Cholangiocarcinoma (CCA) is a malignant tumor originating from cholangiocytes. However, it remains unclear about the pathogenesis of this carcinoma, which may be related to multiple factors. Currently, CCA is mainly treated by surgery, chemotherapy, and radiotherapy. Among them, surgery is the only potentially curative option for CCA. Nevertheless, the high malignancy and asymptomatic nature of CCA may lead to poor treatment outcomes. It has been demonstrated that Chinese medicine (CM) plays a significant role in various antitumor applications. Meanwhile, CM exhibits fewer side effects and high availability. Moreover, the in vitro application of CM monomers has been explored in many domestic and foreign studies. This article mainly reviews the signaling pathways and molecular mechanisms of CM monomers in the treatment of CCA in recent years. These findings are expected to provide new insights into the treatment of CCA.
Cholangiocarcinoma/drug therapy* ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Bile Duct Neoplasms/drug therapy* ; Medicine, Chinese Traditional ; Animals ; Signal Transduction/drug effects*

Peripheral nerve injury (PNI) encompasses damage to nerves located outside the central nervous system, adversely affecting both motor and sensory functions. Although peripheral nerves possess an intrinsic capacity for self-repair, severe injuries frequently result in significant tissue loss and erroneous axonal junctions, thereby impeding complete recovery and potentially causing neuropathic pain. Various therapeutic strategies, including surgical interventions, biomaterials, and pharmacological agents, have been developed to enhance nerve repair processes. While preclinical studies in animal models have demonstrated the efficacy of certain pharmacological agents in promoting nerve regeneration and mitigating inflammation, only a limited number of these agents have been translated into clinical practice to expedite nerve regeneration. Chinese herb medicine (CHM) possesses a longstanding history in the treatment of various ailments and demonstrates potential efficacy in addressing PNI through its distinctive, cost-effective, and multifaceted methodologies. This review critically examines the advancements in the application of CHM for PNI treatment and nerve regeneration. In particular, we have summarized the most commonly employed and rigorously investigated CHM prescriptions, individual herbs, and natural products, elucidating their respective functions and underlying mechanisms in the context of PNI treatment. Furthermore, we have deliberated on the prospective development of CHM in both clinical practice and fundamental research.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Peripheral Nerve Injuries/drug therapy* ; Animals ; Nerve Regeneration/drug effects* ; Medicine, Chinese Traditional

OBJECTIVE: To assess the efficacy and safety of Erzhu Jiedu Decoction (EZJDD) Granules in treating mid-advanced hepatitis B virus-associated primary liver cancer (HBV-PLC) patients with Pi (Spleen)-deficiency and dampness-heat syndrome. METHODS: From January 2021 to June 2023, a cohort of 132 patients were enrolled and randomly assigned to a control group or a EZJDD group according to the random numbers, with 66 patients in each group. The patients in the control group received conventional treatment for 3 months, followed by a 3-month follow-up. In addition to the conventional treatment, patients in the EZJDD group were administered EZJDD Granules (10.9 g/pack, 2 packs twice per day) orally for same duration. Progression-free survival (PFS) as primary outcome was evaluated by Kaplan Meier method. Karnofsky performance status (KPS) scores were used to assess the quality of life in two groups before and after treatment, and survival rates were determined as well. The efficacy of Chinese medicine syndrome was calculated with Nimodipine method. Liver function, tumor indicators and T lymphocyte subsets were measured, respectively. Safety indicators were recorded and assessed. RESULTS: Of the 116 patients who completed the study, 57 were in the control group and 59 in the EZJDD group. The median PFS was 3.53 months (106 days) in the EZJDD group compared to 2.33 months (70 days) in the control group (P=0.005). Six-month survival rate was 52.63% (30/57) in the control group and 69.49% (41/59) in the EZJDD group (P=0.039). The median KPS score in the EZJDD group [70(63, 90)] was higher than that in the control group [70(60, 80)] (P=0.013). The total effective rate of CM syndrome was 52.63% (30/57) in the control group and 77.97% (46/59) in the EZJDD group (P=0.005). The levels of alpha fetoprotein, alpha fetoprotein-L3, alpha-L-fucosidase and protein induced by Vitamin K absence or antagonist- II in the EZJDD group increased less than the control group (P>0.05). CD8⁺ levels were decreased, while CD3⁺ and CD4⁺ levels, as well as CD4⁺/CD8⁺ ratio were significantly increased in the EZZJD group (P<0.05). No treatment-related adverse reactions were observed during the study. CONCLUSION EZJDD Granules significantly prolonged the median PFS and improved 6-month survival rate in patients with mid-advanced HBV-PLC (Registration No. ChiCTR2200056922).
Humans ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Liver Neoplasms/complications* ; Hepatitis B virus/physiology* ; Hepatitis B/complications* ; Treatment Outcome ; Adult ; Spleen/drug effects* ; Quality of Life ; Medicine, Chinese Traditional ; Aged ; Syndrome

