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    ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ํ™œ์šฉํ•œ ์ž๋™ ์˜์ƒ ์žก์Œ ์ œ๊ฑฐ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ณ„์‚ฐ๊ณผํ•™์ „๊ณต, 2020. 8. ๊ฐ•๋ช…์ฃผ.Noise removal in digital image data is a fundamental and important task in the field of image processing. The goal of the task is to remove noises from the given degraded images while maintaining essential details such as edges, curves, textures, etc. There have been various attempts on image denoising: mainly model-based methods such as filtering methods, total variation based methods, non-local mean based approaches. Deep learning have been attracting signi๏ฌcant research interest as they have shown better results than the classical methods in almost all fields. Deep learning-based methods use a large amount of data to train a network for its own objective; in the image denoising case, in order to map the corrupted image to a desired clean image. In this thesis we proposed a new network architecture focusing on white Gaussian noise and real noise cancellation. Our model is a deep and wide network designed by constructing a basic block consisting of a mixture of various types of dilated convolutions and repeatedly stacking them. We did not use a batch normal layer to maintain the original own color information of each input data. Also skip connection was utilized so as not to lose the existing information. Through several experiments and comparisons, it was proved that the proposed network has better performance compared to the traditional and latest methods in image denoising.๋””์ง€ํ„ธ ์˜์ƒ ๋ฐ์ดํ„ฐ ๋‚ด์˜ ์žก์Œ ์ œ๊ฑฐ ๋ฐ ๊ฐ์†Œ๋Š” ์—ดํ™”๋œ ์˜์ƒ์˜ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜๋ฉด์„œ ๋ชจ์„œ๋ฆฌ, ๊ณก์„ , ์งˆ๊ฐ ๋“ฑ๊ณผ ๊ฐ™์€ ํ•„์ˆ˜ ์„ธ๋ถ€ ์ •๋ณด๋ฅผ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์ด ๋ชฉ์ ์ธ ์˜์ƒ ์ฒ˜๋ฆฌ ๋ถ„์•ผ์˜ ๊ธฐ๋ณธ์ ์ด๊ณ  ํ•„์ˆ˜์ ์ธ ์ž‘์—…์ด๋‹ค. ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์˜์ƒ ์žก์Œ ์ œ๊ฑฐ ๋ฐฉ๋ฒ•๋“ค์€ ์—ดํ™”๋œ ์˜์ƒ์„ ์›ํ•˜๋Š” ํ’ˆ์งˆ์˜ ์˜์ƒ์œผ๋กœ ๋งคํ•‘ํ•˜๋„๋ก ๋Œ€์šฉ๋Ÿ‰์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋„คํŠธ์›Œํฌ๋ฅผ ์ง€๋„ํ•™์Šตํ•˜๋ฉฐ ๊ณ ์ „์ ์ธ ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋ฏธ์ง€ ๋””๋…ธ์ด์ง•์— ๋Œ€ํ•œ ์—ฌ๋Ÿฌ ๋ฐฉ๋ฒ•๋“ค์„ ์กฐ์‚ฌํ–ˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ํŠนํžˆ ๋ฐฑ์ƒ‰ ๊ฐ€์šฐ์‹œ์•ˆ ์žก์Œ๊ณผ ์‹ค์ œ ์žก์Œ ์ œ๊ฑฐ ๋ฌธ์ œ์— ์ง‘์ค‘ํ•˜๋ฉด์„œ ๋„คํŠธ์›Œํฌ ์•„ํ‚คํ…์ฒ˜๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ์‹คํ—˜ํ•˜์˜€๋‹ค. ์—ฌ๋Ÿฌ ํ˜•ํƒœ์˜ ๋”œ๋ ˆ์ดํ‹ฐ๋“œ ์ฝ˜๋ณผ๋ฃจ์…˜๋“ค์„ ํ˜ผํ•ฉํ•˜์—ฌ ๊ธฐ๋ณธ ๋ธ”๋ก์„ ๊ตฌ์„ฑํ•˜๊ณ  ์ด๋ฅผ ๋ฐ˜๋ณตํ•˜์—ฌ ์Œ“์•„์„œ ์„ค๊ณ„ํ•œ ๋„คํŠธ์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๊ณ , ๊ฐ๊ฐ ๋ณธ์—ฐ์˜ ์ƒ‰์ƒ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋„๋ก ์—ฌ๋Ÿฌ ์ž…๋ ฅ ์˜์ƒ์„ ํ•˜๋‚˜๋กœ ๋ฌถ์–ด ๊ตฌ์„ฑํ•˜๋Š” ๋ฐฐ์น˜๋ฅผ ํ‰์ค€ํ™”ํ•˜๋Š” ๋ฐฐ์น˜๋…ธ๋ฉ€ ๋ ˆ์ด์–ด๋Š” ์‚ฌ์šฉํ•˜์ง€ ์•Š์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ธ”๋ก์ด ์—ฌ๋Ÿฌ ์ธต ์ง„ํ–‰๋˜๋Š” ๋™์•ˆ ๊ธฐ์กด์˜ ์ •๋ณด๋ฅผ ์†์‹คํ•˜์ง€ ์•Š๋„๋ก ์Šคํ‚ต ์ปค๋„ฅ์…˜์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์—ฌ๋Ÿฌ ์‹คํ—˜๊ณผ ๊ธฐ์กด์— ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๊ณผ ์ตœ์‹  ๋ฒค์น˜ ๋งˆํฌ์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•˜์—ฌ ์ œ์•ˆํ•œ ๋„คํŠธ์›Œํฌ๊ฐ€ ๋…ธ์ด์ฆˆ ๊ฐ์†Œ ๋ฐ ์ œ๊ฑฐ ์ž‘์—…์—์„œ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค๊ณผ ๋น„๊ตํ•˜์—ฌ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ์ž…์ฆํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ์ œ์•ˆํ•œ ์•„ํ‚คํ…์ฒ˜๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„์ ๋„ ๋ช‡ ๊ฐ€์ง€ ์กด์žฌํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋„คํŠธ์›Œํฌ๋Š” ๋‹ค์šด์ƒ˜ํ”Œ๋ง์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š์Œ์œผ๋กœ์จ ์ •๋ณด ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜์˜€์ง€๋งŒ ์ตœ์‹  ๋ฒค์น˜๋งˆํฌ์— ๋น„ํ•˜์—ฌ ๋” ๋งŽ์€ ์ถ”๋ก  ์‹œ๊ฐ„์ด ํ•„์š”ํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ ์ž‘์—…์—๋Š” ์ ์šฉํ•˜๊ธฐ๊ฐ€ ์‰ฝ์ง€ ์•Š๋‹ค. ์‹ค์ œ ์˜์ƒ์—๋Š” ๋‹จ์ˆœํ•œ ์žก์Œ๋ณด๋‹ค๋Š” ์˜์ƒ ํš๋“, ์ €์žฅ ๋“ฑ๊ณผ ๊ฐ™์€ ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฑฐ์น˜๋ฉด์„œ ์—ฌ๋Ÿฌ ์š”์ธ๋“ค๋กœ ์ธํ•œ ๋‹ค์–‘ํ•œ ์žก์Œ, ๋ธ”๋Ÿฌ์™€ ๊ฐ™์€ ์—ดํ™”๊ฐ€ ํ˜ผ์žฌ ๋˜์–ด ์žˆ๋‹ค. ์‹ค์ œ ์žก์Œ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ๊ฐ๋„์—์„œ์˜ ๋ถ„์„๊ณผ ์—ฌ๋Ÿฌ ๋ชจ๋ธ๋ง ์‹คํ—˜, ๊ทธ๋ฆฌ๊ณ  ์˜์ƒ ์žก์Œ ๋ฐ ๋ธ”๋Ÿฌ, ์••์ถ•๊ณผ ๊ฐ™์€ ๋ณตํ•ฉ ๋ชจ๋ธ๋ง์ด ํ•„์š”ํ•˜๋‹ค. ํ–ฅํ›„์—๋Š” ์ด๋Ÿฌํ•œ ์ ๋“ค์„ ๋ณด์™„ํ•จ์œผ๋กœ์จ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ๋„คํŠธ์›Œํฌ์˜ ์กฐ์ •์„ ํ†ตํ•ด ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๊ธฐ๋Œ€ํ•œ๋‹ค.1 Introduction 1 2 Review on Image Denoising Methods 4 2.1 Image Noise Models 4 2.2 Traditional Denoising Methods 8 2.2.1 TV-based regularization 9 2.2.2 Non-local regularization 9 2.2.3 Sparse representation 10 2.2.4 Low-rank minimization 10 2.3 CNN-based Denoising Methods 11 2.3.1 DnCNN 11 2.3.2 FFDNet 12 2.3.3 WDnCNN 12 2.3.4 DHDN 13 3 Proposed models 15 3.1 Related Works 15 3.1.1 Residual learning 15 3.1.2 Dilated convolution 16 3.2 Proposed Network Architecture 17 4 Experiments 21 4.1 Training Details 21 4.2 Synthetic Noise Reduction 23 4.2.1 Set12 denoising 24 4.2.2 Kodak24 and BSD68 denoising 30 4.3 Real Noise Reduction 34 4.3.1 DnD test results 35 4.3.2 NTIRE 2020 real image denoising challenge 42 5 Conclusion and Future Works 46 Abstract (in Korean) 54Docto

    ํ•œ๊ตญ ๋Œ€๋„์‹œ ์†Œ๋ฐฉ๊ด€๋“ค์˜ ์œ ํ•ด๋ฌผ์งˆ ์ง์ ‘, ๊ฐ„์ ‘ ๋…ธ์ถœ๊ณผ ์งˆ๋ณ‘์œ ๋ณ‘๋ฅ 

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑดํ•™์ „๊ณต), 2021. 2. ์„ฑ์ฃผํ—Œ.์„œ๋ก  : ์„ธ๊ณ„๋ณด๊ฑด๊ธฐ๊ตฌ ์‚ฐํ•˜ ๊ตญ์ œ์•”์—ฐ๊ตฌ์†Œ์—์„œ๋Š” ์†Œ๋ฐฉ๊ด€์˜ ์ง์—…์  ๋…ธ์ถœ์„ ์ธ์ฒด๋ฐœ์•”๊ฐ€๋Šฅ์ธ์ž์ธ ๊ทธ๋ฃน2B๋กœ ์ƒ์ฒด๋ฆฌ๋“ฌ ๊ต๋ž€์ธ์ž์ธ ๊ต๋Œ€๊ทผ๋ฌด๋ฅผ ๋ฐœ์•”์ถ”์ •์ธ์ž์ธ ๊ทธ๋ฃน2A๋กœ ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™ธ์—๋„ ์†Œ๋ฐฉ๊ด€์€ ์žฌ๋‚œ ๋“ฑ ์†Œ๋ฐฉํ™œ๋™ํ˜„์žฅ์—์„œ ๋…ธ์ถœ๋˜๋Š” ์œ„ํ—˜๋ฌผ์งˆ์— ์˜ํ•ด์„œ๋„ ๋ฐœ์•”๋ฌผ์งˆ์— ์ถ”๊ฐ€์ ์œผ๋กœ ๋…ธ์ถœ๋  ์ˆ˜ ์žˆ๋‹ค. ์†Œ๋ฐฉ๊ด€์˜ ์ง์—…ํ™˜๊ฒฝ์€ ์ถœ๋™ํ˜„์žฅ, ์ถœ๋™๊ฒฝ๋กœ, ์‚ฌ๋ฌด๊ณต๊ฐ„์œผ๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค. ํŠนํžˆ, ์ถœ๋™ํ˜„์žฅ ์ค‘ ํ•˜๋‚˜์ธ ํ™”์žฌํ˜„์žฅ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ผ์‚ฐํ™”ํƒ„์†Œ, ์‹œ์•ˆํ™”์ˆ˜์†Œ, ํฌ๋ฆ„์•Œ๋ฐํžˆ๋“œ ๋“ฑ 10์—ฌ์ข…์˜ ๊ฐ์ข… ๋…์„ฑ๋ฌผ์งˆ์€ ์ธ์ฒด ๊ฑด๊ฐ•์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฐ•๋ ฅํ•œ ์œ„ํ—˜์ธ์ž ์ค‘ ํ•˜๋‚˜์ธ ๊ฒƒ์œผ๋กœ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์†Œ๋ฐฉ๊ด€๋“ค์€ ์žฌ๋‚œํ˜„์žฅ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์œ„ํ—˜์š”์ธ์— ์ง€์†์ ์œผ๋กœ ๋…ธ์ถœ๋จ์œผ๋กœ์จ ์งˆ๋ณ‘๋ถ€๋‹ด์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๋”ฐ๋ผ์„œ, ์†Œ๋ฐฉ๊ด€์˜ ๊ทผ๋ฌดํ™˜๊ฒฝ๊ณผ ์žฌ๋‚œํ˜„์žฅ ์ง์—…ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์œ„ํ—˜์š”์ธ์— ๋Œ€ํ•œ ์ •๋ณด๊ฐ€ ์žˆ๋‹ค๋ฉด, ์˜ˆ์ธก๊ฐ€๋Šฅํ•œ ๊ฑด๊ฐ•์˜ํ–ฅ์„ ์˜ˆ๋ฐฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€๋„์‹œ์— ๊ทผ๋ฌดํ•˜๋Š” ์†Œ๋ฐฉ๊ด€๋“ค์˜ ์ถœ๋™ํ˜„์žฅ๊ณผ ์‚ฌ๋ฌด๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ง์—…ํ™˜๊ฒฝ์„ ์ธก์ •ํ•˜๊ณ  ๋ฐฉํ™”๋ณต ์‚ฌ์šฉ, ์„ธ์ฒ™, ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ๋ณด๊ฑดํ•™์  ์ธ์‹์„ ํ‰๊ฐ€ํ•œ๋‹ค. ๋”๋ถˆ์–ด ๋Œ€๋„์‹œ ์†Œ๋ฐฉ๊ด€ ์ „์ˆ˜์ฝ”ํ˜ธํŠธ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํŠน์ˆ˜๊ฑด๊ฐ•์ง„๋‹จ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ผ๋ฐ˜์ธ๊ตฌ์ง‘๋‹จ๊ณผ ๋งŒ์„ฑ๋ณ‘๊ณผ ์ง์—…๊ด€๋ จ์„ฑ ์งˆํ™˜์˜ ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋ฅ (๋น„)๋ฅผ ๋น„๊ต ํ‰๊ฐ€ํ•จ์œผ๋กœ์จ ๊ฑด๊ฐ•์ˆ˜์ค€์„ ํ‰๊ฐ€ํ•œ๋‹ค. ๋ฐฉ๋ฒ• : ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š”, ์„œ์šธ์‹œ์— ๊ทผ๋ฌดํ•˜๋Š” ํ™”์žฌ์ง„์••๊ณผ ์ธ๋ช…๊ตฌ์กฐ ์—…๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์†Œ๋ฐฉ๊ด€ 1,097๋ช…์„ ๋Œ€์ƒ์œผ๋กœ 21์ผ๊ฐ„ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์—ฌ 40.3%์˜ ์‘๋‹ต๋ฅ ์„ ๋ณด์˜€๋‹ค. ์ด ์กฐ์‚ฌ์—์„œ ์†Œ๋ฐฉ๊ด€๋“ค์ด ์‚ฌ์šฉํ•˜๋Š” ๊ฐœ์ธ๋ณดํ˜ธ์žฅ๋น„ ์ค‘ ํ•˜๋‚˜์ธ ๋ฐฉํ™”๋ณต์˜ ์‚ฌ์šฉ, ์„ธํƒ, ๊ด€๋ฆฌ์ •๋„์™€ ๋ณด๊ฑดํ•™์  ์ธ์‹๊ฐ„์— ์—ฐ๊ด€์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์†Œ๋ฐฉ๊ด€๋“ค์˜ ์ง์—…ํ™˜๊ฒฝ ์ค‘ ํ•˜๋‚˜์ธ ์žฌ๋‚œํ˜„์žฅ ํ™œ๋™๋‹จ๊ณ„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ตญ๋‚ด์—์„œ ๊ฐ€์žฅ ๋งŽ์€ ๋ฐœ์ƒ์„ ๋ณด์ด๋Š” ์ฃผํƒํ™”์žฌ ๋ฐœ์ƒ์„ ์ง์—…ํ™˜๊ฒฝ์œผ๋กœ ํ™”์žฌํ˜„์žฅ์—์„œ 1์ฐจ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ์˜ค์—ผ๊ณผ 2์ฐจ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ๋ฐฉํ™”๋ณต ๊ต์ฐจ์˜ค์—ผ์— ๋Œ€ํ•˜์—ฌ ์˜ค์—ผ๋ฌผ์งˆ์˜ ์ข…๋ฅ˜์™€ ์–‘์— ๋Œ€ํ•˜์—ฌ ์‹คํ—˜์„ ํ•˜์˜€๋‹ค. ๊ต์ฐจ์˜ค์—ผ ํ‰๊ฐ€๋Š” ๋ฐฉํ™”๋ณต์„ ์ง์—…ํ™˜๊ฒฝ์˜ ์œ„ํ—˜์ •๋„์— ๋”ฐ๋ผ 4๊ฐœ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ธก์ •ํ•˜์˜€๋‹ค. ์ด๋•Œ 1์ฐจ์˜ค์—ผ๊ณผ 2์ฐจ์˜ค์—ผ์—์„œ์˜ ๊ณต๊ธฐํฌ์ง‘์„ ํ†ตํ•ด 12๊ฐœ ํ•ญ๋ชฉ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋ฐฉํ™”๋ณต์— ๋ฌป์€ ์˜ค์—ผ๋ฌผ์งˆ์€ ์„ธํƒ๋ฌผ์„ ์ถ”์ถœํ•˜์—ฌ 24๊ฐœ ํ•ญ๋ชฉ์— ๋Œ€ํ•˜์—ฌ ์ˆ˜์งˆ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ์™€ ์‹คํ—˜์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋„์ถœ๋œ ๋ฌธ์ œ์ ์— ๋Œ€ํ•œ ๋Œ€์ฑ…๋งˆ๋ จ์„ ์œ„ํ•˜์—ฌ ๋ธํŒŒ์ด ์ „๋ฌธ๊ฐ€ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์œผ๋กœ ์‹คํ–‰๊ฐ€๋Šฅ์„ฑ๊ณผ ํšจ๊ณผ์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๊ตฌ์ฒด์ ์ธ ์ •์ฑ…์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ธํŒŒ์ด ์ „๋ฌธ๊ฐ€ ์กฐ์‚ฌ์˜ ์ฐธ์—ฌ๋Œ€์ƒ์ž๋Š” ์†Œ๋ฐฉํ˜„์žฅ๊ณผ ๋ฐฉํ™”๋ณต์— ๋Œ€ํ•œ ์„ ํ—˜์  ์‹ค๋ฌด๊ฒฝํ—˜๊ณผ ์—ฐ๊ตฌ๊ฒฝํ—˜์ด ๋งŽ์€ ๋Œ€ํ•™๊ต์ˆ˜์™€ ์†Œ๋ฐฉ๊ด€์„ 7๋ช…์„ ๋Œ€์ƒ์œผ๋กœ 3๋ผ์šด๋“œ์— ๊ฑธ์ณ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š”, ์†Œ๋ฐฉ๊ด€๋“ค์˜ ์ง์—…ํ™˜๊ฒฝ ์ค‘ ํ•˜๋‚˜์ธ ๊ทผ๋ฌด ์ค‘ ๋Œ€๊ธฐ๊ณต๊ฐ„์ธ ์†Œ๋ฐฉ์ฒญ์‚ฌ ๋‚ด์—์„œ์˜ ๊ทผ๋ฌดํ™˜๊ฒฝ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์†Œ๋ฐฉ๊ด€๋“ค์ด ํ™”์žฌํ˜„์žฅ์—์„œ ์ฒ ์ˆ˜ํ•˜์—ฌ ์†Œ๋ฐฉ์„œ๋กœ ๋ณต๊ท€ํ•œ ํ›„ ์†Œ๋ฐฉ์ฒญ์‚ฌ ๋‚ด๋ถ€์™€ ์†Œ๋ฐฉ์ฐจ๋Ÿ‰์—์„œ 2์ฐจ์ ์œผ๋กœ ๋…ธ์ถœ๋˜๋Š” ํ™”ํ•™์  ์œ ํ•ด๋ฌผ์งˆ์˜ ์‹ค๋‚ด๊ณต๊ธฐ์งˆ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์„œ์šธ์‹œ ์†Œ์žฌ 4๊ฐœ์†Œ๋ฐฉ์„œ๋ฅผ ๋ฌด์ž‘์œ„๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. 