Immune infiltration analysis reveals immune cell signatures in salivary gland tissue of primary Sjögren’s syndrome

IntroductionMouse models are the basis for primary Sjögren’s syndrome (pSS) research. However, the depth of comparisons between mice and humans in salivary gland (SG) immune cells remains limited.MethodsThe gene expression profiles of SGs from normal subjects and pSS patients were downloaded from the Gene Expression Comprehensive Database. The proportion of infiltrating immune cell subsets was then assessed by cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT). An experimental Sjögren’s syndrome (ESS) mouse model was successfully constructed using SG protein. Based on mouse SG tissue RNA-Seq data, the seq-ImmuCC model was used to quantitatively analyze the compositional ratios of 10 immune cells in pSS patients and mouse model SG tissues.ResultsComputed and obtained 31 human data samples using the CIBERSORT deconvolution method. The immune cell infiltration results showed that, compared to normal human SG tissue, the content of gamma delta T cells was significantly different from naive CD4+ T cells and significantly increased, while the plasma cell content decreased. Principal component analysis indicated differences in immune cell infiltration between pSS patients and normal subjects. Meanwhile, for ESS model mouse data analysis, we found that the proportion of macrophages increased, while the proportion of CD4+ T cells, B cells, and monocytes decreased. Furthermore, we found that the proportion of monocytes was decreased, while the proportion of macrophages was increased in the SG tissues of pSS patients and model mice. The infiltration of CD4+ T, CD8+ T, and B cells also showed some differences.DiscussionWe comprehensively analyzed SG immune infiltration in pSS patients and model mice. We demonstrated conserved and nonconserved aspects of the immune system in mice and humans at the level of immune cells to help explain the primary regulation of immune mechanisms during the development of Sjögren’s syndrome

The Driving Waveform Design Method of Power-Law Fluid Piezoelectric Printing Based on Iterative Learning Control

In some applications of piezoelectric three-dimensional inkjet printing, the materials used are power-law fluids as they are shear thinning. Their time-varying viscosities affect the droplet formation, which is determined by the volume flow rate at the nozzle outlet. To obtain a fine printing effect, it is necessary to present a driving waveform design method that considers the shear-thinning viscosities of materials to control the volume flow rate at the nozzle outlet, which lays the foundation for the single and stable droplet generation during the printing process. In this research, we established the relationship between the driving waveform and the volume flow rate at the nozzle outlet by modifying a model that describes the inkjet mechanism of power-law fluid. The modified model was used to present a driving waveform design method based on iterative learning control. The iterative learning law of the method was designed based on the gradient descent algorithm and demonstrated its convergence. The driving waveform design method was verified to be practical and feasible by implementing drop generation experiments