OBJECTIVE: To observe the clinical effect and safety of Lu'e Biyan Formula (LBF) combined with loratadine in the treatment of moderate to severe allergic rhinitis (AR) patients with Fei (Lung)-qi deficiency-coldness (FQDC) syndrome. METHODS: From September 2023 to December 2024, moderate to severe AR patients with FQDC syndrome were recruited from the Outpatient Department of Integrated Traditional Chinese and Western Medicine for Pulmonary Diseases Part 1, China-Japan Friendship Hospital. Participants were randomly assigned to a test group and a control group by using a random number table at a ratio of 1:1. Both groups received oral loratadine tablets (10 mg, once daily) for 2 weeks. In addition, the test group received oral LBF (30 mL, twice daily), and the control group received a placebo of LBF. Changes in the Total Nasal Symptom Score (TNSS), Total Non-nasal Symptom Score (TNNSS), Visual Analog Scale (VAS), Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ), and Chinese medicine (CM) syndrome scores before and after treatment were compared between groups. Moreover, the total effective rates and disease recurrence rates were compared. Adverse events (AEs) during the study period were also recorded. RESULTS: Totally 109 participants were recruited, and the full analysis set included 105 cases, 54 in the test group and 51 in the control group. Compared with the pre-treatment values, the scores of sneezing, runny nose, nasal obstruction, nasal itching, TNSS, TNNSS, VAS, RQLQ, and CM syndrome were significantly reduced in both groups at 1 and 2 weeks post-treatment and 12 weeks post-drug withdrawal (P<0.01). After treatment, the aforementioned scores in the test group were all markedly lower than those in the control group (P<0.01). Moreover, the total effective rate in the test group was higher than that in the control group (98.15% vs. 70.59%, P<0.01). After 12 weeks of drug withdrawal, there was no significant difference in the recurrence rate between groups (13.21% vs. 22.22%, P>0.05). No obvious AEs were observed in either group following treatment. CONCLUSIONS The combination of LBF with loratadine can effectively alleviate the symptoms of moderate to severe AR patients with FQDC syndrome, thereby improving their quality of life. This therapy demonstrated both precise effect and high safety. (Trial registration No. ITMCTR2025000589).
Humans ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Rhinitis, Allergic/drug therapy* ; Female ; Adult ; Double-Blind Method ; Quality of Life ; Qi ; Middle Aged ; Loratadine/therapeutic use* ; Medicine, Chinese Traditional ; Syndrome ; Lung/drug effects* ; Young Adult ; Treatment Outcome

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 formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

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

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

Bisphenol A (BPA) is a kind of exogenous chemicals presenting in the human living environment widely which affects the action of endocrine hormones in the human body. Numerous studies have shown that BPA has reproductive toxicity in the spermatogenic function damage of the testes through a variety of mechanisms such as interfering with endocrine function, inducing oxidative stress, promoting spermatogonial cell apoptosis, destroying the integrity of the blood-testis barrier, and regulating epigenetic inheritance, thereby destroying male fertility. Relevant studies have shown that TCM can improve male fertility by reversing BPA-induced reproductive damage through multi-component, multi-target and multi-mechanisms. However, there is no systematic review on the mechanism of TCM to reduce the reproductive toxicity of BPA. Based on the existing studies, this article will systematically introduce the mechanisms of BPA-induced reproductive impairment in men and the progress of TCM interventions, with a view to providing reference targets and research directions for the development of new Chinese medicines.
Humans ; Benzhydryl Compounds/adverse effects* ; Male ; Phenols/adverse effects* ; Medicine, Chinese Traditional ; Infertility, Male/chemically induced* ; Testis/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Bisphenol A Compounds