2๊ฐœ์†Œ๋Š” ์‹คํ—˜๊ตฐ์œผ๋กœ ํ™”์žฌํ˜„์žฅ ์†Œ๋ฐฉํ™œ๋™์ด ์ข…๋ฃŒ๋˜๊ณ  ์†Œ๋ฐฉ์„œ๋กœ ๊ท€์†Œํ•œ ํ›„์— ์ธก์ •ํ•˜์˜€๋‹ค. ๋‹ค๋ฅธ 2๊ฐœ์†Œ๋Š” ๋Œ€์กฐ๊ตฐ์œผ๋กœ ์ถœ๋™๊ณผ ์ƒ๊ด€์—†์ด ํ‰์†Œ ์ˆ˜์ค€์—์„œ ์‹ค๋‚ด๊ณต๊ธฐ์งˆ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์†Œ๋ฐฉ์•ˆ์ „์ง€๋„ ์ „์‚ฐ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ์„œ์šธ์‹œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ชจ๋“  ํ™”์žฌ์‚ฌ๊ณ ๋ฅผ 24์‹œ๊ฐ„ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜์˜€๊ณ , ์ค‘๊ธ‰๊ทœ๋ชจ ์ด์ƒ์˜ ์‚ฌ๊ณ ์—์„œ ์‹คํ—˜๊ตฐ์ด ์ถœ๋™ํ•˜๊ฒŒ ๋˜๋Š” ๊ฒฝ์šฐ ๊ท€์†Œ ํ›„ ๋ฐ”๋กœ ์‹ค๋‚ด๊ณต๊ธฐ์งˆ์„ ์ธก์ •ํ•˜์˜€๋‹ค. 11๊ฐœ ์œ ํ•ด๋ฌผ์งˆํ•ญ๋ชฉ(๋ฏธ์„ธ๋จผ์ง€, ํฌ๋ฆ„์•Œ๋ฐํžˆ๋“œ, ํœ˜๋ฐœ์„ฑ์œ ๊ธฐํ™”ํ•ฉ๋ฌผ, PAH, VCM, ์‚ฐ๋ฅ˜, ์„๋ฉด, CO, CO2, NO2, O3)์€ ๊ณต์ •์‹œํ—˜๋ฒ•์— ๋”ฐ๋ผ ์ธก์ •ํ•˜์˜€๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์œ„ํ—˜์ง๊ตฐ ์ค‘ ํ•˜๋‚˜๋กœ ๋ถ„๋ฅ˜๋˜์–ด ์žˆ๋Š” ์†Œ๋ฐฉ๊ด€์€ ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅํ•œ ์œ ํ•ด๋ฌผ์งˆ์— ์ง€์†์ , ๋ฐ˜๋ณต์ ์œผ๋กœ ๋…ธ์ถœ๋˜๊ณ  ์žˆ์œผ๋‚˜ ๊ฑด๊ฐ•๊ด€๋ฆฌ๋Š” ๊ฐœ์ธ์  ์˜๋ฌด๋กœ ๊ฐ„์ฃผ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋งค๋…„ ์‹ค์‹œํ•˜๋Š” ์†Œ๋ฐฉ๊ด€ ํŠน์ˆ˜๊ฑด๊ฐ•์ง„๋‹จ ๊ฒฐ๊ณผ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋Œ€๋„์‹œ ์†Œ๋ฐฉ๊ด€๋“ค์˜ ๋งŒ์„ฑ์งˆํ™˜์ธ ๊ณ ํ˜ˆ์••, ๋‹น๋‡จ, ๋Œ€์‚ฌ์ฆํ›„๊ตฐ, ๋น„๋งŒ๊ณผ ์ง์—…์„ฑ ์งˆํ™˜์ธ ํ ํ™˜๊ธฐ์žฅ์• (์ œํ•œ์„ฑ, ํ์‡„์„ฑ, ๋ณตํ•ฉ์„ฑ)์™€ ์†Œ์Œ์„ฑ๋‚œ์ฒญ(4๋ถ„๋ฒ•, 6๋ถ„๋ฒ•)์— ๋Œ€ํ•˜์—ฌ ์ผ๋ฐ˜์ธ๊ตฌ์ง‘๋‹จ์˜ ๊ตญ๊ฑด์˜ ๊ฒ€์ง„๊ฒฐ๊ณผ๋ฅผ ์ด์šฉ, ๋น„๊ตํ•˜์—ฌ ์ง์ ‘ํ‘œ์ค€ํ™” ๋ฐฉ๋ฒ•์œผ๋กœ ์†Œ๋ฐฉ๊ด€๊ณผ ์ผ๋ฐ˜์ธ๊ตฌ์ง‘๋‹จ 1,000๋ช…๋‹น ์„ฑ-์—ฐ๋ น ํ‘œ์ค€ํ™”์œ ๋ณ‘๋ฅ ๊ณผ ๊ฐ„์ ‘ํ‘œ์ค€ํ™” ๋ฐฉ๋ฒ•์œผ๋กœ ์„ฑ-์—ฐ๋ น ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋น„๋ฅผ ๊ฐ๊ฐ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์†Œ๋ฐฉ๊ด€ ํŠน์ˆ˜๊ฑด๊ฐ•์ง„๋‹จ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์—ญํ•™์  ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ : ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ, ์†Œ๋ฐฉ๊ด€๋“ค์€ ์žฌ๋‚œํ˜„์žฅ์—์„œ ๋ฐœ์•”๋ฌผ์งˆ ๋ฐœ์ƒ๊ฐ€๋Šฅ์„ฑ ์ธ์ง€๋„๋Š” 94.4%๋กœ ๋†’์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋ณด๊ฑดํ•™์  ์‚ฌ๊ณ ๊ฐ€ ์†Œ๋ฐฉ๊ด€ ์Šค์Šค๋กœ์˜ ์•ˆ์ „์„ ์ง€์ผœ์ฃผ๋Š” ๋ฐฉํ™”๋ณต์˜ ์‚ฌ์šฉ, ์„ธํƒ, ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ํ–‰๋™์—๋Š” ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‹ค๋งŒ, ๋ฐฉํ™”๋ณต์˜ ์‚ฌ์šฉ์—์„œ, ํ™”์žฌํ˜„์žฅ์—์„œ ์†Œ๋ฐฉํ™œ๋™ ์ข…๋ฃŒ ํ›„ ๋ฐฉํ™”๋ณต์„ ์‹ค์ œ ๋ฒ—์–ด์•ผ ํ•˜๋Š” ์œ„์น˜์— ๋Œ€ํ•œ ์ƒ๊ฐ๊ณผ ๋ณด๊ฑดํ•™์  ์‚ฌ๊ณ ๊ฐ„์—๋Š” ์—ฐ๊ด€์„ฑ์ด ์žˆ์—ˆ๋‹ค (AOR=1.92, CI 1.01-3.68). ํ™”์žฌํ˜„์žฅ๊ณผ ๋ฐฉํ™”๋ณต ์˜ค์—ผ๋„ ํ‰๊ฐ€๊ฒฐ๊ณผ, ํ™”์žฌํ˜„์žฅ์—์„œ๋Š” ๊ธ‰์„ฑ ํ”ผ๋ถ€๋…์„ฑ์„ ์œ ๋ฐœํ•˜๋Š” ์‹œ์•ˆํ™”์ˆ˜์†Œ๊ฐ€ ๋ถˆ์™„์ „์—ฐ์†Œ(ํ›ˆ์†Œํ™”์žฌ)์™€ ์™„์ „์—ฐ์†Œ(ํ™”์—ผํ™”์žฌ) ๋ชจ๋‘์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ์‹œ์•ˆํ™”์ˆ˜์†Œ๋Š” ๋ฐฉํ™”๋ณต ํฌ์ง‘๊ณต๊ธฐ์—์„œ๋„ ๊ฒ€์ถœ๋˜์–ด ํ™”์žฌํ˜„์žฅ์—์„œ์˜ ๋ฐฉํ™”๋ณต ์ฐฉ์šฉ์ด ๋งค์šฐ ์ค‘์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํฌ๋ฆ„์•Œ๋ฐํžˆ๋“œ๋Š” ์™„์ „์—ฐ์†Œ(ํ™”์—ผํ™”์žฌ)์—์„œ ๋…ธ์ถœ๊ธฐ์ค€ TWA 0.3์„ ์ดˆ๊ณผํ•˜์—ฌ ๋ฐฉํ™”๋ณต์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ์ด ์™ธ์—๋„ ์‹œ์•ˆํ™”์ˆ˜์†Œ, ๋‹ˆ์ผˆ์€ ์‹ ๊ทœ ๋ฐฐ์ •๋ฐ›์€ ๋ฐฉํ™”๋ณต์—์„œ๋„ ๊ฒ€์ถœ๋˜์—ˆ์œผ๋‚˜ ์œ„ํ—˜์ˆ˜์ค€์„ ์ดˆ๊ณผํ•˜์ง€๋Š” ์•Š์•˜๋‹ค. ์‹œ์•ˆํ™”์ˆ˜์†Œ๋Š” ๋ฐฉํ™”๋ณต ๋ชจ๋“  ์‹คํ—˜๊ทธ๋ฃน์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๊ณ , ๋‚ฉ, ์‚ฐํ™”์ฒ , ์•Œ๋ฃจ๋ฏธ๋Š„, ์นด๋“œ๋ฎด๊ณผ ๊ฐ™์€ ์ค‘๊ธˆ์† ๋ฌผ์งˆ๋„ ๊ณต๊ธฐํฌ์ง‘์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๋ฐฉํ™”๋ณต ๊ต์ฐจ์˜ค์—ผ ์ˆ˜์งˆ๋ถ„์„ ๊ฒฐ๊ณผ์—์„œ๋Š” ๊ตฌ๋ฆฌ, ๋‹ˆ์ผˆ, ์•„์—ฐ ๋“ฑ์˜ ์ค‘๊ธˆ์†์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ์•„ํฌ๋ฆด๋กœ๋‹ˆํŠธ๋ฆด์€ ํ™”์žฌ์— ํ•œ ๋ฒˆ ๋…ธ์ถœ๋œ ๊ฒฝ์šฐ๋ณด๋‹ค ์—ฐ์†์œผ๋กœ ๋…ธ์ถœ๋œ ๊ฒฝ์šฐ ๊ทธ ์–‘์ด 2.1๋ฐฐ ๋” ๋†’์€ ์–‘-๋ฐ˜์‘๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. 1๊ธ‰ ๋ฐœ์•”๋ฌผ์งˆ์ธ ๋‚˜ํ”„ํƒˆ๋ Œ์€ ํ›ˆ์†Œํ™”์žฌ์™€ ํ™”์—ผํ™”์žฌ ๋ชจ๋‘ ๊ฒ€์ถœ๋˜์—ˆ์œผ๋‚˜ ํ›ˆ์†Œํ™”์žฌ์—์„œ ๋” ๋†’๊ฒŒ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๊ตฌ๋ฆฌ์™€ ๊ทธ ํ™”ํ•ฉ๋ฌผ, ์•ˆํ‹ฐ๋ชฌ, ์•„ํฌ๋ฆด๋กœ๋‹ˆํŠธ๋ฆด, ๋””์—ํ‹ธํ—ฅ์‹คํ”„ํƒˆ๋ ˆ์ดํŠธ 4๊ฐœ ํ•ญ๋ชฉ์ด ๋ฌผํ™˜๊ฒฝ๋ณด์กด๋ฒ• ๊ธฐ์ค€ ํŠน์ •์ˆ˜์งˆ์œ ํ•ด๋ฌผ์งˆ ํ์ˆ˜๋ฐฐ์ถœ์‹œ์„ค ์ ์šฉ๊ธฐ์ค€์„ ์ดˆ๊ณผํ•˜์—ฌ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ์ด๋กœ์จ ํ™”์žฌํ˜„์žฅ์—์„œ์˜ ๋ฐฉํ™”๋ณต ์˜ค์—ผ์ด ์‹ฌ๊ฐํ•จ์„ ํ™•์ธํ•˜์˜€๊ณ  ์ˆ˜์งˆ์˜ค์—ผ ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ƒ๊ธฐ 4๊ฐœ ํ•ญ๋ชฉ์ด ํ์ˆ˜๊ธฐ์ค€์น˜๋ฅผ ์ดˆ๊ณผํ•˜์—ฌ ๊ฒ€์ถœ๋จ์œผ๋กœ์จ ์ค‘์žฌ๊ฐ€๋Šฅ์„ฑ ๋˜๋Š” ์ •์ฑ…์  ์กฐ์น˜๊ฐ€ ํ•„์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ์™€ ์‹คํ—˜์—ฐ๊ตฌ์—์„œ ๋„์ถœ๋œ ๋ฌธ์ œ์˜ ๋Œ€์•ˆ๋งˆ๋ จ์„ ์œ„ํ•˜์—ฌ ๋ธํŒŒ์ด์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ, ์„ค๋ฌธ์กฐ์‚ฌ์™€ ํ™”์žฌํ˜„์žฅ๊ณผ ๋ฐฉํ™”๋ณต ์˜ค์—ผ๋„ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ์ค‘์š”๋„ ๋ถ€๋ฌธ์—์„œ๋Š” ์†Œ๊ด€๋ถ„์•ผ ์˜ˆ์‚ฐํ™•๋ณด์™€ ์žฌ๋‚œํ˜„์žฅ ์˜ค์—ผ๋ฌผ์งˆ ๊ฑด๊ฐ•์˜ํ–ฅ ์œ„ํ—˜์„ฑ์— ๋Œ€ํ•œ ๊ต์œก์‹ค์‹œ๊ฐ€ ๊ฐ€์žฅ ์‹œ๊ธ‰ํ•œ ๊ณผ์ œ๋กœ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ์‹คํ–‰๊ฐ€๋Šฅ์„ฑ ๋ถ€๋ฌธ์—์„œ๋Š” ์žฌ๋‚œํ˜„์žฅ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๊ฑด๊ฐ•์˜ํ–ฅ ์œ„ํ—˜์„ฑ ๊ต์œก์‹ค์‹œ, ๊ฐœ์ธ๋ณดํ˜ธ์žฅ๋น„ 1์ฐจ์ œ์—ผ ๊ต์œก์‹ค์‹œ, ๋ฐฉํ™”๋ณต ๋“ฑ ๊ฐœ์ธ๋ณดํ˜ธ์žฅ๋น„ ๊ด€๋ฆฌ์ฒด๊ณ„ ๊ฐœ์„ ๊ณผ ์ฒด๊ณ„์  ์šด์˜์„ ์œ„ํ•œ ๊ต์œก๊ณผ์ • ๊ฐœ๋ฐœ์ด ๊ฐ€์žฅ ์‹œ๊ธ‰ํ•œ ๊ณผ์ œ๋กœ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ, ์†Œ๋ฐฉ์ฒญ์‚ฌ์—์„œ ์ธก์ •ํ•œ ์œ ํ•ด๋ฌผ์งˆ 11์ข… ์ค‘ 3์ข…์ด ๊ตญ๋‚ดโ€ข์™ธ ๊ธฐ์ค€์„ ์ดˆ๊ณผํ•˜์˜€๊ณ , 1์ข…์€ ๊ตญ์™ธ๊ธฐ์ค€์— ์œก๋ฐ•ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ํŠนํžˆ ์ดํœ˜๋ฐœ์„ฑ์œ ๊ธฐํ™”ํ•ฉ๋ฌผ, ์ด์‚ฐํ™”ํƒ„์†Œ, ํ™ฉ์‚ฐ์€ ๊ฐ 2.5๋ฐฐ, 2.2๋ฐฐ, 1.1๋ฐฐ๊ฐ€ ํ™˜๊ฒฝ๋ถ€์™€ ๊ณ ์šฉ๋…ธ๋™๋ถ€ ๊ธฐ์ค€๋ณด๋‹ค ๋†’์•˜๋‹ค. ๋˜ํ•œ, ํฌ๋ฆ„์•Œ๋ฐํžˆ๋“œ์™€ ํ™ฉ์‚ฐ์˜ ๊ฒฝ์šฐ, ์‹คํ—˜๊ตฐ๋ณด๋‹ค ๋Œ€์กฐ๊ตฐ์—์„œ ๋” ๋†’๊ฒŒ ์ธก์ •๋˜์—ˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ, ๋Œ€๋„์‹œ ์†Œ๋ฐฉ๊ด€ ํŠน์ˆ˜๊ฑด๊ฐ•์ง„๋‹จ๊ฒฐ๊ณผ ์ „์ˆ˜์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ผ๋ฐ˜์ธ๊ตฌ์ง‘๋‹จ๊ณผ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์ง์ ‘ํ‘œ์ค€ํ™” ๋ฐฉ๋ฒ•์œผ๋กœ ์‚ฐ์ถœํ•œ ์งˆ๋ณ‘๋‹จ๊ณ„์˜ ์„ฑ-์—ฐ๋ น๋ณด์ • ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋ฅ ์€ ์†Œ๋ฐฉ๊ด€์—์„œ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ 12.43% (๋‚จ 16.91%, ์—ฌ 7.76), ํํ™˜๊ธฐ์žฅ์• ์—์„œ, ์ œํ•œ์„ฑ์€ 16.87% (์—ฌ 18.32%), ๋ณตํ•ฉ์„ฑ์€ 2.07% (์—ฌ 2.59%) ์ผ๋ฐ˜์ธ๊ตฌ๋ณด๋‹ค ๋†’์•˜๋‹ค. ์ผ๋ฐ˜์ธ๊ตฌ๋Š” ๊ณ ํ˜ˆ์•• 17.93% (๋‚จ 23.44%, ์—ฌ 12.19%), ๋‹น๋‡จ 5.30% (๋‚จ 6.75%, ์—ฌ 3.80%), ๋น„๋งŒ 34.29% (๋‚จ 46.94%, ์—ฌ 21.14%), ํํ™˜๊ธฐ์žฅ์• ์˜ ๊ฒฝ์šฐ, ํ์‡„์„ฑ์—์„œ 4.39% (๋‚จ 6.13%, ์—ฌ 2.61%), ์†Œ์Œ์„ฑ ๋‚œ์ฒญ์˜ ๊ฒฝ์šฐ, 3๋ถ„๋ฒ•์—์„œ ์šฐ์ธก์€ 4.41% (๋‚จ 5.51%, ์—ฌ 3.29%), ์ขŒ์ธก์€ 4.87% (๋‚จ 6.41%, ์—ฌ 3.29%) ์ด์—ˆ๊ณ , 4๋ถ„๋ฒ•์—์„œ ์šฐ์ธก์€ 6.99% (๋‚จ 8.66%, ์—ฌ 5.28%), ์ขŒ์ธก์€ 7.92% (๋‚จ 10.06%, ์—ฌ 5.71%) ์†Œ๋ฐฉ๊ด€๋ณด๋‹ค ๋†’์•˜๋‹ค. ๊ฐ ์งˆํ™˜์˜ ์งˆ๋ณ‘์ „๋‹จ๊ณ„ ์„ฑ-์—ฐ๋ น๋ณด์ • ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋ฅ ์€ ๊ณ ํ˜ˆ์•• 37.26% (๋‚จ 52.45%, ์—ฌ 21.47%), ๋‹น๋‡จ 23.17% (๋‚จ 30.79%, ์—ฌ 15.25%), ๋Œ€์‚ฌ์ฆํ›„๊ตฐ 87.21% (๋‚จ 83.09%, ์—ฌ 91.49%), ๋น„๋งŒ 23.25% (๋‚จ 33.08%, ์—ฌ 13.03%)๋กœ ์ผ๋ฐ˜์ธ๊ตฌ๋ณด๋‹ค ์†Œ๋ฐฉ๊ด€์—์„œ ๋ชจ๋‘ ๋†’์•˜๋‹ค. ๋˜ํ•œ, ๊ฐ„์ ‘ํ‘œ์ค€ํ™”์— ์˜ํ•œ ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋น„์—์„œ๋Š”, ์งˆ๋ณ‘๋‹จ๊ณ„์˜ ๊ฒฝ์šฐ, ์ผ๋ฐ˜์ธ๊ตฌ๋ณด๋‹ค ์†Œ๋ฐฉ๊ด€์—์„œ ๋น„๋งŒ 1.12๋ฐฐ (1.08-1.16), ๋Œ€์‚ฌ์ฆํ›„๊ตฐ 1.33๋ฐฐ (1.26-1.40) ๋†’์•˜๊ณ , ์งˆ๋ณ‘์ „๋‹จ๊ณ„์˜ ๊ฒฝ์šฐ, ์†Œ๋ฐฉ๊ด€์—์„œ ๋น„๋งŒ 1.34๋ฐฐ (1.29-1.40), ๊ณ ํ˜ˆ์•• 1.89๋ฐฐ (1.83-1.96), ๋‹น๋‡จ 1.36๋ฐฐ (1.31-1.41), ๋Œ€์‚ฌ์ฆํ›„๊ตฐ 1.85๋ฐฐ (1.81-1.90) ์ผ๋ฐ˜์ธ๊ตฌ๋ณด๋‹ค ๋†’์•˜๋‹ค. ๊ฒฐ๋ก  : ๋ฐฉํ™”๋ณต ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์†Œ๋ฐฉ๊ด€์˜ ๋ณด๊ฑดํ•™์  ์ƒ๊ฐ๊ณผ ๋ฐฉํ™”๋ณต์˜ ์‚ฌ์šฉ, ์„ธํƒ, ๊ด€๋ฆฌํ–‰๋™๊ฐ„์—๋Š” ์—ฐ๊ด€์„ฑ์ด ์—†์—ˆ์œผ๋‚˜, ๋ณด๊ฑดํ•™์  ์ƒ๊ฐ๊ณผ ๋ฐฉํ™”๋ณต์˜ ์˜ฌ๋ฐ”๋ฅธ ์‚ฌ์šฉ์— ๋Œ€ํ•œ ์ƒ๊ฐ ์‚ฌ์ด์—๋Š” ์—ฐ๊ด€์„ฑํ†ต๊ณ„์  ์œ ์˜์„ฑ์„ ๋ณด์—ฌ ์ƒ๊ฐ์—์„œ ํ–‰๋™์œผ๋กœ ์˜ฎ๊ธธ ์ˆ˜ ์žˆ๋Š” ํ†ต๋กœ๋ฅผ ๋งˆ๋ จํ•ด์ฃผ๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜์—ฌ ์ด์—๋Œ€ํ•œ ๋Œ€์ฑ…๋งˆ๋ จ์ด ํ•„์š”ํ•˜๋‹ค. ์ฃผํƒํ™”์žฌ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ํ™”์žฌํ˜„์žฅ๊ณผ ๋ฐฉํ™”๋ณต ์˜ค์—ผ๋ฌผ์งˆ ๋ฐœ์ƒ ์‹คํ—˜์„ ํ†ตํ•ด ์žฌ๋‚œํ˜„์žฅ์—์„œ์˜ ์†Œ๋ฐฉ๊ด€ ์ง์—…ํ™˜๊ฒฝ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋Š” ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ฃผํƒํ™”์žฌ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์œ ํ•ด๋ฌผ์งˆ์˜ ์ข…๋ฅ˜์™€ ์–‘์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฌผ๋ก  ์ œํ•œ๋œ ๊ณต๊ฐ„์—์„œ ๋ฐœ์ƒํ•œ ์œ ํ•ด๋ฌผ์งˆ์˜ ์ข…๋ฅ˜์™€ ์–‘์œผ๋กœ ๊ณผ์†Œ์ถ”์ • ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์œผ๋‚˜, ์ด ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ์ผ๋ฐ˜์ ์œผ๋กœ ์ฃผํƒํ™”์žฌ ํ˜„์žฅ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์œ ํ•ด๋ฌผ์งˆ์˜ ์ข…๋ฅ˜์™€ ์–‘์— ๋Œ€ํ•œ DB๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ฉฐ, ํ™”์žฌํ˜„์žฅ์—์„œ ๊ฐœ์ธ๋ณดํ˜ธ์žฅ๋น„์˜ ์ •ํ™•ํ•˜๊ณ  ์ฒ ์ €ํ•œ ์ฐฉ์šฉ์ด ์ค‘์š”ํ•จ์„ ์žฌํ™•์ธํ•˜์˜€๋‹ค. ๋”๋ถˆ์–ด, ๋ฐฉํ™”๋ณต ์˜ค์—ผ์— ์˜ํ•œ ์ˆ˜์งˆ์˜ค์—ผ์˜ ๊ฐ€๋Šฅ์„ฑ ํ™•์ธ์„ ํ†ตํ•ด ํ›„์†์—ฐ๊ตฌ์™€ ์ •์ฑ…์‹คํ–‰์ด ํ•„์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์†Œ๋ฐฉ๊ด€์˜ ๊ทผ๋ฌด๋…„์ˆ˜์™€ ์ถœ๋™ํšŸ์ˆ˜์— ๋Œ€ํ•œ ์ •๋ณด๊ฐ€ ์žˆ๋‹ค๋ฉด ํ† ๋Œ€๋กœ ์ฃผํƒํ™”์žฌํ˜„์žฅ์—์„œ ๋…ธ์ถœ๋˜์—ˆ๋˜ ์œ ํ•ด๋ฌผ์งˆ์˜ ๋ˆ„์ ๋…ธ์ถœ์–‘์„ ์‚ฐ์ •ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋ฃŒ๋“ค์€ ์†Œ๋ฐฉ๊ด€์—๊ฒŒ ์งˆ๋ณ‘์ด ๋ฐœ์ƒํ•˜์˜€์„ ๋•Œ ์—…๋ฌด๊ด€๋ จ์„ฑ ์งˆํ™˜์—ฌ๋ถ€๋ฅผ ์ž…์ฆํ•  ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ์ž๋ฃŒ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ, ํ™”์žฌํ˜„์žฅ์—์„œ ๊ต์ฐจ์˜ค์—ผ์ด ๋ฐœ์ƒํ•œ ๋ฐฉํ™”๋ณต์— ๋Œ€ํ•˜์—ฌ ํ˜„์žฅ ๊ธด๊ธ‰์ œ์—ผ์— ๋Œ€ํ•œ ์†Œ๋ฐฉ์ •์ฑ…์„ ์‹คํ–‰ํ•˜๊ธฐ ์œ„ํ•œ ๊ฐ•๋ ฅํ•œ ๊ณผํ•™์  ๊ทผ๊ฑฐ๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ์†Œ๋ฐฉ๊ด€ ๋ฐฉํ™”๋ณต ์•ˆ์ „ํ•œ ์‚ฌ์šฉ๊ณผ ๋ณด๊ฑด์ •์ฑ…์„ ์ถ”์ง„ํ•  ๋•Œ ์ ๊ทน ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์†Œ๋ฐฉ์ฒญ์‚ฌ ์‹ค๋‚ด๊ณต๊ธฐ์งˆ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์†Œ๋ฐฉ๊ด€ ๋Œ€๊ธฐ๊ทผ๋ฌดํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์ง์—…ํ™˜๊ฒฝ ์ธก์ •์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ์‹ค๋‚ด๊ณต๊ฐ„๊ณผ ์ฐจ๊ณ ์—์„œ ๊ธฐ์ค€์„ ์ดˆ๊ณผํ•˜๋Š” ํ•ญ๋ชฉ๋“ค์ด ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์žฌ๋‚œํ˜„์žฅ ์ถœ๋™ ํ›„ ๊ท€์†Œ์™€ ์ƒ๊ด€์—†์ด ์†Œ๋ฐฉ์ฐจ๋Ÿ‰ ๋ฐฐ๊ธฐ๊ฐ€์Šค์™€ ํ‰์†Œ ํ™”ํ•™์  ์œ ํ•ด๋ฌผ์งˆ์˜ ๋ˆ„์ ์ด ์ฒญ์‚ฌ ๋‚ด ์‹ค๋‚ด๊ณต๊ธฐ์งˆ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์žฅโ€ข๋‹จ๊ธฐ์ ์œผ๋กœ ์†Œ๋ฐฉ๊ด€์˜ ๊ฑด๊ฐ•์˜ํ–ฅ์— ๋ฌธ์ œ๋ฅผ ์ค„ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์–ด ์†Œ๋ฐฉ์ฒญ์‚ฌ์˜ ์ฒด๊ณ„์  ์‹ค๋‚ด๊ณต๊ธฐ์งˆ ๊ด€๋ฆฌ๊ฐ€ ํ•„์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ํ–ฅํ›„ ์„œ์šธ์‹œ ์†Œ๋ฐฉ์ฒญ์‚ฌ ๋‚ด ์‹ค๋‚ด๊ณต๊ธฐ์งˆ ๊ด€๋ฆฌ์‹œ์Šคํ…œ์ด ์ •์ฐฉ๊ณผ ๊ฐœ์„ ์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ํŠน์ˆ˜๊ฑด๊ฐ•์ง„๋‹จ๊ฒฐ๊ณผ ๋ถ„์„์„ ํ†ตํ•œ ์†Œ๋ฐฉ๊ด€๊ณผ ์ผ๋ฐ˜์ธ๊ตฌ์ง‘๋‹จ๊ณผ์˜ ํ‘œ์ค€ํ™” ์œ ๋ณ‘๋ฅ  ๋น„๊ต๋ฅผ ํ†ตํ•ด ์งˆ๋ณ‘๋‹จ๊ณ„์˜ ๊ฒฝ์šฐ, ๊ฑด๊ฐ•๊ทผ๋กœ์ž ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์งˆ๋ณ‘์ „๋‹จ๊ณ„์˜ ๊ฒฝ์šฐ ์†Œ๋ฐฉ๊ด€์—์„œ ๋ชจ๋“  ๋งŒ์„ฑ์งˆํ™˜์—์„œ ์ผ๋ฐ˜์ธ๊ตฌ๋ณด๋‹ค ๋†’์€ ์œ ๋ณ‘๋ฅ ์„ ๋ณด์ž„์— ๋”ฐ๋ผ ์ฒด๊ณ„์ ์ธ ๊ฑด๊ฐ•๊ด€๋ฆฌ ํ”„๋กœ๊ทธ๋žจ์ด ํ•„์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํŠนํžˆ, ๊ฑด๊ฐ•๊ทผ๋กœ์ž ํšจ๊ณผ๋ฅผ ๋ณด์ด๋Š” ์ง๊ตฐ์—์„œ๋Š” ๊ฒ€์ง„๊ฒฐ๊ณผ์˜ ์ฝ”ํ˜ธํŠธ ๊ตฌ์ถ•์„ ํ†ตํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ฒด๊ณ„์ ์ธ ์ถ”์ , ๊ด€๋ฆฌ์‹œ์Šคํ…œ ๊ตฌ์ถ•์ด ํ•„์š”ํ•˜๋‹ค. ๋.Introduction : The International Cancer Institute (IARC) under the World Health Organization regulates the occupational exposure of firefighters as group 2B, which is a possible human carcinogenic factor, and group 2A, which is a predicted carcinogenic factor, as the shift work, a factor that disturbs the biological rhythm. In addition, firefighters may be additionally exposed to carcinogens by dangerous substances exposed at fire fighting scene such as disasters. The firefighter's work environment is classified into dispatch scene, emergency response route, and office space. In particular, 10 kinds of toxic substances, such as carbon monoxide, hydrogen cyanide, and formaldehyde, which are generated at the fire scene, which is one of the dispatch scenes, are well known to be one of the strong risk factors that affect human health. Firefighters increase the burden of disease by constantly being exposed to risk factors occurring at the disaster scene. Therefore, if there is information on the risk factors for firefighters' working environment and disaster scene work environment, predictable health effects can be prevented. The purpose of this study is to measure the working environment of firefighters working in metropolitan in the dispatch scene and office space, and to evaluate the health perception of health. In addition, the standardized prevalence rate (ratio) of chronic diseases and work-related diseases is compared and evaluated with the general population with the general population using the results of occupational health examinations targeting the total cohort of firefighters in metropolitan cities to evaluate their health level. Methods : First, a survey was conducted for 1,097 firefighters working in the city of Seoul for fire fighting and rescue work for 21 days, with a response rate of 40.3%. In this survey, health awareness was evaluated on the use, washing, and management of fire fighting protection suit, one of the personal protective equipment used by firefighters. In addition, the level of activity at the disaster scene among firefighters' work environments was evaluated. In the work environment, the most common occurrence of residential fires in Korea was tested for the type and amount of pollutants for the primary contamination and secondary cross-contamination of fire fighting protection suit. Cross-contamination evaluation was measured by dividing fire clothing into 4 groups according to the degree of risk of the work environment. At this time, 12 items were analyzed through air sampling in the primary and secondary contamination environment. In addition, the contaminations on the fire fighting protection suit were extracted from the laundry water and water quality analysis was performed on 23 items. In order to prepare countermeasures for problems derived through questionnaire surveys and experimental studies, a specific policy that can increase the feasibility and effectiveness of the Delphi expert survey method was proposed. Participants in the Delphi expert survey conducted three rounds of university professors and firefighters with a priori practical experience and research experience on firefighting scenes and firefighting suits. Second, firefighters evaluated the working environment at the fire station, a waiting space during work. After firefighters withdrew from the fire scene and returned to the fire station, the indoor air quality of the chemically hazardous substances that were secondarily exposed inside the fire station was evaluated. The research subject randomly selected four fire stations located in Seoul, Korea. The two fire stations were measured after the firefighting activities at the fire scene were terminated and returned to the fire station as an experimental group. The other two sites were controls, and the indoor air quality was measured at the usual level regardless of dispatch. We conducted 24-hour monitoring for all fire accidents that occurred in Seoul Metropolitan using fire safety map computer system. Also, indoor air quality was measured immediately after homecoming if the experiment group was to be dispatched due to an accident of intermediate or larger scale. 11 hazardous substance items such as particulate matter, formaldehyde, volatile organic compounds, PAH, VCM, acidity, asbestos, CO2, NO2, O3 were measured according to the process test method. Thirdly, firefighters classified as one of the dangerous occupations are exposed to unpredictable hazardous substances, but health management is considered a personal obligation. Therefore, in this study, using the results of annual firefighters' occupational health examination, high blood pressure, diabetes mellitus, metabolic syndrome, obesity and work-related diseases such as pulmonary ventilation disorders (limited, obstructive, combined) and noise-induced hearing loss (three tone average and four tone average method). The age- and sex-adjusted standardized prevalence rate per 1,000 firefighters was calculated using the direct standardization method and the age- and sex-adjusted standardized prevalence ratio by the indirect standardization method. Results : In the first study, firefighters had a high 94.4% awareness of the possibility of carcinogens occurring at the disaster scene. It was confirmed that these health awareness did not affect the behavior of the use, washing, and maintenance of fire fighting protective suit that protects their own safety. However, in the use of fire fighting protection suit, there was statistical significance between the thought of the location where fire fighting protection suit should be taken off after the firefighting activity at the fire scene and the health awareness. As a result of the fire scene and fire fighting protection suit contamination assessment, hydrogen cyanide, which causes acute skin toxicity, was detected in both incomplete and complete combustion at the fire scene. Hydrogen cyanide was also detected in the air sampled in fire fighting protection suit, confirming the importance of wearing fire fighting protection suit at the fire scene. Formaldehyde was detected in fire fighting protection suits exceeding the exposure limit TWA 0.3 in complete combustion. In addition, hydrogen cyanide and nickel were detected in newly allocated fire protection clothing, but did not exceed the risk level. Hydrogen cyanide was detected in all experimental groups in fire fighting protection suit, and heavy metals such as lead, iron oxide, aluminum and cadmium were also detected in air sampling. In the results of water quality analysis for cross-contamination of fire fighting protection suit, four items of copper and its compounds, antimony, acrylonitrile, and diethylhexylphthalate were detected in excess of the standards applied to wastewater discharge facilities for specific water quality hazardous substances according to the Water Environment Conservation Act in Korea. As a result, it was confirmed that the contamination of fire fighting protection suit at the fire scene was serious and suggested the possibility of water pollution. In addition, it was confirmed that policy measures were necessary as four items were detected exceeding the wastewater standard. The most urgent task was to secure the budget for the relevant field and conduct education on the risk of health impact of contaminations in the disaster scene in the importance category of the questionnaire survey and the results of the fire scene and fire fighting protection suit contamination level test. In the field of feasibility, education on the risk of health impacts of pollutants at disaster scenes, first-line decontamination education for personal protective equipment, improvement of personal protective equipment management system such as fire fighting protection suit and development of a curriculum for systematic operation were evaluated as the most urgent tasks. In the second study, it was confirmed that three of the 11 hazardous substances measured by the fire department exceeded the domestic and foreign standards, and one was close to the international standards. In particular, total volatile organic compounds, carbon dioxide and sulfuric acids were 2.5 times, 2.2 times and 1.1 times higher than the standard. Also, for formaldehyde and sulfuric acid, it was measured higher in the control group than in the case group. In the third study, as a result of comparison with the general population using the data obtained from occupational health examinations for firefighters in metropolitan cities, the age- and sex- adjusted standardized prevalence rate at the disease stage was 12.43% (male 16.91%, female 7.76%) for metabolic syndrome in firefighters, and in pulmonary ventilation disorders, the restrictive was 16.87% (male 15.45%, female 18.32%), and the combined was 2.07% (female 2.59%), which was higher than that of the general