Atheroprotective potentials of curcuminoids against ginger extract in hypercholesterolaemic rabbits

The anti-atherogenic potentials of total ginger (Zingiber officinale) extract (TGE) or curcuminoids extracted from turmeric (Curcuma longa), members of family Zingiberaceae, were compared in hypercholesterolaemia. Rabbits were fed either normal or atherogenic diet. The rabbits on atherogenic diet received treatments with TGE or curcumenoids and placebo concurrently for 6 weeks (n = 6). The anti-atherogenic effects of curcuminoids and ginger are mediated via multiple mechanisms. This effect was correlated with their ability to lower cholesteryl ester transfer protein activity. Ginger extract exerted preferential effects on plasma lipids, reverse cholesterol transport, cholesterol synthesis and inflammatory status. Curcuminoids, however, showed superior antioxidant activity.     

桔梗皂苷对高脂大鼠血清指标的调节

为研究桔梗皂甙对高脂大鼠血清指标的调节作用,将桔梗粉末以10倍量蒸馏水,浸泡2 h,超声振动处理30 min,滤除药渣,得提取液。同法再次提取一次,合并提取液,减压蒸干,以甲醇溶解,乙醚沉淀,得到皂甙。Wistar大鼠以高脂饲料饲喂,建立高血脂大鼠模型,分组灌胃生理盐水、阳性药物和桔梗皂甙溶液。尾静脉取血测定血清甘油三酯(TG)水平、总胆固醇(TC)水平、低密度脂蛋白胆固醇(LDL-C)水平、高密度脂蛋白胆固醇(HDL-C)水平、载脂蛋白AI(ApoAI)水平和载脂蛋白B(ApoB)水平等指标。结果表明:该法制备桔梗皂甙的方法简便、快速,得率高。桔梗皂甙对血清指标的调节作用显著,提示桔梗皂甙具有降血脂作用,是改善心血管生理功能的良好天然产物来源。     

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC–MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC–MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC–MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC–MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.