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Antidepressant effects of Yuanzhi (Polygalae Radix) extract on chronic unpredictable mild stress-induced depression in rats: modulation of the NLRP3 inflammasome and NF-κB pathway

CHEN Yuzhen ; ZHAO Yongzhi ; ZHANG Yiwen ; CHEN Fang ; Iqbal Choudhary Muhammad ; LIU Xinmin ; JIANG Ning

Digital Chinese Medicine.2024;7(2):184-194. doi: 10.1016/j.dcmed.2024.09.009

Objective: To investigate the antidepressant effects of Yuanzhi (Polygalae Radix, PR) aqueous extract on chronic unpredictable mild stress (CUMS)-induced depression rat models and the underlying mechanisms. Methods: A total of 40 male Sprague Dawley (SD) rats were randomly divided into control, model, low dose of PR (PR-L, 0.5 g/kg), high dose of PR (PR-H, 1 g/kg), and fluoxetine (10 mg/kg) groups, with 8 rats in each group. Except for the rats in control group, those in the other four groups underwent CUMS-induced depression modeling. PR and fluoxetine were administered intragastrically once daily, 30 min prior to the CUMS procedure, for 14 consecutive days until the behavioral tests were performed. After CUMS modeling, the sucrose preference test (SPT), open field test (OFT), novelty-suppressed feeding test (NSFT), forced swim test (FST), and tail suspension test (TST) were employed to assess the pharmacological effects of PR on the mitigation of depressive-like behaviors in rat models. Additionally, the enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the serum levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β in the rats. Western blot analysis was also conducted to evaluate the protein expression levels of nuclear factor kappa-B (NF-κB), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), and caspase-1 in the hippocampal tissues of the rats. Immunofluorescence staining was performed to observe the morphological changes in ionized calcium-binding adapter molecule 1positive (Iba-1+) cells in the dentate gyrus (DG) of rats with CUMS-induced depression. Result: (i) Treatment with PR-H and fluoxetine resulted in significant enhancements in both the total distance and time the rats moved during tests (P < 0.01 and P < 0.05, respectively). Post-administration of PR-H and fluoxetine also led to statistically significant increase in sucrose preference among rats (P < 0.05). Besides, PR-L, PR-H, and fluoxetine treatment markedly decreased the latency of ingestion (P < 0.05, P < 0.05, and P < 0.01, respectively). As observed from the FST, PR-L, PR-H, and fluoxetine presented antidepressant effects on rats with CUMS-induced depression, leading to the reduction in time of their immobility (P < 0.05, P < 0.01, and P < 0.01, respectively). The results of TST indicated reduced immobility time in rats receiving PR-H and fluoxetine treatment as well (P < 0.01). (ii) Rats in model group showed an increase in the levels of Iba-1+ microglia in their left and right brains in comparison with control group (P < 0.01). However, such increase was negated post PR treatment (P < 0.01). Treatment with PR-L, PR-H, and fluoxetine considerably reduced the levels of inflammatory factors (TNF-α, IL-1β, and IL-6, P < 0.01). In addition, treatment of PR-L and PR-H effectively counteracted the elevated levels of NLRP3, ASC, and caspase-1, and markedly down-regulated the expression levels of phosphorylated p65 (p-p65), COX-2, and iNOS in rats’ hippocampus (P < 0.01). Conclusion Collectively, these findings indicate that PR exerts an antidepressant effect on rats with CUMS-induced depression partially through the modulation of the NLRP3 and NF-κB signaling pathways.

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Quantum theory-based physical model of the human body in TCM

Shuna SONG ; Zhensu SHE

Digital Chinese Medicine.2022;5(4):354-359. doi:10.1016/j.dcmed.2022.12.002

In the study, a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence, vigor, and spirit in the human body in traditional Chinese medicine (TCM). It is assumed that there is a macro human order parameter (wave function), and its dynamics are governed by a macro potential field reflecting influences from heaven, earth, and society, and satisfy the generalized Schrodinger equation. This proposed model was applied in the study to interpret basic concepts of human body in TCM, with an aim to unfold the TCM development in the future.