population. The general population was hypertension 17.93% (male 23.44%, female 12.19%), diabetes mellitus 5.30% (male 6.75%, female 3.80%), obesity 34.29% (male 46.94%, female 21.14%), and in the case of pulmonary ventilation disorder, obstructive 4.39% (male 6.13%, female 2.61%), in the case of noise-induced hearing loss by three tone average, the right side was 4.41% (male 5.51%, female 3.29%), and the left side was 4.87% (male 6.41%, female 3.29%). In the four tone average, the right side was 6.99% (male 8.66%, female 5.28%), and the left side 7.92% (male 10.06%, female 5.71%) higher than that of firefighters. The age- and sex- adjusted standardized prevalence rate for each disease was hypertension 37.26% (male 52.45%, female 21.47%), diabetes mellitus 23.17% (male 30.79%, female 15.25%), metabolic syndrome was 87.21% (male 83.09%, female 91.49%) ), obesity was 23.25% (male 33.08%, female 13.03%), which was higher in all firefighters than the general population. In addition, in the standardized prevalence ratio by indirect standardization, in the case of the disease stage, the firefighters were obesity 1.12 times (95% CI 1.08-1.16) and the metabolic syndrome 1.33 times (1.26-1.40) higher than the general population. It was higher than that of the general population (1.29-1.40), hypertension 1.89 times (1.83-1.96), diabetes mellitus 1.36 times (1.31-1.41), and metabolic syndrome 1.85 times (1.81-1.90). Conclusions : There was no statistical significance between the firefighter's health awareness and the use, washing, and management behavior of firefighters through the fire fighting protection suit survey. However, there was statistical significance between the health awareness and the thoughts on the proper use of fire fighting protection suit. Therefore, it is necessary to provide a pathway to move from thought to action. Firefighters' working environment at the disaster scene was measured through experiments on the occurrence of contaminants in fire scenes and fire fighting protection suits that may occur in residential fires. Through the results of this study, we were able to estimate the types and amounts of hazardous substances that can occur in a residential fire. Based on the results of this study, it is possible to establish a database on the types and amounts of hazardous substances that generally occur at residential fire scenes. Furthermore, it was confirmed that follow-up research and policy implementation are necessary through confirmation of the possibility of water pollution due to contamination of fire fighting protection suit. Also, if there is information on the number of years worked and the number of dispatches of firefighters, the cumulative exposure amount of hazardous substances exposed at the residential fire scene can be calculated. These data can be used as evidence to prove whether a firefighter has a disease related to work when a disease occurs. In addition, it will be able to provide a strong scientific evidence for implementing firefighting policies for emergency decontamination on-scene for firefighting suits with cross-contamination at the fire scene. The results of this study can be actively used when promoting the safe use of firefighters' fire fighting protection suit and health policies. Through the evaluation of the indoor air quality of the fire station building, the work environment was measured for the firefighter's standby working environment, and items exceeding the standard were detected in the indoor space and garage. In addition, regardless of returning after dispatch to the disaster scene, the accumulation of fire fighting vehicle exhaust gas and usual chemically hazardous substances has the potential to affect not only the indoor air quality of the building, but also the health of firefighters in the long and short term. It was confirmed that air quality management was necessary. The results of this study suggest that the indoor air quality management system in the Seoul Fire Department can be used for settlement and improvement in the future. Finally, by comparing the standardized prevalence rate between firefighters and the general population through the analysis of the results of occupational health examination, it was possible to estimate that there is an effect of healthy workers in the case of the disease stage. However, in the case of the pre-disease stage, it was confirmed that a systematic health management program was necessary as firefighters showed a higher prevalence than the general population in all chronic diseases. In particular, it is necessary to establish a systematic tracking and management system through research through cohort establishment of examination results in occupational groups showing the effect of healthy workers.Chapter 1. Introduction 1 Chapter 2. Behavior Evaluation of Firefighters Turnout Gear Using, Management and Assessment of Direct & Cross Contamination through Chemical Pollutant Exposures after Fire Occurrence 24 1. Introduction 25 2. Goal of this study 30 3. Frame of study 31 Chapter 2.1. Association between Behavior of Using, Washing, Management of Turnout Gear and Public Health Awareness 33 1. Study Aims 33 2. Literature Reviews 33 3. Methods and Materials 36 4. Results 40 5. Discussion 55 Chapter 2-2. Assessment of Primary and Secondary Contamination of Chemical Pollutants on Fire fighting Protection Suit at Fire Scenes 60 1. Study Aims 60 2. Literature Reviews 60 3. Methods and Materials 66 4. Results 92 5. Discussion 105 Chapter 2-3. A Study on Delphi for Improving the Perception of Firefighting Suit Management and Alternative Policy for Reducing Contamination Level 109 1. Study Aims 109 2. Methods and Materials 109 3. Results 114 4. Discussion and Political Suggestions 128 4. Limitations and Strength 144 5. Conclusion 146 Chapter 3. Characterization of Secondary Exposure to Chemicals and Indoor Air Quality in Fire Station 148 1. Introduction 149 2. Study Objective 151 3. Methods and Materials 151 4. Results 159 5. Discussions 174 6. Limitations and Further study 184 7. Conclusions 186 Chapter 4. The Prevalence of Chronic Disease and Work-related Disease among Korean Firefighters compared with the General Population 188 1. Introduction 189 2. Study Objective 191 3. Methods and Materials 191 4. Results 205 5. Discussions 254 6. Conclusions 259 Chapter 5. Public Health Implications and Further Studies 261 1. Measurement of various occupational environments of firefighters 262 2. Firefighters health status assessment 264 References 266 ๊ตญ๋ฌธ์ดˆ๋ก 271Docto

    Clinical Outcomes of Spousal Donor Kidney Transplantation: Single Center Experience

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    The supply of deceased donors is limited in Korea and most of kidney transplantations are performed using living related or unrelated donors. In this study, we investigated the clinical characteristics and outcomes of spousal donor kidney transplantation at our center. Methods: From January 2000 to August 2008, we performed 909 cases of kidney transplantations. In this study, 475 one-haplomatch living-related donor (LRD) and 50 spousal donor kidney transplantations were retrospectively analyzed. We compared the outcomes of spousal donor group with those of one-haplomatch LRD group. We also compared the outcomes of husband-to wife with those of wife-to-husband subgroup. Results: The number of Human leukocyte antigen (HLA) mismatch was significantly larger in spousal group (3.3ยฑ1.2) than in LRD group (2.7ยฑ0.7). The proportion of tacrolimus use was higher in spousal group (72.0%) than in LRD group (26.6%). The incidence rate of delayed graft function was higher in spousal group (4.0%) than in LRD group (0.4%). There was no significant difference in the incidence of acute rejection between the two groups. Graft survival rates in spousal group (98.0% at 1 year and 91.5% at 5 year) were comparable to those in LRD group (99.6% at 1year and 98.7% at 5 year) (P=0.321). There were no significant differences in the incidence of acute rejection and graft survival rates between the subgroups (husband-to-wife vs. wife-to- husband). Conclusions: We achieved excellent outcomes by using spousal donor as an option to reduce the donor organ shortageope

    ๋ฆฌ๋”โ€”๋ฉค๋ฒ„ ๊ฐˆ๋“ฑ ๋น„๋Œ€์นญ ํ˜„์ƒ์˜ ๊ฐœ์ธ์˜ ์ฐฝ์˜์„ฑ์— ๋Œ€ํ•œ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2017. 2. ์ตœ์ง„๋‚จ.We examine the effects of an overlooked concept of leader-member conflict asymmetry, on a members individual creativity. Considering that the main stream of conflict asymmetry research rarely focused on people at different levels of hierarchical relationships, the present study addressed this limitation by examining the leader-member conflict asymmetry in one-to-multi vertical dyads. Data from 50 leader-member dyads tested our multilevel hypotheses, which showed that leader-member conflict asymmetry (i.e., the member conflict scale subtracted from the leader conflict scale) negatively relates to a members proactive creativity, but positively relates to members responsive creativity. The leaders higher perception of conflict decreased the proactive creativity of members but increased their responsive creativity. A members psychological safety fully mediated the relationship between the leader-member conflict asymmetry and the members proactive creativity.I. INTRODUCTION 1 II. THEORY AND HYPOTHESIS 5 1. Leader-Member Conflict Asymmetry 5 1.1 Leader: the higher perceiver of conflict 5 1.2 Member: the higher perceiver of conflict 7 2. Mediating factors 8 2.1 Psychological Safety 10 2.2 Intrinsic/Extrinsic motivation 13 III. METHODS 16 1. Sample and procedures 16 2. Measures 17 IV. RESULTS 19 1. Leader-member conflict asymmetry and members creativity 20 2. Mediating effects of psychological safety and intrinsic/extrinsic motivation 21 V. SUPPLEMENTARY ANALYSIS 22 1. Multivariate polynomial regression analysis 22 2. Actual conflict and perceived conflict asymmetries 24 3. Task conflict and relationship conflict asymmetries 25 4. Motivation: mediator or moderator 27 5. Pure effects of leader-member conflict asymmetrydepending on its magnitude 28 VI. DISCUSSION 29 1. Implications 31 2. Limitations and future research 32 REFERENCES 34 ABSTRACT IN KOREAN 55Maste

