331 research outputs found
์ฌ์ธต ์ ๊ฒฝ๋ง์ ํ์ฉํ ์๋ ์์ ์ก์ ์ ๊ฑฐ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์์ฐ๊ณผํ๋ํ ํ๋๊ณผ์ ๊ณ์ฐ๊ณผํ์ ๊ณต, 2020. 8. ๊ฐ๋ช
์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๏ฌcant 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.๋์งํธ ์์ ๋ฐ์ดํฐ ๋ด์ ์ก์ ์ ๊ฑฐ ๋ฐ ๊ฐ์๋ ์ดํ๋ ์์์ ๋
ธ์ด์ฆ๋ฅผ ์ ๊ฑฐํ๋ฉด์ ๋ชจ์๋ฆฌ, ๊ณก์ , ์ง๊ฐ ๋ฑ๊ณผ ๊ฐ์ ํ์ ์ธ๋ถ ์ ๋ณด๋ฅผ ์ ์งํ๋ ๊ฒ์ด ๋ชฉ์ ์ธ ์์ ์ฒ๋ฆฌ ๋ถ์ผ์ ๊ธฐ๋ณธ์ ์ด๊ณ ํ์์ ์ธ ์์
์ด๋ค. ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ์์ ์ก์ ์ ๊ฑฐ ๋ฐฉ๋ฒ๋ค์ ์ดํ๋ ์์์ ์ํ๋ ํ์ง์ ์์์ผ๋ก ๋งคํํ๋๋ก ๋์ฉ๋์ ๋ฐ์ดํฐ๋ฅผ ์ด์ฉํ์ฌ ๋คํธ์ํฌ๋ฅผ ์ง๋ํ์ตํ๋ฉฐ ๊ณ ์ ์ ์ธ ๋ฐฉ๋ฒ๋ค๋ณด๋ค ๋ฐ์ด๋ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ๊ณ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ด๋ฏธ์ง ๋๋
ธ์ด์ง์ ๋ํ ์ฌ๋ฌ ๋ฐฉ๋ฒ๋ค์ ์กฐ์ฌํ์ ๋ฟ๋ง ์๋๋ผ, ํนํ ๋ฐฑ์ ๊ฐ์ฐ์์ ์ก์๊ณผ ์ค์ ์ก์ ์ ๊ฑฐ ๋ฌธ์ ์ ์ง์คํ๋ฉด์ ๋คํธ์ํฌ ์ํคํ
์ฒ๋ฅผ ์ค๊ณํ๊ณ ์คํํ์๋ค. ์ฌ๋ฌ ํํ์ ๋๋ ์ดํฐ๋ ์ฝ๋ณผ๋ฃจ์
๋ค์ ํผํฉํ์ฌ ๊ธฐ๋ณธ ๋ธ๋ก์ ๊ตฌ์ฑํ๊ณ ์ด๋ฅผ ๋ฐ๋ณตํ์ฌ ์์์ ์ค๊ณํ ๋คํธ์ํฌ๋ฅผ ์ ์ํ์๊ณ , ๊ฐ๊ฐ ๋ณธ์ฐ์ ์์์ ์ ์งํ ์ ์๋๋ก ์ฌ๋ฌ ์
๋ ฅ ์์์ ํ๋๋ก ๋ฌถ์ด ๊ตฌ์ฑํ๋ ๋ฐฐ์น๋ฅผ ํ์คํํ๋ ๋ฐฐ์น๋
ธ๋ฉ ๋ ์ด์ด๋ ์ฌ์ฉํ์ง ์์๋ค. ๊ทธ๋ฆฌ๊ณ ๋ธ๋ก์ด ์ฌ๋ฌ ์ธต ์งํ๋๋ ๋์ ๊ธฐ์กด์ ์ ๋ณด๋ฅผ ์์คํ์ง ์๋๋ก ์คํต ์ปค๋ฅ์
์ ์ฌ์ฉํ์๋ค. ์ฌ๋ฌ ์คํ๊ณผ ๊ธฐ์กด์ ์ ์๋ ๋ฐฉ๋ฒ๊ณผ ์ต์ ๋ฒค์น ๋งํฌ์์ ๋น๊ต๋ฅผ ํตํ์ฌ ์ ์ํ ๋คํธ์ํฌ๊ฐ ๋
ธ์ด์ฆ ๊ฐ์ ๋ฐ ์ ๊ฑฐ ์์
์์ ๊ธฐ์กด์ ๋ฐฉ๋ฒ๋ค๊ณผ ๋น๊ตํ์ฌ ์ฐ์ํ ์ฑ๋ฅ์ ๊ฐ์ง๊ณ ์์์ ์
์ฆํ์๋ค. ํ์ง๋ง ์ ์ํ ์ํคํ
์ฒ๋ฐฉ๋ฒ์ ํ๊ณ์ ๋ ๋ช ๊ฐ์ง ์กด์ฌํ๋ค. ์ ์ํ ๋คํธ์ํฌ๋ ๋ค์ด์ํ๋ง์ ์ฌ์ฉํ์ง ์์์ผ๋ก์จ ์ ๋ณด ์์ค์ ์ต์ํํ์์ง๋ง ์ต์ ๋ฒค์น๋งํฌ์ ๋นํ์ฌ ๋ ๋ง์ ์ถ๋ก ์๊ฐ์ด ํ์ํ์ฌ ์ค์๊ฐ ์์
์๋ ์ ์ฉํ๊ธฐ๊ฐ ์ฝ์ง ์๋ค. ์ค์ ์์์๋ ๋จ์ํ ์ก์๋ณด๋ค๋ ์์ ํ๋, ์ ์ฅ ๋ฑ๊ณผ ๊ฐ์ ํ๋ก์ธ์ค๋ฅผ ๊ฑฐ์น๋ฉด์ ์ฌ๋ฌ ์์ธ๋ค๋ก ์ธํ ๋ค์ํ ์ก์, ๋ธ๋ฌ์ ๊ฐ์ ์ดํ๊ฐ ํผ์ฌ ๋์ด ์๋ค. ์ค์ ์ก์์ ๋ํ ๋ค์ํ ๊ฐ๋์์์ ๋ถ์๊ณผ ์ฌ๋ฌ ๋ชจ๋ธ๋ง ์คํ, ๊ทธ๋ฆฌ๊ณ ์์ ์ก์ ๋ฐ ๋ธ๋ฌ, ์์ถ๊ณผ ๊ฐ์ ๋ณตํฉ ๋ชจ๋ธ๋ง์ด ํ์ํ๋ค. ํฅํ์๋ ์ด๋ฌํ ์ ๋ค์ ๋ณด์ํจ์ผ๋ก์จ ์ฑ๋ฅ์ ํฅ์์ํค๊ณ ๋คํธ์ํฌ์ ์กฐ์ ์ ํตํด ์ค์๊ฐ์ผ๋ก ์ ์ฉ๋ ์ ์์์ ๊ธฐ๋ํ๋ค.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
ํ๊ตญ ๋๋์ ์๋ฐฉ๊ด๋ค์ ์ ํด๋ฌผ์ง ์ง์ , ๊ฐ์ ๋ ธ์ถ๊ณผ ์ง๋ณ์ ๋ณ๋ฅ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๋ณด๊ฑด๋ํ์ ๋ณด๊ฑดํ๊ณผ(๋ณด๊ฑดํ์ ๊ณต), 2021. 2. ์ฑ์ฃผํ.์๋ก : ์ธ๊ณ๋ณด๊ฑด๊ธฐ๊ตฌ ์ฐํ ๊ตญ์ ์์ฐ๊ตฌ์์์๋ ์๋ฐฉ๊ด์ ์ง์
์ ๋
ธ์ถ์ ์ธ์ฒด๋ฐ์๊ฐ๋ฅ์ธ์์ธ ๊ทธ๋ฃน2B๋ก ์์ฒด๋ฆฌ๋ฌ ๊ต๋์ธ์์ธ ๊ต๋๊ทผ๋ฌด๋ฅผ ๋ฐ์์ถ์ ์ธ์์ธ ๊ทธ๋ฃน2A๋ก ๊ท์ ํ๊ณ ์๋ค. ์ด์ธ์๋ ์๋ฐฉ๊ด์ ์ฌ๋ ๋ฑ ์๋ฐฉํ๋ํ์ฅ์์ ๋
ธ์ถ๋๋ ์ํ๋ฌผ์ง์ ์ํด์๋ ๋ฐ์๋ฌผ์ง์ ์ถ๊ฐ์ ์ผ๋ก ๋
ธ์ถ๋ ์ ์๋ค. ์๋ฐฉ๊ด์ ์ง์
ํ๊ฒฝ์ ์ถ๋ํ์ฅ, ์ถ๋๊ฒฝ๋ก, ์ฌ๋ฌด๊ณต๊ฐ์ผ๋ก ๋ถ๋ฅ๋๋ค. ํนํ, ์ถ๋ํ์ฅ ์ค ํ๋์ธ ํ์ฌํ์ฅ์์ ๋ฐ์ํ๋ ์ผ์ฐํํ์, ์์ํ์์, ํฌ๋ฆ์๋ฐํ๋ ๋ฑ 10์ฌ์ข
์ ๊ฐ์ข
๋
์ฑ๋ฌผ์ง์ ์ธ์ฒด ๊ฑด๊ฐ์ํฅ์ ์ฃผ๋ ๊ฐ๋ ฅํ ์ํ์ธ์ ์ค ํ๋์ธ ๊ฒ์ผ๋ก ์ ์๋ ค์ ธ ์๋ค. ์๋ฐฉ๊ด๋ค์ ์ฌ๋ํ์ฅ์์ ๋ฐ์ํ๋ ์ํ์์ธ์ ์ง์์ ์ผ๋ก ๋
ธ์ถ๋จ์ผ๋ก์จ ์ง๋ณ๋ถ๋ด์ ์ฆ๊ฐ์ํจ๋ค. ๋ฐ๋ผ์, ์๋ฐฉ๊ด์ ๊ทผ๋ฌดํ๊ฒฝ๊ณผ ์ฌ๋ํ์ฅ ์ง์
ํ๊ฒฝ์ ๋ํ ์ํ์์ธ์ ๋ํ ์ ๋ณด๊ฐ ์๋ค๋ฉด, ์์ธก๊ฐ๋ฅํ ๊ฑด๊ฐ์ํฅ์ ์๋ฐฉํ ์ ์์ ๊ฒ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ๋๋์์ ๊ทผ๋ฌดํ๋ ์๋ฐฉ๊ด๋ค์ ์ถ๋ํ์ฅ๊ณผ ์ฌ๋ฌด๊ณต๊ฐ์ ๋ํ ์ง์
ํ๊ฒฝ์ ์ธก์ ํ๊ณ ๋ฐฉํ๋ณต ์ฌ์ฉ, ์ธ์ฒ, ๊ด๋ฆฌ์ ๋ํ ๋ณด๊ฑดํ์ ์ธ์์ ํ๊ฐํ๋ค. ๋๋ถ์ด ๋๋์ ์๋ฐฉ๊ด ์ ์์ฝํธํธ๋ฅผ ๋์์ผ๋ก ํน์๊ฑด๊ฐ์ง๋จ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ์ฌ ์ผ๋ฐ์ธ๊ตฌ์ง๋จ๊ณผ ๋ง์ฑ๋ณ๊ณผ ์ง์
๊ด๋ จ์ฑ ์งํ์ ํ์คํ ์ ๋ณ๋ฅ (๋น)๋ฅผ ๋น๊ต ํ๊ฐํจ์ผ๋ก์จ ๊ฑด๊ฐ์์ค์ ํ๊ฐํ๋ค.
