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Postmenopausal osteoporosis (PMOP) poses a significant threat to women’s health worldwide. However, detailed molecular mechanism and therapeutic strategy for PMOP remain insufficient. Accumulating evidence suggests that miR-48-5p is implicated in the pathogenesis of osteoporosis. The present study aimed to determine the role and mechanism of miR-483-5p in PMOP. Results from PMOP patients demonstrated that miR-483-5p was up-regulated and SATB2 was down-regulated. Luciferase reporter assay identified SATB2 as a direct target gene of miR-483-5p. Experiments in MC3T3-E1 cells indicated that miR-483-5p mimic markedly inhibited cell viability as well as the expressions of OPG, RUNX2 and BMP2. And miR-483-5p inhibitor, SATB2-overexpressed lentiviruses (Lv-SATB2) or LY294002 (PI3K/AKT inhibitor) significantly reversed the above results. Similarly, PI3K/AKT signaling was activated by miR-483-5p mimic, and was inhibited in miR-483-5p inhibitor, Lv-SATB2 or LY294002 treated cells. In vivo experiments showed that miR-483-5p inhibitor significantly increased the bone mineral density and biomechanical parameters of femurs in ovariectomized (OVX) rats by targeting SATB2. In addition, the osteogenic differentiation and PI3K/AKT signaling were also regulated by miR-483-5p-SATB2 axis. Taken together, our findings indicated that miR-483-5p contributed to the pathogenesis of PMOP by inhibiting SATB2 and activating PI3K/AKT pathway. MiR-483-5p/SATB2 could be selected as a potential therapeutic target for PMOP.

Background: We developed a potential useful alternative prediction model based on the support vector machine (SAM) algorithm to predict the risk of preoperative deep vein thrombosis (DVT) in non-fractured patients awaiting total joint arthroplasty (TJA). Methods: From March 2015 to August 2020, a retrospective review of the preoperative ultrasound examination findings of lower extremity venous vessels was performed on non-fractured patients of 369 elective TJA. Based on the ultrasound examination findings of preoperative lower extremely venous vessels, these patients were divided into two groups: the DVT group and the Non-DVT group. We collected the clinical, imaging, and laboratory findings from an electronic medical record system. These variables were imported into univariate, multivariate and logistic regression analysis to identify the risk factor for preoperative DVT. According to published literature and clinical experience, a series of variables were selected to construct a prediction model based on the SVM machine learning algorithm. Results: Among the 369 patients, preoperative DVT was observed in 21 patients (5.7%). The Multivariate regression analysis showed the following 5 independent factors associated with preoperative DVT: preoperative fibrinogen odds ratio [OR] = 7.306), age (OR = 1.133), history of hypertension (OR = 3.848), preoperative hematocrit (OR = 0.315), and D-dimer (OR = 2.032). The SVM model achieved a maximum and average area under the receiver operating characteristic curve (AUC) of 0.94 and 0.77 in the 10-fold cross-validation. Meanwhile, the accuracy, precision, and recall of the model were 0.98, 0.92, and 0.93, respectively. Additionally, the confusion matrix showed the classification results of the discriminant analysis.Conclusions: SVM machine modeling is a promising method for the prediction of the risk of DVT in non-fractured patients awaiting TJA. However, future external validation is needed.