ABSTRACT
Objective To study the content difference of avicularin,quercitrin and quercetin in Herba Taxilli from different host plants. Methods The contents of avicularin,quercitrin and quercetin in Herba Taxilli were determined by RP-HPLC and the samples were prepared by ultrasonic extraction with methanol. The analytical column was UltImate XB-C18(250 mm×4.6 mm,5μm), the mobile phase was acetonitrile-methanol-0.1%phosphate with gradient elution,at a flow rate of 1.0 ml/min. The column tempera-ture was set at 25℃,the detection wavelength was 254 nm for avicularin and quercitrin,and 365 nm for quercetin. Results The lin-ear range of the above three ingrediets were 0.0992-1.9840(r=0.9999),0.2254-4.4580(r=0.9999)and 0.1258-2.5160μg(r=0.9997) and the average recovery rates(n=5)were 98.39%,97.08%and 98.159%,respectively. Their contents from different host plants were 0.0000-0.0398,0.3977-0.7639 and 0.0068-0.0231 mg/g in branches and 0.0167-0.1704,1.8626-11.0041,and 0.0185-0.1841 mg/g in leaves. Conclusion The method is accurate,simple and reproducible,and can be used for the quality control of Herba Taxilli.
ABSTRACT
<p><b>OBJECTIVE</b>We purpose a novel factor analysis method based on kinetic cluster and α-divergence measure for extracting the blood input function and the time-activity curve of the regional tissue from dynamic myocardial positron emission computed tomography(PET) images.</p><p><b>METHODS</b>Dynamic PET images were decomposed into initial factors and factor images by minimizing the α-divergence between the factor model and actual image data. The kinetic clustering as a priori constraint was then incorporated into the model to solve the nonuniqueness problem, and the tissue time-activity curves and the tissue space distributions with physiological significance were generated.</p><p><b>RESULTS</b>The model was applied to the 82RbPET myocardial perfusion simulation data and compared with the traditional model-based least squares measure and the minimal spatial overlap constraint. The experimental results showed that the proposed model performed better than the traditional model in terms of both accuracy and sensitivity.</p><p><b>CONCLUSION</b>This method can select the optimal measure by α value, and incorporate the prior information of the kinetic clustering of PET image pixels to obtain the accurate time-activity curves of the tissue, which has shown good performance in visual evaluation and quantitative evaluation.</p>