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Lunasin is prevalent in barley and is bioavailable and bioactive in in vivo and in vitro studies.
Jeong, Hyung Jin,Jeong, Jin Boo,Hsieh, Chia Chien,Hernandez-Ledesma, Blanca,de Lumen, Ben O Lawrence Erlbaum Associates, Publishers [etc.] 2010 Nutrition and cancer Vol.62 No.8
<P>Lunasin, a unique 43-amino acid peptide found in a number of seeds, has been shown to be chemopreventive in mammalian cells and in a skin cancer mouse model. To elucidate the role of cereals in cancer prevention, we report here the prevalence, bioavailability, and bioactivity of lunasin from barley. Lunasin is present in all cultivars of barley analyzed. The liver and kidney of rats fed with lunasin-enriched barley (LEB) show the presence of lunasin in Western blot. Lunasin extracted from the kidney and liver inhibits the activities of HATs (histone acetyl transferases), yGCN5 by 20% and 18% at 100 nM, and PCAF activity by 25% and 24% at 100 nM, confirming that the peptide is intact and bioactive. Purified barley lunasin localizes in the nuclei of NIH 3T3 cells. Barley lunasin added to NIH 3T3 cells in the presence of the chemical carcinogen MCA activates the expression of tumor suppressors p21 and p15 by 45% and 47%, decreases cyclin D1 by 98%, and inhibits Rb hyperphosphorylation by 45% compared with the MCA treatment alone. We conclude that lunasin is prevalent in barley, bioavailable, and bioactive and that consumption of barley could play an important role of cancer prevention in barley-consuming populations.</P>
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Purkayastha Subhanik,Xiao Yanhe,Jiao Zhicheng,Thepumnoeysuk Rujapa,Halsey Kasey,Wu Jing,Tran Thi My Linh,Hsieh Ben,Choi Ji Whae,Wang Dongcui,Vallières Martin,Wang Robin,Collins Scott,Feng Xue,Feldman 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.7
Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
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