๋ฐฉ๋ฒ : ์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ๋, ์์ธ์์ ๊ทผ๋ฌดํ๋ ํ์ฌ์ง์๊ณผ ์ธ๋ช
๊ตฌ์กฐ ์
๋ฌด๋ฅผ ์ํํ๋ ์๋ฐฉ๊ด 1,097๋ช
์ ๋์์ผ๋ก 21์ผ๊ฐ ์ค๋ฌธ์กฐ์ฌ๋ฅผ ์ค์ํ์ฌ 40.3%์ ์๋ต๋ฅ ์ ๋ณด์๋ค. ์ด ์กฐ์ฌ์์ ์๋ฐฉ๊ด๋ค์ด ์ฌ์ฉํ๋ ๊ฐ์ธ๋ณดํธ์ฅ๋น ์ค ํ๋์ธ ๋ฐฉํ๋ณต์ ์ฌ์ฉ, ์ธํ, ๊ด๋ฆฌ์ ๋์ ๋ณด๊ฑดํ์ ์ธ์๊ฐ์ ์ฐ๊ด์ฑ์ ํ๊ฐํ์๋ค. ๋ํ, ์๋ฐฉ๊ด๋ค์ ์ง์
ํ๊ฒฝ ์ค ํ๋์ธ ์ฌ๋ํ์ฅ ํ๋๋จ๊ณ๋ฅผ ํ๊ฐํ์๋ค. ๊ตญ๋ด์์ ๊ฐ์ฅ ๋ง์ ๋ฐ์์ ๋ณด์ด๋ ์ฃผํํ์ฌ ๋ฐ์์ ์ง์
ํ๊ฒฝ์ผ๋ก ํ์ฌํ์ฅ์์ 1์ฐจ์ ์ผ๋ก ๋ฐ์ํ๋ ์ค์ผ๊ณผ 2์ฐจ์ ์ผ๋ก ๋ฐ์ํ๋ ๋ฐฉํ๋ณต ๊ต์ฐจ์ค์ผ์ ๋ํ์ฌ ์ค์ผ๋ฌผ์ง์ ์ข
๋ฅ์ ์์ ๋ํ์ฌ ์คํ์ ํ์๋ค. ๊ต์ฐจ์ค์ผ ํ๊ฐ๋ ๋ฐฉํ๋ณต์ ์ง์
ํ๊ฒฝ์ ์ํ์ ๋์ ๋ฐ๋ผ 4๊ฐ ๊ทธ๋ฃน์ผ๋ก ๋๋์ด ์ธก์ ํ์๋ค. ์ด๋ 1์ฐจ์ค์ผ๊ณผ 2์ฐจ์ค์ผ์์์ ๊ณต๊ธฐํฌ์ง์ ํตํด 12๊ฐ ํญ๋ชฉ์ ๋ถ์ํ์๋ค. ๋ํ, ๋ฐฉํ๋ณต์ ๋ฌป์ ์ค์ผ๋ฌผ์ง์ ์ธํ๋ฌผ์ ์ถ์ถํ์ฌ 24๊ฐ ํญ๋ชฉ์ ๋ํ์ฌ ์์ง๋ถ์์ ์ํํ์๋ค. ์ค๋ฌธ์กฐ์ฌ์ ์คํ์ฐ๊ตฌ๋ฅผ ํตํด ๋์ถ๋ ๋ฌธ์ ์ ์ ๋ํ ๋์ฑ
๋ง๋ จ์ ์ํ์ฌ ๋ธํ์ด ์ ๋ฌธ๊ฐ ์กฐ์ฌ ๋ฐฉ๋ฒ์ผ๋ก ์คํ๊ฐ๋ฅ์ฑ๊ณผ ํจ๊ณผ์ฑ์ ๋์ผ ์ ์๋ ๊ตฌ์ฒด์ ์ธ ์ ์ฑ
์ ์ ์ํ์๋ค. ๋ธํ์ด ์ ๋ฌธ๊ฐ ์กฐ์ฌ์ ์ฐธ์ฌ๋์์๋ ์๋ฐฉํ์ฅ๊ณผ ๋ฐฉํ๋ณต์ ๋ํ ์ ํ์ ์ค๋ฌด๊ฒฝํ๊ณผ ์ฐ๊ตฌ๊ฒฝํ์ด ๋ง์ ๋ํ๊ต์์ ์๋ฐฉ๊ด์ 7๋ช
์ ๋์์ผ๋ก 3๋ผ์ด๋์ ๊ฑธ์ณ ์ํํ์๋ค. ๋ ๋ฒ์งธ ์ฐ๊ตฌ๋, ์๋ฐฉ๊ด๋ค์ ์ง์
ํ๊ฒฝ ์ค ํ๋์ธ ๊ทผ๋ฌด ์ค ๋๊ธฐ๊ณต๊ฐ์ธ ์๋ฐฉ์ฒญ์ฌ ๋ด์์์ ๊ทผ๋ฌดํ๊ฒฝ์ ํ๊ฐํ์๋ค. ์๋ฐฉ๊ด๋ค์ด ํ์ฌํ์ฅ์์ ์ฒ ์ํ์ฌ ์๋ฐฉ์๋ก ๋ณต๊ทํ ํ ์๋ฐฉ์ฒญ์ฌ ๋ด๋ถ์ ์๋ฐฉ์ฐจ๋์์ 2์ฐจ์ ์ผ๋ก ๋
ธ์ถ๋๋ ํํ์ ์ ํด๋ฌผ์ง์ ์ค๋ด๊ณต๊ธฐ์ง์ ํ๊ฐํ์๋ค. ์์ธ์ ์์ฌ 4๊ฐ์๋ฐฉ์๋ฅผ ๋ฌด์์๋ก ์ ์ ํ์๋ค. 2๊ฐ์๋ ์คํ๊ตฐ์ผ๋ก ํ์ฌํ์ฅ ์๋ฐฉํ๋์ด ์ข
๋ฃ๋๊ณ ์๋ฐฉ์๋ก ๊ท์ํ ํ์ ์ธก์ ํ์๋ค. ๋ค๋ฅธ 2๊ฐ์๋ ๋์กฐ๊ตฐ์ผ๋ก ์ถ๋๊ณผ ์๊ด์์ด ํ์ ์์ค์์ ์ค๋ด๊ณต๊ธฐ์ง์ ์ธก์ ํ์๋ค. ์๋ฐฉ์์ ์ง๋ ์ ์ฐ์์คํ
์ ์ด์ฉํ์ฌ ์์ธ์์์ ๋ฐ์ํ๋ ๋ชจ๋ ํ์ฌ์ฌ๊ณ ๋ฅผ 24์๊ฐ ๋ชจ๋ํฐ๋งํ์๊ณ , ์ค๊ธ๊ท๋ชจ ์ด์์ ์ฌ๊ณ ์์ ์คํ๊ตฐ์ด ์ถ๋ํ๊ฒ ๋๋ ๊ฒฝ์ฐ ๊ท์ ํ ๋ฐ๋ก ์ค๋ด๊ณต๊ธฐ์ง์ ์ธก์ ํ์๋ค. 11๊ฐ ์ ํด๋ฌผ์งํญ๋ชฉ(๋ฏธ์ธ๋จผ์ง, ํฌ๋ฆ์๋ฐํ๋, ํ๋ฐ์ฑ์ ๊ธฐํํฉ๋ฌผ, PAH, VCM, ์ฐ๋ฅ, ์๋ฉด, CO, CO2, NO2, O3)์ ๊ณต์ ์ํ๋ฒ์ ๋ฐ๋ผ ์ธก์ ํ์๋ค. ์ธ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์ํ์ง๊ตฐ ์ค ํ๋๋ก ๋ถ๋ฅ๋์ด ์๋ ์๋ฐฉ๊ด์ ์์ธก ๋ถ๊ฐ๋ฅํ ์ ํด๋ฌผ์ง์ ์ง์์ , ๋ฐ๋ณต์ ์ผ๋ก ๋
ธ์ถ๋๊ณ ์์ผ๋ ๊ฑด๊ฐ๊ด๋ฆฌ๋ ๊ฐ์ธ์ ์๋ฌด๋ก ๊ฐ์ฃผ๋๊ณ ์๋ค. ๋ฐ๋ผ์ ๋ณธ ์ฐ๊ตฌ์์๋ ๋งค๋
์ค์ํ๋ ์๋ฐฉ๊ด ํน์๊ฑด๊ฐ์ง๋จ ๊ฒฐ๊ณผ์๋ฃ๋ฅผ ์ด์ฉํ์ฌ ๋๋์ ์๋ฐฉ๊ด๋ค์ ๋ง์ฑ์งํ์ธ ๊ณ ํ์, ๋น๋จ, ๋์ฌ์ฆํ๊ตฐ, ๋น๋ง๊ณผ ์ง์
์ฑ ์งํ์ธ ํ ํ๊ธฐ์ฅ์ (์ ํ์ฑ, ํ์์ฑ, ๋ณตํฉ์ฑ)์ ์์์ฑ๋์ฒญ(4๋ถ๋ฒ, 6๋ถ๋ฒ)์ ๋ํ์ฌ ์ผ๋ฐ์ธ๊ตฌ์ง๋จ์ ๊ตญ๊ฑด์ ๊ฒ์ง๊ฒฐ๊ณผ๋ฅผ ์ด์ฉ, ๋น๊ตํ์ฌ ์ง์ ํ์คํ ๋ฐฉ๋ฒ์ผ๋ก ์๋ฐฉ๊ด๊ณผ ์ผ๋ฐ์ธ๊ตฌ์ง๋จ 1,000๋ช
๋น ์ฑ-์ฐ๋ น ํ์คํ์ ๋ณ๋ฅ ๊ณผ ๊ฐ์ ํ์คํ ๋ฐฉ๋ฒ์ผ๋ก ์ฑ-์ฐ๋ น ํ์คํ ์ ๋ณ๋น๋ฅผ ๊ฐ๊ฐ ์ฐ์ถํ์๋ค. ๋ํ, ์๋ฐฉ๊ด ํน์๊ฑด๊ฐ์ง๋จ๊ฒฐ๊ณผ์ ๋ํ ์ญํ์ ํน์ฑ์ ๋ถ์ํ์๋ค.
