Objective To determine the biological traits and optimal condition for the induction and differentiation of endothelial progenitor cells from peripheral blood in healthy adults. Methods Mononuelear cells were isolated from peripheral blood of healthy adults by Ficoll-density eentrifugation. The isolated ceils were cultured in 1640 medium supplemented with VECF165 and bFGF. The EPC specific surface mark CD34 and KDR were assessed by fluorescence activated cell sorter(FACS)analysis: EPC were characterized as adherent cells double positive for DiL-acLDL uptake and lectin binding by direct fluorescent staining under a hser scanning confocal microscope. EPC migration were assayed by MTr assay. Result The number and migration ability of EPC were increased by VEGFl65 and bFGF. Conclusion Endothelial progenitors cells can be derived from mononuclear cells of peripheral blood at specific conditions.

Objective To propose a distribution feature extraction algorithm based on wavelet packet coefficients to reconstruct the signal energy sequence for effectively identifying the pathological features of heart sounds,thereby realizing the early screening of heart diseases.Methods The original heart sound signal was decomposed into 10 layers using wavelet packet decomposition algorithm.After obtaining the wavelet packet coefficients of each layer,each coefficient was reconstructed,and the energy of the reconstructed signal was calculated and arranged in the original order to form the energy sequence.The distribution characteristics of the energy sequence of the reconstructed signals at each layer were analyzed,and distribution features were taken as classification features.Support vector machine,K-nearest neighbor,and decision tree were used to classify and recognize normal heart sounds and the heart sound signals of various diseases.Results The combination of the distribution features of the reconstructed signal energy sequence and decision tree classifier had an accuracy of 93.6%for classifying 5 types of heart sounds on the public dataset,and the accuracy was 95.6%for identifying normal heart sounds and hypertrophic cardiomyopathy heart sounds.Conclusion The proposed algorithm can extract the effective pathological information of abnormal heart sounds,providing a reference for clinical cardiac auscultation.