The field of acupuncture is currently facing paradigmatic issues, including the difficulty of turning theoretical strengths into technical discourse power, a gap between mechanistic research and clinical imperatives, and the fragmentation of research activities. Therefore, constructing a research paradigm grounded in traditional Chinese medicine theory and validated through contemporary scientific methods is essential. Hand Twelve Jing-Well Points (HTWP) acupuncture, an ancient therapy known for its rapid efficacy and practical application, offers an optimal solution for these challenges. In this study, the theoretical foundations, clinical effectiveness, biological explanations, and Translational applications of HTWP acupuncture were consolidated by integrating a four-step research framework. Initially, we developed the "Well point-Brain Connection" framework rooted in classical theories and informed by the "Root-Knot" concept. Subsequent clinical practice provided evidence of its effectiveness in modulating awareness and facilitating motor-cognitive rehabilitation for patients with central nervous system issues. Moreover, mechanistic research provides scientific verification of a "Sensation-Transmission-Effect" cascade that encompasses the activation of arousal circuits and the repair of the blood-brain barrier. Ultimately, the promotion of translation, standardization of systems, and the use of wearable technologies have aided the transition from passive therapy to proactive health management. This closed-loop concept offers compelling evidence for the "Well point-Brain Connection" and provides an applicable framework for scientific innovation and global distribution of innovative acupuncture hypotheses.

Puerariae Lobatae Radix (PLR) is a traditional Chinese medicinal herb included in China's inaugural list of substances recognized as both food and medicine. PLR has a long history of use and a wide range of medicinal applications. Its use is associated with a broad range of health-promoting effects. Recent studies have shown that PLR contains abundant bioactive compounds including isoflavones and polysaccharides. These findings demonstrate significant potential for the prevention and treatment of metabolic diseases (MDs). The potential mechanisms underlying these effects involve several targets and pathways, including activation of the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) and AMPK signaling pathways; regulation of key factors, such as peroxisome proliferator-activated receptors; and modulation of the gut microbiota composition. This systematic review examines the botanical characteristics and primary chemical constituents of PLR, with particular emphasis on recent research advances, mechanisms of action, and the clinical applications of its active components in MDs intervention. This review aims to provide theoretical guidance for further development, quality improvement, and application of PLR in the prevention and treatment of MDs.

Traditional Chinese medicine (TCM) possesses unique advantages in disease prevention and treatment, yet its inherent complexity and diversity pose tremendous challenges for structural elucidation, mechanism research, and bioactivity characterization. High-resolution mass spectrometry (HRMS) technology demonstrates immense potential in TCM analysis due to its high sensitivity, high resolution, high throughput, and high efficiency. However, its application in TCM research remains constrained by the lack of intelligent analytical methods and unified standardized databases. Therefore, this paper focuses on the integration of artificial intelligence (AI) and mass spectrometry, providing a systematic review of the applications and potential of AI-driven mass spectrometry analysis in structural elucidation, data resource integration, multi-omics mechanism studies, and chemical biology. Furthermore, this article emphasizes that by leveraging AI models to learn the complex mapping from chemical structures to biological functions, fragment-based characterization has emerged as the bridge connecting chemical structures with biological activities. Molecular fragments themselves serve as the core "knowledge units" that carry bioactive information. Future research will focus on establishing high-quality mass spectrometry databases for the complete chemical profiles of TCM and promoting the standardization and open sharing of mass spectrometry databases, thereby advancing the integration of AI and mass spectrometry in TCM analysis and providing new tools for TCM research.

Network pharmacology provides a transformative framework for decoding multi-target, system-level mechanisms of the food-medicine homology (FMH) substances, overcoming the limitations of reductionist approaches by integrating multi-omics data, computational modeling, and network analysis. Central to this paradigm is the "Network Targets" theory, which conceptualizes therapeutic intervention as the reconfiguration of disease-associated biological networks rather than the modulation of isolated single targets. Artificial intelligence accelerates this process by enabling high-dimensional data integration, predictive modeling of synergistic combinations, and the identification of active constituents. This review outlines the key databases and computational tools that operationalize network pharmacology in FMH research and systematically categorizes their applications, including material screening, ingredient identification, synergy analysis, quality standard establishment, safety assessment, formula optimization, functional food discovery, and personalized recommendation, supported by experimental validation across numerous FMH items. Despite the challenges in data standardization and dynamic modeling, the integration of multi-omics, dynamic networks, and centralized repositories will further advance the field. Ultimately, network pharmacology will bridge traditional FMH wisdom with contemporary mechanistic rigor, positioning FMH as the cornerstone of precision nutrition and preventive medicine.