OBJECTIVE: To investigate the clinical effects of thread-dragging therapy on gangrene of non-ischemic diabetic foot ulcers (NIDFU). METHODS: A total of 136 patients with NIDFU were recruited from the Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between June 21, 2021 and February 1, 2023, and randomized into an intervention group and a control group, with 68 cases in each group. Both groups received basic treatment. The intervention group was treated with thread-dragging therapy, while the control group was treated with debridement combined with routine dressing changes after surgery. Both groups were treated continuously for 2 months. The amputation rates and changes in the ulcer area were compared between the groups. The inflammatory response index including peripheral white blood cells (WBCs), neutrophil percentage (NEUT%), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), and interleukin 6 (IL-6) were compared between the two groups. RESULTS: After treatment, the ulcer areas in the intervention group were significantly smaller than that of the control group (8.50±3.88 cm² vs. 10.11±4.61 cm², P<0.05). The amputation rates of the two groups were not statistically significant (4.4% vs. 5.9%, P>0.05). Differences of WBCs count, CRP, and ESR before and after therapy in the intervention group were better than the control group (P<0.05). However, there were no significant differences in changes of NEUT%, PCT, and IL-6 between the two groups (P>0.05). CONCLUSION Thread-dragging therapy may be effective in the treatment of NIDFU, with the additional advantages of less tissue damage after healing. (Registration No. ChiCTR2100047496).
Humans ; Diabetic Foot/blood* ; Male ; Female ; Middle Aged ; Gangrene/therapy* ; Medicine, Chinese Traditional/methods* ; Aged ; C-Reactive Protein/metabolism* ; Amputation, Surgical ; Wound Healing ; Treatment Outcome ; Interleukin-6/blood*

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

The advances of modern medicine have inadvertently led to a globally ageing population plagued primarily with non-communicable diseases. In addition to traditional medical approaches, nature-based therapy is becoming an increasingly attractive option, with its potential to holistically address physical and mental facets of health and well-being, and to complement 'preventive' and 'population health' strategies, both of which form the bedrock of sustainable healthcare. However, at present, there is vast heterogeneity in the implementation of nature-based therapy, limiting its widespread and sustainable use. The aim of this review is to practically examine and provide a focussed summary of the current evidence with a view to identifying existing gaps and limitations, and to propose directions for future research and implementation within the healthcare setting.
Humans ; Singapore ; Tertiary Care Centers ; Hospitals, Public ; Delivery of Health Care ; Nature ; Complementary Therapies/methods* ; Medicine, Traditional/methods*

Treatment based on meridian differentiation is a characteristic method in clinical acupuncture diagnosis and treatment. Accurately defining and explaining its main content and core concepts is essential for effective clinical guidance. This paper reviews the historical and contemporary understanding, concepts, and primary content of treatment based on meridian differentiation. It proposes a four-step process for clinical application: meridian examination, treatment based on meridian differentiation, acupoint selection, and appropriate treatment methods, with TCM syndrome differentiation applied throughout. Constructing a diagnostic and treatment system which is based on meridian differentiation and suited to clinical acupuncture is significant for enhancing therapeutic efficacy and maximizing the benefits of acupuncture in disease treatment.
Humans ; Meridians ; Acupuncture Therapy/methods* ; Acupuncture Points ; Medicine, Chinese Traditional

Transcutaneous electrical stimulation equipment is a kind of characteristic therapeutic devices developed on the basis of the integration of traditional Chinese medicine (TCM) theory and modern science and technology, which is widely used in clinical practice. Significant breakthroughs have been made in the development of related devices such as transcutaneous electrical acupoint stimulation (TEAS) devices, transcutaneous electrical nerve stimulation (TENS) devices, and transcutaneous auricular vagus nerve stimulation (taVNS) devices in recent years. Although the market for these devices is vast, there are still limitations that need to be optimized in terms of electrode materials and power supply methods, bulky instrument size, cumbersome wiring, restricted applications, and inadequate intelligent functionality. In the future, it is still necessary to further build upon the theoretical foundation of TCM acupuncture, integrate a variety of modern scientific technologies to advance the intelligence and modernization of acupuncture equipment, and thereby improving its capabilities to support clinical practice and research.
Humans ; Transcutaneous Electric Nerve Stimulation/methods* ; Acupuncture Points ; Acupuncture Therapy/instrumentation* ; Medicine, Chinese Traditional