RESUMO
BACKGROUND: Post-traumatic capsular contracture is a common complication of joint injury and surgery. Post-traumatic capsular contracture is associated with fibrosis characterized by excessive differentiation and proliferation of myofibroblasts and abnormal secretion and accumulation of extracellular matrix. Previous studies have suggested that IL11 plays a role in myocardial fibrosis. We thus hypothesized that IL11 may play a fibrotic role during capsular contracture, in order to discover new targets for preventing joint capsule contracture METHODS: We constructed a post-traumatic contracture model by excessively extending the knee joint and fixing the joint in the flexion position, and a post-traumatic joint capsule contracture model was constructed in the wild-type, IL11-/-, IL11R -/-, α-SMA-cre-IL11fl/fl, α-SMA-cre-IL11Rfl/fl mouse strain, with wild-type mice without any treatment of the knee joint as the control group. Fibrotic markers and the expression of IL11 and IL11R in knee joint tissue were detected in each group of mice. The NIH3T3 cell line was used for in vitro analyses. The expression of fibrosis markers, IL11, TGFß and ERK1/2 were detected by western blot, ELISA and RT-qPCR. RESULTS: Inhibition of IL11 inhibited ERK1/2 phosphorylation, reduced the secretion of collagen in the joint capsule, and inhibited the excessive differentiation and proliferation of myofibroblasts in the post-traumatic joint capsule contracture, thus alleviating the joint capsule contracture and obtaining better joint mobility. CONCLUSION: Downregulation of IL11 in traumatic joint capsule contracture inhibits ERK1/2 phosphorylation, thus significantly relieving joint capsule contracture. Our findings indicate the TGFß/IL11/ERK1/2 axis is an important pathway for the differentiation of fibroblasts into myofibroblasts. Anti-IL11 treatment is an effective means to prevent traumatic joint capsule contracture.
RESUMO
RATIONALE AND OBJECTIVES: To analyse the MRI-based radiomics and delta-radiomics features to establish radiomics models for predicting the radiographic progression of osteoarthritis (OA). MATERIALS AND METHODS: The data used in this research come from the dataset of the FNIH Biomarker Consortium Project within the Osteoarthritis Initiative (OAI). 565 participants randomly divided into training and validation groups at a 7:3 ratio. The training cohort consisted of 395 participants and included 202 cases. The validation cohort consisted of 170 participants and included 87 cases. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Support vector machine (SVM) was used to establish radiomics models and clinical and biomarker models for predicting the radiographic progression of OA. The predictive ability of the model was evaluated by the area under the curve (AUC). RESULTS: The baseline, 24 M, Delta, and two combination radiomics models (Baseline and Delta, 24 M and Delta) all showed good predictive performance in the training and validation cohorts, with the combination model exhibiting the best performance. In the training cohort, the AUCs were 0.851 (95% CI: 0.812-0.890), 0.825 (95% CI: 0.784-0.865), 0.804 (95% CI: 0.761-0.847), 0.892 (95% CI: 0.860-0.924) and 0.884 (95% CI: 0.851-0.917), respectively. The AUCs in the validation cohort were 0.741 (95% CI: 0.667-0.814), 0.786 (95% CI: 0.716-0.856), 0.745 (95% CI: 0.671-0.819), 0.781 (95% CI: 0.711-0.851) and 0.802 (95% CI: 0.736-0.869), respectively. As compared, the clinical and biomarker models have AUC < 0.74. The DeLong test showed that the predictive performance of the radiomics models in the training and validation cohorts was significantly better than that of the clinical and biomarker models (P < 0.001). CONCLUSION: The MRI-based radiomics models of the patella all showed good predictive performance performed better than the clinical and biomarker models in predicting the radiographic progression of OA. Delta radiomics can improve the predictive performance of the single time model, the combined model of 24 M and Delta provided the best predictive performance.
