Exploring the Rules of Related Parameters in Transcutaneous Electrical Nerve Stimulation for Cancer Pain Based on Data Mining

Safety and Effectiveness of Electroacupuncture During Colon Endoscopic Submucosal Dissection: A Randomized Controlled Trial

Background

Endoscopic treatment of early colon neoplasms has evolved as a valid and less traumatic alternative to surgical resection. It can usually be performed with sedation on an outpatient basis. The present study was performed to determine the safety and effectiveness of electroacupuncture (EA) versus propofol sedation during endoscopic submucosal dissection (ESD) for early colon neoplasm.

Methods

A total of 150 adult outpatients undergoing ESD were selected and divided into the EA combined with propofol group (EP group), remifentanil combined with propofol group (RP group), and propofol group (SP group), with 50 patients in each group. All patients received standard sedation with propofol. Acupuncture was performed before intravenous propofol injection in the EP group. A density wave of 1-3 mA, 2/100 hz current was administered for 20 min before the induction of anesthesia. The effectiveness of sedation was measured by satisfaction levels, and pain and sedation scores were measured by questionnaires. Respiratory and hemodynamic complications were monitored and compared as indices of safety.

Results

Demographic data were comparable among the three groups. The total dose of propofol and the percentage of body movement in the EP group were lower than in the SP and RP groups (P<0.01). The incidence of hypotension and bradycardia in the SP and RP groups was higher than in the EP group. Patients who received the EA intervention showed a significant reduction in hypoxemia. The endoscopists felt that the procedure was more favorable in the EP group, but, there was no significant difference of patient satisfaction scores among three groups.

Conclusion

Sedation with EA is effective and safe for patients undergoing ESD, and could improve the satisfaction levels of patients and gastroendoscopists.

Integrative research on the mechanisms of acupuncture mechanics and interdisciplinary innovation

As a traditional therapeutic approach, acupuncture benefits from modern biomechanics, which offers a unique perspective for understanding its mechanisms by investigating the mechanical properties of biological tissues and cells under force, deformation, and movement. This review summarizes recent advancements in the biomechanics of acupuncture, focusing on three main areas: the mechanical effects of acupuncture, the transmission mechanisms of mechanical signals, and the personalization and precision of acupuncture treatments. First, the review introduces the structural basis of the tissues involved in acupuncture; analyzes the mechanical responses of the skin, dermis, and subcutaneous tissues from needle insertion to point activation; and discusses how these responses impact acupuncture efficacy. Second, the phenomenon of mechanical coupling during acupuncture is discussed in detail, especially the role of connective tissues, including the wrapping and self-locking of collagen fibers, the remodeling of the cytoskeleton and the regulation of mitochondrial function triggered by acupuncture. Third, this article examines the mechanisms of mechanical signal transmission in acupuncture, explaining how mechanosensitive ion channels are activated during the procedure and subsequently initiate a cascade of biochemical responses. Finally, the review highlights the numerical simulation methods used in acupuncture, including the mechanical modeling of skin tissues, the exploration of the mechanical mechanisms of acupuncture, and visualization studies of the needling process. By integrating multidisciplinary research findings, this paper delves into the entire mechanical process of acupuncture, from skin penetration to point stimulation, and analyzes tissue responses to provide a solid theoretical foundation for the scientific study of acupuncture. In addition, directions for future research to further refine acupuncture techniques for clinical applications are proposed.

Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT

In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model’s “precision,” “recall,” and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets.

Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default

Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT

In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model's "precision," "recall," and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets. Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default.