Kidney transplantation is an ideal treatment for patients with end-stage renal disease. Circulating alloantibodies against donor human leukocyte antigens and blood group antigens can impair allografts, shorten allograft survival, and limit access to kidney transplantation. Furthermore, the presence of donor specific antibodies is associated with increased incidence of antibody-mediated rejection and decreased graft survival following transplantation. Plasmapheresis, an extracorporeal therapy directed at removing plasma proteins that has been found to minimize the effects of perioperative sensitization in kidney transplantation. Plasmapheresis enables transplantation across the barrier of ABO blood group incompatibility. In addition, it is also an important approach for the treatment of antibody-mediated rejection. Therefore, studying the application of plasmapheresis in perioperative period of kidney transplantation is expected to increase the chance of transplantation and improve the outcomes following transplantation. This article introduces the application of plasmapheresis in the perioperative period of kidney transplantation.
Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.