The process of multi-parametric flow cytometry data analysis is complicate and time-consuming, which requires well-trained professionals to operate on. To overcome this limitation, a method for multi-parameter flow cytometry data processing based on kernel principal component analysis (KPCA) was proposed in this paper. The dimensionality of the data was reduced by nonlinear transform. After the new characteristic variables were obtained, automatical clustering can be achieved using improvedK-means algorithm. Experimental data of peripheral blood lymphocyte were processed using the principal component analysis (PCA)-based method and KPCA-based method and then the influence of different feature parameter selections was explored. The results indicate that the KPCA can be successfully applied in the multi-parameter flow cytometry data analysis for efficient and accurate cell clustering, which can improve the efficiency of flow cytometry in clinical diagnosis analysis.
As the largest barrier organ in the human body, once skin defect occur, it not only affects appearance but also cause clinical problems such as infections. Traditional skin defect repair methods, such as autologous skin transplantation and allogeneic skin transplantation, have shortcomings such as limited donor sources, potential immune rejection, and limited repair effects, and are difficult to meet the individualized treatment needs of complex wounds. Bioprinting technology, as a breakthrough approach in tissue engineering in recent years, can accurately control the spatial distribution of seed cells and biomaterials within scaffolds based on digital models, achieving personalized biomimetic structure of skin tissue. This article aims to summarize the application and research progress of bioprinting technology in skin tissue engineering, providing a theoretical basis for its further clinical application.