Objective Peripheral helper T (Tph) cells interact with B cells and promote immune responses at sites of ectopic lymphoid structures (ELSs). To assess the characteristics of Tph cells, we investigated the phenotype of T helper (Th) cells in patients with systemic lupus erythematosus (SLE) and the underlying competitive binding mechanisms using cytokines-mediated signal transducer and activator of transcription (STAT) factors. Methods Peripheral blood mononuclear cells from SLE patients and healthy controls were analyzed for phenotype identifying. Serum cytokine levels were detected using Luminex assays. In vitro culture was performed to assess cytokine-induced conversion of phenotypes and transcriptional regulation using flow cytometry and PCR. Chromatin immunoprecipitation was used to evaluate STATs binding and histone modifications. Results CXCR5-PD-1+Tph-like cells were increased in SLE patients and showed strong association with disease activity and renal involvement. Serum IFN-α levels were increased and associated with Tph frequency. IFN-α promoted the differentiation of IL-10-producing CXCR5-PD-1+Tph-like cells, increased the responsiveness of IL-2, and induced the conversion of Tfh-like cells to Tph-like cells. STAT5 gained advantage and bound to BCL6 locus at expense of STAT1, accompanied by suppression of H3K4me3. Finally, anti-IFNAR1 decreased the differentiation of Tph-like cells, thereby suppressing the generation of CD38highCD27highplasmablasts. Conclusion Tph cells might be crucial makers to effectively reflect disease activity level in SLE patients. The finding that synergy of IFN-α and IL-2 increases Tph cells through competitive transcriptional regulation, could be one of the mechanisms responsible for pathologic formation of ELSs and helpful for selection of individualized therapeutic approaches for SLE.

Older people in China have a poor understanding of hospital signage. To address this problem, in this study, we combined the theories of situated cognition and cognitive commonness in order to introduce the three main factors that affect the generation of situational cognitive commonness: composition of the situation, familiarity, and concreteness. We used these theories to construct a methodological framework for the design of geriatric hospital wayfinding signs that were based on situational cognitive commonness. The design of nine healthcare signs for Chinese national standards were used as examples in the study. First, users who were familiar with medical scenarios were asked to draw concrete cognitive conception graphics for the purposes of individual wayfinding targets from both physical and social situations. Next, we coded and grouped the generated graphics based on their situational features in order to extract groups of representative common graphics. Finally, we reorganized the common graphics and developed concrete designs, which were tested by the judgment test. The wayfinding signs designed according to the methodological framework of this study effectively improved the understanding of hospital signage among older Chinese people. This study took geriatric hospital wayfinding signs as the examples to provide a feasible theoretical basis and research reference for symbol design.

BackgroundIgAV, the most common systemic vasculitis in childhood, is an immunoglobulin A-associated immune complex-mediated disease and its underlying molecular mechanisms are not fully understood. This study attempted to identify differentially expressed genes (DEGs) and find dysregulated immune cell types in IgAV to find the underlying pathogenesis for IgAVN.MethodsGSE102114 datasets were obtained from the Gene Expression Omnibus (GEO) database to identify DEGs. Then, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. And key hub genes were identified by cytoHubba plug-in, performed functional enrichment analyses and followed by verification using PCR based on patient samples. Finally, the abundance of 24 immune cells were detected by Immune Cell Abundance Identifier (ImmuCellAI) to estimate the proportions and dysregulation of immune cell types within IgAVN.ResultA total of 4200 DEGs were screened in IgAVN patients compared to Health Donor, including 2004 upregulated and 2196 downregulated genes. Of the top 10 hub genes from PPI network, STAT1, TLR4, PTEN, UBB, HSPA8, ATP5B, UBA52, and CDC42 were verified significantly upregulated in more patients. Enrichment analyses indicated that hub genes were primarily enriched in Toll-like receptor (TLR) signaling pathway, nucleotide oligomerization domain (NOD)-like receptor signaling pathway, and Th17 signaling pathways. Moreover, we found a diversity of immune cells in IgAVN, consisting mainly of T cells. Finally, this study suggests that the overdifferentiation of Th2 cells, Th17 cells and Tfh cells may be involved in the occurrence and development of IgAVN.ConclusionWe screened out the key genes, pathways and maladjusted immune cells and associated with the pathogenesis of IgAVN. The unique characteristics of IgAV-infiltrating immune cell subsets were confirmed, providing new insights for future molecular targeted therapy and a direction for immunological research on IgAVN.

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19.

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