The therapeutic effect of manual acupuncture (MA) is closely related to the stimulation amount. In the clinical studies, the stimulation amount is often difficult to be determined. The reason is that there are many parameters affecting the stimulation amount, including manipulation selection, treatment time, needling velocity, and force, and no complete and reasonable scheme is available for the measurement of stimulation parameters. This paper reviewed the theoretical and laboratory measurement studies on MA stimulation, summarized 4 types of available parameters according to the theory of physics, and compared the advantages and disadvantages of the existing methods of parameter measurement. Such efforts are hoped for providing reference for the establishment of the stimulation parameter system of MA and possible technical solutions for future measurement experiments.

Objective: This study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia.Methods: We analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors.Results: The RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions.Conclusion: The results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

As a minimally invasive method of physical stimulation, manual acupuncture (MA) is used globally as a sort of therapy for neuromusculoskeletal disorders. In addition to selecting appropriate acupoints, acupuncturists should also determine the stimulation parameters of needling, such as the manipulation (lifting-thrusting or twirling), needling amplitude, velocity, and stimulation time. At present, most studies focus on acupoint combination and mechanism of MA, the relationship between stimulation parameters and their therapeutic effects, as well as the influence on mechanism of action are relatively scattered, and lack of systematic summary and analysis. This paper reviewed the three types of stimulation parameters of MA, their common options and values, corresponding effects and potential mechanisms of action. The purpose of such efforts is to provide a useful reference for the dose-effect relationship of MA and the quantification and standardization of its clinical treatment of neuromusculoskeletal disorders to further promote the application of acupuncture in the world.

Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination () was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.