Objective: To investigate the protective effects of Gouqizi (Lycii Fructus, GQZ)-Danshen (Salviae Miltiorrhizae Radix et Rhizoma, DS) against hydrogen peroxide (H2O2)-induced oxidative injury in 661W retinal photoreceptor cells and to explore whether these effects involve angiopoietin-like 4 (ANGPTL4). Methods: Liquid chromatography-mass spectrometry (LC-MS) was used to identify the major components of GQZ-DS aqueous extract. An H2O2-induced oxidative injury model was established in 661W cells. Working concentrations of H2O2 and GQZ-DS were determined using the cell counting kit-8 (CCK-8) assay. Cells were subjected to GQZ-DS, ANGPTL4 knockdown, or ANGPTL4 overexpression as indicated. Flow cytometry was used to analyze cell cycle distribution, apoptotic rate, and intracellular reactive oxygen species (ROS). Colorimetric determination of malondialdehyde (MDA) content and superoxide dismutase (SOD) activity using the thiobarbituric acid (TBA) method was performed. The protein expression level of ANGPTL4 was assessed by immunofluorescence. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to quantify the mRNA level of ANGPTL4. Western blot was used to detect the protein levels of ANGPTL4 and cleaved caspase-3. Results: LC-MS identified five major constituents in the GQZ-DS aqueous extract: 2-O-β-D-glucopyranosyl-L-ascorbic acid, rutin, D-galactose, salvianolic acid A, and tanshinone IIA. The CCK-8 method selected 300 μmol/L H2O2 as the oxidative stress condition for experiments, and 0.05 and 0.1 g/mL were selected as the low and high doses of GQZ-DS, respectively, for subsequent experiments. The intervention with H2O2 resulted in reduced cell viability, elevated ROS and MDA levels, decreased SOD activity, increased apoptotic rate, upregulated cleaved caspase-3 expression, and G0/G1 phase cell cycle arrest of 661W cells. Both low and high doses of GQZ-DS alleviated these alterations, with high dose exhibiting stronger protective effects (P < 0.05 or P < 0.01). GQZ-DS also downregulated ANGPTL4 expression at both the mRNA and protein levels. Following plasmid transfection of cells, the study further revealed that ANGPTL4 knockdown mitigated oxidative stress and apoptosis-related injury, whereas ANGPTL4 overexpression exacerbated these pathological changes. Moreover, GQZ-DS partially reversed the degree of cellular oxidative damage induced by ANGPTL4 overexpression. [ Conclusion: GQZ-DS can alleviate the H2O2-induced oxidative injury of 661W retinal photoreceptor cells, and the effects are related to the down-regulation of ANGPTL4 expression.
Objective: To address the lack of fine-grained clinical recognition for specific Yang deficiency syndrome subtypes and the limitations of conventional object detection models in extracting irregular, low-contrast tongue phenotypes. This study aims to develop an objective subtype recognition framework based on an improved You Only Look Once nano (YOLO11n) architecture, using a standardized visual phenotype matrix to translate macroscopic traditional Chinese medicine (TCM) descriptions into quantifiable clinical targets. Methods: This cross-sectional diagnostic study consecutively enrolled adult inpatients admitted to the Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital), between September 1, 2024 and June 1, 2025, who were suspected of having Yang deficiency constitution based on initial TCM consultation. Clinical tongue image data were collected for analysis. Based on an Expert Visual Phenotype Annotation Matrix, a five-category recognition system was established, including the following TCM syndrome subtypes: spleen-dampness exuberance syndrome, mild kidney Yang deficiency syndrome, upper heat and lower cold syndrome, simultaneous Yin-Yang deficiency syndrome, and Yin deficiency and fluid depletion syndrome (negative control). The proposed Yang deficiency YOLO (YD-YOLO) model, built upon the YOLO11n baseline, integrates the Cross Stage Partial with kernel size 2 (C3k2)-GhostBottleneck-Dynamic Convolution (GBDC) module into the backbone to adaptively extract low-contrast features, and embeds the multipath aggregation coordinate attention (MACA) mechanism into the neck to suppress background interference through multi-scale spatial coordination. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize feature attribution and evaluate the biological plausibility of the model’s focus. Model performance was evaluated through ablation and comparative experiments using mean average precision (mAP), precision, recall, F1 score, inference speed (frames per second, FPS), overall accuracy, Cohen’s kappa, and the area under the receiver operating characteristic (ROC) curve (AUC). Results: Based on the final inclusion of 1 186 clinical cases, the YD-YOLO model had an overall accuracy of 91.5%, a Cohen’s kappa of 0.912, and an mAP@50 of 0.731 [higher than the YOLO11n baseline (0.681)], with AUC ranging from 0.91 to 0.97 across all TCM syndrome subtypes. Among the TCM syndrome subtypes, the mild kidney Yang deficiency syndrome had the highest mAP@50 (0.900), and the inference speed reached 89.00 FPS. Grad-CAM analysis showed that the model localized activation to key TCM pathological features, such as marginal tooth marks and focal root coatings, while suppressing non-diagnostic oral background noise. Conclusion: The YD-YOLO model demonstrates the feasibility of deep learning for the fine-grained classification of TCM Yang deficiency subtypes. By integrating visual phenotype quantification with model interpretability, the proposed framework provides an objective basis for syndrome differentiation, supporting the development of standardized digital diagnostic systems and the provision of clinical decision support in TCM practice.
