목적 : 온도감응성 나노구조체를 제조하고 이를 적용하여, 보관시에는 항생제가 용출되지 않고, 안구착용시에 만 온도감응성으로 항생제를 용출하는 스마트 콘택트렌즈를 제조하고자 한다. 방법 : 에멀젼중합하여 p(NIPAAm)-기반의 나노구조체를 합성하였고, 이를 샌드위치 공법을 통해 콘택트렌즈 에 도입하였다. Soaking 방법을 통해 항생제인 levofloxacin(LVF)를 콘택트렌즈에 탑재하여 온도에 따라 항생제 용출 특성을 분석하였다. 결과 : sodium n-dodecyl sulfate (SDS) 마이셀 템플레이트를 활용한 에멀젼중합을 통해 20-40 nm 크기의 온도감응형 p(NIPAAm)-기반의 나노구조체를 합성하였고 이는 TEM과 입도분석기를 통해 확인하였다. 샌드위치 공법을 통해 콘택트렌즈에 나노구조체를 도입하였고, soaking 방법을 통해 항생제를 렌즈안의 나노구조체에 탑재 하였다. 25 oC와 35 oC에서 각각 항생제의 방출 특성을 분석하였다. 상온에서는 항생제를 3 ug 이내로 방출하였 지만, 35 oC에서는 2시간이내에 대부분의 항생제를 방출하였고 10 ug까지 방출하였다. 결론 : 본 연구에서는 온도감응형 나노구조체를 합성하고, 이를 콘택트렌즈에 적용 및 항생제를 탑재하여, 온 도감응형 스마트 항생제용출 콘택트렌즈를 제조하였다. 온도감응형 나노구조체는 콘택트렌즈안에서 항생제를 성 공적으로 탑재할 수 있었고, 상온에서 상당량의 항생체를 보관하고, 온도증가시 10 ug까지의 많은 양의 항생제를 방출하였다. 본 연구결과는 약물전달용 스마트 안과의료기기 및 콘택트렌즈의 개발 및 상용화에 큰 역할을 할 것 으로 기대된다.

목적 : 반응성 청색광차단 염료를 하이드로겔 콘택트렌즈에 화학적으로 고정시켰고, 제조된 콘택트렌즈의 청색 광차단 기능의 분석 및 첨가된 염료를 정량하고자 한다. 방법 : Vinyl sulfone-기반의 반응성 염료인 Reactive Orange 16 Dye를 다량의 알콜 작용기를 함유하는 하 이드로겔 콘택트렌즈에 화학적으로 결합시켰다. 콘택트렌즈의 청색광차단 특성 및 염료의 정량은 UV-vis spectrophotometer를 이용하여 확인하였다. 결과 : 청색광차단기능의 Reactive Orange 16 Dye가 성공적으로 하이드로겔 콘택트렌즈에 결합되었다. UV-vis spectra 분석을 통해 염료를 함유한 콘택트렌즈들이 우수한 청색광차단 기능을 보임을 확인하였다. Beer-Lambert의 법칙을 이용하여, 콘택트렌즈에 첨가된 Reactive Orange 16 Dye를 정량하였으며, 반응 염료 의 농도 조절을 통해, 콘택트렌즈의 청색광차단율을 조절할 수 있었다. 결론 : 본 연구에서는 청색광차단 기능의 반응성 염료를 하이드로겔 콘택트렌즈에 화학적 결합을 통해 고정시 키고, 청색광차단 기능을 분석하였다. 화학적 반응에서 청색광차단 염료의 농도가 증가할수록, 380~500 nm 사 이의 청색광 파장 영역에서의 차단 세기와 첨가된 염료의 양이 같이 증가함을 확인하였다. 대량생산이 가능한 청 색광차단 콘택트렌즈의 개발은 기능성 안광학의료기기 개발에 큰 역할을 할 것으로 기대된다.