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A new interpretation of TCM pulse diagnosis based on quantum physical model of the human body

Shuna SONG ; Zhensu SHE

Digital Chinese Medicine.2022;5(4):360-366. doi:10.1016/j.dcmed.2022.12.009

Following the quantum theory-based physical model of the human body, a new interpretation of the traditional Chinese medicine (TCM) principle of “Cunkou reads viscera” is presented. Then, a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes, and to quantitatively capture the degree of Yin-Yang attributes of 13 pulse shapes. Furthermore, the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity, to characterize the hierarchical resonance between the human body and the environment. The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state, and diagnose its health state according to the pulse deviation from its equilibrium state, to truly achieve the high level of TCM: “knowing the normal state and reaching the change”.

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Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Xiaotong CHEN ; Yeuk-Lan Alice LEUNG ; Jiangang SHEN

Digital Chinese Medicine.2022;5(4):367-376. doi:10.1016/j.dcmed.2022.12.003

Cardiovascular diseases (CVDs) are major disease burdens with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, traditional Chinese medicine (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.

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Data-driven based four examinations in TCM: a survey

Dong SUI ; Lei ZHANG ; Fei YANG

Digital Chinese Medicine.2022;5(4):377-385. doi:10.1016/j.dcmed.2022.12.004

Traditional Chinese medicine (TCM) diagnosis is a unique disease diagnosis method with thousands of years of TCM theory and effective experience. Its thinking mode in the process is different from that of modern medicine, which includes the essence of TCM theory. From the perspective of clinical application, the four diagnostic methods of TCM, including inspection, auscultation and olfaction, inquiry, and palpation, have been widely accepted by TCM practitioners worldwide. With the rise of artificial intelligence (AI) over the past decades, AI based TCM diagnosis has also grown rapidly, marked by the emerging of a large number of data-driven deep learning models. In this paper, our aim is to simply but systematically review the development of the data-driven technologies applied to the four diagnostic approaches, i.e. the four examinations, in TCM, including data sets, digital signal acquisition devices, and learning based computational algorithms, to better analyze the development of AI-based TCM diagnosis, and provide references for new research and its applications in TCM settings in the future.

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Research on knowledge reasoning of TCM based on knowledge graphs

Zhiheng GUO ; Qingping LIU ; Beiji ZOU

Digital Chinese Medicine.2022;5(4):386-393. doi:10.1016/j.dcmed.2022.12.005

With the widespread use of Internet, the amount of data in the field of traditional Chinese medicine (TCM) is growing exponentially. Consequently, there is much attention on the collection of useful knowledge as well as its effective organization and expression. Knowledge graphs have thus emerged, and knowledge reasoning based on this tool has become one of the hot spots of research. This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning, and explores the significance of knowledge reasoning. Secondly, the mainstream knowledge reasoning methods, including knowledge reasoning based on traditional rules, knowledge reasoning based on distributed feature representation, and knowledge reasoning based on neural networks are introduced. Then, using stroke as an example, the knowledge reasoning methods are expounded, the principles and characteristics of commonly used knowledge reasoning methods are summarized, and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out. Finally, we summarize the problems faced in the development of knowledge reasoning in TCM, and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

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Construction and application of knowledge graph of Treatise on Febrile Diseases

Dongbo LIU ; Changfa WEI ; Shuaishuai XIA ; Junfeng YAN

Digital Chinese Medicine.2022;5(4):394-405. doi:10.1016/j.dcmed.2022.12.006

Objective: To establish the knowledge graph of “disease-syndrome-symptom-method-formula” in Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) for reducing the fuzziness and uncertainty of data, and for laying a foundation for later knowledge reasoning and its application. Methods: Under the guidance of experts in the classical formula of traditional Chinese medicine (TCM), the method of “top-down as the main, bottom-up as the auxiliary” was adopted to carry out knowledge extraction, knowledge fusion, and knowledge storage from the five aspects of the disease, syndrome, symptom, method, and formula for the original text of Treatise on Febrile Diseases, and so the knowledge graph of Treatise on Febrile Diseases was constructed. On this basis, the knowledge structure query and the knowledge relevance query were realized in a visual manner. Results: The knowledge graph of “disease-syndrome-symptom-method-formula” in the Treatise on Febrile Diseases was constructed, containing 6 469 entities and 10 911 relational triples, on which the query of entities and their relationships can be carried out and the query result can be visualized. Conclusion The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system, and improves the completeness and accuracy of the knowledge representation, and the connection between “disease-syndrome-symptom-treatment-formula”, which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.