๊ฒฐ๊ณผ : ์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ์์, ์๋ฐฉ๊ด๋ค์ ์ฌ๋ํ์ฅ์์ ๋ฐ์๋ฌผ์ง ๋ฐ์๊ฐ๋ฅ์ฑ ์ธ์ง๋๋ 94.4%๋ก ๋์๋ค. ์ด๋ฌํ ๋ณด๊ฑดํ์ ์ฌ๊ณ ๊ฐ ์๋ฐฉ๊ด ์ค์ค๋ก์ ์์ ์ ์ง์ผ์ฃผ๋ ๋ฐฉํ๋ณต์ ์ฌ์ฉ, ์ธํ, ๊ด๋ฆฌ์ ๋ํ ํ๋์๋ ์ํฅ์ ์ฃผ์ง ์๋ ๊ฒ์ผ๋ก ํ์ธ๋์๋ค. ๋ค๋ง, ๋ฐฉํ๋ณต์ ์ฌ์ฉ์์, ํ์ฌํ์ฅ์์ ์๋ฐฉํ๋ ์ข
๋ฃ ํ ๋ฐฉํ๋ณต์ ์ค์ ๋ฒ์ด์ผ ํ๋ ์์น์ ๋ํ ์๊ฐ๊ณผ ๋ณด๊ฑดํ์ ์ฌ๊ณ ๊ฐ์๋ ์ฐ๊ด์ฑ์ด ์์๋ค (AOR=1.92, CI 1.01-3.68). ํ์ฌํ์ฅ๊ณผ ๋ฐฉํ๋ณต ์ค์ผ๋ ํ๊ฐ๊ฒฐ๊ณผ, ํ์ฌํ์ฅ์์๋ ๊ธ์ฑ ํผ๋ถ๋
์ฑ์ ์ ๋ฐํ๋ ์์ํ์์๊ฐ ๋ถ์์ ์ฐ์(ํ์ํ์ฌ)์ ์์ ์ฐ์(ํ์ผํ์ฌ) ๋ชจ๋์์ ๊ฒ์ถ๋์๋ค. ์์ํ์์๋ ๋ฐฉํ๋ณต ํฌ์ง๊ณต๊ธฐ์์๋ ๊ฒ์ถ๋์ด ํ์ฌํ์ฅ์์์ ๋ฐฉํ๋ณต ์ฐฉ์ฉ์ด ๋งค์ฐ ์ค์ํจ์ ํ์ธํ์๋ค. ํฌ๋ฆ์๋ฐํ๋๋ ์์ ์ฐ์(ํ์ผํ์ฌ)์์ ๋
ธ์ถ๊ธฐ์ค TWA 0.3์ ์ด๊ณผํ์ฌ ๋ฐฉํ๋ณต์์ ๊ฒ์ถ๋์๋ค. ์ด ์ธ์๋ ์์ํ์์, ๋์ผ์ ์ ๊ท ๋ฐฐ์ ๋ฐ์ ๋ฐฉํ๋ณต์์๋ ๊ฒ์ถ๋์์ผ๋ ์ํ์์ค์ ์ด๊ณผํ์ง๋ ์์๋ค. ์์ํ์์๋ ๋ฐฉํ๋ณต ๋ชจ๋ ์คํ๊ทธ๋ฃน์์ ๊ฒ์ถ๋์๊ณ , ๋ฉ, ์ฐํ์ฒ , ์๋ฃจ๋ฏธ๋, ์นด๋๋ฎด๊ณผ ๊ฐ์ ์ค๊ธ์ ๋ฌผ์ง๋ ๊ณต๊ธฐํฌ์ง์์ ๊ฒ์ถ๋์๋ค. ๋ฐฉํ๋ณต ๊ต์ฐจ์ค์ผ ์์ง๋ถ์ ๊ฒฐ๊ณผ์์๋ ๊ตฌ๋ฆฌ, ๋์ผ, ์์ฐ ๋ฑ์ ์ค๊ธ์์ด ๋ฐ๊ฒฌ๋์๋ค. ์ํฌ๋ฆด๋ก๋ํธ๋ฆด์ ํ์ฌ์ ํ ๋ฒ ๋
ธ์ถ๋ ๊ฒฝ์ฐ๋ณด๋ค ์ฐ์์ผ๋ก ๋
ธ์ถ๋ ๊ฒฝ์ฐ ๊ทธ ์์ด 2.1๋ฐฐ ๋ ๋์ ์-๋ฐ์๊ด๊ณ๋ฅผ ๋ณด์๋ค. 1๊ธ ๋ฐ์๋ฌผ์ง์ธ ๋ํํ๋ ์ ํ์ํ์ฌ์ ํ์ผํ์ฌ ๋ชจ๋ ๊ฒ์ถ๋์์ผ๋ ํ์ํ์ฌ์์ ๋ ๋๊ฒ ๊ฒ์ถ๋์๋ค. ๊ตฌ๋ฆฌ์ ๊ทธ ํํฉ๋ฌผ, ์ํฐ๋ชฌ, ์ํฌ๋ฆด๋ก๋ํธ๋ฆด, ๋์ํธํฅ์คํํ๋ ์ดํธ 4๊ฐ ํญ๋ชฉ์ด ๋ฌผํ๊ฒฝ๋ณด์กด๋ฒ ๊ธฐ์ค ํน์ ์์ง์ ํด๋ฌผ์ง ํ์๋ฐฐ์ถ์์ค ์ ์ฉ๊ธฐ์ค์ ์ด๊ณผํ์ฌ ๊ฒ์ถ๋์๋ค. ์ด๋ก์จ ํ์ฌํ์ฅ์์์ ๋ฐฉํ๋ณต ์ค์ผ์ด ์ฌ๊ฐํจ์ ํ์ธํ์๊ณ ์์ง์ค์ผ ๊ฐ๋ฅ์ฑ์ ์์ฌํ์๋ค. ๋ํ, ์๊ธฐ 4๊ฐ ํญ๋ชฉ์ด ํ์๊ธฐ์ค์น๋ฅผ ์ด๊ณผํ์ฌ ๊ฒ์ถ๋จ์ผ๋ก์จ ์ค์ฌ๊ฐ๋ฅ์ฑ ๋๋ ์ ์ฑ
์ ์กฐ์น๊ฐ ํ์ํจ์ ํ์ธํ์๋ค. ์ค๋ฌธ์กฐ์ฌ์ ์คํ์ฐ๊ตฌ์์ ๋์ถ๋ ๋ฌธ์ ์ ๋์๋ง๋ จ์ ์ํ์ฌ ๋ธํ์ด์กฐ์ฌ๋ฅผ ์ํํ ๊ฒฐ๊ณผ, ์ค๋ฌธ์กฐ์ฌ์ ํ์ฌํ์ฅ๊ณผ ๋ฐฉํ๋ณต ์ค์ผ๋ ์คํ๊ฒฐ๊ณผ๋ฅผ ์ค์๋ ๋ถ๋ฌธ์์๋ ์๊ด๋ถ์ผ ์์ฐํ๋ณด์ ์ฌ๋ํ์ฅ ์ค์ผ๋ฌผ์ง ๊ฑด๊ฐ์ํฅ ์ํ์ฑ์ ๋ํ ๊ต์ก์ค์๊ฐ ๊ฐ์ฅ ์๊ธํ ๊ณผ์ ๋ก ํ๊ฐ๋์๋ค. ์คํ๊ฐ๋ฅ์ฑ ๋ถ๋ฌธ์์๋ ์ฌ๋ํ์ฅ ์ค์ผ๋ฌผ์ง์ ๊ฑด๊ฐ์ํฅ ์ํ์ฑ ๊ต์ก์ค์, ๊ฐ์ธ๋ณดํธ์ฅ๋น 1์ฐจ์ ์ผ ๊ต์ก์ค์, ๋ฐฉํ๋ณต ๋ฑ ๊ฐ์ธ๋ณดํธ์ฅ๋น ๊ด๋ฆฌ์ฒด๊ณ ๊ฐ์ ๊ณผ ์ฒด๊ณ์ ์ด์์ ์ํ ๊ต์ก๊ณผ์ ๊ฐ๋ฐ์ด ๊ฐ์ฅ ์๊ธํ ๊ณผ์ ๋ก ํ๊ฐ๋์๋ค. ๋ ๋ฒ์งธ ์ฐ๊ตฌ์์, ์๋ฐฉ์ฒญ์ฌ์์ ์ธก์ ํ ์ ํด๋ฌผ์ง 11์ข
์ค 3์ข
์ด ๊ตญ๋ดโข์ธ ๊ธฐ์ค์ ์ด๊ณผํ์๊ณ , 1์ข
์ ๊ตญ์ธ๊ธฐ์ค์ ์ก๋ฐํ๋ ๊ฒ์ผ๋ก ํ์ธ๋์๋ค. ํนํ ์ดํ๋ฐ์ฑ์ ๊ธฐํํฉ๋ฌผ, ์ด์ฐํํ์, ํฉ์ฐ์ ๊ฐ 2.5๋ฐฐ, 2.2๋ฐฐ, 1.1๋ฐฐ๊ฐ ํ๊ฒฝ๋ถ์ ๊ณ ์ฉ๋
