Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.
The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.
Background: Ischemic stroke (IS) is a leading cause of death and disability worldwide, posing a serious threat to human health. The herbal pair Astragali Radix–Chuanxiong Rhizoma (HQ–CX) is commonly used in the treatment of IS. However, the synergistic effects and underlying mechanisms of this herbal combination remain unclear. Objective: This study aimed to evaluate the therapeutic effects of the HQ–CX herbal pair on IS and to investigate its underlying mechanisms. Methods: A rat model of middle cerebral artery occlusion (MCAO) combined with Qi deficiency and blood stasis syndrome (QDBS) was established. The effects of HQ–CX on QDBS-MCAO rats, including improvements in QDBS symptoms, neurological function, and blood flow recovery in the ischemic area, were assessed by Qi deficiency syndrome score, blood stasis syndrome score, modified neurological severity score, cerebral blood flow measurement, and Nissl staining. Transcriptome analysis was conducted to predict the molecular mechanisms underlying the therapeutic effects of HQ–CX in IS. Additional experimental techniques, including enzyme-linked immunosorbent assay, immunofluorescence, Western blot analysis, and treatment with the pharmacological inhibitor deoxyribonuclease I, were employed to validate the efficacy of HQ–CX and explore its potential molecular targets. Furthermore, the intestinally absorbed chemical constituents of HQ–CX were identified by UPLC-Orbitrap-MS analysis, and its effects on neutrophil extracellular trap (NET) formation in vitro were evaluated through enzyme-linked immunosorbent assay, Western blot, and immunofluorescence assays. Results: In this study, HQ–CX not only alleviated symptoms associated with QDBS but also enhanced blood flow restoration in ischemic regions, thereby reducing infarct size in MCAO rats after ischemia. Transcriptomic analysis revealed that HQ–CX may exert neuroprotective effects by inhibiting NET formation, a potential therapeutic target for IS, through suppression of complement component 5a receptor 1, peptidyl arginine deiminase 4, and integrin subunit beta 3 expression. Furthermore, HQ–CX significantly reduced plasma levels of cell-free DNA and histone-DNA complexes in MCAO rats, and decreased the expression of myeloperoxidase and citrullinated histone H3 in the pia mater, cortex, and striatum of the ischemic hemisphere. Interestingly, HQ–CX failed to provide additional neuroprotection when NETs were degraded by deoxyribonuclease I, suggesting that its neuroprotective effects against ischemic injury are primarily mediated through inhibition of NET formation. Conclusion: HQ–CX exerts protective effects against IS by inhibiting NET formation through the complement component 5a/complement component 5a receptor 1 signaling pathway. These findings provide scientific evidence supporting the clinical efficacy of this herbal pair in the treatment of IS.
Background: Advances in intensive care have markedly improved survival after severe brain injury, leading to a growing population of patients with prolonged disorders of consciousness (pDOC). Current management of pDOC remains largely supportive, and evidence-based neuromodulatory interventions are limited; moreover, existing guidelines provide insufficiently explicit recommendations regarding mechanisms of action and objective biomarkers of treatment response. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential noninvasive intervention; however, its modulatory effects on brain function in pDOC are not yet well characterized, and the paucity of integrative mechanistic evidence has constrained its translation into routine clinical practice. Objectives: Within a multimodal assessment framework, this study aims to systematically elucidate the neurobiological mechanisms by which taVNS modulates brain function and autonomic activity in patients with pDOC, and to evaluate its clinical potential to enhance levels of consciousness. Methods: In this randomized controlled trial, 60 patients with vegetative state/minimally conscious state will be enrolled and randomly allocated to a taVNS group, a transcutaneous nonauricular vagus nerve stimulation group (sham), or a control group ( n = 20 per group) for a 4-week intervention. The primary outcome will be changes in the Coma Recovery Scale-Revised scores from baseline to weeks 1, 2, and 4 of treatment. Secondary outcomes will include functional brain activity assessed by electroencephalography and functional near-infrared spectroscopy, as well as autonomic modulation indexed by heart rate variability. Functional prognosis will be evaluated using the Glasgow Outcome Scale-Extended at the end of treatment and at a 6-month follow-up. Safety will be assessed by continuous monitoring and documentation of adverse events throughout the study period. Results and Discussion: By integrating electroencephalography–functional near-infrared spectroscopy with heart rate variability, this study will characterize the effects of taVNS on functional brain networks and consciousness recovery in pDOC across complementary behavioral, electrophysiological, hemodynamic, and autonomic domains, while interrogating potential sources of clinical and neurobiological heterogeneity. The findings are expected to provide a mechanistic and evidence-based foundation for the mechanism-driven clinical implementation of taVNS and the optimization of stimulation protocols in pDOC. Clinical Trial Registration: International Traditional Medicine Clinical Trial Registry, ITMCTR20250021041, https://itmctr.ccebtcm.org.cn.
Licorice ( Glycyrrhiza ) is a medicinal herb utilized globally, distributed across all continents except Antarctica, with a history of medicinal application spanning over 3000 years. It has been documented in traditional medical systems across various countries and regions for centuries. Over the course of history, the applications of licorice have evolved from traditional medicinal uses to encompass modern industrial applications. Licorice is incorporated into food products as a sweetener, preservative, and stabilizer, and is used in the cosmetics industry because of its antioxidant properties. Its antibacterial effects also render it a valuable component in toothpaste for maintaining oral hygiene, thereby contributing to human health. In agriculture, licorice plays a multifaceted role by promoting plant growth, improving livestock quality, and enhancing antibacterial capacity and immunity in aquaculture species. Furthermore, it is utilized to enhance the properties of composite materials. The extensive applications of licorice across various sectors have also stimulated trade growth. In addition, licorice contributes to environmental improvement while providing unique ecological benefits. However, future application trends and safety concerns warrant further research. This paper reviews the literature and ancient texts on the traditional efficacy, contemporary multi-industry applications, and safe use of licorice, aiming to provide a scientific basis for the development of licorice-related products and the sustainable utilization of licorice resources.