    2014๋…„ ์ธ๊ตฌ๋ณด๊ฑด์กฐ์‚ฌ(DHS)๋ฅผ ํ™œ์šฉํ•œ ๋‹ค์ˆ˜์ค€ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต),2019. 8. ๊น€์„ ์˜.๋ชจ์„ฑ์‚ฌ๋ง์€ ์„ธ๊ณ„์ ์œผ๋กœ ์—ฌ์ „ํžˆ ์‹ฌ๊ฐํ•œ ๋ณด๊ฑด ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์ด๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๋ง์ด ์˜ˆ๋ฐฉ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์—์„œ, ๋˜ํ•œ ์ „์ฒด ๋ชจ์„ฑ์‚ฌ๋ง์˜ 99%๊ฐ€ ๊ฐœ๋ฐœ๋„์ƒ๊ตญ์—์„œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋”์šฑ ์ฃผ๋ชฉํ•ด์•ผ ํ•  ์ธก๋ฉด์ด ์กด์žฌํ•œ๋‹ค. ์‚ฐ์ „๊ด€๋ฆฌ(antenatal care, ANC), ์‚ฐํ›„๊ด€๋ฆฌ(postnatal care, PNC) ๋“ฑ์˜ ๊ธฐ์ดˆ์ ์ธ ๋ชจ์„ฑ ๊ด€๋ฆฌ ์„œ๋น„์Šค๊ฐ€ ์ „๋ฐ˜์ ์ธ ๋ชจ์„ฑ๊ฑด๊ฐ• ๊ฐœ์„ ์— ํšจ๊ณผ์ ์œผ๋กœ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ๊ณผ๊ฑฐ ๋งŽ์€ ์—ฐ๊ตฌ์—์„œ ์ž…์ฆ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—ฌ์ „ํžˆ ๊ฐœ๋ฐœ๋„์ƒ๊ตญ ๋“ฑ์ง€์—์„œ๋Š” ์„œ๋น„์Šค ์ด์šฉ์— ์ง€์—ฐ์„ ์ผ์œผํ‚ค๋Š” ์—ฌ๋Ÿฌ ์ฐจ์›์˜ ์žฅ์• ๋ฌผ๋“ค๋กœ ์ธํ•ด ์‚ฐ๋ชจ์˜ ์„œ๋น„์Šค ์ถ”๊ตฌ ์ˆ˜์ค€์ด ๋‚ฎ์€ ์ƒํ™ฉ์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์ง€์ธ ๊ฐ€๋‚˜์™€ ์ผ€๋ƒ๋Š” ์ „์ฒด ๋ชจ์„ฑ์‚ฌ๋ง ๋ถ€๋‹ด์˜ ์ƒ๋‹น ๋ถ€๋ถ„(66%)์„ ์ฐจ์ง€ํ•˜๋Š” ์‚ฌํ•˜๋ผ ์ด๋‚จ ์•„ํ”„๋ฆฌ์นด ์ง€์—ญ ๋‚ด ์œ„์น˜ํ•œ๋‹ค. ๋‘ ๊ตญ๊ฐ€ ๋ชจ๋‘ ์ €์ค‘์†Œ๋“๊ตญ(lower middle income country)์— ์†ํ•˜๋ฉฐ ๊ตญ๊ฐ€ ์†Œ๋“์ˆ˜์ค€(GNI per capita) ๋˜ํ•œ ์œ ์‚ฌํ•˜๋‚˜, ๋‘ ๊ตญ๊ฐ€์˜ ๋ชจ์„ฑ์‚ฌ๋ง๋น„ (maternal mortality ratio)์—๋Š” ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€๋‚˜์™€ ์ผ€๋ƒ ์—ฌ์„ฑ์˜ ๋ชจ์„ฑ๊ฑด๊ฐ• ์„œ๋น„์Šค ์ด์šฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์ด ๋ฌด์—‡์ธ์ง€์™€, ๊ฐ๊ตญ ์˜ํ–ฅ ์š”์ธ์˜ ์–‘์ƒ์ด ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅธ์ง€ ์‚ดํŽด๋ด„์œผ๋กœ์จ ๊ตญ๊ฐ€๊ฐ„ ๋น„๊ต ์—ฐ๊ตฌ๋ฅผ ์‹ค์‹œํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„์„ ์œ„ํ•œ ์›์ž๋ฃŒ๋Š” 2014๋…„ ๊ฐ€๋‚˜ ๋ฐ ์ผ€๋ƒ ์ธ๊ตฌ๋ณด๊ฑด์กฐ์‚ฌ(Demographic and Health Survey, DHS)์˜ ์—ฌ์„ฑ ์„ค๋ฌธ์ง€ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ณ€์ˆ˜๋Š” ANC, ์‹œ์„ค ์ถœ์‚ฐ ๋ฐ PNC์˜ ์ด์šฉ ์ˆ˜์ค€์„ ํ†ตํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•œ ๋ชจ์„ฑ ๊ฑด๊ฐ•๊ด€๋ฆฌ ์ถ”๊ตฌ ํ–‰์œ„(maternal health care seeking behavior, MSB)๋กœ์„œ, ์ด๋Š” ์ด 4๊ฐœ ์ˆ˜์ค€ โ€“ lowest, mid-low, mid-high, highest โ€“ ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ˆœ์„œํ˜• ๋ณ€์ˆ˜์ด๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” ์ตœ๊ทผ 5๋…„ ๋‚ด ์ถœ์‚ฐ ๊ฒฝํ—˜์ด ์žˆ๋Š” ๊ฐ€๋‚˜ ๋ฐ ์ผ€๋ƒ ๋‚ด 15-49์„ธ ์—ฌ์„ฑ ์ด 5,484๋ช…(๊ฐ€์ค‘์น˜ ์ ์šฉ ์‹œ ์ด 5,144๋ช…)์ด๋ฉฐ, ์•ค๋”์Šจ ํ–‰๋™ ๋ชจํ˜•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์—ฐ๊ตฌ์˜ ๊ฐœ๋… ํ‹€์„ ์ž‘์„ฑํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„์˜ ๊ฒฝ์šฐ ๋จผ์ € ๊ธฐ์ˆ ๋ถ„์„์„ ํ†ตํ•ด ๊ฐ€๋‚˜์™€ ์ผ€๋ƒ ๋‚ด ์ „์ฒด์ ์ธ MSB ๋ถ„ํฌ ๋ฐ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด์™€ ํ•จ๊ป˜ ์นด์ด์ œ๊ณฑ ๊ฒ€์ •์„ ์‹ค์‹œํ•˜์—ฌ MSB์™€ ๊ฐ ์˜ˆ์ธก๋ณ€์ˆ˜ ๊ฐ„์˜ ์—ฐ๊ด€์„ฑ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ดํ›„ ๋‘ ๊ตญ๊ฐ€ MSB์˜ ์˜ํ–ฅ ์š”์ธ์„ ๋ถ„์„ํ•˜๋Š”๋ฐ ์žˆ์–ด DHS ์ž๋ฃŒ์˜ ์œ„๊ณ„์  ํŠน์„ฑ ๋ฐ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ์ˆœ์œ„์  ์†์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋‹ค์ˆ˜์ค€ ์ˆœ์„œํ˜• ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€(multilevel ordinal logistic regression) ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ฐœ์ธ ์ˆ˜์ค€์˜ MSB๋ฅผ ์„ค๋ช…ํ•จ์— ์žˆ์–ด ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์˜ํ–ฅ์„ ํ•จ๊ป˜ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์ „์ฒด ๊ฐ€๋‚˜ ์—ฌ์„ฑ ์ค‘ highest์— ์†ํ•œ ๋น„์œจ์ด ์•ฝ 65%, ์ผ€๋ƒ์˜ ๊ฒฝ์šฐ ๊ทธ ๋น„์œจ์ด ์•ฝ 31%๋กœ ๋‚˜ํƒ€๋‚˜ ์ „๋ฐ˜์ ์œผ๋กœ ๊ฐ€๋‚˜์˜ MSB ์ˆ˜์ค€์ด ์ผ€๋ƒ๋ณด๋‹ค ๋†’์€ ํŽธ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์ˆ˜์ค€ ๋ถ„์„ ์‹œ ์ด 4๊ฐœ ๋ชจํ˜•์„ ์ ํ•ฉํ•˜์˜€์œผ๋ฉฐ, ๊ฐ€์žฅ ๋จผ์ € ์˜๋ชจํ˜•(null model)์˜ ์ ํ•ฉ์„ ํ†ตํ•ด ๊ธ‰๋‚ด์ƒ๊ด€๊ณ„์ˆ˜(intraclass correlation coefficient)๋ฅผ ์‚ฐ์ถœ, ๋‹ค์ˆ˜์ค€ ๋ถ„์„ ๋ฐฉ๋ฒ•์˜ ์ ํ•ฉ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฐ€๋‚˜์™€ ์ผ€๋ƒ ๊ฐ๊ฐ์˜ 4๊ฐœ ๋ชจํ˜• ๋ชจ๋‘์—์„œ ์ง‘๋‹จ๊ฐ„ ๋ถ„์‚ฐ์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์—ฌ, ์ปค๋ฎค๋‹ˆํ‹ฐ ํŠน์„ฑ์ด ๊ฐœ์ธ ์ˆ˜์ค€์˜ MSB์— ์˜ํ–ฅ์„ ๋ผ์นœ๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฐœ์ธ ๋ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ ์ˆ˜์ค€์˜ ๋ณ€์ˆ˜๋ฅผ ๋ชจ๋‘ ํˆฌ์ž…ํ•œ Model 4 ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ฐ€๋‚˜์™€ ์ผ€๋ƒ ์—ฌ์„ฑ์˜ MSB์— ๊ณตํ†ต์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ์—ฌ์„ฑ์˜ ๊ต์œก์ˆ˜์ค€, ๊ฐ€์ •์˜ ๊ฒฝ์ œ์ˆ˜์ค€, ์ž„์‹ ์— ๋Œ€ํ•œ ์š•๊ตฌ, ์ปค๋ฎค๋‹ˆํ‹ฐ ๋นˆ๊ณค ์ˆ˜์ค€ ๋ฐ ํ–‰์ •๊ตฌ์—ญ์ด์—ˆ๋‹ค. ์ด ์™ธ ๊ฐ€๋‚˜์˜ ๊ฒฝ์šฐ ์ข…๊ต, ๊ฑด๊ฐ•๋ณดํ—˜ ๊ฐ€์ž… ์—ฌ๋ถ€ ๋ฐ ์˜๋ฃŒ ์„œ๋น„์Šค ์ ‘๊ทผ์— ๋Œ€ํ•œ ์–ด๋ ค์›€์ด, ์ผ€๋ƒ์˜ ๊ฒฝ์šฐ ์—ฐ๋ น, ์ธ์ข…, ๋ฐฐ์šฐ์ž/ํŒŒํŠธ๋„ˆ์˜ ๊ต์œก์ˆ˜์ค€, ๋ฏธ๋””์–ด์— ๋Œ€ํ•œ ์ ‘๊ทผ, ํ”ผ์ž„ ์—ฌ๋ถ€ ๋ฐ ๊ฑฐ์ฃผ์ง€์—ญ์ด MSB์™€ ์œ ์˜ํ•œ ์—ฐ๊ด€์„ฑ์„ ์ง€๋‹Œ ์š”์ธ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ฐ€๋‚˜๋Š” ๋น„๊ต์  ์šฐํ˜ธ์ ์ธ ์ •์ฑ… ํ™˜๊ฒฝ๊ณผ ์—ฌ๋Ÿฌ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์˜ ์„ฑ๊ณผ๋กœ ๋‹ค์†Œ ๋†’์€ ์ˆ˜์ค€์˜ MSB๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ์ผ€๋ƒ๋Š” ์ „๋ฐ˜์ ์œผ๋กœ ์—ฌ์„ฑ์˜ ๋ชจ์„ฑ ์„œ๋น„์Šค ์ด์šฉ์ด ๋‚ฎ์€ ์ˆ˜์ค€์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋กœ ๋ฏธ๋ฃจ์–ด ๋ณด์•„ ๋‘ ๊ตญ๊ฐ€ ๋ชจ๋‘ ๊ต์œก ์‹œ์Šคํ…œ์˜ ๊ฐ•ํ™” ๋ฐ ์งˆ ์ œ๊ณ , ๊ตญ๊ฐ€ ์ „์ฒด์˜ ๊ท ํ˜• ๋ฐœ์ „์„ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜๋Š” ๋™์‹œ์— ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ธฐ๋ฐ˜์˜ ์†Œ๋“์ฐฝ์ถœ, ํ˜„๊ธˆ์ง€์› ์‚ฌ์—… ๋“ฑ์„ ํ†ตํ•ด ์†Œ์™ธ๊ณ„์ธต ์—ฌ์„ฑ์„ ๋ณ„๋„๋กœ ์ง€์›ํ•จ์ด ํ•„์š”ํ•˜๋‹ค. ๋˜ํ•œ ์ง€์—ญ๊ฐ„ ๊ฑด๊ฐ• ๋ถˆํ‰๋“ฑ์ด ๋‚˜ํƒ€๋‚˜๋Š” ์›์ธ์„ ์‹ฌ์ธต ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ฐ ์ง€์—ญ์˜ ๋งฅ๋ฝ์— ๋”ฐ๋ผ ๋ชจ์„ฑ๊ฑด๊ฐ• ์‚ฌ์—…์˜ ํ™•๋Œ€๋ฅผ ์œ„ํ•œ ํƒ€๋‹นํ•œ ์ „๋žต์„ ์ˆ˜๋ฆฝํ•ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ํ•œํŽธ, ๊ฐ€๋‚˜์˜ ๊ฒฝ์šฐ ํ† ์†์‹ ์•™์— ๊ทผ๊ฑฐํ•œ ์ž˜๋ชป๋œ ๋ณด๊ฑด ์ง€์‹ ๋ฐ ์‹ค์ฒœ์„ ๋ฐ”๋กœ์žก๊ธฐ ์œ„ํ•œ ์ปค๋ฎค๋‹ˆํ‹ฐ ์ฐจ์›์˜ ๊ฑด๊ฐ•์ฆ์ง„ ๊ต์œก, ๋นˆ๊ณค์ธต์— ๋Œ€ํ•œ ๊ฑด๊ฐ•๋ณดํ—˜ ์ง€์› ๊ฐ•ํ™” ๋ฐ ๊ธ‰์—ฌ์ฒด๊ณ„ ๊ฐœ์„ , ์ง€๋ฆฌ์ ์œผ๋กœ ์ ‘๊ทผ์ด ์–ด๋ ค์šด ๋งˆ์„ ๋Œ€์ƒ์˜ ๊ฐ€์ • ๋ฐฉ๋ฌธ ๋ฐ ์‚ฌํšŒ๋™์› ์บ ํŽ˜์ธ ๋“ฑ์˜ ์ „๋žต์„ ๊ณ ๋ คํ•ด๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, ์ผ€๋ƒ์˜ ๊ฒฝ์šฐ ์ธ์ข… ๊ฐ„ ๋ฌธํ™”์  ์ฐจ์ด๋ฅผ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์˜ํ•œ ๋ณด๊ฑด ๊ต์œก์ด ์ œ๊ณต๋˜์–ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ์œผ๋ฉฐ, ๋ณด๋‹ค ๋งŽ์€ ์ฃผ๋ฏผ๋“ค์—๊ฒŒ ์ฃผ์š” ๊ฑด๊ฐ• ๋ฉ”์‹œ์ง€๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ „๋‹ฌํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐ์ข… ๋ฏธ๋””์–ด๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ „๋žต์„ ์ˆ˜๋ฆฝํ•จ๊ณผ ํ•จ๊ป˜ ๊ตญ๊ฐ€ ์ „๋ฐ˜์— ๊ฑธ์ณ ๋ฏธ๋””์–ด ์ ‘๊ทผ ํ–ฅ์ƒ์„ ์œ„ํ•œ ํ†ต์‹  ์ธํ”„๋ผ ๊ฐœ์„  ์‚ฌ์—…์„ ์ถ”์ง„ํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ด์™€ ๊ฐ™์€ ์ •์ฑ…์  ํ•จ์˜๋ฅผ ์ œ๊ณตํ•จ๊ณผ ํ•จ๊ป˜, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€๋‚˜ ๋ฐ ์ผ€๋ƒ ์—ฌ์„ฑ์˜ ๋ชจ์„ฑ๊ฑด๊ฐ• ์„œ๋น„์Šค ์ด์šฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ๋ถ„์„ํ•จ์— ์žˆ์–ด ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์˜ํ–ฅ์„ ์ค‘์š”ํ•˜๊ฒŒ ๊ณ ๋ คํ•˜์—ฌ ์ด์˜ ์˜ํ–ฅ์„ ๋ฐํ˜€๋ƒˆ๊ณ , ์—ฌ์„ฑ์˜ ์‚ฐ์ „, ์ถœ์‚ฐ ๋ฐ ์‚ฐํ›„๊ธฐ์˜ ๋ชจ์„ฑ ์„œ๋น„์Šค ์ด์šฉ์„ MSB๋ผ๋Š” ๋‹จ์ผ๋ณ€์ˆ˜ ๋‚ด ๋™์‹œ์— ๊ณ ๋ คํ•˜์—ฌ ์ด์˜ ๊ฒฐ์ • ์š”์ธ์„ ํ†ตํ•ฉ์ ์œผ๋กœ ์ œ์‹œํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ๊ทธ ์˜์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค.Background: It is quite evident that most of maternal deaths occur in lower- and middle-income countries, where the level of maternal health care utilization remains much lower than high income countries. In addition to this disparity by income, significant differences in womens health status are being observed between regions. In 2015, 66% of all maternal deaths took place within Sub-Saharan Africa (SSA) alone. However, previous literatures have iteratively emphasized that the great majority of maternal deaths could have been averted if essential maternal care were provided in a timely manner. Above all, antenatal care (ANC), delivery care (DC), and postnatal care (PNC) turned out to be most effective in preventing urgent complications during prenatal, delivery and postpartum periods. Our study setting, Ghana and Kenya, are located in SSA region and regarded as a lower middle-income country, while their level of maternal mortality does not exhibit similar levels. For this reason, this study aims to explore factors associated with maternal health care utilization in Ghana and Kenya, focusing on the influences of communities. Methods: We utilized data acquired from Demographic and Health Survey (DHS) conducted in 2014. Study population includes a total of 5,484 women (5,144 women if weights applied) aged 15-49 years who had at least one birth in past five years. Outcome variable, named as maternal health care seeking behavior (MSB) was adapted from the prior research and composed of four levels โ€“ lowest, mid-low, mid-high and highest โ€“ by considering the level of utilization of ANC, DC and PNC in a comprehensive way. We developed the conceptual framework of the study based on Andersens behavioral model. Multilevel ordinal logistic regression was performed through Stata SE 15.0 software to address the study objective. Results: Overall, about 65% of Ghanaian women belonged to the highest level of MSB, compared to 31% in Kenya. This indicates that the level of utilization of maternal health care is relatively higher in Ghana. Through the multilevel analysis employed in this study, we could examine the impact of community characteristics on the individual-level MSB as originally intended. When women had a secondary level of education, the higher wealth index and desire for pregnancy, and the lower the community poverty level, the odds of being beyond a