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The Anti-Inflammatory Effect of Pegmatite by in Vivo and in Vitro Study
이민혁,김석권,권용석,이장호,이근철,Lee, Min-Hyuk,Kim, Seok-Kwun,Kwon, Yong-Seok,Lee, Jang-Ho,Lee, Keun-Cheol Korean Society of Plastic and Reconstructive Surge 2010 Archives of Plastic Surgery Vol.37 No.1
Purpose: This work aimed to elucidate the anti-inflammatory effect of pegmatite in vitro and in vivo. Methods: Author evaluated the suppressive effects of pegmatite on lipopolysaccharide (LPS)-stimulated nitric oxide (NO) production, TNF-${\alpha}$ and IL-6 release in the RAW 264.7 murinemacrophages. Results: Treatment of RAW 264.7 cells with pegmatite significantly reduced LPS-stimulated NO production and inflammatory cytokine such as TNF-${\alpha}$ and IL-6 secretion in a concentration-dependent manner. Also pegmatite showed topical anti-inflammatory activity in the arachidonic acid (AA)-induced ear edema and acetic acid-induced increase in capillary permeability assessment in mice. It was also found that pegmatite (10 mg per ear in DW) inhibited arachidonic acid induced edema at 24 h more profoundly than 1 h by topical application. Furthermore, the vascular permeability increase induced by acetic acid was significantly reduced in mice that received pegmatite in 50 mg per mouse. Conclusion: Therefore the results of the present study suggest that pegmatite is a potent inhibitor of the LPS-induced NO and inflammatory cytokine in RAW 264.7 macrophages and showed anti-inflammatory activities in vivo animal model.
은닉 마코프 랜덤 모델 기반의 전달 맵을 이용한 안개 제거
이민혁(Min-Hyuk Lee),권오설(Oh-Seol Kwon) 대한전자공학회 2014 전자공학회논문지 Vol.51 No.1
본 논문에서는 한 장의 영상에서 안개를 제거하는 알고리즘을 제안한다. 기존의 Dark Channel Prior(DCP) 알고리즘은 영상의 어두운 정보를 계산하여 전달량을 추정한 후, 매팅(matting) 기법을 사용하여 안개 영역을 보완하여 검출한다. 이 과정에서 블록현상이 발생하는 문제가 있으며 이로 인해 안개를 효율적으로 제거하는데 한계점이 있다. 이 문제를 해결하기 위해 본 논문에서는 Hidden Markov Random Field(HMRF) 와 Expectation-Maximization(EM) 알고리즘을 이용하여 매팅 과정에서 발생하는 블록문제를 해결하고자 하였다. 실험 결과를 통하여 제안한 방법은 기존 방법보다 안개제거에서 더 향상된 결과를 얻을 수 있음을 확인하였다. This paper proposes an image haze removal algorithm for a single image. The conventional Dark Channel Prior(DCP) algorithm estimates a transmission map using the dark information in an image, and the haze regions are then detected using a matting algorithm. However, since the DCP algorithm uses block-based processing, block artifacts are invariably formed in the transmission map. To solve this problem, the proposed algorithm generates a modified transmission map using a Hidden Markov Random Field(HMRF) and Expectation-Maximization(EM) algorithm. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal.