Objective: To assess the activity of cathepsin D (CAT-D) and alpha-1 antitrypsin (AAT) in blood in patients with hip or knee osteoarthritis, and to explore whether these two enzymes could be served as serum biomarkers for cartilage degeneration. Methods: hTe activity of CAT-D and AAT in blood serum of 44 women and 26 men with hip or knee osteoarthritis was determined by the method of ELISA before total joint replacement and on the 10th day atfer the surgery. One hundred healthy volunteers were chosen as the control. All datawere analyzed by using SPSS19.0 sotfware. Results: Compared with the controls, the activity of CAT-D in patients with osteoarthritis was decreased by 25% (P<0.05) and 50% (P<0.05) before and atfer the surgery, respectively. hTe activity of AAT in the osteoarthritis patients before the surgery was not signiifcantly changed compared with the control group (P>0.05), but it was increased by 80% after the surgery than that in the control group (P<0.05). hTere was no signiifcant difference in the activities of 2 enzymes between hip and knee osteoarthritis (P>0.05). hTe gender, hypertension, diabetes and age did not affect the activities of the 2 enzymes (P>0.05). Conclusion: AAT might be a possible inflammatory indicator in the osteoarthritis. CAT-D and AAT enzymes are not affected by gender, age, hypertension and diabetes, etc, and they might be served as potential biomarkers for cartilage degradation.

Objective:To investigate the efficacy of a machine learning model based on radiomics of brain lesions on T 2WI in differentiating multiple sclerosis (MS) from neuromyelitis optica spectrum disorders (NMOSD). Methods:Totally 223 MS and NMOSD patients who were treated from January 2009 to September 2018 in Beijing Tiantan Hospital Affiliated to Capital Medical University, Donghu Branch of the First Affiliated Hospital of Nanchang University, Tianjin Medical University General Hospital, and Xuanwu Hospital of Capital Medical University were analyzed retrospectively, and according to the proportion of 7∶3, 223 patients were completely randomly divided into training set (156 cases) and test set (67 cases). A total of 74 patients with MS and NMOSD who were treated in Huashan Hospital Affiliated to Fudan University and China-Japan Friendship Hospital of Jilin University from January 2009 to September 2018 and in Xianghu Branch of the First Affiliated Hospital of Nanchang University from March 2020 to September 2021 were collected as an independent external validation set. All patients underwent brain cross-sectional MR T 2WI, radiomics features were extracted from T 2WI, and features were selected by max-relevance and min-redundancy and least absolute shrinkage and selection operator algorithms. Then various machine learning classifier models (logistic regression, decision tree, AdaBoost, random forest or support vector machine) were constructed to differentiate MS from NMOSD. The area under curve (AUC) of receiver operating characteristics was used to evaluate the performance of each classifier model in the training set, test set and external validation set. Results:Based on multi-center T 2WI, a total of 11 radiomics features related to the discrimination between MS and NMOSD were extracted and classifier models were constructed. Among them, the random forest model had the best efficiency in distinguishing MS from NMOSD, and its AUC values for distinguishing MS from NMOSD in the training set, test set and external validation set were 1.000, 0.944 and 0.902, with specificity of 100%, 76.9% and 86.0%, and sensitivity of 100%, 92.1% and 79.7%, respectively. Conclusion:The random forest model based on the radiomic features of T 2WI of brain lesions can effectively distinguish MS from NMOSD.