๋กœ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์—ฌ์„ฑ ๊ฐ„๊ฒฝ๋ณ€์ฆ ํ™˜์ž์—์„œ ์ง€๋ฐฉ์กฐ์ง์˜ ๋ถ„ํฌ๊ฐ€ ์˜ˆํ›„์— ์ค‘์š”ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.open์„

    Characterization of P2Y Purinergic Receptors in Dopaminergic SN4741 cells

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