ธ๋๋ถ ๊ธฐ์ค๋ณด๋ค ๋์๋ค. ๋ํ, ํฌ๋ฆ์๋ฐํ๋์ ํฉ์ฐ์ ๊ฒฝ์ฐ, ์คํ๊ตฐ๋ณด๋ค ๋์กฐ๊ตฐ์์ ๋ ๋๊ฒ ์ธก์ ๋์๋ค. ์ธ ๋ฒ์งธ ์ฐ๊ตฌ์์, ๋๋์ ์๋ฐฉ๊ด ํน์๊ฑด๊ฐ์ง๋จ๊ฒฐ๊ณผ ์ ์์๋ฃ๋ฅผ ์ด์ฉํ์ฌ ์ผ๋ฐ์ธ๊ตฌ์ง๋จ๊ณผ ๋น๊ต๋ฅผ ํตํด ์ง์ ํ์คํ ๋ฐฉ๋ฒ์ผ๋ก ์ฐ์ถํ ์ง๋ณ๋จ๊ณ์ ์ฑ-์ฐ๋ น๋ณด์ ํ์คํ ์ ๋ณ๋ฅ ์ ์๋ฐฉ๊ด์์ ๋์ฌ์ฆํ๊ตฐ 12.43% (๋จ 16.91%, ์ฌ 7.76), ํํ๊ธฐ์ฅ์ ์์, ์ ํ์ฑ์ 16.87% (์ฌ 18.32%), ๋ณตํฉ์ฑ์ 2.07% (์ฌ 2.59%) ์ผ๋ฐ์ธ๊ตฌ๋ณด๋ค ๋์๋ค. ์ผ๋ฐ์ธ๊ตฌ๋ ๊ณ ํ์ 17.93% (๋จ 23.44%, ์ฌ 12.19%), ๋น๋จ 5.30% (๋จ 6.75%, ์ฌ 3.80%), ๋น๋ง 34.29% (๋จ 46.94%, ์ฌ 21.14%), ํํ๊ธฐ์ฅ์ ์ ๊ฒฝ์ฐ, ํ์์ฑ์์ 4.39% (๋จ 6.13%, ์ฌ 2.61%), ์์์ฑ ๋์ฒญ์ ๊ฒฝ์ฐ, 3๋ถ๋ฒ์์ ์ฐ์ธก์ 4.41% (๋จ 5.51%, ์ฌ 3.29%), ์ข์ธก์ 4.87% (๋จ 6.41%, ์ฌ 3.29%) ์ด์๊ณ , 4๋ถ๋ฒ์์ ์ฐ์ธก์ 6.99% (๋จ 8.66%, ์ฌ 5.28%), ์ข์ธก์ 7.92% (๋จ 10.06%, ์ฌ 5.71%) ์๋ฐฉ๊ด๋ณด๋ค ๋์๋ค. ๊ฐ ์งํ์ ์ง๋ณ์ ๋จ๊ณ ์ฑ-์ฐ๋ น๋ณด์ ํ์คํ ์ ๋ณ๋ฅ ์ ๊ณ ํ์ 37.26% (๋จ 52.45%, ์ฌ 21.47%), ๋น๋จ 23.17% (๋จ 30.79%, ์ฌ 15.25%), ๋์ฌ์ฆํ๊ตฐ 87.21% (๋จ 83.09%, ์ฌ 91.49%), ๋น๋ง 23.25% (๋จ 33.08%, ์ฌ 13.03%)๋ก ์ผ๋ฐ์ธ๊ตฌ๋ณด๋ค ์๋ฐฉ๊ด์์ ๋ชจ๋ ๋์๋ค. ๋ํ, ๊ฐ์ ํ์คํ์ ์ํ ํ์คํ ์ ๋ณ๋น์์๋, ์ง๋ณ๋จ๊ณ์ ๊ฒฝ์ฐ, ์ผ๋ฐ์ธ๊ตฌ๋ณด๋ค ์๋ฐฉ๊ด์์ ๋น๋ง 1.12๋ฐฐ (1.08-1.16), ๋์ฌ์ฆํ๊ตฐ 1.33๋ฐฐ (1.26-1.40) ๋์๊ณ , ์ง๋ณ์ ๋จ๊ณ์ ๊ฒฝ์ฐ, ์๋ฐฉ๊ด์์ ๋น๋ง 1.34๋ฐฐ (1.29-1.40), ๊ณ ํ์ 1.89๋ฐฐ (1.83-1.96), ๋น๋จ 1.36๋ฐฐ (1.31-1.41), ๋์ฌ์ฆํ๊ตฐ 1.85๋ฐฐ (1.81-1.90) ์ผ๋ฐ์ธ๊ตฌ๋ณด๋ค ๋์๋ค.
๊ฒฐ๋ก : ๋ฐฉํ๋ณต ์ค๋ฌธ์กฐ์ฌ๋ฅผ ํตํด ์๋ฐฉ๊ด์ ๋ณด๊ฑดํ์ ์๊ฐ๊ณผ ๋ฐฉํ๋ณต์ ์ฌ์ฉ, ์ธํ, ๊ด๋ฆฌํ๋๊ฐ์๋ ์ฐ๊ด์ฑ์ด ์์์ผ๋, ๋ณด๊ฑดํ์ ์๊ฐ๊ณผ ๋ฐฉํ๋ณต์ ์ฌ๋ฐ๋ฅธ ์ฌ์ฉ์ ๋ํ ์๊ฐ ์ฌ์ด์๋ ์ฐ๊ด์ฑํต๊ณ์ ์ ์์ฑ์ ๋ณด์ฌ ์๊ฐ์์ ํ๋์ผ๋ก ์ฎ๊ธธ ์ ์๋ ํต๋ก๋ฅผ ๋ง๋ จํด์ฃผ๋ ๊ฒ์ด ํ์ํ์ฌ ์ด์๋ํ ๋์ฑ
๋ง๋ จ์ด ํ์ํ๋ค. ์ฃผํํ์ฌ์์ ๋ฐ์ํ ์ ์๋ ํ์ฌํ์ฅ๊ณผ ๋ฐฉํ๋ณต ์ค์ผ๋ฌผ์ง ๋ฐ์ ์คํ์ ํตํด ์ฌ๋ํ์ฅ์์์ ์๋ฐฉ๊ด ์ง์
ํ๊ฒฝ์ ํ๊ฐํ์๋ค. ์ฐ๋ฆฌ๋ ๋ณธ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํตํด ์ฃผํํ์ฌ์์ ๋ฐ์ํ ์ ์๋ ์ ํด๋ฌผ์ง์ ์ข
๋ฅ์ ์์ ์ถ์ ํ ์ ์์๋ค. ๋ฌผ๋ก ์ ํ๋ ๊ณต๊ฐ์์ ๋ฐ์ํ ์ ํด๋ฌผ์ง์ ์ข
๋ฅ์ ์์ผ๋ก ๊ณผ์์ถ์ ๊ฐ๋ฅ์ฑ์ด ์์ผ๋, ์ด ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ ๋๋ก ์ผ๋ฐ์ ์ผ๋ก ์ฃผํํ์ฌ ํ์ฅ์์ ๋ฐ์ํ๋ ์ ํด๋ฌผ์ง์ ์ข
๋ฅ์ ์์ ๋ํ DB๋ฅผ ๊ตฌ์ถํ ์ ์์ ๊ฒ์ด๋ฉฐ, ํ์ฌํ์ฅ์์ ๊ฐ์ธ๋ณดํธ์ฅ๋น์ ์ ํํ๊ณ ์ฒ ์ ํ ์ฐฉ์ฉ์ด ์ค์ํจ์ ์ฌํ์ธํ์๋ค. ๋๋ถ์ด, ๋ฐฉํ๋ณต ์ค์ผ์ ์ํ ์์ง์ค์ผ์ ๊ฐ๋ฅ์ฑ ํ์ธ์ ํตํด ํ์์ฐ๊ตฌ์ ์ ์ฑ
์คํ์ด ํ์ํจ์ ํ์ธํ์๋ค. ๋ํ, ์๋ฐฉ๊ด์ ๊ทผ๋ฌด๋
์์ ์ถ๋ํ์์ ๋ํ ์ ๋ณด๊ฐ ์๋ค๋ฉด ํ ๋๋ก ์ฃผํํ์ฌํ์ฅ์์ ๋
ธ์ถ๋์๋ ์ ํด๋ฌผ์ง์ ๋์ ๋
ธ์ถ์์ ์ฐ์ ํ ์ ์์ ๊ฒ์ด๋ค. ์ด๋ฌํ ์๋ฃ๋ค์ ์๋ฐฉ๊ด์๊ฒ ์ง๋ณ์ด ๋ฐ์ํ์์ ๋ ์
๋ฌด๊ด๋ จ์ฑ ์งํ์ฌ๋ถ๋ฅผ ์
์ฆํ ์ ์๋ ๊ทผ๊ฑฐ์๋ฃ๋ก ํ์ฉํ ์ ์์ ๊ฒ์ด๋ค. ๋ํ, ํ์ฌํ์ฅ์์ ๊ต์ฐจ์ค์ผ์ด ๋ฐ์ํ ๋ฐฉํ๋ณต์ ๋ํ์ฌ ํ์ฅ ๊ธด๊ธ์ ์ผ์ ๋ํ ์๋ฐฉ์ ์ฑ
์ ์คํํ๊ธฐ ์ํ ๊ฐ๋ ฅํ ๊ณผํ์ ๊ทผ๊ฑฐ๋ฅผ ์ ๊ณตํ ์ ์์ ๊ฒ์ด๋ค. ๋ณธ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ ์๋ฐฉ๊ด ๋ฐฉํ๋ณต ์์ ํ ์ฌ์ฉ๊ณผ ๋ณด๊ฑด์ ์ฑ
์ ์ถ์งํ ๋ ์ ๊ทน ํ์ฉํ ์ ์์ ๊ฒ์ด๋ค. ์๋ฐฉ์ฒญ์ฌ ์ค๋ด๊ณต๊ธฐ์ง ํ๊ฐ๋ฅผ ํตํด ์๋ฐฉ๊ด ๋๊ธฐ๊ทผ๋ฌดํ๊ฒฝ์ ๋ํ ์ง์
ํ๊ฒฝ ์ธก์ ์ ์ํํ์๊ณ , ์ค๋ด๊ณต๊ฐ๊ณผ ์ฐจ๊ณ ์์ ๊ธฐ์ค์ ์ด๊ณผํ๋ ํญ๋ชฉ๋ค์ด ๊ฒ์ถ๋์๋ค. ๋ํ, ์ฌ๋ํ์ฅ ์ถ๋ ํ ๊ท์์ ์๊ด์์ด ์๋ฐฉ์ฐจ๋ ๋ฐฐ๊ธฐ๊ฐ์ค์ ํ์ ํํ์ ์ ํด๋ฌผ์ง์ ๋์ ์ด ์ฒญ์ฌ ๋ด ์ค๋ด๊ณต๊ธฐ์ง ๋ฟ๋ง ์๋๋ผ ์ฅโข๋จ๊ธฐ์ ์ผ๋ก ์๋ฐฉ๊ด์ ๊ฑด๊ฐ์ํฅ์ ๋ฌธ์ ๋ฅผ ์ค ์ ์๋ ๊ฐ๋ฅ์ฑ์ด ์์ด ์๋ฐฉ์ฒญ์ฌ์ ์ฒด๊ณ์ ์ค๋ด๊ณต๊ธฐ์ง ๊ด๋ฆฌ๊ฐ ํ์ํจ์ ํ์ธํ์๋ค. ๋ณธ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ ํฅํ ์์ธ์ ์๋ฐฉ์ฒญ์ฌ ๋ด ์ค๋ด๊ณต๊ธฐ์ง ๊ด๋ฆฌ์์คํ
์ด ์ ์ฐฉ๊ณผ ๊ฐ์ ์ ํ์ฉ๋ ์ ์์ ๊ฒ์ด๋ค. ๋ง์ง๋ง์ผ๋ก, ํน์๊ฑด๊ฐ์ง๋จ๊ฒฐ๊ณผ ๋ถ์์ ํตํ ์๋ฐฉ๊ด๊ณผ ์ผ๋ฐ์ธ๊ตฌ์ง๋จ๊ณผ์ ํ์คํ ์ ๋ณ๋ฅ ๋น๊ต๋ฅผ ํตํด ์ง๋ณ๋จ๊ณ์ ๊ฒฝ์ฐ, ๊ฑด๊ฐ๊ทผ๋ก์ ํจ๊ณผ๊ฐ ์์์ ์ถ์ ํ ์ ์์๋ค. ๊ทธ๋ฌ๋ ์ง๋ณ์ ๋จ๊ณ์ ๊ฒฝ์ฐ ์๋ฐฉ๊ด์์ ๋ชจ๋ ๋ง์ฑ์งํ์์ ์ผ๋ฐ์ธ๊ตฌ๋ณด๋ค ๋์ ์ ๋ณ๋ฅ ์ ๋ณด์์ ๋ฐ๋ผ ์ฒด๊ณ์ ์ธ ๊ฑด๊ฐ๊ด๋ฆฌ ํ๋ก๊ทธ๋จ์ด ํ์ํจ์ ํ์ธํ์๋ค. ํนํ, ๊ฑด๊ฐ๊ทผ๋ก์ ํจ๊ณผ๋ฅผ ๋ณด์ด๋ ์ง๊ตฐ์์๋ ๊ฒ์ง๊ฒฐ๊ณผ์ ์ฝํธํธ ๊ตฌ์ถ์ ํตํ ์ฐ๊ตฌ๋ฅผ ํตํด ์ฒด๊ณ์ ์ธ ์ถ์ , ๊ด๋ฆฌ์์คํ
๊ตฌ์ถ์ด ํ์ํ๋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
๊ตญ๋ฌธ์ด๋ก 271Docto
Clinical Outcomes of Spousal Donor Kidney Transplantation: Single Center Experience
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
๋ฆฌ๋โ๋ฉค๋ฒ ๊ฐ๋ฑ ๋น๋์นญ ํ์์ ๊ฐ์ธ์ ์ฐฝ์์ฑ์ ๋ํ ํจ๊ณผ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ฒฝ์ํ๊ณผ, 2017. 2. ์ต์ง๋จ.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 ์ธ๊ตฌ๋ณด๊ฑด์กฐ์ฌ(DHS)๋ฅผ ํ์ฉํ ๋ค์์ค ๋ถ์
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๋ณด๊ฑด๋ํ์ ๋ณด๊ฑดํ๊ณผ(๋ณด๊ฑด์ ์ฑ
๊ด๋ฆฌํ์ ๊ณต),2019. 8. ๊น์ ์.๋ชจ์ฑ์ฌ๋ง์ ์ธ๊ณ์ ์ผ๋ก ์ฌ์ ํ ์ฌ๊ฐํ ๋ณด๊ฑด ๋ฌธ์ ์ค ํ๋์ด๋ค. ์ด๋ ๋๋ถ๋ถ์ ์ฌ๋ง์ด ์๋ฐฉ ๊ฐ๋ฅํ๋ค๋ ์ ์์, ๋ํ ์ ์ฒด ๋ชจ์ฑ์ฌ๋ง์ 99%๊ฐ ๊ฐ๋ฐ๋์๊ตญ์์ ๋ฐ์ํ๊ณ ์๋ค๋ ์ ์์ ๋์ฑ ์ฃผ๋ชฉํด์ผ ํ ์ธก๋ฉด์ด ์กด์ฌํ๋ค. ์ฐ์ ๊ด๋ฆฌ(antenatal care, ANC), ์ฐํ๊ด๋ฆฌ(postnatal care, PNC) ๋ฑ์ ๊ธฐ์ด์ ์ธ ๋ชจ์ฑ ๊ด๋ฆฌ ์๋น์ค๊ฐ ์ ๋ฐ์ ์ธ ๋ชจ์ฑ๊ฑด๊ฐ ๊ฐ์ ์ ํจ๊ณผ์ ์ผ๋ก ๊ธฐ์ฌํ ์ ์๋ค๋ ๊ฒ์ ๊ณผ๊ฑฐ ๋ง์ ์ฐ๊ตฌ์์ ์
์ฆ๋์๋ค. ๊ทธ๋ฌ๋ ์ฌ์ ํ ๊ฐ๋ฐ๋์๊ตญ ๋ฑ์ง์์๋ ์๋น์ค ์ด์ฉ์ ์ง์ฐ์ ์ผ์ผํค๋ ์ฌ๋ฌ ์ฐจ์์ ์ฅ์ ๋ฌผ๋ค๋ก ์ธํด ์ฐ๋ชจ์ ์๋น์ค ์ถ๊ตฌ ์์ค์ด ๋ฎ์ ์ํฉ์ ๋จธ๋ฌผ๋ฌ ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋์์ง์ธ ๊ฐ๋์ ์ผ๋๋ ์ ์ฒด ๋ชจ์ฑ์ฌ๋ง ๋ถ๋ด์ ์๋น ๋ถ๋ถ(66%)์ ์ฐจ์งํ๋ ์ฌํ๋ผ ์ด๋จ ์ํ๋ฆฌ์นด ์ง์ญ ๋ด ์์นํ๋ค. ๋ ๊ตญ๊ฐ ๋ชจ๋ ์ ์ค์๋๊ตญ(lower middle income country)์ ์ํ๋ฉฐ ๊ตญ๊ฐ ์๋์์ค(GNI per capita) ๋ํ ์ ์ฌํ๋, ๋ ๊ตญ๊ฐ์ ๋ชจ์ฑ์ฌ๋ง๋น (maternal mortality ratio)์๋ ์ ์ํ ์ฐจ์ด๊ฐ ๋ํ๋๊ณ ์๋ค.
