ABSTRACT
Objective:To investigate the effect and mechanism of Fuzheng Jiedu Tongluofang on the expression of MMP-2,MMP-9 and invasion of human hepatoma HepG2 cell.Methods:Hepatioma HepG2 cell was treated with Fuzheng Jiedu Tongluofang.The cell viability was measured by CCK8.The invasion of HepG2 was detected by Transwell assay.The expression of MMP-2 and MMP-9 was analyzed by ELISA.Results:In the CCK8 assay,Fuzheng Jiedu Tongluofang could inhibit the proliferation of HepG2 cells at a density and time measure.In the Transwell assay,the inhibitory rate of invasion was 52.45% when treated with Fuzheng Jiedu Tongluofang at a concentration of 2.65 mg/ml. The ELISA assay indicated that the expression of MMP-2 and MMP-9 decreased significantly after treated with Fuzheng Jiedu Tongluofang(P<0.05). Conclusion:Fuzheng Jiedu Tongluofang suppressed the invasion of HepG2 with the possible mechanism of down-regulating the expression of MMP-2 and MMP-9 in HepG2.
ABSTRACT
Medical image registration is a highlight of actual research on medical image processing. Based onsimilarity measure of Shannon entropy, a new generalized distance measurement based on Rényi entropy applied to image rigid registration is introduced and is called here generalized mutual information (GMI). It is used in three dimensional cerebral image registration experiments. The simulation results show that generalized distance measurement and Shannon entropy measurement apply to different areas; that the registration measure based o n generalized distance is a natural extension of mutual information of Shannon entropy. The results prove that generalized mutual information uses less time than simple mutual information does, and the new similarity measure manifests higher degree of consistency between the two cerebral registration images. Also, the registration results provide the clinical diagnoses with more important references. In conclusion, generalized mutual information has satisfied the demands of clinical application to a wide extent.
Subject(s)
Humans , Algorithms , Brain , Pathology , Diagnostic Imaging , Methods , Entropy , Image Interpretation, Computer-Assisted , Methods , Image Processing, Computer-Assisted , Methods , Imaging, Three-Dimensional , Magnetic Resonance Imaging , Pattern Recognition, Automated , MethodsABSTRACT
Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. These noises bring obstacle to the diagnosis of cardiovascular diseases. To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.