Yin-Yang theory is one of the core components of the foundational theory of traditional Chinese medicine (TCM), yet its classification as philosophical or scientific has long been controversial. To move beyond the longstanding either-philosophy-or-science debate surrounding Yin-Yang theory in TCM, this article, for the first time, proposes a three-level framework spanning macro, meso, and micro perspectives. At the macro level, Yin and Yang are a theoretical achievement of ancient Chinese natural philosophy, used to explain universal laws governing how the cosmos operates, which constitutes Yin-Yang theory in philosophy; at the meso level, Huangdi Neijing (《黄帝内经》, Inner Canon of Huangdi) translated Yin-Yang from a cosmological framework into medical discourse, making it a core methodological basis for clinical pattern differentiation and treatment, which constitutes Yin-Yang theory in medicine; at the micro level, Yin-Yang theory in TCM has increasingly converged with modern science, showing scientific features that are testable and reproducible, which constitutes Yin-Yang theory in science. The article further demonstrates that Yin-Yang theory in TCM differs markedly from Yin-Yang as a general philosophical doctrine in core application and practical orientation: in the course of its medical transformation, elements related to life phenomena were selectively incorporated, while, within a medical context, abstract propositions such as cosmological speculation were bracketed and rendered concrete, thereby achieving a practice-oriented transition from philosophy to medicine. Based on this, the present study conducts analyses from four aspects, namely philosophical roots, clinical application, modern scientific interpretation, and implications for life sciences; it substantiates the logical basis for Yin-Yang theory in TCM as the overarching framework for clinical pattern differentiation and treatment, reviews modern research progress from perspectives such as systems science and network regulation, and explores its potential value in advancing a new paradigm of state-based medicine. The study suggests that an accurate understanding of Yin-Yang theory in TCM requires moving beyond the either-philosophy-or-science binary and analyzing specific issues on a case-by-case basis. Across different perspectives, Yin-Yang theory in TCM has philosophical, medical, and scientific attributes. Only by grounding the theory in actual clinical efficacy and engaging with modern science can the innovative development of Yin-Yang theory in TCM be promoted, thereby providing valuable Eastern insights for building a life science system with original Chinese contributions.
Objective: To elucidate the biological basis of traditional Chinese medicine (TCM) syndromes from the perspective of “same syndrome, different diseases” in patients with chronic hepatitis B (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC), thereby providing a complementary approach for the diagnosis and treatment of chronic liver diseases (CLD). Methods: To investigate the dynamic characteristics of TCM syndromes in CLD, transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) was performed from patients with CHB, LC, or HCC presenting with three TCM syndromes: liver gallbladder dampness heat syndrome (LGDHS), liver depression spleen deficiency syndrome (LDSDS), and liver kidney Yin deficiency syndrome (LKYDS). These participants were recruited at Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between August 1, 2018 and December 31, 2021. Differentially expressed genes (DEGs) were identified using the random variance model (RVM) F test with false discovery rate (FDR) correction. Principal component analysis (PCA) and unsupervised hierarchical clustering were applied to visualize sample grouping. Dynamic network biomarkers (DNB) analysis was employed to detect critical transition stages during syndrome evolution, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to characterize the functional roles and pathway involvement of the DNB members. Random forest (RF) analysis and the area under the receiver operating characteristic (ROC) curves (AUC) were used to rank the importance of candidate genes. External validation was performed using microarray data from an independent CLD cohort (GSE89377) and RNA-seq data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Additionally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on an independent cohort of LC patients to validate the expression levels of the candidate genes. Results: The study included a total of 132 participants. DNB analysis identified LDSDS stage as a critical tipping point during TCM syndrome evolution across CHB, LC, and HCC. The phosphoinositide 3-kinase/protein kinase B (PI3K-AKT) signaling pathway was consistently enriched in the DNB analysis across all three types of CLD, suggesting its potential involvement in the critical transition of TCM syndromes. Among the 24 core DNB members of the PI3K-AKT pathway, four genes—integrin subunit beta 1 (ITGB1), collagen type IV alpha 1 chain (COL4A1), collagen type IV alpha 2 chain (COL4A2), and DNA damage