particular category of MSB increased for both countries. Furthermore, an unequal distribution of MSB level was identified among administrative divisions. Along with these factors, religion, health insurance coverage and difficulty in accessing health care were significantly associated with MSB for Ghanaian women. On the other hand, mothers age, ethnicity, husband/partners educational attainment, access to media, use of contraceptives and place of residence tended to increase or decrease the odds of practicing a higher level of MSB in Kenya. Conclusion: Findings of this study clearly show that there is a need to strengthen the overall educational system and health infrastructure as well as pursue a more balanced socioeconomic development in both countries. At the same time, income generation and cash transfer programs could be designed and implemented for the less privileged women. It might be mutually crucial for the central and local governments to analyze what directly and indirectly causes maternal health disparities within each country and develop relevant policies on the basis of evidence collected. In Ghana, health education to correct womens wrong perceptions, NHIS strengthening to support more poor women and home visits or social mobilization campaigns to reach mothers in underserved areas could be executed in order to promote maternal health utilization. For Kenyan women, we recommend that future health initiatives might consider developing more culturally sensitive materials reflecting each ethnic groups traditions for health promotion and taking advantage of mass-media to convey key information on maternal health to the general public, along with the improvement of communications infrastructure at the national scale.1. ์„œ๋ก  1 1. 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 1. 2. ์—ฐ๊ตฌ๋ชฉ์  6 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 7 2. 1. ๋ชจ์„ฑ์‚ฌ๋ง๊ณผ ์ดํ™˜ 7 2. 2. ๋ชจ์„ฑ๊ฑด๊ฐ• ์„œ๋น„์Šค ์ด์šฉ ์š”์ธ 10 2. 3. ๊ฐ€๋‚˜์˜ ๋ณด๊ฑด ํ˜„ํ™ฉ 13 2. 4. ์ผ€๋ƒ์˜ ๋ณด๊ฑด ํ˜„ํ™ฉ 19 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 26 3. 1. ์—ฐ๊ตฌ์ž๋ฃŒ 26 3. 2. ์—ฐ๊ตฌ๋Œ€์ƒ 28 3. 3. ์—ฐ๊ตฌ๋ชจํ˜• 29 3. 4. ๋ณ€์ˆ˜์˜ ์ •์˜ 32 (1) ์ข…์†๋ณ€์ˆ˜ 32 (2) ์„ค๋ช…๋ณ€์ˆ˜ 35 (3) ๋ถ„์„๋ฐฉ๋ฒ• 43 (4) ์œค๋ฆฌ์  ๊ณ ๋ ค 45 4. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 46 4. 1. ์ „์ฒด MSB ์ˆ˜์ค€ ๋ถ„ํฌ 46 (1) ๊ฐ€๋‚˜ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 46 (2) ์ผ€๋ƒ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 46 4. 2. ๊ธฐ์ˆ ๋ถ„์„ ๋ฐ ์นด์ด์ œ๊ณฑ ๋ถ„์„ ๊ฒฐ๊ณผ 48 (1) ๊ฐ€๋‚˜ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 48 (2) ์ผ€๋ƒ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 54 4. 3. ๋‹ค์ˆ˜์ค€ ๋ถ„์„ ๊ฒฐ๊ณผ 60 (1) ๊ฐ€๋‚˜ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 60 (2) ์ผ€๋ƒ DHS ๋ถ„์„ ๊ฒฐ๊ณผ 67 5. ํ† ์˜ ๋ฐ ๊ฒฐ๋ก  74 5. 1. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ ๋ฐ ํ•จ์˜ 74 5. 2. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ์ œ์–ธ 83 ์ฐธ๊ณ ๋ฌธํ—Œ 85 ๋ถ€ ๋ก 98 Abstract 112Maste

    Posttransplant Lymphoproliferative Disorders in Kidney Transplant Patients: Report from a Single-center over 25 Years

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    Background: Posttranplant lymphoproliferative disorder (PTLD) is a fatal complication of organ transplantation and standard treatment is either ineffective or too toxic to tolerate. This study aims to evaluate the characteristics of PTLD patients retrospectively. Methods: We enrolled 2,630 kidney recipients who underwent transplantation from April 1979 to June 2007. And we retrospectively reviewed clinical manifestations of PTLD. Results: Among one hundred ninety post-transplant malignancies from 2,630 renal recipients, 11 PTLD were diagnosed during 195.3ยฑ11.5 months (0โˆผ388 months) of mean follow up duration. PTLD predominantly occurred in male (Male : Female=10 : 1) and mean age of PTLD patients at the time of PTLD diagnosis was 51ยฑ15 year (18โˆผ71 year). Mean time interval to PTLD diagnosis were 126.6ยฑ74.8 months (6โˆผ240 months). In aspect of WHO classification, there were no early lesion, 1 polymorphic PTLD (9.1%), 10 monomorphic PTLD (90.9%) and no other types. In aspect of involved organ, GI tract was involved in 1 case, lung in 2 cases, bone in 2 cases, spleen in 2 cases, neck node in 2 cases, liver in 1 case, and multiple organs in 1 case. Conclusions: Our findings showed that the prevalence of PTLD was 0.46%, which was less than reports from Western countries. We also found that the late onset PTLD was more than early onset one, which was another difference from previous reports.ope

    ์„œ์šธ์‹œ ์•„ํŒŒํŠธ๋ฅผ ๋Œ€์ƒ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ(๋„์‹œ๋ฐ์ง€์—ญ๊ณ„ํš์ „๊ณต), 2015. 8. ์ตœ๋ง‰์ค‘.์˜ค๋Š˜๋‚  ์„ธ๊ณ„์˜ ๋„์‹œ๋Š” ๋„˜์ณ๋‚˜๋Š” ์ž๊ฐ€์šฉ์œผ๋กœ ์ธํ•ด ๊ตํ†ต ํ˜ผ์žก, ๋Œ€๊ธฐ์˜ค์—ผ, ์†Œ์Œ, ์ด์ƒํ™”ํƒ„์†Œ ๋ฐœ์ƒ ๋ฐ ์—๋„ˆ์ง€์†Œ๋น„ ๋“ฑ ์‚ฌํšŒยท๊ฒฝ์ œ ๋ฐ ํ™˜๊ฒฝ์— ๊ฑธ์ณ ๋‹ค์–‘ํ•œ ๋ฌธ์ œ๋ฅผ ์•ˆ๊ณ  ์žˆ์œผ๋ฉฐ 1970๋…„๋Œ€๋ถ€ํ„ฐ ์ง€์†์ ์œผ๋กœ ์ด์–ด์ ธ์˜จ ์ž๊ฐ€์šฉ์˜ ์ฆ๊ฐ€๋Š” ๋” ๋งŽ์€ ์ฐจ๋Ÿ‰๊ณผ ๋„๋กœํ™•์žฅ์„ ๋ถ€์ถ”๊ฒจ ๋„์‹œ๋ฅผ ํ™•์‹ ์‹œํ‚ค๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ตํ†ต๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋„์‹œ ๋‚ด ์ž๋™์ฐจ์˜ ์ด์šฉ์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ๋Œ€์ค‘๊ตํ†ต์˜ ์ด์šฉ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ํšจ์œจ์ ์ธ ๋„์‹œ๊ฐœ๋ฐœ ๋ฐ ๊ตํ†ต์ฒด๊ณ„๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ณด๋‹ค ํšจ์œจ์ ์ด๋ฉฐ ์ง€์†๊ฐ€๋Šฅํ•œ ๋„์‹œ๊ฐœ๋ฐœ ๋ฐ ๊ตํ†ต์ฒด๊ณ„์— ๋Œ€ํ•œ ๋Œ€์•ˆ์„ ์ œ์‹œํ•˜๊ธฐ ์œ„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ์งง์€ ๊ธฐ๊ฐ„ ๋™์•ˆ ์ง‘์•ฝ์ ์ธ ํ† ์ง€ ์ด์šฉ์„ ๋ฐ”ํƒ•์œผ๋กœ ์„ธ๊ณ„์  ์ˆ˜์ค€์˜ ์ง€ํ•˜์ฒ ์„ ๊ตฌ์ถ•ํ•œ ์„œ์šธ์‹œ์˜ ๊ณ ๋ฐ€๋„๋„์‹œ๊ฐœ๋ฐœ์ด ์„œ์šธ์‹œ ์ง€ํ•˜์ฒ  ์ด์šฉ์ž ์ˆ˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์‹ค์ฆ ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์˜ ์š”์•ฝ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์„œ์šธ์‹œ๋Š” ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„๋ฅผ ๊ฐ์ข… ๊ฐœ๋ฐœ ๋ฐ ์ •๋น„ ์‚ฌ์—…์œผ๋กœ ํ†ตํ•ด ๊ด€๋ฆฌํ•ด์™”์œผ๋ฉฐ, 2010๋…„์„ ๊ธฐ์ค€์œผ๋กœ ์„œ์šธ์‹œ์˜ ์ „์ฒด ์ฃผ๊ฑฐ์œ ํ˜• ์ค‘ ์•„ํŒŒํŠธ๊ฐ€ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ์ด ์•ฝ 60%๋กœ ๊ณ ๋ฐ€๋„์ฃผ๊ฑฐ์œ ํ˜•์ธ ์•„ํŒŒํŠธ๋ฅผ ๊พธ์ค€ํžˆ ๋ณด๊ธ‰ํ•ด ์˜ค๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ์„œ์šธ์‹œ ๋Œ€์ค‘๊ตํ†ต ๋ถ„๋‹ด๋ฅ ์€ 1990๋…„๋Œ€๋ถ€ํ„ฐ 60%๋ฅผ ์œ ์ง€ํ•ด์™”๊ณ , ๊ทธ ์ค‘ ์ง€ํ•˜์ฒ ์˜ ๋ถ„๋‹ด๋ฅ ์€ 2010๋…„ ๊ธฐ์ค€์œผ๋กœ 36%๋ฅผ ์ฐจ์ง€ํ•˜๋ฉด์„œ ์„œ์šธ์‹œ์˜ ๋Œ€ํ‘œ์ ์ธ ๋Œ€์ค‘๊ตํ†ต์œผ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ์…‹์งธ, 2014๋…„ ํ˜„์กดํ•˜๋Š” ์•„ํŒŒํŠธ์˜ ํ‰๊ท ์ค€๊ณต์—ฐ๋„์™€ ์„œ์šธ์‹œ ์ง€ํ•˜์ฒ  215๊ฐœ ์—ญ์„ธ๊ถŒ์˜ ์ค€๊ณต๋…„๋„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ง€ํ•˜์ฒ  ์—ญ์„ธ๊ถŒ์˜ ๊ฐœ๋ฐœ ํŒจํ„ด์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ „์ฒด ์—ญ์„ธ๊ถŒ ์ค‘ ์•ฝ 80%์˜ TOD ๊ฐœ๋ฐœ ๋ฐฉ์‹์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด๋Š” ์„œ์šธ์‹œ ๋„์‹œ๊ฐœ๋ฐœ์ด ์ง€ํ•˜์ฒ  ์ค€๊ณต์ด ์ด๋ฃจ์–ด์ง„ ํ›„ ์•„ํŒŒํŠธ๊ฐ€ ๊ฐœ๋ฐœ๋˜๋Š” TOD ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์ง€๊ณ  ์žˆ์Œ์„ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋„ท์งธ, ๋‹ค์ค‘ํšŒ๊ท€ ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ฐœ๋ฐœ๋ฐ€๋„์˜ ์ง€ํ‘œ๋กœ ์‚ฌ์šฉ๋œ ์„œ์šธ์‹œ์˜ ์„ธ๋Œ€ ์ˆ˜ ๋ฐ€๋„๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์ง€ํ•˜์ฒ ์˜ ์ด์šฉ์ž ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ์ง€ํ•˜์ฒ  ํ™˜์Šน๊ณผ ์—ฐ๊ณ„๋œ ๋ฒ„์Šค์˜ ๊ฒฝ์šฐ ๋ฒ„์Šค์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋ณด์™„ ํ˜น์€ ๋Œ€์ฒด๊ด€๊ณ„์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์ƒ์˜ ๋ถ„์„๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ์„œ์šธ์‹œ๋Š” ๋Œ€์ค‘๊ตํ†ต์„ ์œ„์ฃผ๋กœ ๊ฐœ๋ฐœ์ด ์ด๋ฃจ์–ด์ง€๋Š” TOD ๋ฐฉ์‹์œผ๋กœ ๋„์‹œ๊ฐœ๋ฐœ์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ณ ๋ฐ€๋„์ฃผ๊ฑฐ์ง€๋ฅผ ์—ญ์„ธ๊ถŒ์— ๋ฐฐ์น˜ํ•˜์—ฌ ๋Œ€์ค‘๊ตํ†ต ์ˆ˜์š”๋ฅผ ์ถฉ์กฑ ์‹œ์ผฐ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ํšจ์œจ์ ์ธ ๋Œ€์ค‘๊ตํ†ต์˜ ์ด์šฉ์ด ์ด๋ฃจ์–ด์ง€๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ญ์„ธ๊ถŒ์„ ์œ„์ฃผ๋กœ ๊ณ ๋ฐ€ ์ฃผ๊ฑฐ์ง€๋ฅผ ๋ฐฐ์น˜ํ•˜๋Š” ๊ฒƒ์ด ํšจ์œจ์ ์ž„์„ ํ™•์ธํ•˜๊ณ , ์ด๋Š” ๋” ๋‚˜์•„๊ฐ€ ์ˆ˜ํ‰์  ํ˜•ํƒœ๋กœ ๊ฐœ๋ฐœ๋˜์–ด ๋Œ€์ค‘๊ตํ†ต์˜ ์ž…์ง€ ์„ ์ •์ด ์–ด๋ ค์šด ๊ฐœ๋ฐœ๋„์ƒ๊ตญ์— ๋ณด๋‹ค ํšจ์œจ์ ์ธ ๋„์‹œ๊ฐœ๋ฐœ ๋ฐ ๊ตํ†ต์ฒด๊ณ„์˜ ๋ณธ๋ณด๊ธฐ๋กœ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 2 1) ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 2 2) ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ํ๋ฆ„๋„ 3 โ…ก. ์ด๋ก  ๋ฐ ์„ ํ–‰ ์—ฐ๊ตฌ ๊ณ ์ฐฐ 5 1. ์ž…์ง€์ด๋ก  5 2. ๋„์‹œ๊ฐœ๋ฐœ๋ฐ€๋„์™€ ๋Œ€์ค‘๊ตํ†ต 6 1) ๋Œ€์ค‘๊ตํ†ต์ง€ํ–ฅ๊ฐœ๋ฐœ(TOD)๊ณผ ๊ฐœ๋ฐœ์ง€ํ–ฅ ๋Œ€์ค‘๊ตํ†ต(DOT) 6 2) ๋Œ€๋Ÿ‰๊ณ ์†์ˆ˜์†ก ์‹œ์Šคํ…œ์˜ ๋ณด๊ธ‰ 7 3. ์„ ํ–‰์—ฐ๊ตฌ 9 โ…ข. ์„œ์šธ์‹œ ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„ ๋ฐ ๋Œ€์ค‘๊ตํ†ต 12 1. ์„œ์šธ์‹œ ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„ ๋ฐ ๋Œ€์ค‘๊ตํ†ต ์ด์šฉ ๋ณ€ํ™” 12 1) ๋„์‹œ๊ฐœ๋ฐœ์— ๋”ฐ๋ฅธ ๋Œ€์ค‘๊ตํ†ต์˜ ๋ณ€ํ™” ๋ฐ ์ง€ํ•˜์ฒ  ๊ฑด์„ค 12 2) ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„์˜ ๋ณ€ํ™” 13 2. ์„œ์šธ์‹œ ์•„ํŒŒํŠธ ๋ฐ ์ง€ํ•˜์ฒ  ๋ณด๊ธ‰ ๋ฐ ๋„์‹œ๊ฐœ๋ฐœ ํŒจํ„ด 14 1) ์„œ์šธ์‹œ ์•„ํŒŒํŠธ ๋ฐ ์ง€ํ•˜์ฒ  ๋ณด๊ธ‰ํ˜„ํ™ฉ 14 2) ์„œ์šธ์‹œ ์ง€ํ•˜์ฒ  ์—ญ์„ธ๊ถŒ ๋„์‹œ๊ฐœ๋ฐœ ํŒจํ„ด ๋ถ„์„ 20 3. ์†Œ๊ฒฐ 22 โ…ฃ. ์‹ค์ฆ๋ถ„์„ 23 1. ์ฃผ์š” ๋ณ€์ˆ˜ ๋ฐ ์ž๋ฃŒ์˜ ๊ตฌ์ถ• 23 1) ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„ ๋ฐ ์ง€ํ•˜์ฒ  ์ด์šฉ์ž ์‚ฐ์ • 23 2) ์—ญ์„ธ๊ถŒ ๋ฐ˜๊ฒฝ ๋‚ด/์™ธ ์ฃผ๊ฑฐ๊ฐœ๋ฐœ๋ฐ€๋„ ์ฐจ ๋น„๊ต๋ถ„์„ 28 3) ์ฃผ๊ฑฐ์ค‘์‹ฌ ์—ญ์„ธ๊ถŒ ๋ถ„๋ฅ˜ 32 4) ๋ณ€์ˆ˜์˜ ๊ตฌ์„ฑ 34 2. ์‹ค์ฆ๋ถ„์„ 37 1) ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„ 37 2) ๋‹ค์ค‘ํšŒ๊ท€ ๋ถ„์„ 39 โ…ค. ๊ฒฐ๋ก  44 1. ์—ฐ๊ตฌ์˜ ์š”์•ฝ 44 2. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ์‹œ์‚ฌ์  45 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ 46Maste

