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Objective: To explore the development and research hotspots on the application of artificial intelligence (AI) in traditional Chinese medicine (TCM) diagnosis and predict research trends in the area. Methods: All articles were retrieved from China National Knowledge Infrastructure (CNKI), Wanfang Data (Wanfang), China Science and Technology Journal Database (VIP), and Web of Science Core Collection (WoSCC). All related papers published in journals from the foundation of the databases to December 31, 2022 were included. NoteExpress, Co-Occurrence (COOC), VOSviewer, and CiteSpace were used to visualize data about publication volumes, journals, authors, research institutions, and keywords as well as to analyze hotspots trending topics in the field. Results: A total of 686 articles were retrieved from the databases, among which 610 papers were published in Chinese and 76 in English. In terms of the journals in which these papers were published, 238 of them were Chinese journals and 52 were English ones. The number of the papers published in journals presented a slow growth. According to the results from Chinese article analysis, WANG Yiqin from Shanghai University of Traditional Chinese Medicine published the most papers in the field. The authors of Chinese papers belonged to six long-term research teams, led by WANG Yiqin and XU Jiatuo (Shanghai University of Traditional Chinese Medicine), WEI Yuke (Guangdong University of Technology), LI Gang (Tianjin University), XI Guangcheng (Institute of Automation of the Chinese Academy of Sciences), and NIU Xin (Beijing University of Chinese Medicine), respectively. In accordance with results from English paper analysis, four authors equally publishing the most papers were YAN Haixia, HU Xiaojuan, and JIANG Tao (Shanghai University of Traditional Chinese Medicine), and WEN Chuanbiao (Chengdu University of Traditional Chinese Medicine). The authors of English papers were from two major research teams in the field of Shanghai University of Traditional Chinese Medicine. Currently, research hotspots on AI such as neural networks, data mining, machine learning, feature recognition, image processing, and expert systems, have been centered on tongue diagnosis, pulse diagnosis, and syndrome research in TCM. Additionally, it was found that research on the topic was gradually evolving from explorations of a single diagnosis method to investigations on the combination of multiple TCM diagnosis methods. Conclusion: Research on AI application in TCM diagnosis is still in a slowly growing stage. As technology develops, AI has been applied to many aspects of TCM diagnosis. Therefore, how to combine the two for improving TCM diagnosis is something worthy of our brainstorming and exploring.

Aiming at the problem of fatigue fracture of pipeline connectors, a finite element model of pipeline connectors in aircraft hydraulic system is established. Based on the theory of continuous damage mechanics, the vibration fatigue life of pipeline connectors with assembly stress is predicted. The results show that under normal assembly state, the fatigue crack initiation point of pipeline connectors is located at the contact part between flared conduit and flat nozzle. With the increase of vibration load amplitude, the fatigue life of pipeline connections decreases gradually. When there is axial assembly deviation, due to the influence of stress concentration, the fatigue crack initiation point of pipeline connector is located at the connection between pipe flare and pipe body. The axial assembly deviation weakens the fatigue strength of pipeline connectors. The larger the axial deviation is, the smaller the fatigue life of pipeline structure is.

Objective To investigate the effect of " One syndrome and One Product " combined with medical care integration mode in improving the quality of nursing care for osteoporotic patients with spleen-kidney deficiency and blood stasis syndrome. Methods Totally 60 osteoporotic patients with spleen-kidney deficiency and blood stasis syndrome were randomly divided into control group and observation group, with 30 cases in each group. The control group was given routine nursing intervention, and the observation group was given nursing intervention based on " One syndrome and One Product " combined with medical care integration mode. The patient’s level of knowledge on disease, degree of pain, nursing satisfaction and activity of daily living were measured and compared between two groups. Results After intervention, patients in the observation group achieved a higher level of knowledge on disease, lower score of pain assessment and higher score of nursing satisfaction compared with those in the control group (P＜0. 05). After intervention, Barthel Index in both groups was higher than that before intervention (P

The feature size of circuits was gradually reduced to a few nanometers, which is prone to lead to the failure of the metal circuit even upon a low bias voltage due to the electromigration. Therefore, it is essential to understand the electrical transport characteristics of a narrow metal wire shrunk to atomic scale due to electromigration. To this end, we report that the approach for metal deposition and the underneath substrate play a critical role in determining the electron transport behavior. It is observed that the conductance of the narrow metal wire fabricated on a SiO2 substrate first rises and then decreases during the electromigration process when the cross section of the metal wire is reduced to a few atoms. However, such a phenomenon is not observed for the metal wire fabricated on a polyimide substrate. Assisted by component analysis technology, it is revealed that the metal atoms can penetrate into the underneath substrate during the metal deposition process, and the metal atoms buried in the different substrates result in distinguished conductance behavior. [ABSTRACT FROM AUTHOR]

Hydrodynamic force loading platforms based on acoustofluidics have been developed to study the mechanical deformation of cancer cells and to control cell behavior. However, so far there have been no experimental measurements on living plant cells using such an acoustic approach. Unique structures, including cell walls, allow plant cells to exhibit more variation in mechanical resistance. In this work, we analyzed plant cell deformation and membrane permeability using a gigahertz (GHz) acoustofluidic system. By recording the proportion of intact cells in the cell population, we evaluated the mechanical resistance of the protoplasts to the hydrodynamic forces of the acoustic streaming. The results showed that a regenerated primary cell wall (PCW) could significantly improve the mechanical resistance of individual plant cells within 24 h compared to the freshly prepared protoplasts without walls. The results of enzymatic degradation showed that three main components of the primary cell wall contribute to different degrees to the improvement of the mechanical properties of the cells, in decreasing order: cellulose, hemicellulose, and pectin. Furthermore, we have shown that such an acoustofluidic system can alter the permeability of the protoplast membrane in a controllable manner for transient gene expression. [ABSTRACT FROM AUTHOR]