inducible transcript 3 (DDIT3)—were identified by RF analysis (Gini score > 1) and ROC analysis. ROC analysis demonstrated high discriminative ability for distinguishing LGDHS from LKYDS in CHB patients, with AUC of 0.7891 for ITGB1, 0.7070 for COL4A1, 0.7148 for COL4A2, and 0.8945 for DDIT3. In the independent CLD cohort (GSE89377), all four genes showed significant stepwise upregulation from normal to CHB, LC, and HCC (all P < 0.05). In the TCGA-LIHC dataset, their expression progressively increased with tumor stage. RT-qPCR validation in an independent LC cohort (30 LGDHS vs. 30 LKYDS) confirmed that ITGB1, COL4A2, and DDIT3 were significantly upregulated in LKYDS compared with LGDHS (P = 0.0152, 0.0186, and 0.0247, respectively), whereas COL4A1 showed a non-significant upward trend (P = 0.1201). Conclusion: This study introduces a novel approach to understanding the molecular features underlying TCM syndrome evolution in CLD. The PI3K-AKT pathway and four identified genes (ITGB1, COL4A1, COL4A2, and DDIT3) play crucial roles in the transition from excess (LGDHS) to deficiency (LKYDS) via the critical LDSDS stage. These findings offer potential quantitative biomarkers and therapeutic targets for TCM syndrome differentiation and may help arrest syndrome progression in CLD.
Objective: To perform a comparative evaluation of Mamdani and Takagi-Sugeno (TS) fuzzy inference systems for predicting peripheral vascular resistance from photoplethysmogram (PPG) signals, incorporating diagnostic parameters from Persian medicine (PM) pulsology. Methods: Both fuzzy inference systems were implemented in MATLAB R2021b and validated using leave-one-out cross-validation (LOOCV) on clinical PM pulse diagnostic data collected from 35 healthy volunteers. The dataset included pulse frequency scale (1 – 6) and weakness scale (1 – 4) alongside corresponding PPG-derived peripheral vascular resistance indices (range: 0.019 – 0.983). The Mamdani system (with 35 rules) was designed using trapezoidal and triangular membership functions, a singleton fuzzifier, a product inference engine, and a centroid defuzzifier. The TS system (with 6 rules) was configured as a first-order model with Gaussian input membership functions. System performance was quantitatively evaluated using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2). Statistical significance of performance differences was assessed using a paired t test. Results: he Mamdani inference system showed much higher prediction accuracy than the TS system. Comparative analysis revealed a substantial advantage for the Mamdani system (MAE = 0.007 63 ± 0.001 20, RMSE = 0.008 58 ± 0.001 50, R2 = 0.998 40 ± 0.000 80) over the TS system (MAE = 0.015 40 ± 0.002 10, RMSE = 0.022 48 ± 0.002 80, R2 = 0.989 20 ± 0.001 50). A paired t test comparing the absolute errors of the 35 LOOCV folds confirmed statistical significance [t (34) = 5.07, P < 0.001]. Conclusion: he findings suggest the potential suitability of the Mamdani system for certain precision-oriented analytical tasks. Both systems exhibit practical utility for integrative medicine diagnostics and wearable health-monitoring applications for healthy individuals, enabling a modern computational translation of PM pulsology.
Objective: This study proposes a clustering framework for Chinese materia medica (CMM) based on a large language model (LLM), aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory. Methods: First, a CMM property knowledge base was constructed based on Chinese Materia Medica, including 567 commonly used CMMs characterized by four properties, five flavors, and meridian tropism. Then, 49 CMMs derived from 10 prescriptions for Zangdu (脏毒, pathogenic toxins) recorded in Waike Zhengzong (《外科正宗》, Orthodox Manual of External Medicine) and Yangke Xinde Ji (《疡科心得集》, Collected Insights on Ulcer Medicine) were selected as the experimental dataset. Five semantic representation methods—One-Hot, Word2Vec, Bidirectional Encoder Representations from Transformers (BERT), Beijing Academy of Artificial Intelligence General Embedding (BGE), and Qwen—were applied to encode CMM property information into vector representations. Subsequently, t-distributed Stochastic Neighbor Embedding (t-SNE) was used for nonlinear dimensionality reduction on high-dimensional semantic vectors, followed by k-means clustering (k = 7). Clustering performance was evaluated using the Silhouette Score (SS), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). Results: The Qwen-based clustering method, CMM-EmbedCluster, achieved the highest SS (0.607 4) and CHI (158.057 2), as well as the lowest DBI (0.499 5), indicating improved cluster separation and compactness compared with other methods. Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space, with strong inter-cluster discrimination and high intra-cluster functional consistency. Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties, five flavors, and meridian tropism, forming functional partitions consistent with CMM property theory. Conclusion: CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory, providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics.
