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101.
Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases. 相似文献
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A mixed‐method study of effects of a therapeutic play intervention for children on parental anxiety and parents' perceptions of the intervention
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Context The root of Helicteres angustifolia L. (Sterculiaceae) has been used as folk herbal drug to treat cancer, bacterial infections, inflammatory, and flu in China. However, there is no report on its antidiabetic activity.Objective This study evaluates the antidiabetic activity of ethanol extract from H. angustifolia root.Materials and methods The promoting effect of H. angustifolia root ethanol extract (25, 50, and 100 μg/mL) on glucose uptake was evaluated using HepG2 cell, differentiated C2C12 myotubes, and differentiated 3T3-L1 adipocytes. The antidiabetic activity of the extract was assessed in vivo using STZ-induced diabetic rats by orally administration of the extract (200 and 400?mg/kg b.w.) once per day for 28 d. Blood glucose, TG, TC, TP, HDL-C, UA, BUN, AST, ALT, insulin, and HOMA-IR were analyzed.Results The results showed that the extract increased glucose uptake in C2C12 myotubes and 3T3-L1 adipocytes with an IC50 value of 79.95 and 135.96 μg/mL, respectively. And about 12%, 19%, and 10% (p < 0.05) in HepG2 cells when compared with the control at the concentration of 25, 50, and 100 μg/mL, respectively. After 28 days’ treatment with the extract, significant reduction was observed in blood glucose, HOMA-IR, TC, TG, UA, BUN, AST, and ALT levels, while the levels of TP and HDL cholesterol increased.Discussion and conclusion These results suggest that H. angustifolia root ethanol extract possess potent antidiabetic activity, which is the first report on antidiabetic activity of this plant. 相似文献
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目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法(bootstrap)的最小绝对值收敛和选择算子嵌套逻辑回归(LASSO‐LR)及1000次bootstrap的赤池信息量准则(AIC)向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77(95%CI为0.65~0.87),独立验证集AUC为0.72(95%CI为0.64~0.81)。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。 相似文献