Objective Helicobacter pylori infection has been reported to aggravate rheumatoid arthritis (RA), but the relevant mechanism remains unclear. This study aimed to investigate the underlying pathogenic mechanism of H. pylori infection in the progression of RA. Methods The Disease Activity Score (DAS-28) and serum anticitrullinated protein antibody (ACPA) levels were compared between H. pylori-negative and H. pylori-positive patients with RA. MH7A cells were stimulated with polyclonal ACPA purified from the peripheral blood of patients with RA. The citrullination levels were assessed by western blot in GES-1 cells and sera. ChIP, luciferase reporter assays, mass spectrometry and ELISA were applied to explore the molecular mechanism of H. pylori infection in RA progression. Results The DAS-28 and ACPA levels of patients with RA in the H. pylori-positive group were significantly higher than those in the H. pylori-negative group. Polyclonal ACPA derived from H. pylori-positive patients promoted cell proliferation and induced secretion of IL-6 and IL-8. For the first time, we found that H. pylori infection induces cellular protein citrullination by upregulating protein arginine deiminase type 4 (PAD4). Furthermore, we confirmed a direct functional binding of hypoxia-inducible factor 1α on the PADI4 gene promoter. We demonstrated that PAD4 interacts with and citrullinates keratin 1 (K1), and serum and synovial fluid levels of anti-Cit-K1 antibody were markedly increased in H. pylori-infected patients with RA. Conclusion Our findings reveal a novel mechanism by which H. pylori infection contributes to RA progression. Therapeutic interventions targeting H. pylori may be a viable strategy for the management of RA.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune responses to guide vaccination strategies. Although neutralizing antibodies (NAbs) have garnered increasing attention, T-cells are crucial for conferring long-lasting immunity, especially their resilience against viral mutations. However, assessing T-cell responses clinically has been hindered by cost and complexity. In this study, we recruited a cohort of 134 healthy adults, who had been immunized with three doses of the SARS-CoV-2 inactivated vaccine. Cellular immunity elicited by a comprehensive array of overlapping peptides covering the entire sequence of the virus's structural proteins was assessed by intracellular cytokine staining (ICS). Additionally, a dataset including demographic information, routine blood indices, and immune cell indicators comprising 32 variables was collected. Multivariate analysis revealed age and days post-vaccination as key factors influencing the strength of the T-cell response. Importantly, random forest (RF) and classification and regression tree (CART) algorithms were employed, along with 8 easily accessible indicators to formulate predictive models for the SARS-CoV-2-specific CD4⁺ and CD8⁺ T-cell responses. Besides, these models demonstrated substantial accuracy (r > 0.9) in both the training and testing sets. Our findings offer an efficient and economical methodology for evaluating the T-cell reactions in healthy adults following inactivated SARS-CoV-2 vaccination, which is visualizable and easy to use, providing a novel strategy for assessing cellular immunity after vaccination.

This study aimed to establish a predictive model and nomogram based on routine laboratory blood indicators and clinical symptoms, subsequently providing a rapid risk assessment of norovirus (NoV) infection in children. This retrospective study enrolled 307 pediatric patients with symptoms of acute gastroenteritis and detected NoV using real-time quantitative polymerase chain reaction. Significant indicators selected by multivariate logistic regression, including routine blood tests and consultation symptoms, were used to develop the nomogram. We divided the sample into training and internal validation sets and performed external validation of the final model. Furthermore, we evaluated the clinical performance using the Akaike information criterion (AIC), area under the curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity, specificity, concordance rate, positive predictive value, and negative predictive value. Overall, 153 cases were NoV-PCR-positive, and 154 were negative. The multivariate logistic regression included five predictors of NoV infection, including symptoms of vomiting, upper respiratory tract infection, and indicators of white blood cells, lymphocyte absolute counts, and platelet counts. The nomogram showed a significant predictive value with overall internal set diagnosis, with an AUC of 0.827 (95% confidence interval (CI): 0.785–0.868), and 0.812 (95% CI: 0.755–0.869) with 0.799 (95% CI: 0.705–0.894) in the training and internal validation sets, respectively. Nevertheless, the AUC in the external validation set was higher (0.915; 95% CI: 0.862–0.968). This nomogram is a useful tool for risk assessment for NoV infection. Moreover, the evaluated indicators are accessible, substantially reducing the time for laboratory testing.