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MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation

Zhenchao CUI ; Shujie SONG ; Jing QI

Digital Chinese Medicine.2022;5(4):406-418. doi:10.1016/j.dcmed.2022.12.008

Objective: For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed. Methods: To obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of encoder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyramid pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation networks. Results: The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels. Conclusion Based on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features, which can provide another diagnosis method for computer-aided Chinese medical diagnosis.

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Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network

Junfeng YAN ; Zhihua WEN ; Beiji ZOU

Digital Chinese Medicine.2022;5(4):419-428. doi:10.1016/j.dcmed.2022.12.007

Objective: To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) dataset and explore an optimal learning method represented with node attributes based on graph convolutional network (GCN). Methods: Clauses that contain symptoms, formulas, and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs, which were used to propose a node representation learning method based on GCN − the Traditional Chinese Medicine Graph Convolution Network (TCM-GCN). The symptom-formula, symptom-herb, and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes, and thus acquiring the nodes’ sum-aggregations of symptoms, formulas, and herbs to lay a foundation for the downstream tasks of the prediction models. Results: Comparisons among the node representations with multi-hot encoding, non-fusion encoding, and fusion encoding showed that the Precision@10, Recall@10, and F1-score@10 of the fusion encoding were 9.77%, 6.65%, and 8.30%, respectively, higher than those of the non-fusion encoding in the prediction studies of the model. Conclusion Node representations by fusion encoding achieved comparatively ideal results, indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

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QingNangTCM: a parameter-efficient fine-tuning large language model for traditional Chinese medicine

Xuming TONG ; Liyan LIU ; Yanhong YUAN ; Xiaozheng DING ; Huiru JIA ; Xu YANG ; Sio Kei IM ; Mini Han WANG ; Zhang XIONH ; Yapeng WANG

Digital Chinese Medicine.2026;9(1):1-12. doi:10.1016/j.dcmed.2026.02.002

Objective: To develop QingNangTCM, a specialized large language model (LLM) tailored for expert-level traditional Chinese medicine (TCM) question-answering and clinical reasoning, addressing the scarcity of domain-specific corpora and specialized alignment. Methods: We constructed QnTCM_Dataset, a corpus of 100 000 entries, by integrating data from ShenNong_TCM_Dataset and SymMap v2.0, and synthesizing additional samples via retrieval-augmented generation (RAG) and persona-driven generation. The dataset comprehensively covers diagnostic inquiries, prescriptions, and herbal knowledge. Utilizing P-Tuning v2, we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM. A multi-dimensional evaluation framework, assessing accuracy, coverage, consistency, safety, professionalism, and fluency, was established using metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), and LLM-as-a-Judge with expert review. Qualitative analysis was conducted across four simulated clinical scenarios: symptom analysis, disease treatment, herb inquiry, and failure cases. Baseline models included GLM-4-9B-Chat, DeepSeek-V2, HuatuoGPT-II (7B), and GLM-4-9B-Chat (freeze-tuning). Results: QingNangTCM achieved the highest scores in BLEU-1/2/3/4 (0.425/0.298/0.137/0.064), ROUGE-1/2 (0.368/0.157), and METEOR (0.218), demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy, coverage, and consistency. Although its ROUGE-L score (0.299) was lower than that of HuatuoGPT-II (7B) (0.351), it significantly outperformed domain-specific models in expert-validated win rates for professionalism (86%) and safety (73%). Qualitative analysis confirmed that the model strictly adheres to the “symptom-syndrome-pathogenesis-treatment” reasoning chain, though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes. Conclusion Combining domain-specific corpus construction with parameter-efficient prompt tuning enhances the reasoning behavior and domain adaptation of LLMs for TCM-related tasks. This work provides a technical framework for the digital organization and intelligent utilization of TCM knowledge, with potential value for supporting diagnostic reasoning and medical education.

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