기후 변화의 가속화로 국내 제주 및 남부 지방을 중심으로 열대 및 아열대 작물의 재배 및 소비가 증가하는 추세이다.오크라는 식용적 가치가 있을 뿐만 아니라, 품종별 고유의 꼬 투리와 잎의 색은 관상적 가치가 있어 조경 식물로 많이 사용 되고 있다. 본 연구는 도심 내 옥상 온실에 관상식물인 오크 라를 도입하기 위해 피트모스와 펄라이트 비율에 따른 기초 종자 발아 및 육묘기 실험을 진행하였다. 피트모스와 펄라이 트 비율은 PT:PL=1:2, 1:1, 2:1, 4:1 네 가지로 조성하였다. 발 아율은 파종 후 7일차부터 다른 처리구에 비해 피트모스:펄라 이트=1:2의 처리구에서 가장 높았다. 파종 후 28일차와 70일 차에 줄기길이, 경경, 뿌리길이, 지상부와 지하부의 생체중과 건물중 그리고 엽면적은 피트모스:펄라이트=1:2 처리구에서 가장 높았다. 그러나, 파종 후 56일 이후부터 엽수는 피트모 스:펄라이트=1:2, 2:1, 4:1 처리구에서 감소하였다. 피트모스: 펄라이트=1:1, 2:1, 4:1 처리구에서 식물이 필요로 하는 피트 모스의 비율이 지나치게 높아 높은 피트모스 비율로 인하여 수분함수량이 과도하게 높아 식물이 고사한 것으로 판단된다. 따라서 오크라를 관상식물로 활용하기 위해서는 피트모스와 펄라이트를 1:2로 혼합한 배지가 가장 적합하다.

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.

Royal jelly (RJ) is a gelatinous substance that bees produce to feed bees and queen bees. It’s frequently sold as a dietary supplement to treat a variety of physical ailments and chronic diseases. While it has long been used in traditional medicine, its applications in Western medicine remain controversial. The inhibitory effect of royal jelly on osteoarthritis was investigated in primary cultured rat cartilage cells and monosodium-iodoacetate (MIA)-induced arthritis rat model 10-hydroxy-2-decenoic acid (10-HAD) is the main fatty acid present in RJ. Among the criteria for RJ quality analysis, 10-HAD content has been proposed as a freshness parameter. We investigated the effect of RJ on the improvement of osteoarthritis on SD rats and they were divided into five groups. In this study, we examined the effect of enzymatic royal jelly (ERJ) administration on osteoarthritis. To determine the antiinflammatory effects of RJ, tumor necrosis factor alpha (TNF-α) and Interleukin-6 (IL-6) expression were measured after lipopolysaccharide (LPS) activation in RAW 264.7 cells. In in vivo animal study, osteoarthritis was induced by intra-articular injection of MIA into knee joints of rats. As a results, ERJ showed that TNF-α and IL-6 levels were decreased by ERJ treatment in a dosedependent manner. In conclusion, ERJ extract was able to inhibit articular cartilage degeneration by preventing extracellular matrix degradation and cartilage cell damage. It was considered that ERJ extract may be a potential therapeutic treatment for degenerative osteoarthritis.