RESUMEN
Hemolytic anemia is a common clinical disease with diverse pathogenesis. In recent years, the incidence of hemolytic anemia is increasing dramatically. The present clinical treatment of immunosuppressive agents or splenectomy is effective to some extent; however, the accompanied clinical adverse reactions are also significant. Traditional Chinese medicine (TCM) has beneficial therapeutic effect on hemolytic anemia, with the obvious advantages including curative effect, less adverse reactions, and low price. The pathogenesis of hemolytic anemia as well as the pharmacological effects and mechanisms of the compound, single herb, and monomer composition of TCM in the treatment of hemolytic anemia were reviewed, aiming to provide the basis for the clinical treatment of hemolytic anemia and the modern research on the mechanism of TCM.
RESUMEN
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.