    ํ˜•์งˆ์ „ํ™˜๋ณต์ œ๋ผ์ง€ ์ƒ์‚ฐ์„ ์œ„ํ•œ piggyBac transposon system์˜ ์ ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜์˜ํ•™๊ณผ, 2017. 2. ์ด๋ณ‘์ฒœ.PiggyBac transposon has been widely employed to generation transgenic animals using a "cut and paste" mechanism that is occurred by piggyBac transposase recognizing transposon-specific inverted terminal repeat sequences (ITRs) located on both ends of targeted gene. The targeted gene is cut, and then transferred in to TTAA chromosomal sites. In this study, to investigate whether this system can be applied to transgenic cell line establishment, generation of cloned embryos by somatic cell nuclear transfer (SCNT) and also trial of re-differentiation of cell that was generated by transcription factor for donor in producing transgenic pig SCNT cloned embryos. Firstly, to verify the application of piggyBac transposon to establish transgenic cell line and generate its cloned embryos by SCNT, a report gene, RFP (DsRed2), inducible expression vector was constructed using piggyBac transposon system, it was transferred to porcine fetal fibroblast, after enrichment, the transgenic cell line was established. Homogenously RFP expression was well controlled according the fact of existence or nonexistence of doxycycline in established cell line. Also cloned embryos were shown homogenous expression pattern of RFP using the cell line for SCNT donor. Furthermore the cloned embryos were shown normal development without damage by piggyBac transposon system. Octamer-binding transcription factor 4 (Oct4) is a critical molecule for the self-renewal and pluripotency in undifferentiated cell lines like as embryonic stem cell. For investigate the effect of Oct4-overexpression on cell proliferation and development of cloned embryos, Oct4 was firstly transferred to porcine fibroblasts before the introduction of 4 transcription factors in further. Oct4 expression was validated by the immunostaining of Oct4. Cell morphology was changed to sharp, and both proliferation and migration abilities were enhanced in Oct4-overexpressed cells and p16, Bcl2 and Myc were also upregulated. SCNT was performed using Oct4-overexpressed cells, and the development of Oct4 embryos was compared to that of wild-type cloned embryos. The cleavage and blastocyst formation rates were significantly improved in the Oct4 embryos. Interestingly, blastocyst formation of the Oct4 embryos was observed as early as Day 5 in culture, while blastocysts were observed from Day 6 in wild-type cloned embryos. For validating the availability of piggyBac transposon application to generate porcine induced pluripotent cells, coding domain region of porcine Oct4, Sox2, c-Myc and Klf4 from pig ovarian cDNA by PCR was cloned, constructed to piggyBac expression vector, which is inducible expressed by doxycycline and transduced to porcine fetal fibroblast. After 24h transfection, 2 ยตg/ml doxycycline was added in DMEM/F12 culture media contained 15% FBS, 10ng/ml bFGF and transfected cells were cultured for 2 weeks more. From about 10 days after, outgrowing colonies were formed, picked, digested to single cell and cultured on CF1 feeder cells treated with mitomycin C. They are routinely single cell passaged 1:4~6 at every 2-3 day by enzyme. We verified the colony formation from single cell culture and maintained the colonies up to passage (> 37th). To compare the developmental efficiency of differentiated cells and established cell lines. SCNT was carried out using two types of cells as followed to our previous established protocol. In the results, the cleavage rate and blastocyst formation rate were not shown any significant differences. But total cell number of blastocyst that one of parameter evaluating of quality of embryos was shown significantly increased in established cell lines group. In conclusion, piggyBac transposon system could be applied to establish the transgenic cell lines and cloned embryos for producing of transgenic pig, also it could be used to generated illimitable self-renewal cell line and produce the its cloned embryos. As this gene delivery system will be valuably used in gene modification availability and stability or safety of generating pig models for human biomedical research.GENERAL INTRODUCTION 1 1. Literature review 2 2. General objective 32 GENERAL METHODOLOGY 33 1. Chemicals and materials 34 2. Vector construction 34 3. General cell culture 38 4. Preparation of somatic cell nuclear transfer in pig 39 5. Statistical analysis 41 INTRODUCING TRANSPOSON SYSTEM IN PORCINE CELL 43 Chapter I. Confirmation of piggyBac transposon system to generate transgenic cell line and cloned embryos in pig 44 1. Introduction 44 2. Materials and methods 46 3. Results 48 4. Discussion 52 Chapter โ…ก. The Key factor of pluripotency, Oct4 enhance proliferation of porcine fibroblasts and development of cloned embryos 54 1. Introduction 54 2. Materials and methods 56 3. Results 60 4. Discussion 69 Chapter โ…ข. Establishment of transgenic cell line with 4 transcriptional factors using piggyBac transposon and evaluation of development the SCNT cloned embryos. 72 1. Introduction 72 2. Materials and methods 76 3. Results 82 4. Discussion 92 FINAL CONCLUSION 96 REFERENCES 98 ๊ตญ๋ฌธ์ดˆ๋ก 122Docto

    ๋™์‹œ์กฐ์ ˆ ์œ ์ „์  ์ƒํ˜ธ์ž‘์šฉ ๋ฐœ๊ตด์„ ์œ„ํ•œ ํ•˜์ดํผ๊ทธ๋ž˜ํ”„ ๋ชจ๋ธ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ์ƒ๋ฌผ์ •๋ณดํ•™์ „๊ณต, 2014. 2. ์žฅ๋ณ‘ํƒ.A comprehensive understanding of biological systems requires the analysis of higher-order interactions among many genomic factors. Various genomic factors cooperate to affect biological processes including cancer occurrence, progression and metastasis. However, the complexity of genomic interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, this dissertation addresses the problem of analyzing complex relationships among many genomic factors in biological processes including cancers. We propose a hypergraph approach for modeling, learning and extracting: explicitly modeling higher-order genomic interactions, efficiently learning based on evolutionary methods, and effectively extracting biological knowledge from the model. A hypergraph model is a higher-order graphical model explicitly representing complex relationships among many variables from high-dimensional data. This property allows the proposed model to be suitable for the analysis of biological and medical phenomena characterizing higher-order interactions between various genomic factors. This dissertation proposes the advanced hypergraph-based models in terms of the learning methods and the model structures to analyze large-scale biological data focusing on identifying co-regulatory genomic interactions on a genome-wide level. We introduce an evolutionary approach based on information-theoretic criteria into the learning mechanisms for efficiently searching a huge problem space reflecting higher-order interactions between factors. This evolutionary learning is explained from the perspective of a sequential Bayesian sampling framework. Also, a hierarchy is introduced into the hypergraph model for modeling hierarchical genomic relationships. This hierarchical structure allows the hypergraph model to explicitly represent gene regulatory circuits as functional blocks or groups across the level of epigenetic, transcriptional, and post-transcriptional regulation. Moreover, the proposed graph-analyzing method is able to grasp the global structures of biological systems such as genomic modules and regulatory networks by analyzing the learned model structures. The proposed model is applied to analyzing cancer genomics considered as a major topic in current biology and medicine. We show that the performance of our model competes with or outperforms state-of-the-art models on multiple cancer genomic data. Furthermore, the propose model is capable of discovering new or hidden patterns as candidates of potential gene regulatory circuits such as gene modules, miRNA-mRNA networks, and multiple genomic interactions, associated with the specific cancer. The results of these analysis can provide several crucial evidences that can pave the way for identifying unknown functions in the cancer system. The proposed hypergraph model will contribute to elucidating core regulatory mechanisms and to comprehensive understanding of biological processes including cancers.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .i 1 Introduction 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problems to be Addressed . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 The Proposed Approach and its Contribution . . . . . . . . . . . . . . 4 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . 6 2 Related Work 2.1 Analysis of Co-Regulatory Genomic Interactions from Omics Data . . 9 2.2 Probabilistic Graphical Models for Biological Problems . . . . . . . . 11 2.2.1 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Higher-order Graphical Models for Biological Problems . . . . . . . . 16 2.3.1 Higher-Order Models . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.2 Hypergraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Hypergraph Classifiers for Identifying Prognostic Modules in Cancer 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Analyzing Gene Modules for Cancer Prognosis Prediction . . . . . . 24 3.3 Hypergraph Classifiers for Identifying Cancer Gene Modules . . . . 26 3.3.1 Hypergraph Classifiers . . . . . . . . . . . . . . . . . . . . . . 26 3.3.2 Bayesian Evolutionary Algorithm . . . . . . . . . . . . . . . . 27 3.3.3 Bayesian Evolutionary Learning for Hypergraph Classifiers . 29 3.4 Predicting Cancer Clinical Outcomes Based on Gene Modules . . . . 34 3.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 34 3.4.2 Prediction Performance . . . . . . . . . . . . . . . . . . . . . . 36 3.4.3 Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4.4 Identification of Prognostic Gene Modules . . . . . . . . . . . 44 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4 Hypergraph-based Models for Constructing Higher-Order miRNA-mRNA Interaction Networks in Cancer 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.2 Analyzing Relationships between miRNAs and mRNAs from Heterogeneous Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3 Hypergraph-based Models for Identifying miRNA-mRNA Interactions 57 4.3.1 Hypergraph-based Models . . . . . . . . . . . . . . . . . . . . 