์ด๋ฌํ ์ธก๋ฉด์์ ๋ณธ ์ฐ๊ตฌ๋ ๊ฐ๋์ ์ผ๋ ์ฌ์ฑ์ ๋ชจ์ฑ๊ฑด๊ฐ ์๋น์ค ์ด์ฉ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ด ๋ฌด์์ธ์ง์, ๊ฐ๊ตญ ์ํฅ ์์ธ์ ์์์ด ์ด๋ป๊ฒ ๋ค๋ฅธ์ง ์ดํด๋ด์ผ๋ก์จ ๊ตญ๊ฐ๊ฐ ๋น๊ต ์ฐ๊ตฌ๋ฅผ ์ค์ํ๊ณ ์ ํ์๋ค. ๋ถ์์ ์ํ ์์๋ฃ๋ 2014๋
๊ฐ๋ ๋ฐ ์ผ๋ ์ธ๊ตฌ๋ณด๊ฑด์กฐ์ฌ(Demographic and Health Survey, DHS)์ ์ฌ์ฑ ์ค๋ฌธ์ง ์๋ฃ๋ฅผ ํ์ฉํ์๋ค. ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ๋ณ์๋ ANC, ์์ค ์ถ์ฐ ๋ฐ PNC์ ์ด์ฉ ์์ค์ ํตํฉ์ ์ผ๋ก ๊ณ ๋ คํ ๋ชจ์ฑ ๊ฑด๊ฐ๊ด๋ฆฌ ์ถ๊ตฌ ํ์(maternal health care seeking behavior, MSB)๋ก์, ์ด๋ ์ด 4๊ฐ ์์ค โ lowest, mid-low, mid-high, highest โ ์ผ๋ก ๊ตฌ์ฑ๋ ์์ํ ๋ณ์์ด๋ค. ์ฐ๊ตฌ ๋์์๋ ์ต๊ทผ 5๋
๋ด ์ถ์ฐ ๊ฒฝํ์ด ์๋ ๊ฐ๋ ๋ฐ ์ผ๋ ๋ด 15-49์ธ ์ฌ์ฑ ์ด 5,484๋ช
(๊ฐ์ค์น ์ ์ฉ ์ ์ด 5,144๋ช
)์ด๋ฉฐ, ์ค๋์จ ํ๋ ๋ชจํ์ ๊ธฐ๋ฐ์ผ๋ก ์ฐ๊ตฌ์ ๊ฐ๋
ํ์ ์์ฑํ์๋ค. ์๋ฃ ๋ถ์์ ๊ฒฝ์ฐ ๋จผ์ ๊ธฐ์ ๋ถ์์ ํตํด ๊ฐ๋์ ์ผ๋ ๋ด ์ ์ฒด์ ์ธ MSB ๋ถํฌ ๋ฐ ์ฐ๊ตฌ ๋์์์ ํน์ฑ์ ํ์ธํ์์ผ๋ฉฐ, ์ด์ ํจ๊ป ์นด์ด์ ๊ณฑ ๊ฒ์ ์ ์ค์ํ์ฌ MSB์ ๊ฐ ์์ธก๋ณ์ ๊ฐ์ ์ฐ๊ด์ฑ์ ์ดํด๋ณด์๋ค. ์ดํ ๋ ๊ตญ๊ฐ MSB์ ์ํฅ ์์ธ์ ๋ถ์ํ๋๋ฐ ์์ด DHS ์๋ฃ์ ์๊ณ์ ํน์ฑ ๋ฐ ๊ฒฐ๊ณผ๋ณ์์ ์์์ ์์ฑ์ ๊ณ ๋ คํ์ฌ ๋ค์์ค ์์ํ ๋ก์ง์คํฑ ํ๊ท(multilevel ordinal logistic regression) ๋ถ์์ ์ํํ์๋ค. ์ด๋ฅผ ํตํด ๊ฐ์ธ ์์ค์ MSB๋ฅผ ์ค๋ช
ํจ์ ์์ด ์ปค๋ฎค๋ํฐ์ ์ํฅ์ ํจ๊ป ์ดํด๋ณด๊ณ ์ ํ์๋ค.
๋ถ์ ๊ฒฐ๊ณผ, ์ ์ฒด ๊ฐ๋ ์ฌ์ฑ ์ค highest์ ์ํ ๋น์จ์ด ์ฝ 65%, ์ผ๋์ ๊ฒฝ์ฐ ๊ทธ ๋น์จ์ด ์ฝ 31%๋ก ๋ํ๋ ์ ๋ฐ์ ์ผ๋ก ๊ฐ๋์ MSB ์์ค์ด ์ผ๋๋ณด๋ค ๋์ ํธ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ค์์ค ๋ถ์ ์ ์ด 4๊ฐ ๋ชจํ์ ์ ํฉํ์์ผ๋ฉฐ, ๊ฐ์ฅ ๋จผ์ ์๋ชจํ(null model)์ ์ ํฉ์ ํตํด ๊ธ๋ด์๊ด๊ณ์(intraclass correlation coefficient)๋ฅผ ์ฐ์ถ, ๋ค์์ค ๋ถ์ ๋ฐฉ๋ฒ์ ์ ํฉ์ฑ์ ํ์ธํ์๋ค. ๋ํ ๊ฐ๋์ ์ผ๋ ๊ฐ๊ฐ์ 4๊ฐ ๋ชจํ ๋ชจ๋์์ ์ง๋จ๊ฐ ๋ถ์ฐ์ด ํต๊ณ์ ์ผ๋ก ์ ์ํ์ฌ, ์ปค๋ฎค๋ํฐ ํน์ฑ์ด ๊ฐ์ธ ์์ค์ MSB์ ์ํฅ์ ๋ผ์น๋ค๋ ๊ฒ์ ๊ฒ์ฆํ ์ ์์๋ค. ๊ฐ์ธ ๋ฐ ์ปค๋ฎค๋ํฐ ์์ค์ ๋ณ์๋ฅผ ๋ชจ๋ ํฌ์
ํ Model 4 ๋ถ์ ๊ฒฐ๊ณผ, ๊ฐ๋์ ์ผ๋ ์ฌ์ฑ์ MSB์ ๊ณตํต์ ์ผ๋ก ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ ์ฌ์ฑ์ ๊ต์ก์์ค, ๊ฐ์ ์ ๊ฒฝ์ ์์ค, ์์ ์ ๋ํ ์๊ตฌ, ์ปค๋ฎค๋ํฐ ๋น๊ณค ์์ค ๋ฐ ํ์ ๊ตฌ์ญ์ด์๋ค. ์ด ์ธ ๊ฐ๋์ ๊ฒฝ์ฐ ์ข
๊ต, ๊ฑด๊ฐ๋ณดํ ๊ฐ์
์ฌ๋ถ ๋ฐ ์๋ฃ ์๋น์ค ์ ๊ทผ์ ๋ํ ์ด๋ ค์์ด, ์ผ๋์ ๊ฒฝ์ฐ ์ฐ๋ น, ์ธ์ข
, ๋ฐฐ์ฐ์/ํํธ๋์ ๊ต์ก์์ค, ๋ฏธ๋์ด์ ๋ํ ์ ๊ทผ, ํผ์ ์ฌ๋ถ ๋ฐ ๊ฑฐ์ฃผ์ง์ญ์ด MSB์ ์ ์ํ ์ฐ๊ด์ฑ์ ์ง๋ ์์ธ์ผ๋ก ํ์ธ๋์๋ค.
