miR-486-5p Attenuates Steroid-Induced Adipogenesis and Osteonecrosis of the Femoral Head Via TBX2/P21 Axis.

Enhanced adipogenic differentiation of mesenchymal stem cells (MSCs) is considered as a major risk factor for steroid-induced osteonecrosis of the femoral head (SOFNH). The role of microRNAs during this process has sparked interest. miR-486-5p expression was down-regulated significantly in femoral head bone tissues of both SONFH patients and rat models. The purpose of this study was to reveal the role of miR-486-5p on MSCs adipogenesis and SONFH progression. The present study showed that miR-486-5p could significantly inhibit adipogenesis of 3T3-L1 cells by suppressing mitotic clonal expansion (MCE). And upregulated expression of P21, which was caused by miR-486-5p mediated TBX2 decrease, was responsible for inhibited MCE. Further, miR-486-5p was demonstrated to effectively inhibit steroid-induced fat formation in the femoral head and prevented SONFH progression in a rat model. Considering the potent effects of miR-486-5p on attenuating adipogenesis, it seems to be a promising target for the treatment of SONFH.

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis.

BACKGROUND: This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated whether machine learning-derived radiomic features recognize LHB tendinitis. PATIENTS AND METHODS: A total of 170 patients were reviewed. All LHB tendinitis patients were diagnosed under arthroscopy. Radiomic features were extracted from preoperative magnetic resonance imaging (MRI), and the input dataset was divided into a training set and a test set. For feature selection, the t test and least absolute shrinkage and selection operator (LASSO) methods were used, and random forest (RF) and support vector machine (SVM) were used as machine learning classifiers. The sensitivity, specificity, accuracy, and area under the curve (AUC) of each model's receiver operating characteristic (ROC) curves were calculated to evaluate model performance. RESULTS: In total, 851 radiomic features were extracted, with 109 radiomic features extracted using a t test and 20 radiomic features extracted using the LASSO method. The random forest classifier shows the highest sensitivity, specificity, accuracy, and AUC (0.52, 0.92, 0.73, and 0.72). CONCLUSION: The classifier contract by 20 radiomic features demonstrated a good ability to predict extra-articular LHB tendinitis.However because of poor segmentation reliability, the value of Radiomic in LHB tendinitis still needs to be further explored.