Cement is widely used as representative industrial material. In Korea, about 50 million tons of cement are consumed every year. In the manufacture of cement, raw materials containing NORM such as fly ash and bauxite are used. Therefore, the workers can be subjected to radiation exposure. The major exposure pathway in NORM industries is internal exposure due to inhalation of aerosol. Internal radiation dose due to aerosol inhalation varies depending on physicochemical properties of the aerosol. Therefore, the objective of this study was to investigate aerosol properties influencing inhalation dose in cement industries. In this study, aerosol properties were measured for two cement manufacturers. A particulate size distribution and concentration at various processing areas in cement manufacturing industries in Korea were analyzed using a cascade impactor. The mass density of raw materials and byproducts were measured using pycnometer. Shape of particulates was analyzed using SEM. The radioactivity concentration of Ra-226, Ra-228 for U/Th decay series was measured using HPGe. Particulate concentration by size was distributed log-normally with maximum at particle size about 7.2 μm in manufacturer A and 5.2 μm in manufacturer B. The mass density of fly ash and cement were 2.3±0.06, 3.2±0.02 g/cm3 respectively in manufacturer A. In manufacturer B, the mass density of bauxite and cement were 3.4±0.02, 2.9±0.01 g/cm3 respectively. The shape of particulates appeared as spherical shape in manufacturer A and B regardless of sampling area. Thus, a shape factor of unity could be assumed. The radioactivity concentrations of Ra-226, Ra-228 were 82±9, 82±8 Bq/kg for fly ash, and 25±4, 23±3 Bq/kg for cement in manufacturer A. In manufacturer B, the radioactivity concentrations of Ra-226, Ra-228 were 344±34, 391±32 Bq/kg for bauxite, and 122±13, 145±12 Bq/kg for cement. The radioactivity concentrations of Ra-226, Ra-228 in cement were less than raw materials such as fly ash and bauxite. It is because the dilution of the radioactivity concentration occurred during mixing with other raw materials in cement production process. This study results will be used as database for accurate dose assessment due to airborne particulate inhalation by workers in cement industries.

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

The optimal determination of seeding rate is critical to minimizing uncertainties about the large variations observed in forage quality and productivity when alfalfa is cultivated under different geographical areas and growing conditions. The objective of this investigation was to provide information about the proper seeding rate according to harvest timing for alfalfa cultivation in the Northern regions of Korea. Alfalfa was sown in September 2018 at a seeding rate of 20, 30 or 40 kg/ha and harvested four times in 2019: May 3, July 2, September 11, and October 13. Regardless of seeding rate, alfalfa plant height was longest at the third harvest (113 cm) and the shortest in the last annual harvest (43.8 cm). However, seeding rate had no effect on alfalfa plant height at any harvest. Forage relative feed value was increased in the first cutting but decreased in the third cuttings as seeding rate increased. However, seeding rate had slight effect on alfalfa forage quality components at the second and fourth cuttings. Total annual DM and crude protein production (in 4 harvests) was greater at higher seeding rates. Plots seeded at a rate of 40 kg/ha produced on average 1,257 and 2,620 kg/ha more forage (DM basis) than those seeded at a rate of 30 or 20 kg/ha, respectively. Forage DM production at the first, second, third, and fourth harvests accounted for 36.1, 24.0, 27.1, and 12.8 % of total annual DM production, respectively. Overall, small differences were seen when alfalfa seeding rate was different but maximum forage DM production (in four harvests) was detected when seeding rate was 40 kg/ha. These data could be useful to the alfalfa growers by allowing them to make more accurate trade-offs between seed price and the expected magnitude of forage yield gains in order to select the best seeding rate.

Cutting management has been identified as a critical factor in the alfalfa production systems because it has a significant impact on maximizing yield and maintaining the forage quality. The objective of this experiment was to determine the proper cutting height according to harvesting time for optimizing nutrient yield and forage nutritive quality of alfalfa grown in alpine regions of Korea. Alfalfa was sown at a seeding rate of 30 kg/ha in August 2018 and harvested at four cuttings in 2019 (3 May, 2 July, 11 September, and 13 October). Cutting heights were adjusted at 5, 15, and 25 cm above the soil surface. Alfalfa plant was tallest at the third cutting (109 cm), which was on average 35 cm taller than the first or second cutting. Relative feed value (RFV) remained unaffected by cutting height at the first harvest, but increased consistently in subsequent harvests as cutting height increased. Alfalfa collected at the first and fourth cuttings had the highest RFV (mean 152), which was on average 8 and 67 units higher than the second and third harvests, respectively. At each harvest, in vitro dry matter digestibility was highest in alfalfa cut at a 25-cm height. Dry matter (DM) production at each cutting height was highest in the first cutting, accounting for on average 36-37% of total annual DM production, and lowest in the fourth harvest, accounting for about 11-13% of the total DM yield. The total dry matter production (in four harvests) was 4,218 kg/ha higher when alfalfa was subjected to a cutting height of 5 cm rather than 25 cm. Cutting height had no effect on total crude protein yield, but from the first to fourth cutting, the protein yield followed a decreasing trend. Finally, there were visible declines in forage nutritive quality when alfalfa was cut at a shorter height. However, the magnitude of difference in total forage yield may outweigh the slight decline in forage quality when alfalfa is cut at a lower height. The findings of this study could help the alfalfa growers make better harvest management decisions.