๊ฐ๋๋ ๋น๊ต์ ์ฐํธ์ ์ธ ์ ์ฑ
ํ๊ฒฝ๊ณผ ์ฌ๋ฌ ์ด๋์
ํฐ๋ธ์ ์ฑ๊ณผ๋ก ๋ค์ ๋์ ์์ค์ MSB๋ฅผ ๋ณด์ด๊ณ ์๋ค. ์ด์ ๋ฐํด ์ผ๋๋ ์ ๋ฐ์ ์ผ๋ก ์ฌ์ฑ์ ๋ชจ์ฑ ์๋น์ค ์ด์ฉ์ด ๋ฎ์ ์์ค์ ๋จธ๋ฌผ๋ฌ ์๋ค. ๋ณธ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ก ๋ฏธ๋ฃจ์ด ๋ณด์ ๋ ๊ตญ๊ฐ ๋ชจ๋ ๊ต์ก ์์คํ
์ ๊ฐํ ๋ฐ ์ง ์ ๊ณ , ๊ตญ๊ฐ ์ ์ฒด์ ๊ท ํ ๋ฐ์ ์ ์ํด ๋
ธ๋ ฅํ๋ ๋์์ ์ปค๋ฎค๋ํฐ ๊ธฐ๋ฐ์ ์๋์ฐฝ์ถ, ํ๊ธ์ง์ ์ฌ์
๋ฑ์ ํตํด ์์ธ๊ณ์ธต ์ฌ์ฑ์ ๋ณ๋๋ก ์ง์ํจ์ด ํ์ํ๋ค. ๋ํ ์ง์ญ๊ฐ ๊ฑด๊ฐ ๋ถํ๋ฑ์ด ๋ํ๋๋ ์์ธ์ ์ฌ์ธต ๋ถ์ํจ์ผ๋ก์จ ๊ฐ ์ง์ญ์ ๋งฅ๋ฝ์ ๋ฐ๋ผ ๋ชจ์ฑ๊ฑด๊ฐ ์ฌ์
์ ํ๋๋ฅผ ์ํ ํ๋นํ ์ ๋ต์ ์๋ฆฝํด์ผ ํ ๊ฒ์ผ๋ก ๋ณด์ธ๋ค. ํํธ, ๊ฐ๋์ ๊ฒฝ์ฐ ํ ์์ ์์ ๊ทผ๊ฑฐํ ์๋ชป๋ ๋ณด๊ฑด ์ง์ ๋ฐ ์ค์ฒ์ ๋ฐ๋ก์ก๊ธฐ ์ํ ์ปค๋ฎค๋ํฐ ์ฐจ์์ ๊ฑด๊ฐ์ฆ์ง ๊ต์ก, ๋น๊ณค์ธต์ ๋ํ ๊ฑด๊ฐ๋ณดํ ์ง์ ๊ฐํ ๋ฐ ๊ธ์ฌ์ฒด๊ณ ๊ฐ์ , ์ง๋ฆฌ์ ์ผ๋ก ์ ๊ทผ์ด ์ด๋ ค์ด ๋ง์ ๋์์ ๊ฐ์ ๋ฐฉ๋ฌธ ๋ฐ ์ฌํ๋์ ์บ ํ์ธ ๋ฑ์ ์ ๋ต์ ๊ณ ๋ คํด๋ณผ ์ ์๋ค. ํํธ, ์ผ๋์ ๊ฒฝ์ฐ ์ธ์ข
๊ฐ ๋ฌธํ์ ์ฐจ์ด๋ฅผ ๋ฏผ๊ฐํ๊ฒ ๋ฐ์ํ ๋ณด๊ฑด ๊ต์ก์ด ์ ๊ณต๋์ด์ผ ํ ํ์๊ฐ ์์ผ๋ฉฐ, ๋ณด๋ค ๋ง์ ์ฃผ๋ฏผ๋ค์๊ฒ ์ฃผ์ ๊ฑด๊ฐ ๋ฉ์์ง๋ฅผ ํจ๊ณผ์ ์ผ๋ก ์ ๋ฌํ ์ ์๋๋ก ๊ฐ์ข
๋ฏธ๋์ด๋ฅผ ํ์ฉํ๋ ์ ๋ต์ ์๋ฆฝํจ๊ณผ ํจ๊ป ๊ตญ๊ฐ ์ ๋ฐ์ ๊ฑธ์ณ ๋ฏธ๋์ด ์ ๊ทผ ํฅ์์ ์ํ ํต์ ์ธํ๋ผ ๊ฐ์ ์ฌ์
์ ์ถ์งํด์ผ ํ ๊ฒ์ด๋ค. ์ด์ ๊ฐ์ ์ ์ฑ
์ ํจ์๋ฅผ ์ ๊ณตํจ๊ณผ ํจ๊ป, ๋ณธ ์ฐ๊ตฌ๋ ๊ฐ๋ ๋ฐ ์ผ๋ ์ฌ์ฑ์ ๋ชจ์ฑ๊ฑด๊ฐ ์๋น์ค ์ด์ฉ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ ๋ถ์ํจ์ ์์ด ์ปค๋ฎค๋ํฐ์ ์ํฅ์ ์ค์ํ๊ฒ ๊ณ ๋ คํ์ฌ ์ด์ ์ํฅ์ ๋ฐํ๋๊ณ , ์ฌ์ฑ์ ์ฐ์ , ์ถ์ฐ ๋ฐ ์ฐํ๊ธฐ์ ๋ชจ์ฑ ์๋น์ค ์ด์ฉ์ MSB๋ผ๋ ๋จ์ผ๋ณ์ ๋ด ๋์์ ๊ณ ๋ คํ์ฌ ์ด์ ๊ฒฐ์ ์์ธ์ ํตํฉ์ ์ผ๋ก ์ ์ํ์๋ค๋ ์ ์์ ๊ทธ ์์๋ฅผ ์ฐพ์ ์ ์๋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. ์๋ก 1
1. 1. ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ ๋ฐ ํ์์ฑ 1
1. 2. ์ฐ๊ตฌ๋ชฉ์ 6
2. ์ ํ์ฐ๊ตฌ ๊ณ ์ฐฐ 7
2. 1. ๋ชจ์ฑ์ฌ๋ง๊ณผ ์ดํ 7
2. 2. ๋ชจ์ฑ๊ฑด๊ฐ ์๋น์ค ์ด์ฉ ์์ธ 10
2. 3. ๊ฐ๋์ ๋ณด๊ฑด ํํฉ 13
2. 4. ์ผ๋์ ๋ณด๊ฑด ํํฉ 19
3. ์ฐ๊ตฌ๋ฐฉ๋ฒ 26
3. 1. ์ฐ๊ตฌ์๋ฃ 26
3. 2. ์ฐ๊ตฌ๋์ 28
3. 3. ์ฐ๊ตฌ๋ชจํ 29
3. 4. ๋ณ์์ ์ ์ 32
(1) ์ข
์๋ณ์ 32
(2) ์ค๋ช
๋ณ์ 35
(3) ๋ถ์๋ฐฉ๋ฒ 43
(4) ์ค๋ฆฌ์ ๊ณ ๋ ค 45
4. ์ฐ๊ตฌ๊ฒฐ๊ณผ 46
4. 1. ์ ์ฒด MSB ์์ค ๋ถํฌ 46
(1) ๊ฐ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 46
(2) ์ผ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 46
4. 2. ๊ธฐ์ ๋ถ์ ๋ฐ ์นด์ด์ ๊ณฑ ๋ถ์ ๊ฒฐ๊ณผ 48
(1) ๊ฐ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 48
(2) ์ผ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 54
4. 3. ๋ค์์ค ๋ถ์ ๊ฒฐ๊ณผ 60
(1) ๊ฐ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 60
(2) ์ผ๋ DHS ๋ถ์ ๊ฒฐ๊ณผ 67
5. ํ ์ ๋ฐ ๊ฒฐ๋ก 74
5. 1. ์ฐ๊ตฌ๊ฒฐ๊ณผ ์์ฝ ๋ฐ ํจ์ 74
5. 2. ์ฐ๊ตฌ์ ํ๊ณ ๋ฐ ์ ์ธ 83
์ฐธ๊ณ ๋ฌธํ 85
๋ถ ๋ก 98
Abstract 112Maste
Posttransplant Lymphoproliferative Disorders in Kidney Transplant Patients: Report from a Single-center over 25 Years
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 (0โผ388 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 (18โผ71 year). Mean time interval to PTLD diagnosis were 126.6ยฑ74.8 months (6โผ240 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
์์ธ์ ์ํํธ๋ฅผ ๋์์ผ๋ก
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ํ๊ฒฝ๋ํ์ : ํ๊ฒฝ๋ํ์ ํ๊ฒฝ๊ณํํ๊ณผ(๋์๋ฐ์ง์ญ๊ณํ์ ๊ณต), 2015. 8. ์ต๋ง์ค.์ค๋๋ ์ธ๊ณ์ ๋์๋ ๋์ณ๋๋ ์๊ฐ์ฉ์ผ๋ก ์ธํด ๊ตํต ํผ์ก, ๋๊ธฐ์ค์ผ, ์์, ์ด์ํํ์ ๋ฐ์ ๋ฐ ์๋์ง์๋น ๋ฑ ์ฌํยท๊ฒฝ์ ๋ฐ ํ๊ฒฝ์ ๊ฑธ์ณ ๋ค์ํ ๋ฌธ์ ๋ฅผ ์๊ณ ์์ผ๋ฉฐ 1970๋
๋๋ถํฐ ์ง์์ ์ผ๋ก ์ด์ด์ ธ์จ ์๊ฐ์ฉ์ ์ฆ๊ฐ๋ ๋ ๋ง์ ์ฐจ๋๊ณผ ๋๋กํ์ฅ์ ๋ถ์ถ๊ฒจ ๋์๋ฅผ ํ์ ์ํค๊ณ ์๋ค. ์ด๋ฌํ ๊ตํต๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋์ ๋ด ์๋์ฐจ์ ์ด์ฉ์ ๊ฐ์์ํค๊ณ , ๋์ค๊ตํต์ ์ด์ฉ์ ์ฆ๊ฐ์ํค๋ ํจ์จ์ ์ธ ๋์๊ฐ๋ฐ ๋ฐ ๊ตํต์ฒด๊ณ๋ฅผ ํ์๋ก ํ๋ค. ์ด์ ๋ณธ ์ฐ๊ตฌ๋ ๋ณด๋ค ํจ์จ์ ์ด๋ฉฐ ์ง์๊ฐ๋ฅํ ๋์๊ฐ๋ฐ ๋ฐ ๊ตํต์ฒด๊ณ์ ๋ํ ๋์์ ์ ์ํ๊ธฐ ์ํด ์๋์ ์ผ๋ก ์งง์ ๊ธฐ๊ฐ ๋์ ์ง์ฝ์ ์ธ ํ ์ง ์ด์ฉ์ ๋ฐํ์ผ๋ก ์ธ๊ณ์ ์์ค์ ์งํ์ฒ ์ ๊ตฌ์ถํ ์์ธ์์ ๊ณ ๋ฐ๋๋์๊ฐ๋ฐ์ด ์์ธ์ ์งํ์ฒ ์ด์ฉ์ ์์ ๋ฏธ์น๋ ์ํฅ์ ๋ํ ์ค์ฆ ๋ถ์์ ํ์๋ค. ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ์ ์์ฝ์ ๋ค์๊ณผ ๊ฐ๋ค.
