Tetrandrine Inhibits Titanium Particle-Induced Inflammatory Osteolysis through the Nuclear Factor-κB Pathway.

Peri-implant osteolysis (PIO) and the subsequent aseptic loosening are the main reasons for artificial joint implant failure. Existing methods for treating aseptic loosening are far from satisfactory, necessitating advanced drug exploration. This study is aimed at investigating the effect and underlying mechanism of tetrandrine (Tet) on inflammatory osteolysis. We established a Ti particle-induced inflammatory osteolysis mouse model and administered Tet or an equal volume of phosphate-buffered saline (PBS). Two weeks later, specimens were collected. Histological staining showed that Tet administration inhibited Ti-stimulated osteolysis. Tartrate-resistant acid phosphate (TRAP) staining and transmission electron microscopy (TEM) demonstrated that osteoclast formation was remarkably inhibited in the groups treated with Tet in a dose-dependent manner. In addition, relevant inflammatory cytokines (tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, and IL-6) were also significantly reduced in the calvaria of the Tet-treated groups. Exposure of receptor activator for nuclear factor-κB ligand- (RANKL-) induced bone marrow-derived macrophages (BMMs) and RAW264.7 cells to Tet significantly reduced osteoclast formation, F-actin ring formation, bone resorption, and the expression of relevant genes (matrix metallopeptidase 9 (MMP-9), TRAP, and nuclear factor of activated T-cells, cytoplasmic 1 (NFATc1)) during osteoclastogenesis in vitro. Mechanistic studies using Western blotting demonstrated that Tet inhibited the nuclear factor (NF)-κB signaling pathway by decreasing the phosphorylation of inhibitor of NF-κB α (IκBα) and p65, which play important roles in osteoclast formation. Collectively, our data indicate that Tet suppressed Ti-induced inflammatory osteolysis and osteoclast formation in mice, suggesting that Tet has the potential to be developed to treat and prevent wear particle-induced inflammatory osteolysis.

Development of a nomogram to predict medication nonadherence risk in patients with rheumatoid arthritis.

OBJECTIVES: Poor adherence among patients with chronic diseases including inflammatory rheumatic diseases (IRDs) is a complex and serious global health care problem. This study aimed to develop an intelligent nomogram using retrospectively collected patient clinical data for predicting nonadherence to biologic treatment in rheumatoid arthritis (RA) patients. METHODS: The clinical characteristics of 102 RA patients were collected from outpatients and inpatients at the Orthopedic Departments of Ningxia General Hospital of Ningxia Medical University and Ningxia Hui Autonomous Region People's Hospital from October 2020 to September 2021. Adherence was evaluated using the proportion of treatment days covered within 6 months as the outcome event. A least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify risk predictors, and then multivariate logistic regression analysis was applied to construct the risk prediction model. Furthermore, the nomogram was plotted by multivariable logistic regression. RESULTS: Among the 102 patients analyzed, 43 patients did not adhere to biologic therapy for various reasons. LASSO regression analysis identified age, sex, education level, disease activity, monthly income, medical insurance, and adverse drug reactions as the significant risk predictors. By incorporating these factors, the nomogram was plotted which showed good discrimination, calibration, and clinical value. The C-index was 0.759 (95% CI: 0.665-0.853), and the area under the receiver operating characteristic (ROC) curve was 0.7416 with a good calibration ability. Decision curve analysis showed that the prediction effect of this model could benefit about 75% of the patients without compromising the interests of other patients. CONCLUSIONS: This nomogram could help medical staff identify patients with higher risk of nonadherence early, so that intervention measures can be taken in time.