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Lee, Eunjung,Jung, Dae Young,Kim, Jong Hun,Patel, Payal R.,Hu, Xiaodi,Lee, Yongjin,Azuma, Yoshihiro,Wang, Hsun-Fan,Tsitsilianos, Nicholas,Shafiq, Umber,Kwon, Jung Yeon,Lee, Hyong Joo,Lee, Ki Won,Kim, The Federation of American Societies for Experimen 2015 The FASEB Journal Vol.29 No.8
<P>Insulin resistance is a major characteristic of obesity and type 2 diabetes, but the underlying mechanism is unclear. Recent studies have shown a metabolic role of capsaicin that may be mediated <I>via</I> the transient receptor potential vanilloid type-1 (TRPV1) channel. In this study, TRPV1 knockout (KO) and wild-type (WT) mice (as controls) were fed a high-fat diet (HFD), and metabolic studies were performed to measure insulin and leptin action. The TRPV1 KO mice became more obese than the WT mice after HFD, partly attributed to altered energy balance and leptin resistance in the KO mice. The hyperinsulinemic-euglycemic clamp experiment showed that the TRPV1 KO mice were more insulin resistant after HFD because of the ∼40% reduction in glucose metabolism in the white and brown adipose tissue, compared with that in the WT mice. Leptin treatment failed to suppress food intake, and leptin-mediated hypothalamic signal transducer and activator of transcription (STAT)-3 activity was blunted in the TRPV1 KO mice. We also found that the TRPV1 KO mice were more obese and insulin resistant than the WT mice at 9 mo of age. Taken together, these results indicate that lacking TRPV1 exacerbates the obesity and insulin resistance associated with an HFD and aging, and our findings further suggest that TRPV1 has a major role in regulating glucose metabolism and hypothalamic leptin’s effects in obesity.—Lee, E., Jung, D. Y., Kim, J. H., Patel, P. R., Hu, X., Lee, Y., Azuma, Y., Wang, H.-F., Tsitsilianos, N., Shafiq, U., Kwon, J. Y., Lee, H. J., Lee, K. W., Kim, J. K. Transient receptor potential vanilloid type-1 channel regulates diet-induced obesity, insulin resistance, and leptin resistance.</P>
The incidence of tuberculosis after a measles outbreak.
Lee, Chang-Hoon,Lee, Eun Gyu,Lee, Ju-Young,Park, KeeHo,Lee, Beom Hee,Han, Hwasoon,Oh, Eunjung,Kim, Hee-Jin,Kang, Mi-Kyoung,Oh, Soo Yon,Bai, Jeong Ym,Bai, Gill-Han,Lee, Duk-Hyoung,Oh, Dae-Kyu,Lee, Jong The University of Chicago Press 2008 Clinical infectious diseases Vol.46 No.6
<P>Among 53,974 cases of measles that occurred during the 2000-2001 outbreak in Korea, the incidence of tuberculosis following measles was 47 cases per 214,949.6 person-years, which was significantly lower than that in the general population (standardized incidence ratio, 0.73; 95% confidence interval, 0.54-0.96). In conclusion, we did not find a positive relationship between measles and tuberculosis.</P>
Predicting disease phenotypes based on the molecular networks with condition-responsive correlation.
Lee, Sejoon,Lee, Eunjung,Lee, Kwang H,Lee, Doheon Inderscience 2011 International journal of data mining and bioinform Vol.5 No.2
<P>Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.</P>
EUNJUNG LEE,Soyoung Shin,JEE-YOUNG LEE,Sojung Lee,Jin-Kyoung Kim,윤도영,Eun-Rhan WOO,김양미 대한화학회 2012 Bulletin of the Korean Chemical Society Vol.33 No.7
Human peroxisome proliferator-activated receptor gamma (hPPARγ) has been implicated in numerous pathologies, including obesity, diabetes, and cancer. Previously, we verified that amentoflavone is an activator of hPPARγ and probed the molecular basis of its action. In this study, we investigated the mechanism of action of amentoflavone in cancer cells and demonstrated that amentoflavone showed strong cytotoxicity against MCF-7 and HeLa cancer cell lines. We showed that hPPARγ expression in MCF-7 and HeLa cells is specifically stimulated by amentoflavone, and suggested that amentoflavone-induced cytotoxic activities are mediated by activation of hPPARγ in these two cancer cell lines. Moreover, amentoflavone increased PTEN levels in these two cancer cell lines, indicating that the cytotoxic activities of amentoflavone are mediated by increasing of PTEN expression levels due to hPPARγ activation.
Assuring explainability on demand response targeting via credit scoring
Lee, Kyungeun,Lee, Hyesu,Lee, Hyoseop,Yoon, Yoonjin,Lee, Eunjung,Rhee, Wonjong Elsevier 2018 ENERGY Vol.161 No.-
<P><B>Abstract</B></P> <P>As data-driven innovation becomes a main trend in the energy sector, explainability of data-driven actions is becoming a major fairness issue for the residential applications, and it is expected to become a requirement for regulatory compliance. Explainability, however, often demands a sacrifice in prediction performance and affects the effectiveness of data-driven actions. In this study, we consider data-driven customer targeting in an incentive-based residential demand response program, and investigate the explainability-performance tradeoff when using simple-rule based, machine learning, and credit scoring methods. Credit scoring, that has been a popular solution in the finance discipline for over 60 years, is a scorecard based modeling method that can surely provide explainability. We first provide the detailed steps of applying credit scoring to the demand response problem. Then, we use a dataset of 14,525 households obtained from a real demand response program and analyze two prediction problems – participation prediction and behavior change prediction. The results show that credit scoring can achieve a comparable performance as the best-performing machine learning methods while providing full explainability. Our results suggest that credit scoring can be a promising explainability option for broader energy sector problems.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A quantitative analysis of data-driven targeting in residential DR. </LI> <LI> Explainability of data-driven actions and its relation to fairness. </LI> <LI> Details of implementing credit scoring, which has good explainability, for DR. </LI> <LI> A case study of incentive DR, where the DR was operated through a smartphone app. </LI> <LI> Credit scoring can achieve a comparable performance as machine learning methods. </LI> </UL> </P>