http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Chae, Jung-Woo,Chua, Peh Siang,Ng, Terence,Yeo, Angie Hui Ling,Shwe, Maung,Gan, Yan Xiang,Dorajoo, Sreemanee,Foo, Koon Mian,Loh, Kiley Wei-Jen,Koo, Si-Lin,Chay, Wen Yee,Tan, Tira Jing Ying,Beh, Sok Yu Springer-Verlag 2018 Breast cancer research and treatment Vol.168 No.3
<P>This is the first study to show that the reduction of mtDNA content in peripheral blood is associated with the onset of CRF in patients receiving chemotherapy. Further validation studies are required to confirm the findings.</P>
Wen, Wanqing,Zheng, Wei,Okada, Yukinori,Takeuchi, Fumihiko,Tabara, Yasuharu,Hwang, Joo-Yeon,Dorajoo, Rajkumar,Li, Huaixing,Tsai, Fuu-Jen,Yang, Xiaobo,He, Jiang,Wu, Ying,He, Meian,Zhang, Yi,Liang, Jun IRL Press 2014 Human molecular genetics Vol.23 No.20
<P>Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by <I>in silico</I> and <I>de novo</I> replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the <I>KCNQ1</I> (rs2237892, <I>P</I> = 9.29 × 10<SUP>−13</SUP>), <I>ALDH2/MYL2</I> (rs671, <I>P</I> = 3.40 × 10<SUP>−11</SUP>; rs12229654, <I>P</I> = 4.56 × 10<SUP>−9</SUP>), <I>ITIH4</I> (rs2535633, <I>P</I> = 1.77 × 10<SUP>−10</SUP>) and <I>NT5C2</I> (rs11191580, <I>P</I> = 3.83 × 10<SUP>−8</SUP>) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (<I>P</I> < 5.0 × 10<SUP>−8</SUP>) and an additional 14 at <I>P</I> < 1.0 × 10<SUP>−3</SUP> with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity.</P>
Hui Xing Tan,Desmond Chun Hwee Teo,이동윤,김청수,Jing Wei Neo,Cynthia Sung,Haroun Chahed,Pei San Ang,Doreen Su Yin Tan,박래웅,Sreemanee Raaj Dorajoo 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.2
Objectives: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to acommon data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefitriskassessments in post-market regulatory evaluation and decisions. Methods: EMRs from January 2013 to December 2016were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappingswere applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a priorOMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrialfibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed torepresent the comparative effectiveness, safety and utilization of the drugs. Results: Over 90% of records were mapped ontothe OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. Intotal, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable viathe proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfullyinform regulatory decision-making. Conclusions: While the structure of the OMOP-CDM and its accessory tools facilitatereal-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-riskassessments, may require layering on additional analytic tools and visualization techniques.