์ฒซ์งธ, ์์ธ์๋ ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋๋ฅผ ๊ฐ์ข
๊ฐ๋ฐ ๋ฐ ์ ๋น ์ฌ์
์ผ๋ก ํตํด ๊ด๋ฆฌํด์์ผ๋ฉฐ, 2010๋
์ ๊ธฐ์ค์ผ๋ก ์์ธ์์ ์ ์ฒด ์ฃผ๊ฑฐ์ ํ ์ค ์ํํธ๊ฐ ์ฐจ์งํ๋ ๋น์จ์ด ์ฝ 60%๋ก ๊ณ ๋ฐ๋์ฃผ๊ฑฐ์ ํ์ธ ์ํํธ๋ฅผ ๊พธ์คํ ๋ณด๊ธํด ์ค๊ณ ์๋ค.
๋์งธ, ์์ธ์ ๋์ค๊ตํต ๋ถ๋ด๋ฅ ์ 1990๋
๋๋ถํฐ 60%๋ฅผ ์ ์งํด์๊ณ , ๊ทธ ์ค ์งํ์ฒ ์ ๋ถ๋ด๋ฅ ์ 2010๋
๊ธฐ์ค์ผ๋ก 36%๋ฅผ ์ฐจ์งํ๋ฉด์ ์์ธ์์ ๋ํ์ ์ธ ๋์ค๊ตํต์ผ๋ก ์๋ฆฌ ์ก์๋ค.
์
์งธ, 2014๋
ํ์กดํ๋ ์ํํธ์ ํ๊ท ์ค๊ณต์ฐ๋์ ์์ธ์ ์งํ์ฒ 215๊ฐ ์ญ์ธ๊ถ์ ์ค๊ณต๋
๋๋ฅผ ๋ฐํ์ผ๋ก ์งํ์ฒ ์ญ์ธ๊ถ์ ๊ฐ๋ฐ ํจํด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ์ ์ฒด ์ญ์ธ๊ถ ์ค ์ฝ 80%์ TOD ๊ฐ๋ฐ ๋ฐฉ์์ผ๋ก ๋ํ๋ฌ์ผ๋ฉฐ, ์ด๋ ์์ธ์ ๋์๊ฐ๋ฐ์ด ์งํ์ฒ ์ค๊ณต์ด ์ด๋ฃจ์ด์ง ํ ์ํํธ๊ฐ ๊ฐ๋ฐ๋๋ TOD ๋ฐฉ์์ผ๋ก ์ด๋ฃจ์ง๊ณ ์์์ ์ ์ถํ ์ ์๋ค.
๋ท์งธ, ๋ค์คํ๊ท ๋ถ์ ๊ฒฐ๊ณผ, ๊ฐ๋ฐ๋ฐ๋์ ์งํ๋ก ์ฌ์ฉ๋ ์์ธ์์ ์ธ๋ ์ ๋ฐ๋๊ฐ ์ฆ๊ฐํ ์๋ก ์งํ์ฒ ์ ์ด์ฉ์ ์๊ฐ ์ฆ๊ฐํ์๊ณ , ์งํ์ฒ ํ์น๊ณผ ์ฐ๊ณ๋ ๋ฒ์ค์ ๊ฒฝ์ฐ ๋ฒ์ค์ ํน์ฑ์ ๋ฐ๋ผ ๋ณด์ ํน์ ๋์ฒด๊ด๊ณ์ธ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค.
์ด์์ ๋ถ์๊ฒฐ๊ณผ๋ค์ ํตํด ์์ธ์๋ ๋์ค๊ตํต์ ์์ฃผ๋ก ๊ฐ๋ฐ์ด ์ด๋ฃจ์ด์ง๋ TOD ๋ฐฉ์์ผ๋ก ๋์๊ฐ๋ฐ์ด ์ด๋ฃจ์ด์ง๊ณ ์์ผ๋ฉฐ, ์ด๋ฅผ ๋ฐํ์ผ๋ก ๊ณ ๋ฐ๋์ฃผ๊ฑฐ์ง๋ฅผ ์ญ์ธ๊ถ์ ๋ฐฐ์นํ์ฌ ๋์ค๊ตํต ์์๋ฅผ ์ถฉ์กฑ ์์ผฐ์ ๊ฒ์ผ๋ก ์์๋๋ค. ์ด๋ฅผ ํตํด ํจ์จ์ ์ธ ๋์ค๊ตํต์ ์ด์ฉ์ด ์ด๋ฃจ์ด์ง๊ธฐ ์ํด์๋ ์ญ์ธ๊ถ์ ์์ฃผ๋ก ๊ณ ๋ฐ ์ฃผ๊ฑฐ์ง๋ฅผ ๋ฐฐ์นํ๋ ๊ฒ์ด ํจ์จ์ ์์ ํ์ธํ๊ณ , ์ด๋ ๋ ๋์๊ฐ ์ํ์ ํํ๋ก ๊ฐ๋ฐ๋์ด ๋์ค๊ตํต์ ์
์ง ์ ์ ์ด ์ด๋ ค์ด ๊ฐ๋ฐ๋์๊ตญ์ ๋ณด๋ค ํจ์จ์ ์ธ ๋์๊ฐ๋ฐ ๋ฐ ๊ตํต์ฒด๊ณ์ ๋ณธ๋ณด๊ธฐ๋ก ์ ์ํ ์ ์์ ๊ฒ์ด๋ค.โ
. ์๋ก 1
1. ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 1
2. ์ฐ๊ตฌ์ ๋ฒ์ ๋ฐ ๋ฐฉ๋ฒ 2
1) ์ฐ๊ตฌ์ ๋ฒ์ 2
2) ์ฐ๊ตฌ์ ๋ฐฉ๋ฒ ๋ฐ ํ๋ฆ๋ 3
โ
ก. ์ด๋ก ๋ฐ ์ ํ ์ฐ๊ตฌ ๊ณ ์ฐฐ 5
1. ์
์ง์ด๋ก 5
2. ๋์๊ฐ๋ฐ๋ฐ๋์ ๋์ค๊ตํต 6
1) ๋์ค๊ตํต์งํฅ๊ฐ๋ฐ(TOD)๊ณผ ๊ฐ๋ฐ์งํฅ ๋์ค๊ตํต(DOT) 6
2) ๋๋๊ณ ์์์ก ์์คํ
์ ๋ณด๊ธ 7
3. ์ ํ์ฐ๊ตฌ 9
โ
ข. ์์ธ์ ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋ ๋ฐ ๋์ค๊ตํต 12
1. ์์ธ์ ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋ ๋ฐ ๋์ค๊ตํต ์ด์ฉ ๋ณํ 12
1) ๋์๊ฐ๋ฐ์ ๋ฐ๋ฅธ ๋์ค๊ตํต์ ๋ณํ ๋ฐ ์งํ์ฒ ๊ฑด์ค 12
2) ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋์ ๋ณํ 13
2. ์์ธ์ ์ํํธ ๋ฐ ์งํ์ฒ ๋ณด๊ธ ๋ฐ ๋์๊ฐ๋ฐ ํจํด 14
1) ์์ธ์ ์ํํธ ๋ฐ ์งํ์ฒ ๋ณด๊ธํํฉ 14
2) ์์ธ์ ์งํ์ฒ ์ญ์ธ๊ถ ๋์๊ฐ๋ฐ ํจํด ๋ถ์ 20
3. ์๊ฒฐ 22
โ
ฃ. ์ค์ฆ๋ถ์ 23
1. ์ฃผ์ ๋ณ์ ๋ฐ ์๋ฃ์ ๊ตฌ์ถ 23
1) ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋ ๋ฐ ์งํ์ฒ ์ด์ฉ์ ์ฐ์ 23
2) ์ญ์ธ๊ถ ๋ฐ๊ฒฝ ๋ด/์ธ ์ฃผ๊ฑฐ๊ฐ๋ฐ๋ฐ๋ ์ฐจ ๋น๊ต๋ถ์ 28
3) ์ฃผ๊ฑฐ์ค์ฌ ์ญ์ธ๊ถ ๋ถ๋ฅ 32
4) ๋ณ์์ ๊ตฌ์ฑ 34
2. ์ค์ฆ๋ถ์ 37
1) ๊ธฐ์ ํต๊ณ ๋ถ์ 37
2) ๋ค์คํ๊ท ๋ถ์ 39
โ
ค. ๊ฒฐ๋ก 44
1. ์ฐ๊ตฌ์ ์์ฝ 44
2. ์ฐ๊ตฌ์ ํ๊ณ ๋ฐ ์์ฌ์ 45
โ ์ฐธ๊ณ ๋ฌธํ 46Maste
ํ์ง์ ํ๋ณต์ ๋ผ์ง ์์ฐ์ ์ํ piggyBac transposon system์ ์ ์ฉ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์์ํ๊ณผ, 2017. 2. ์ด๋ณ์ฒ.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
๊ตญ๋ฌธ์ด๋ก 122Docto
๋์์กฐ์ ์ ์ ์ ์ํธ์์ฉ ๋ฐ๊ตด์ ์ํ ํ์ดํผ๊ทธ๋ํ ๋ชจ๋ธ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ํ๋๊ณผ์ ์๋ฌผ์ ๋ณดํ์ ๊ณต, 2014. 2. ์ฅ๋ณํ.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
์ด๋ก . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Docto
Development of radical-stable peroxidases based on peroxidase inactivation mechanism
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ํ๋๊ณผ์ ๋ฐ์ด์ค์์ง๋์ด๋ง์ ๊ณต, 2015. 2. ํ์ง์.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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