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Wei, Qing,Liu, Sanzhen,Huang, Jianfeng,Mao, Xueying,Chu, Xiaohui,Wang, Yu,Qiu, Minyan,Mao, Yumin,Xie, Yi,Li, Yao Korean Society for Biochemistry and Molecular Biol 2004 Journal of biochemistry and molecular biology Vol.37 No.4
Double stranded targets on the cDNA microarray contain representatives of both the coding and noncoding strands, which will introduce hybridization competition with probes. Here, the effect of double and single strands of targets on the signal intensity and the ratios of Cy5/Cy3 within the same slide were compared. The results show that single stranded targets can increase the hybridization efficiency without changing the Cy5/Cy3 ratio. Based on these results, a new strategy was established by generating cDNA targets with asymmetric PCR, instead of conventional PCR, to increase the sensitivity of the cDNA microarray. Furthermore, the feasibility of this approach was validated. The results indicate that the cDNA microarray system based on asymmetric PCR is more sensitive, with no decrease in the reliability and reproducibility as compared with that based on conventional symmetric PCR.
Qianwen Huang,Daizhi Yang,Hongrong Deng,Hua Liang,Xueying Zheng,Jinhua Yan,Wen Xu,Xiangwen Liu,Bin Yao,Sihui Luo,Jianping Weng 대한당뇨병학회 2022 Diabetes and Metabolism Journal Vol.46 No.1
Background: Both type 1 diabetes mellitus (T1DM) and metabolic syndrome (MetS) are associated with an elevated risk of morbidity and mortality yet with increasing heterogeneity. This study primarily aimed to evaluate the prevalence of MetS among adult patients with T1DM in China and investigate its associated risk factors, and relationship with microvascular complications.Methods: We included adult patients who had been enrolled in the Guangdong T1DM Translational Medicine Study conducted from June 2010 to June 2015. MetS was defined according to the updated National Cholesterol Education Program criterion. Logistic regression models were used to estimate the odds ratio (OR) for the association between MetS and the risk of diabetic kidney disease (DKD) and diabetic retinopathy (DR).Results: Among the 569 eligible patients enrolled, the prevalence of MetS was 15.1%. While female gender, longer diabetes duration, higher body mass index, and glycosylated hemoglobin A1c (HbA1c) were risk factors associated with MetS (OR, 2.86, 1.04, 1.14, and 1.23, respectively), received nutrition therapy education was a protective factor (OR, 0.46). After adjustment for gender, age, diabetes duration, HbA1c, socioeconomic and lifestyle variables, MetS status was associated with an increased risk of DKD and DR (OR, 2.14 and 3.72, respectively; both P<0.05).Conclusion: Although the prevalence of MetS in adult patients with T1DM in China was relatively low, patients with MetS were more likely to have DKD and DR. A comprehensive management including lifestyle modification might reduce their risk of microvascular complications in adults with T1DM.
Song Zuhua,Guo Dajing,Tang Zhuoyue,Liu Huan,Li Xin,Luo Sha,Yao Xueying,Song Wenlong,Song Junjie,Zhou Zhiming 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.3
Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initialNCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.