IncRNA MALAT1 Regulates the Proliferation, Apoptosis, Migration, and Invasion of Osteosarcoma Cells by Targeting miR-873-5p/ROCK1.

The malignant bone tumor osteosarcoma (OS) was one of the most aggressive tumors. Despite breakthroughs in treatment options for OS recently, the survival rate of patients with metastasis or reoccurring disease has remained unchanged over the last 25 years, at around 20%. lncRNA expression dysregulation is linked to carcinogenesis, advancement, and metastasis. Additionally, the fundamental mechanism of lncRNAs in regulating OS cell biological activity and progression is still being investigated. The expression of miR-873-5p and MALAT1 were detected by quantitative real-time polymerase chain reaction (qRT-PCR) in OS. The relationship between the expression level of MALAT1 and the survival rate of OS individuals was evaluated by the Kaplan-Meier plotter. The tumor cell's capability of proliferation was determined using the CCK-8. Transwell was used to test the migratory and invasive properties of tumor cells. ROCK1 protein expression was analyzed by western blot, while qRT-PCR was used to detect ROCK1 mRNA expression. Targeted genes of MALAT1 or miR-873-5p were predicted by StarBase2.0. The target association among miR-873-5p and MALAT1 or ROCK1 was confirmed using the luciferase assay. The relationship between ROCK1 and MALAT1 or miR-873-5p expression in OS was investigated using Spearman's correlation analysis. MALAT1 was up-regulated and was linked to a lower survival rate of patients in OS. The malignant behaviors of cells were inhibited by down-regulated MALAT1 in vitro. Dual-luciferase gene experiments confirmed the presence of MALAT1/miR-873-5p/ROCK1 axis. The up-regulated miR-873-5p blocked the promoted effects of MALAT1 on cell behaviors. Over-expressed MALAT1 promoted the malignant behaviors of cells by miR-873-5p/ROCK1 axis in OS.

Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning-Based Development and Validation Study.

BACKGROUND: The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. OBJECTIVE: The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. METHODS: We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. RESULTS: A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual's probability of ONFH. CONCLUSIONS: Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.

[Research progress on the application of lower limb alignment in discoid meniscus injury].

Discoid meniscus injury is a kind of common sports injury. Its treatment methods include arthroscopic discoid meniscus plasty, discoid meniscus subtotal resection, discoid meniscus total resection and so on. Although the short-term clinical effect is good, the long-term clinical effect is not ideal. At present, different scholars have different views on the choice of surgical methods for discoid meniscus injury. In recent years, many scholars have shown that the choice of operation and the change of lower limb force line are related to the therapeutic effect of discoid meniscus injury. This paper mainly summarizes the current situation of the treatment of discoid meniscus injury and the changes of the force line of the lower limbs after operation, and expounds therole of the evaluation of the force line of the lower limbs in the treatment of discoid meniscus, so as to provide the basis for the clinical individualized treatment of discoid meniscus injury.

[The risk factors of periprosthetic fracture after hip arthroplasty:a meta-analysis].

OBJECTIVE: To explore risk factors of the periprosthetic fracture after hip arthroplasty. METHODS: Potential studies were searched in databases including Pubmed, Embase, Cochrane Library, CNKI as well as Wanfang Database up to November 2018 and references in related literatures. The methodological quality of literature was estimated by Newcastle-Ottawa Scale. Raw data were merged and tested mainly by Revmain 5.3. RESULTS: Seventeen studies in total were appropriate with 90 632 patients. The results revealed that it increased the risk of periprosthetic fracture after hip arthroplasty, including female (OR=1.62, 95%CI:1.44 to 1.82, P<0.01), revision(OR=3.78, 95%CI:1.88 to 7.58, P<0.01), preoperative diagnosis of rheumatoid arthritis(OR=1.60, 95%CI:1.07 to 2.37, P=0.02). Conversely, patients involved with cemented prosthesis fixation(OR=0.43, 95%CI:0.27 to 0.68, P<0.01) were less likely to suffer periprosthetic fracture after hip arthroplasty. Other factors were not significantly relevant to periprosthetic fracture after hip arthroplasty, including the age, preoperative diagnosis(femoral head necrosis, osteoarthritis, developmental dysplasia of the hip, femoral fracture, concomitant heart diseases) and American Society of Anesthesiologists >=3. CONCLUSIONS: Orthopedics doctors should constantly be cantious about the risk factors including female, revision and diagnosis of rheumatoid arthritis. They are supposed to prevent the periprosthetic fracture by gentle operation during hip arthroplasty and monitoring the functional exercise after operations when the above risk factors occur.