[Multi-index evaluation of different parts of Amomum villosum].

To investigate the quality differences between the seeds and husks of Amomum villosum and explore the rationality of using the seeds without husks, this study determined the content of protocatechuic acid, vanillic acid, epicatechin, quercitrin, volatile oil, water extract, and ethanol extract. The 2,2-diphenyl-1-picrylhydrazyl(DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS), and hydroxyl radical scavenging activities were determined to evaluate the antioxidant activities of seeds and husks. The quality differences between the seeds and husks were assessed through orthogonal partial least squares-discriminant analysis(OPLS-DA) and analytic hierarchy process(AHP) combined with the entropy weight method(EWM). Significant differences(P<0.05) were observed in all 10 indicators between the seeds and husks. The levels of epicatechin, quercetin, and volatile oil were higher in the seeds, whereas those of protocatechuic acid, vanillic acid, water extract, and ethanol extract were higher in the husks. The seeds showed stronger scavenging ability against DPPH and ABTS radicals, while the husks showed a stronger scavenging effect on hydroxyl radicals. OPLS-DA significantly discriminated between the seeds and husks. Furthermore, volatile oil, water extract, DPPH radical scavenging rate, quercitrin, ABTS radical scavenging rate, hydroxyl radical scavenging rate, and vanillic acid were selected as the main differential indicators by variable importance in projection(VIP). Comprehensive scores calculated by AHP combined with EWM indicated that the seeds were superior to husks in terms of overall quality. However, there are still some dominant components and a certain antioxidant effect in the husks. Therefore, it is suggested to using Amomi Fructus with a certain amount of husks or utilizing the husks for other purposes.

In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.

Drug-induced mitochondrial toxicity has become one of the key reasons for which some drugs fail to enter market or are withdrawn from market. Thus early identification of new chemical entities that injure mitochondrial function grows to be very necessary to produce safer drugs and directly reduce attrition rate in later stages of drug development. In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient method (CG) for parameter optimization (GA-CG-SVM), has been employed to develop prediction model of mitochondrial toxicity. We firstly collected 288 compounds, including 171 MT+ and 117 MT-, from different literature resources. Then these compounds were randomly separated into a training set (253 compounds) and a test set (35 compounds). The overall prediction accuracy for the training set by means of 5-fold cross-validation is 84.59%. Further, the SVM model was evaluated by using the independent test set. The overall prediction accuracy for the test set is 77.14%. These clearly indicate that the mitochondrial toxicity is predictable. Meanwhile impacts of the feature selection and SVM parameter optimization on the quality of SVM model were also examined and discussed. The results implicate the potential of the proposed GA-CG-SVM in facilitating the prediction of mitochondrial toxicity.