The purpose of this study was to investigate the efficacy of tranexamic acid (TXA) in patients undergoing open-wedge high tibial osteotomy (OWHTO). Patients from August 2018 to May 2020 were retrospectively studied. Clinical data were obtained including gender, age, height, weight, body mass index (BMI), smoking, alcohol consumption, hypertension, diabetes, history of aspirin, prepostoperative hematocrit (Hct) and hemoglobin (Hb), thrombotic events, blood transfusion requirement, hospital length of stay, size of osteotomy gap, and wound complications such as wound hematoma and infection. 52 patients were enrolled in the tranexamic acid group (TA group), and 48 patients were enrolled in the nontranexamic acid group (NTA group); there were no significant differences between both groups in terms of gender, age, BMI, preoperative Hb, size of osteotomy gap, incidence of smoking, alcohol consumption, hypertension, diabetes, history of aspirin, thrombotic events, blood transfusion requirement, and wound hematoma and infection. The mean hospital length of stay was 9.4 ± 1.0 days in the TA group and 11.0 ± 1.2 days in the NTA group ( P < 0.001 ), the blood loss was 296.0 ± 128.7  ml in the TA group and 383.3 ± 181.3  ml in the NTA group ( P < 0.05 ), and the postoperative Hb level was 120.8 ± 15.0  g/l in the TA group and 109.5 ± 13.8  g/l in the NTA group ( P < 0.001 ). In conclusion, the administration of TXA is beneficial to patients undergoing OWHTO via decreasing hospital length of stay, reducing blood loss, and maintaining higher postoperative Hb levels.

Osteoarthritis (OA) is the most common degenerative joint disease, and causes major pain and disability in adults. It has been reported that muscle weakness and inflammation contribute to osteoarthritis development and progression. Oxidative stress plays important roles in muscle dysfunction and inflammation in osteomyelitis. Baicalin, the major active constituent of the isolated root of Scutellarialateriflora Georgi, has been shown to have anti-oxidative and anti-inflammatory effects. In this study, we evaluated the potential effects of baicalin on osteoarthritis. We established experimental osteoarthritis rat model, applied baicalin to the rats, and then explored the potential protective effect of baicalin on osteoarthritis severity, muscle dysfunction, and oxidative stress. Baicalin alleviated severity of OA in rats. Baicalin application attenuated muscle dysfunction in OA rats by increasing citrate synthase activity, myosin heavy chain IIa expression, and decreasing interleukin 6 production. Baicalin decreased muscular reactive oxygen species generation in OA rats. Baicalin inhibited nuclear factor erythroid-derived 2-like 2 expression in OA rats. Baicalin attenuated osteoarthritis in rat by inhibiting oxidative stress.

Purpose We aim to present unsupervised machine learning-based analysis of clinical features, bone mineral density (BMD) features, and medical care costs of Rotator cuff tears (RCT). Patients and Methods Fifty-three patients with RCT were reviewed, the clinical features, BMD features, and medical care costs were collected and analyzed by descriptive statistics. Furtherly, unsupervised machine learning (UML) algorithm was used for dimensionality reduction and cluster analysis of the RCT data. Results There were 26 males and 27 females. The patients were divided into four subgroups using the UML algorithm. There were significant differences among four subgroups regarding trauma exposure, full-thickness supraspinatus tendon tears, infraspinatus tendon tear, subscapularis tendon tear, BMD distribution, medial row anchors, lateral row anchors, total medical care costs, and consumables costs. We observed the highest frequency of trauma exposure, infraspinatus tendon tear, subscapularis tendon tear, osteoporosis, the highest number of medial row anchors, lateral row anchors, total medical care costs, and consumables costs in subgroup II. Conclusion The unsupervised machine learning-based analysis of RCT can provide clinically meaningful classification, which shows good interpretability and contribute to a better understanding of RCT. The significance of the results is limited due to the small number of samples, a larger follow-up study is needed to confirm the encouraging results.

The purpose of the study was to identify patient characteristics related to blood loss following high tibial osteotomy (HTO). We evaluated 48 patients undergoing HTO from August 2018 to August 2019. The data of 48 patients were collected, including gender, age, height, weight, body mass index (BMI), smoking, alcohol consumption, hypertension, diabetes, history of aspirin, and pre-postoperative hematocrit (Hct). Multiple linear regression analysis was used to analyze the risk factors related to blood loss in HTO. The mean age of patients was 56.6±10.2 years, including 22 males and 26 females. The mean BMI was 28.5±4.2 kg/m2, and the mean blood loss volume was 383.3±181.3 mL, 13 patients with smoking (27.1%), 15 patients with alcohol consumption (31.3%), 23 patients with hypertension (47.9%), 10 patients with diabetes mellitus (20.8%), and 12 patients with history of aspirin (25.0%). Multiple linear regression model suggested alcohol consumption and BMI were associated with blood loss in HTO, R2=0.451, F9,38=3.462 (P<0.05). Our study indicates that alcohol consumption and BMI are important risk factors related to blood loss in HTO.

Purpose We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model. The t -test and least absolute shrinkage and selection operator (LASSO) method were used for feature selection, random forest and support vector machine (SVM) were used as machine learning classifiers. For each model, the sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate model performance. Results In total, 5 demographic features were recorded and 106 radiomics features were extracted. By applying the unsupervised machine learning algorithm, patients were divided into 5 groups. Group 5 had the highest incidence of ACL rupture and left knee involvement. There were significant differences in left knee involvement among the groups. Forty-three radiomics features were extracted using t -test and 7 radiomics features were extracted using LASSO method. We found that the combination of LASSO selection method and random forest classifier has the highest sensitivity, specificity, accuracy, and AUC. The 7 radiomics features extracted by LASSO method were potential predictors for ACL rupture. Conclusion We validated the clinical application of unsupervised machine learning involving ACL rupture. Moreover, we found 7 radiomics features which were potential predictors for ACL rupture. The study indicated that radiomics could be a valuable method in the prediction of ACL rupture.