This review tried to find regular acupointselection in the treatment of cervical vertigo through literature search. Bibliometric analysis and data mining were referenced. There were a total of 42 relative articles, which cov-ered 86 acupoints, with thefrequency of 382. The acupointsselection of the highest frequency were GB20-Fengchi, EX-Jingjiaji, DU20-Baihui. It was concluded that study on the regular acupoint selection in the treatment of cervical vertigo providedbasis for rational clinical acupoint combination. It had important meaning in the standardization and research of cervical vertigo.

Rabbit hemorrhagic disease was described in China in 1984 and can cause hemorrhagic necrosis of the liver within two or three days after infection. The etiological agent, rabbit hemorrhagic disease virus (RHDV), belongs to the Lagovirus genus in the Caliciviridae family. Compared to other calicivirus, such as rNV and SMSV, the structure of Lagovirus members is not well characterized. In this report, structures of two types of wild RHDV particles, the intact virion and the core-like particle (CLP), were reconstructed by cryo-electron microscopy at 11 &0A and 17 &0A, respectively. This is the first time the 3D structure of wild caliciviruses CLP has been provided, and the 3D structure of intact RHDV virion is the highest resolution structure in Lagovirus. Comparison of the intact virion and CLP structures clearly indicated that CLP was produced from the intact virion with the protrusion dissociated. In contrast with the crystal structures of recombinant Norovirus and San Miguel sea lion virus, the capsomers of RHDV virion exhibited unique structural features and assembly modes. Both P1 and P2 subdomains have interactions inside the AB capsomer, while only P2 subdomains have interaction inside CC capsomer. The pseudo atomic models of RHDV capsomers were constructed by homology modeling and density map fitting, and the rotation of RHDV VP60 P domain with respect to its S domain, compared with SMSV, was observed. Collectively, our cryo-electron microscopic studies of RHDV provide close insight into the structure of Lagovirus, which is important for functional analysis and better vaccine development in the future.
Amino Acid Sequence ; Animals ; Caliciviridae Infections ; virology ; China ; Cryoelectron Microscopy ; Hemorrhagic Disease Virus, Rabbit ; ultrastructure ; Molecular Sequence Data ; Rabbits ; Sequence Alignment ; Viral Structural Proteins ; chemistry ; Virion ; ultrastructure

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.