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A2 milk consumption and its health benefits: an update
정휘진,박영서,윤성식 한국식품과학회 2024 Food Science and Biotechnology Vol.33 No.3
Milk is a widely consumed nutrient-rich food containing protein variants such as casein A2 and A1. A1 differs from A2 in an amino acid at position 67 (Pro67 to His67). The breakdown of β-casein yields β-casomorphins (BCM), among which BCM-7 is extensively studied for its effects on the human body. Animal studies have shown that A1 β-casein milk increases digestive transit time and enhances myeloperoxidase activity. Individuals with lactose intolerance prefer A2 milk to conventional A1 milk, as BCM-7 in A1 milk can lead to inflammation and discomfort in sensitive individuals. A2 milk, which contains A2 β-casein, is believed to be more easily digestible than A1 β-casein. Its popularity has grown owing to reports linking A1 casein to diseases such as type 1 diabetes, heart disease, and autism. A2 milk has gained popularity as an alternative to A1 milk, primarily because of its potential benefits for individuals with certain diseases. This review aims to provide an updated understanding of A2 milk consumption and its health benefits. This review aims to provide an updated understanding of A2 milk consumption and its health benefits.
삼차원 심층 콘볼루션 신경망을 이용한 컴퓨터 단층 촬영 영상 내 폐 결절 분류
정휘진(Hwejin Jung),김범수(Bumsoo Kim),이인엽(Inyeop Lee),이준현(Junhyun Lee),강재우(Jaewoo Kang) 한국정보과학회 2018 정보과학회 컴퓨팅의 실제 논문지 Vol.24 No.12
전 세계 암 발병의 큰 비중을 차지하는 폐암을 조기에 예방하기 위해서는 폐 결절을 찾아내 악성 여부를 검사해야 한다. 본 연구에서는 삼차원 시층 콘볼루션 신경망을 이용해 결절의 악성 여부를 판단하는 모델을 제안한다. 숏컷 연결을 이용한 모델을 사용했고, 분류 성능 향상을 위해 앙상블 기법을 이용한다. 본 모델을 LUng Nodule Analysis 2016 대회 데이터에 적용하여 모델의 성능을 측정하고 정확도를 검증한다. 본 모델은 대회의 평가 지표인 Competition Performance Metric 기준 0.899를 기록하였고, 이는 기존 참가자들의 성능과 비교하였을 때 우수한 결과이다. Early detection and examination of pulmonary nodules is the most effective ways to prevent lung cancer, accounting for more than a quarter of all cancer deaths. In this paper, we propose a 3D deep convolutional neural network for pulmonary nodule recognition. We use deep convolutional neural network that uses shortcut connections and the ensemble method is used to boost recognition performance. Proposed models are trained and tested on Lung Nodule Analysis 2016 competition dataset. We evaluate performance of models and verify preciseness. Proposed model produces 0.899 of Competition Performance Metric value, that is evaluation criteria of competition. It is outperforming value than that of other participants.
전라북도 지역 대학생의 식습관과 한식에 대한 인식 및 기호도 연구
민경진,정휘진,이예지,김문숙,최일숙 한국식품조리과학회 2017 한국식품조리과학회지 Vol.33 No.5
Purpose: The aim of this study was to investigate the relationship between the university students’ eating habits, perception, and acceptance of Korean food in Jeollabuk-do province. Methods: The subjects were 313 students (123 male, 190 female) who answered questionnaires during May 2016. Results: This study showed that male students showed significantly higher scores for exercise frequency, whereas female students showed higher scores for ‘snack frequency’ and ‘selective eating’ (p<0.05). Even though many students viewed Korean food as healthy, female students showed significantly higher scores for taste likeness, smell likeness, and color likeness (p<0.05). For acceptance of Korean food menu items, male students showed higher scores for roasted or steamed Korean foods such as Galbi jjim and Bulgogi, whereas female students showed significantly higher scores in soup, dessert, and street food (p<0.05). In the principal component analysis, ‘food interest’ was positively correlated with ‘roasted or steamed’, ‘dessert’, and ‘street food’, whereas ‘health interest’ was correlated with low calorie menu items, such as ‘Kimchi and salad’ and ‘soup’. For improving Korean food, female students showed higher scores for education of eating attitudes, food safety, and development of fusion food as well as significantly high scores for globalization (p<0.05). Conclusion: Student showing health interest and regularity of breakfast showed high scores for Korean food acceptance, menu and improving Korean food. Meanwhile the student with unbalanced eating habits showed different patterns. The result show the need to extend education on eating habits and health related to perception of wellbeing value of Korean food.
김영곤,송인혜,이현나,김성철,양동현,김남국,신동호,유연수,이교운,김다혜,정휘진,조현빈,이현규,김태우,최종현,서창원,한성일,이영제,이영서,유형련,이용주,박정환,오소희,공경엽 대한암학회 2020 Cancer Research and Treatment Vol.52 No.4
Purpose Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n=144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.