목적 : 청년층 건성안의 원인을 평가하고자, 정상안과 건성안의 눈물막을 비교분석하였다. 방법 : 청년 24명(48안, 남 11명, 여 13명, 평균연령 22.04±2.01세)을 대상으로 SPEED 건성안 설문지를 이 용하여 건성안을 판별하고, 눈물검사값을 정상안의 검사값과 비교하였다. 눈물의 양은 OCCUTUBE로, 눈물막 안 정성은 눈물막파괴시간검사(TBUT)로 평가하였다. 눈물지방층두께(LLT)와 눈깜박임 양상 및 마이봄샘 이상은 LIPIVIEW IIⓇ로 측정하고 Image JⓇ 프로그램을 이용하여 분석하였다. 결과: 건성안의 SPEED 설문값은 10.63±3.78점으로 높게 측정되었고, TBUT는 3.39±0.89초, OCCUTUBE 측정값은 2.67±0.92 mm로 정상안 (4.04±0.83초, 3.44±1.42 mm)보다 유의하게 짧았다(p=0.019, p=0.039). 불완전한 눈깜빡임 비율은 건성안의 경우 66.88±33.43%로 정상안의 23.63±25.66%보다 높았으나 LLT와 마이 봄샘 손상은 유의한 차이가 없었다. 결론 : SPEED 설문지로 건성안을 판별한 결과 청년층에서 건성안의 비율은 매우 높았으며, 건성안 증상은 눈 물의 양, TBUT 및 불완전한 눈깜빡임과 연관이 있었다.

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

To date, the development of anticancer drugs has been conducted using two-dimensional (2D) cell culture systems. However, since cancer cells in the body are generated and developed in three-dimensional (3D) microenvironments, the use of 2D anticancer drug screening can make it difficult to accurately evaluate the anticancer effects of drug candidates. Therefore, as a step towards developing a cancer cellfriendly 3D microenvironment based on a combination of vinylsulfone-functionalized polyethylene glycol (PEG-VS) with dicysteine-containing crosslinker peptides with an intervening matrix metalloproteinase (MMP)-specific cleavage site, the types of MMPs secreted from human hepatocarcinoma HepG2 cells, a representative cancer cell, were analyzed transcriptionally and translationally. MMP3 was confirmed to be the most highly expressed protease secreted by HepG2 cells. This knowledge will be important in the design of a crosslinker necessary for the construction of PEG-based hydrogels customized for the 3D culture of HepG2 cells.