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Tong-Fu Wang,* De-Sheng Chen,* Jia-wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jia-wang ZhuDepartment of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of ChinaEmail zhulingelou@126.comPurpose: 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.Keywords: unsuper

De-Sheng Chen,* Tong-Fu Wang,* Jia-Wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jia-Wang ZhuDepartment of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of ChinaEmail zhulingelou@126.comPurpose: 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 th

Si-Feng Shi,1 Jing-Fu Jia,2 Xiao-Kui Guo,3 Ya-Ping Zhao,2 De-Sheng Chen,1 Yong-Yuan Guo,1 Tao Cheng,1 Xian-Long Zhang11Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital, School of Medicine, 2School of Chemistry and Chemical Technology, 3Department of Medical Microbiology and Parasitology, School of Medicine, Shanghai Jiao Tong University Shanghai, ChinaBackground: Bone disorders (including osteoporosis, loosening of a prosthesis, and bone infections) are of great concern to the medical community and are difficult to cure. Therapies are available to treat such diseases, but all have drawbacks and are not specifically targeted to the site of disease. Chitosan is widely used in the biomedical community, including for orthopedic applications. The aim of the present study was to coat chitosan onto iron oxide nanoparticles and to determine its effect on the proliferation and differentiation of osteoblasts.Methods: Nanoparticles were characterized using transmission electron microscopy, dynamic light scattering, x-ray diffraction, zeta potential, and vibrating sample magnetometry. Uptake of nanoparticles by osteoblasts was studied by transmission electron microscopy and Prussian blue staining. Viability and proliferation of osteoblasts were measured in the presence of uncoated iron oxide magnetic nanoparticles or those coated with chitosan. Lactate dehydrogenase, alkaline phosphatase, total protein synthesis, and extracellular calcium deposition was studied in the presence of the nanoparticles.Results: Chitosan-coated iron oxide nanoparticles enhanced osteoblast proliferation, decreased cell membrane damage, and promoted cell differentiation, as indicated by an increase in alkaline phosphatase and extracellular calcium deposition. Chitosan-coated iron oxide nanoparticles showed good compatibility with osteoblasts.Conclusion: Further research is necessary to optimize magnetic nanoparticles for the treatment of bone disease.Keywords: chitosan-coate