Alpha-ketoglutarate ameliorates induced premature ovarian insufficiency in rats by inhibiting apoptosis and upregulating glycolysis.

RESEARCH QUESTION: What are the effects of alpha-ketoglutarate (α-KG) treatment on the ovarian morphology and ovarian reserve function of rats with cyclophosphamide (CTX)-induced premature ovarian insufficiency (POI)? DESIGN: Thirty female Sprague Dawley rats were randomly allocated to a control group (n = 10) and a POI group (n = 20). Cyclophosphamide was administered for 2 weeks to induce POI. The POI group was then divided into two groups: a CTX-POI group (n = 10), administered normal saline, and a CTX-POI + α-KG group (n = 10), administered α-KG 250 mg/kg per day for 21 days. Body mass and fertility was assessed at the end of the study. Serum samples were collected for hormone concentration measurement, and biochemical, histopathological, TUNEL, immunohistochemical and glycolytic pathway analyses were conducted for each group. RESULTS: The α-KG treatment increased body mass and ovarian index of rats, partially normalized their disrupted estrous cycles, prevented follicular loss, restored ovarian reserve, and increased pregnancy rate and litter sizes of rats with POI. It significantly reduced serum concentration of FSH (P < 0.001), increased that of oestradiol (P<0.001) and reduced apoptosis of granulosa cells (P = 0.0003). Moreover, α-KG increased concentrations of lactate (P = 0.015) and ATP (P = 0.025), reduced that of pyruvate (P<0.001) and increased expression of rate-limiting enzymes of glycolysis in the ovary. CONCLUSIONS: α-KG treatment ameliorates the deleterious effects of CTX on the fertility of female rats, possibly by reducing the apoptosis of ovarian granulosa cells and restoring glycolysis.

Research on improved convolutional wavelet neural network.

Artificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.