Acupotomy inhibits aberrant formation of subchondral bone through regulating osteoprotegerin/receptor activator of nuclear factor-κB ligand pathway in rabbits with knee osteoarthritis induced by modified Videman method.

OBJECTIVE: To investigate the effects of acupotomy on inhibiting abnormal formation of subchondral bone in rabbits with knee osteoarthritis (KOA). METHODS: A total of 24 New Zealand rabbits were randomly divided into four groups of 6 rabbits each [control, model, electroacupuncture (EA) and acupotomy]. Eighteen KOA model rabbits were established using a modified Videman method. Rabbits in EA and acupotomy groups received the intervention for 3 weeks. Then, the cartilage and subchondral bone unit were obtained and the histomorphological changes were recorded. Osteo-protegerin (OPG) and receptor activator of nuclear factor-κB ligand (RANKL) in subchondral bone were evaluated by Western blotting, real-time polymerase chain reaction and immunohistochemistry. RESULTS: Compared with the model group, both the acupotomy and EA groups showed a significant decrease in the Lequesne index (both 0.01) and Mankin score ( 0.01, < 0.05). In addition, both EA and acupotomy groups had a higher expression of total articular cartilage (TAC) ( 0.05, < 0.01) and lower expression of articular calcified cartilage (ACC)/TAC ( 0.05, < 0.05) compared with the model group. The thickness of the subchondral bone plate in EA and acupotomy groups were decreased (both 0.01) compared to the model group. Moreover, trabecular bone volume (BV/TV), protein and relative expression of OPG and the ratio of OPG/RANKL in the subchondral bone of acupotomy group were decreased statistically significant, while these parameters were not significantly changed in the EA group compared with the model group. CONCLUSIONS: In the rabbit model of KOA, acupotomy inhibits aberrant formation of subchondral bone by suppressing OPG/RANKL ratio as a potential therapy for KOA.

Cases of Ultrasound-Diagnosed Right Aortic Arch with Right Arterial Duct and the Treatment.

This paper aims to discuss the value of ultrasound to diagnose right aortic arch with right arterial duct. A retrospective analysis of fetal echocardiography characteristics of 10 fetuses who were diagnosed as right aortic arch with right arterial duct from December 2016 to March 2021 is made, and focus is put on the relationship between the aortic arch and arterial duct, and the position of aortic arch, arterial duct arch and trachea on the three vessels and trachea view (3VT). As a result, all 10 cases with right aortic arch and right arterial duct do not show aberrant left subclavian artery, and aortic arch with arterial duct are still connected as "V-shaped", and do not get vascular rings. In conclusion, 3VT can simply and clearly detect the right aortic arch, and the key to diagnosing the right aortic arch and right arterial duct is thorough inspection of the aortic arch, arterial duct, and trachea in their respective positions.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.