57 4.3.2 Learning Hypergraph-based Models . . . . . . . . . . . . . . . 61 4.3.3 Building Interaction Networks from Hypergraphs . . . . . . . 64 4.4 Constructing miRNA-mRNA Interaction Networks Based on Higher- Order Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 66 4.4.2 Classification Performance . . . . . . . . . . . . . . . . . . . . 68 4.4.3 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 70 CONTENTS iii 4.4.4 Constructed Higher-Order miRNA-mRNA Interaction Networks in Prostate Cancer . . . . . . . . . . . . . . . . . . . . . 74 4.4.5 Functional Analysis of the Constructed Interaction Networks 78 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Hierarchical Hypergraphs for Identifying Higher-Order Genomic Interactions in Multilevel Regulation 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2 Analyzing Epigenetic and Genetic Interactions from Multiple Genomic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3 Hierarchical Hypergraphs for Identifying Epigenetic and Genetic Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3.1 Hierarchical Hypergraphs . . . . . . . . . . . . . . . . . . . . . 92 5.3.2 Learning Hierarchical Hypergraphs . . . . . . . . . . . . . . . 95 5.4 Identifying Higher-Order Genomic Interactions in Multilevel Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 100 5.4.2 Identified Higher-Order miRNA-mRNA Interactions Induced by DNA Methylation in Ovarian Cancer . . . . . . . . . . . . 102 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Concluding Remarks 6.1 Summary of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . 107 6.2 Directions for Further Research . . . . . . . . . . . . . . . . . . . . . . 109 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 ์ดˆ๋ก . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Docto

    Development of radical-stable peroxidases based on peroxidase inactivation mechanism

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2015. 2. ํ•œ์ง€์ˆ™.Peroxidases catalyze a variety of oxidative transformations of many aromatic compounds and thus have potential in biosynthesis and other biotechnological applications. However, the usefulness of these versatile enzymes is limited, as the enzyme is quickly inactivated during the oxidation reaction of aromatic compounds. This low stability of peroxidases results in low product yield due to the incomplete reaction and increased production costs. Many researchers have studied this, and three possible pathways for peroxidase inactivation have been proposed: reaction with excess hydrogen peroxide, sorption by polymer product, and reaction with radical intermediates. The first two pathways have been corroborated with extensive evidencehowever, the free radical-mediated mechanism of peroxidase inactivation has not been fully elucidated. Thus, the dominant inactivation mechanism in the oxidation reaction of phenolic compounds must be revealed. An understanding of the molecular mechanism of radical-mediated inactivation is necessary for protein engineering to improve peroxidase stability. Firstly, the dominant mechanism of peroxidase inactivation during phenol oxidation was determined. Two peroxidases, Coprinus cinereus peroxidase (CiP) and horseradish peroxidase isozyme C (HRPC), showed much higher inactivation rates after the simultaneous addition of phenol and hydrogen peroxide. After the oxidation reaction of phenol, the molecular weights of polypeptides originating from the inactivated peroxidases were slightly increased, and a large fraction of heme from the two inactivated peroxidases remained intact. These findings support the hypothesis that the inactivation of peroxidase during the oxidation of phenol occurs by the coupling of phenoxyl radicals with peroxidase polypeptides. Secondly, the radical coupling site of CiP was identified, and the radical stability of CiP was improved by site-directed mutagenesis. The residue F230 of CiP modified with the phenoxyl radical was mutated to amino acids (Ala) that resist radical coupling. The F230A mutant showed the highest stability against the radical attack, retaining 80% of its initial activity, while the wild-type protein was almost completely inactivated. In addition, no structural changes were observed in CiP after radical coupling. Thirdly, HRPC was also engineered to enhance the radical stability. Phenylalanine residues that are vulnerable to modification by phenoxyl radicals were identified and then changed to Ala to prevent radical coupling. The F68A/F142A/F143A/F179A mutant exhibited dramatic enhancement of radical stability, retaining 41% of its initial activity compared to the wild type, which was completely inactivated. Radical coupling did not change the secondary structure or the active site structure of HRPC. Structure and sequence alignment revealed that radical-vulnerable Phe residues were conserved in homologous peroxidases. Fourthly, the radical-stable CiP mutant, F230A, was applied to the major practical applications, such as the removal of phenol, the decolorization of dye, and the synthesis of polymers. As expected, the removal efficiency of phenol and the decolorization efficiency of Reactive Black 5 were increased four- and five-fold, respectively, compared with that of the wild type. In addition, the phenolic polymer having the highest molecular mass (8850 Da) was synthesized by the F230A mutant in a 50% v/v isopropanol-buffer mixture. A novel engineering strategy to eliminate the radical coupling site increased the radical stability of two peroxidases, CiP and HRPC. This implies that phenoxyl radicals covalently bind to critical Phe residues and inactivate peroxidase by blocking substrate access to the active site of the enzyme.ABSTRACT i CONTENTS iv LIST OF TABLES x LIST OF FIGURES xii LIST OF ABBREVIATIONS xv CHAPTER 1 INTRODUCTION 1 1. 1 Research Backgrounds 2 1. 2 Research Objectives 5 CHAPTER 2 LITERATURE SURVEY 9 2. 1 Peroxidase 10 2. 1. 1 Heme peroxidase classification 10 2. 1. 2 Catalytic mechanism of peroxidase 11 2. 1. 3 Horseradish peroxidase (HRP) 14 2. 1. 4 Peroxidase from C. cinereus 16 2. 2 Applications of Peroxidases 18 2. 3 Inactivation Mechanism of Peroxidase 24 2. 3. 1 Inactivation by hydrogen peroxide 24 2. 3. 2 Inactivation by reaction product 28 2. 3. 3 Inactivation by free phenoxyl radical 30 2. 4 Improvement of Peroxidase Stability through Protein Engineering 31 CHAPTER 3 PEROXIDASE INACTIVAION BY COVALENT MODIFICATION WITH PHENOXYL RADICAL DURING PHENOL OXIDATION 36 3. 1 Introduction 37 3. 2 Materials and Methods 40 3. 2. 1 Chemicals and reagents 40 3. 2. 2 Enzymes 40 3. 2. 3 Peroxidase stability 41 3. 2. 4 Peroxidase-catalyzed reactions 42 3. 2. 5 SDS-PAGE 42 3. 2. 6 HPLC analysis 43 3. 3 Results and Discussion 44 3. 3. 1 Inactivation factors for peroxidase during the phenol oxidation reaction 44 3. 3. 2 The modification of peroxidase polypeptide 47 3. 3. 3 Heme destruction of peroxidase 50 3. 4 Conclusion 53 CHAPTER 4 DEVELOPMENT OF THE RADICAL-STABLE COPRINUS CINEREUS PEROXDIASE (CIP) BY BLOCKING THE RADICAL ATTACK 54 4. 1 Introduction 55 4. 2 Materials and Methods 58 4. 2. 1 Peroxidase expression, purification, and activity assay 58 4. 2. 2 Mass spectrometry analysis 58 4. 2. 3 Molecular docking simulation 61 4. 2. 4 Turnover capacity and radical stability 62 4. 2. 5 Kinetic parameters 63 4. 2. 6 Spectroscopic analysis 64 4. 3 Results and Discussion 66 4. 3. 1 Inactivation of CiP during phenol oxidation 66 4. 3. 2 Formation of an inactive adduct between F230 and the phenoxyl radicals 68 4. 3. 3 Improving radical stability by engineering F230 mutants 72 4. 3. 4 Kinetic studies. 81 4. 3. 5 Molecular docking simulation 83 4. 3. 6 Structure of CiP after the phenol modification 85 4. 4 Conclusion 91 CHAPTER 5 ENGINEERING A HORSERADISH PEROXIDASE C STABLE TO RADICAL ATTACKS BY MUTATING MUTIPLE RADICAL COUPLING SITES 92 5. 1 Introduction 93 5. 2 Materials and Methods 96 5. 2. 1 Materials 96 5. 2. 2 Expression of recombinant HRPC 96 5. 2. 3 Refolding of inclusion body and purification 97 5. 2. 4 Peroxidase activity assay 98 5. 2. 5 Mass spectrometry analysis 98 5. 2. 6 Spectroscopic analysis of HRPC 99 5. 2. 7 Turnover capacity and radical stability 100 5. 2. 8 Molecular docking simulation 101 5. 2. 9 Protein modeling of horseradish peroxidase isoenzyme A2 102 5. 3 Results and Discussion 104 5. 3. 1 Inactivation of HRPC during the phenol oxidation 104 5. 3. 2 Peptide modification of HRPC by radical attack 106 5. 3. 3 Effect of radical modification on structure of HRPC 112 5. 3. 4 Improving the radical stability of HRPC by site-directed mutagenesis of multiple Phe residues 116 5. 3. 5 Kinetic characterization of HRPC wild-type and mutants 124 5. 3. 6 Molecular docking simulation of HRPC wild-type and quadruple mutant 127 5. 3. 7 Highly conversed Phe residues in homologous peroxidases 130 5. 4 Conclusion 135 CHAPTER 6 IMPROVED PRACTICAL USEFULNESS OF PEROXIDASE FROM COPRINUS CINEREUS BY MUTIAON OF PHE230 136 6. 1. Introduction 137 6. 2. Materials and Methods 140 6. 2. 1 Materials 140 6. 2. 2. Peroxidase 140 6. 2. 3 Peroxidase stability 141 6. 2. 4 Removal of phenol 141 6. 2. 5 Decolorization of RB5 142 6. 2. 6 Phenol polymerization 143 6. 2. 7. Kinetic studies 144 6. 3. Results and Discussion 145 6. 3. 1. Phenol removal form aqueous solution 145 6. 3. 2. Decolorization of Reactive Black 5 149 6. 3. 3. Enzymatic polymerization of phenol 151 6. 3. 4. Effect of organic solvent on enzyme stability 156 6. 3. 5. Enzyme stability during phenol oxidation in solvent mixtures 159 6. 3. 6. Kinetic study 162 6. 4. Conclusion 165 CHAPTER 7 OVERALL DISCUSSIONS AND RECOMMENDATIONS 166 BIBLIOGRAPHIES 172 ABSTRACT IN KOREAN 193Docto
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