Toxicity of Buxue Shengru Granules on perinatal rats / 中成药

AIM To investigate the toxicity of Buxue Shengru Granules (BSG,Astragali Radix,Angelicae sinensis Radix,Paeoniae Radix alba,etc.) to perinatal SD rats and their offspring to determine its safety.METHODS The respective BSG administration of 0,1,3,10 g/kg to the four groups rats,each with twenty-four female rats,started from the day 15 of gestation (GD15) through weaning when they were procured for the identification of the general condition,body weight and food ingestion.The offspring had their postnatal appearance observed the physiological development,reflexes development,behavior and fertility detected as well.RESULTS BSG gave neither significant influence on the rats' general condition,time of birth,body weight,and histological changes of organs during perinatal period;nor remarkable impact on the offspring's growth,development,nerves,endocrine,and reproductive system.CONCLUSION Data on BSG to rats maternal weight gain and food intake and to their offspring developmental landmarks,sexual maturation,or reflexes suggest that BSG gives no perinatal toxicity.

Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus / 药学学报

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.