Development and validation of a risk prediction model for valve regurgitation in Behçet's disease.

BACKGROUND: In Behçet's disease (BD), mild-to-severe valvular regurgitation (VR) poses a serious complication that contributes significantly to heart failure and eventually death. The accurate prediction of VR is crucial in the early stages of BD subjects for improved prognosis. Accordingly, this study aimed to develop a nomogram that can detect VR early in the course of BD. METHODS: One hundred seventy-two patients diagnosed with Behçet's disease (BD) were conducted to assess cardiac valve regurgitation as the primary outcome. The severity of regurgitation was classified as mild, moderate, or severe. The parameters related to the diagnostic criteria were used to develop model 1. The combination of stepAIC, best subset, and random forest approaches was employed to identify the independent predictors of VR and thus establish model 2 and create a nomogram for predicting the probability of VR in BD. Receiver operating characteristics (ROC) and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS: Thirty-four patients experienced mild-to-severe VR events. Model 2 was established using five variables, including arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. In comparison with model 1 (0.635, 95% CI: 0.512-0.757), the ROC of model 2 (0.879, 95% CI: 0.793-0.966) was improved significantly. DCA suggested that model 2 was more feasible and clinically applicable than model 1. CONCLUSION: A predictive model and a nomogram for predicting the VR of patients with Behçet's disease were developed. The good performance of this model can help us identify potential high-risk groups for heart failure. Key Points • In this study, the predictors of VR in BD were evaluated, and a risk prediction model was developed for the early prediction of the occurrence of VR in patients with BD. • The VR prediction model included the following indexes: arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. • The risk model that we developed was better and more optimized than the models built with diagnostic criteria parameters, and visualizing and personalizing the model, a nomogram, provided clinicians with an easy and intuitive tool for practical prediction.

Tumor-associated neutrophils suppress CD8+ T cell immunity in urothelial bladder carcinoma through the COX-2/PGE2/IDO1 Axis.

BACKGROUND: Many urothelial bladder carcinoma (UBC) patients don't respond to immune checkpoint blockade (ICB) therapy, possibly due to tumor-associated neutrophils (TANs) suppressing lymphocyte immune response. METHODS: We conducted a meta-analysis on the predictive value of neutrophil-lymphocyte ratio (NLR) in ICB response and investigated TANs' role in UBC. We used RNA-sequencing, HALO spatial analysis, single-cell RNA-sequencing, and flow cytometry to study the impacts of TANs and prostaglandin E2 (PGE2) on IDO1 expression. Animal experiments evaluated celecoxib's efficacy in targeting PGE2 synthesis. RESULTS: Our analysis showed that higher TAN infiltration predicted worse outcomes in UBC patients receiving ICB therapy. Our research revealed that TANs promote IDO1 expression in cancer cells, resulting in immunosuppression. We also found that PGE2 synthesized by COX-2 in neutrophils played a key role in upregulating IDO1 in cancer cells. Animal experiments showed that targeting PGE2 synthesis in neutrophils with celecoxib enhanced the efficacy of ICB treatment. CONCLUSIONS: TAN-secreted PGE2 upregulates IDO1, dampening T cell function in UBC. Celecoxib targeting of PGE2 synthesis represents a promising approach to enhance ICB efficacy in UBC.