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IntroductionKnee osteoarthritis (KOA) is a chronic degenerative bone metabolic disease that primarily affects older adults, leading to chronic pain and disability that affect patients’ daily activities. Electroacupuncture (EA) is a commonly used method for the treatment of chronic pain in clinical practice. Previous studies indicate that the endocannabinoid system is involved in EA analgesia, but whether endocannabinopeptide VD-hemopressin (α) and RVD-hemopressin (α) derived from hemoglobin chains are involved in EA analgesia is unclear.MethodsRNA-seq technology was used to screen which genes involved in EA analgesia. The expression of hemoglobin α chain and 26S proteasome were determined by Western blotting. The level of VD-hemopressin (α) and RVD-hemopressin (α) were measured by UPLC-MS/MS. Microinjection VD-Hemopressin (α), RVD-Hemopressin (α) and 26S proteasome inhibitor MG-132 into vlPAG, then observe mechanical and thermal pain thresholds.ResultsTherefore, we used RNA-seq to obtain differentially expressed genes Hba-a1 and Hba-a2 involved in EA analgesia in the periaqueductal gray (PAG), which were translated into the hemoglobin α chain. EA significantly increased the expression of the hemoglobin α chain and the level of hemopressin (α) and RVD-hemopressin (α). Microinjection of VD-hemopressin (α) and RVD-hemopressin (α) into the ventrolateral periaqueductal gray (vlPAG) mimicked the analgesic effect of EA, while CB1 receptor antagonist AM251 reversed this effect. EA significantly increased the expression of 26S proteasome in KOA mice. Microinjection of 26S proteasome inhibitor MG132 before EA prevented both the anti-allodynic effect and upregulation of the concentration of RVD-hemopressin (α) by EA treatment and upregulated the expression of the hemoglobin α chain.DiscussionOur data suggest that EA upregulated the concentration of VD-hemopressin (α) and RVD-hemopressin (α) through enhancement of the hemoglobin α chain degradation by 26S proteasome in the PAG, then activated the CB1 receptor, thereby exerting inhibition of chronic pain in a mouse model of KOA. These results provide new insights into the EA analgesic mechanisms and reveal possible targets for EA treatment of chronic pain.

In current smart classroom research, numerous studies focus on recognizing hand-raising, but few analyze the movements to interpret students’ intentions. This limitation hinders teachers from utilizing this information to enhance the effectiveness of smart classroom teaching. Assistive teaching methods, including robotic and artificial intelligence teaching, require smart classroom systems to both recognize and thoroughly analyze hand-raising movements. This detailed analysis enables systems to provide targeted guidance based on students’ hand-raising behavior. This study proposes a morphology-based analysis method to innovatively convert students’ skeleton key point data into several one-dimensional time series. By analyzing these time series, this method offers a more detailed analysis of student hand-raising behavior, addressing the limitations of deep learning methods that cannot compare classroom hand-raising enthusiasm or establish a detailed database of such behavior. This method primarily utilizes a neural network to obtain students’ skeleton estimation results, which are then converted into time series of several variables using the morphology-based analysis method. The YOLOX and HrNet models were employed to obtain the skeleton estimation results; YOLOX is an object detection model, while HrNet is a skeleton estimation model. This method successfully recognizes hand-raising actions and provides a detailed analysis of their speed and amplitude, effectively supplementing the coarse recognition capabilities of neural networks. The effectiveness of this method has been validated through experiments.