ABSTRACT
Objective:To investigate the effects of scutellarin on Bcl-2 and Bax expression in human tongue squamous carcinoma Tca-8113 cell.Methods:Tca8113 cells were treated with 80,120 and 160μg/ml scutellarin for 48 h respectively.Immunocytochem-istry method and RT-PCR were applied to observe the expression of Bcl-2 and Bax protein and mRNA in the cells.Results:With the increase of scutellarin concentration,Bcl-2 protein and mRNA decreased(P<0.05),Bax protein and mRNA increased(P<0.05). Conclusion:Scutellarin may downregulate Bcl-2 expression and upregulate Bax expression in Tca-8113 cells.
ABSTRACT
Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.
Subject(s)
Algorithms , Image Processing, Computer-Assisted , Imaging, Three-Dimensional , Normal Distribution , SoftwareABSTRACT
To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.