Expression of hsa_circ_0128298 in hepatocellular carcinoma and its functional roles in tumor growth / 临床检验杂志

Objective@#To investigate the expression of circular RNA hsa_circ_0128298 in hepatocellular harcinoma (HCC) and evaluate its function in the growth process of HCC. @*Methods@#qPCR was used to detect the expressions of hsa_circ_0128298 in 50 HCC tissues, corresponding paracancerous tissues and HCC cells. The effects of hsa_circ_0128298 on HCC growth was analyzed by CCK-8 kit, flow cytometry and western blot assays. The values of hsa_circ_0128298 for the diagnosis and prognosis of HCC were assessed by ROC curve and survival curve analyses. @*Results@#Compared with the paracancerous tissues, the expression of hsa_circ_0128298 was significantly increased in HCC tissues ( U =846.0， P =0.005). The area under ROC curve (AUCROC) was 0.661, 95% confidence interval ( CI ) was 0.560 to 0.753, sensitivity was 80.0% and specificity was 50.0%. HCC patients with high expression of hsa_circ_0128298 displayed less overall survival time than those with low expression of hsa_circ_0128298 ( χ 2=6.294, P =0.012). RNA interference of hsa_circ_0128298 (si-hsa_circ_0128298) significantly inhibited HCC cell proliferation and induced cell cycle arrest at G0/G1, and also induced mRNA upregulation and protein expression of cyclin p21. Meanwhile，si-p21 partially reversed the proliferation inhibition and cell cycle arrest of HCC cells induced by si-hsa_circ_0128298, and restore its growth and proliferation potential. @*Conclusion@#The highly expressed hsa_circ_0128298 in HCC could promote the growth of HCC by regulating the expression of p21 and promise to be a new target for diagnosis and treatment of HCC.

Medical image processing based on wavelet characteristics and edge blur detection / 生物医学工程学杂志

To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability.