Obejective To study the effect of daidzein(Daid) on the enzymes of bone metabolism and bone rebuilding in ovariectomized rats.Method Sixty 3-mon female SD rats were divided into 6 groups:Sham,OVX,OVX-D1～D4 groups.Every day the rats in Sham and OVX groups were given distilled water,and the rats in OVX-D1,OVX-D2,OVX-D3,OVX-D4 groups were given 25,50,75,100 mg/kg bw Daid respectively.After 6 mon,all of rats were killed,and the blood,liver and femur were collected.The content of calcium and phosphorus in serum and the enzymes of bone metabolism were measured.Results The content of calcium,phosphorus and ATPase in serum was increased,but the activity of serum alkaline phosphatase(ALP) and tartrate-resistant acid phosphatase(TRAP-5b) was decreased dose-dependently in OVX-D groups compared with OVX group.Conclusion The expression of bone metabolism-associated enzymes could be inhibited,which resulted in inhibition of bone formation and absorbsion in ovariectomized rats.The new balance between bone formation and absorption could be rebuilt by Daid,through the mechanism of decreasing rate of bone conversion.

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q ^AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging/methods* ; Algorithms