Objective: To address the challenges of systematically identifying herb pairs in traditional Chinese medicine (TCM), we proposed HerbGL, a framework for predicting potential herb pairs that integrates network propagation and graph regularization. Methods: Based on the assumption that herbal actions induce subtle perturbations in biological systems, a framework named HerbGL was proposed. Random walk with restart (RWR) was first applied to the protein-protein interaction (PPI) network to reconstruct herb-specific perturbation effects and generate weighted subnetworks. Then, to quantify affinity between herb pairs, two network-proximity metrics, Closeness and PageRank, were computed from the weighted subnetworks to construct herb-pair affinity matrices. Finally, these matrices, together with known herb pairs (derived from co-occurrence analysis of TCM formulas with a threshold determined from the inflection point of the frequency distribution), were incorporated into a graph regularization model to predict potential herb pairs. Model performance was assessed through baseline comparison, ablation and robustness experiment under different ratios of positive and negative samples, using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, and precision as evaluation metrics. Furthermore, the predicted herb pairs were validated through both literature evidence and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Results: The weighted subnetworks constructed by RWR provided a refined simulation of herb-specific perturbation effects, which formed the basis for subsequent affinity modeling and prediction. Analysis of herb pair co-occurrence frequencies revealed a marked change around 150, which was selected as the threshold to distinguish herb pairs from non-herb pairs. HerbGL exhibited superior predictive performance compared with baseline models (AUROC = 0.970 5, AUPRC = 0.955 5, accuracy = 0.726 6, precision = 0.970 6). Ablation results showed that removing the Closeness and PageRank metrics substantially degraded performance (AUROC = 0.819 1, AUPRC = 0.876 8), confirming their necessity. Robustness evaluation under an imbalanced positive-to-negative sample ratio of 1 : 5 yielded AUROC = 0.969 6 and AUPRC = 0.840 4, indicating stable predictive ability. Moreover, multiple case studies further validated the rationality of the predicted herb pairs, such as Fangfeng (Saposhnikoviae Radix) and Qingpi (Citri Reticulatae Pericarpium Viride) which are recorded in Liangpeng Huiji (《良朋汇集》, Collection of Excellent Recipes) Vol. 3: Fangfeng Shengma Tang (防风升麻汤). Additionally, pathway enrichment analysis of the Renshen (Ginseng Radix et Rhizoma) and Lianqiao (Forsythiae Fructus) pair further supported the biological plausibility of their compatibility. Conclusion: HerbGL offers an effective and biologically informed framework for identifying herb pairs in TCM. Beyond improving herb pair prediction, the framework also provides data support for research on herb compounds and mechanisms, thereby supporting data-driven exploration of TCM compatibility.
Traditional Chinese medicine (TCM) pulse diagnosis is a non-invasive approach used to infer cardiovascular status, but its interpretation is relatively subjective, limiting reproducibility and diagnostic precision. This review summarizes progress in digitized radial pulse assessment using modern sensors and artificial intelligence (AI), and evaluates reported applications in cardiovascular screening and decision support. We searched PubMed, IEEE Xplore, and Web of Science Core Collection from inception through November 30, 2025, for studies that acquired wrist/radial pulse signals with electronic devices and applied quantitative analysis or machine learning/deep learning to characterize pulse patterns or assess cardiovascular conditions. Across the literature, pressure-sensor arrays, wearable photoplethysmography (PPG) surrogates, and hybrid platforms enabled more standardized pulse acquisition, while AI models reported promising performance for tasks such as blood pressure estimation, hypertension screening, coronary artery disease identification, heart failure risk stratification, and arrhythmia detection. However, methodological heterogeneity, limited sample sizes, inconsistent labeling standards, and insufficient external validation remain key barriers to clinical translation. Overall, AI-enhanced digital pulse diagnosis may improve the objectivity of TCM pulse assessment and complement conventional cardiovascular diagnostics, provided that future studies adopt rigorous protocols, transparent reporting, and clinically meaningful prospective validation.
Objective: To map the research landscape of artificial intelligence (AI)-assisted tongue diagnosis through bibliometric analysis and to quantify its diagnostic accuracy and clinical interpretability through a diagnostic test accuracy (DTA) meta-analysis. Methods: For the bibliometric analysis, the Web of Science Core Collection (WoSCC) was queried for English-language articles and reviews on AI-assisted tongue diagnosis published between January 1, 2014 and December 31, 2025, and analysed using Bibliometrix, VOSviewer, and CiteSpace, with major output dimensions including annual publication output and disciplinary distribution, journal and citation characteristics, country/region and institutional collaboration, author networks, keyword co-occurrence, and keyword burst detection. For the DTA meta-analysis, four databases [Scopus, PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI)] were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidelines. A bivariate random-effects model hierarchical summary receiver operating characteristic (HSROC) was used to pool sensitivity and specificity, with subgroup analyses by disease category, AI model architecture, and sample-size strata. Methodological quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) tool, and publication bias was evaluated by Deeks’ funnel plot asymmetry test. Results: A total of 198 publications met the bibliometric eligibility criteria. Annual output increased 24.5-fold (from 2 in 2014 to 49 in 2025), with the period 2022 – 2025 alone accounting for 65.2% of all publications. China contributed approximately 83.5% of all institutional affiliations, with Shanghai University of Traditional Chinese Medicine and Jiatuo Xu being the most productive institution and author, respectively. Keyword analysis identified four thematic clusters (AI and deep-learning architectures, image processing and segmentation, traditional Chinese medicine (TCM)-specific applications, and disease-specific applications) and a temporal evolution from traditional machine learning to deep learning and transformer-based, explainable, and multimodal AI architectures. Sixteen DTA meta-analysis studies (14 755 participants) covering metabolic and hepatic disorders, oncological and oral lesions, cardiovascular risk, diabetes, and other clinical applications were included in the DTA meta-analysis. The pooled sensitivity was 90.3% [95% confidence interval (CI): 86.7% – 93.1%] and the pooled specificity was 93.0% (95% CI: 90.6% – 94.7%); the area under the summary receiver operating characteristic (SROC) curve (AUC) was 0.961. Heterogeneity was substantial (I2 = 95.8% for sensitivity; I2 = 92.1% for specificity). Subgroup performance was broadly consistent across disease categories, AI architectures, and sample-size strata, and Deeks’ test indicated no significant publication bias (P = 0.258). Conclusion: AI-assisted tongue diagnosis has progressed rapidly and shows pooled diagnostic performance comparable to established screening modalities, supporting its potential as a complementary and easily accessible decision-support tool.
Objective: To systematically evaluate the clinical efficacy of topical preparations of Mongolian medicine Manggari hot compress therapy (hereafter referred to as Manggari hot compress therapy) in treating cervical spondylotic radiculopathy (CSR) and explore the possible pharmacological material basis in the formula, providing evidence for the clinical application of Mongolian medicine in the treatment of CSR. Methods: The clinical trial employed a randomized, controlled, open-label, and outcome-assessor-blinded design. The CSR patients who were treated at the Department of Traditional Therapeutics Outpatient Clinic, Xilinguole Meng Mongolian General Hospital between July 1, 2024 and August 31, 2025, were enrolled. They were randomly assigned to three groups: an oral control group (administration of oral administration of mecobalamin tablets combined with cervical electric traction), an experimental group (Manggari external hot compress), and a patch control group (flurbiprofen gel plaster). The intervention lasted two weeks. Before and after treatment, the following subjective indicators were recorded: Mongolian Medicine Syndrome (MMS) score, Visual Analog Scale (VAS) score, Northwick Park Neck Pain Questionnaire (NPQ) score, and tongue morphology. Serum levels of inflammatory markers [tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β)] and oxidative stress markers [malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, and glutathione peroxidase (GSH-Px) activity] were measured using enzyme-linked immunosorbent assay (ELISA). Overall therapeutic efficacy was evaluated. One month after treatment completion, a follow-up assessment was conducted, and the MMS, VAS, and NPQ scores were recorded again for all patients. For the pharmacological substance exploration, ultra-high-performance liquid chromatography-Q-exactive orbitrap-mass (UHPLC-QE-MS) was employed to analyze blood-absorbed prototype components of Manggari, under both positive and negative ion modes. The targeting relationship between the core active compounds and the target protein was validated using molecular docking. Results: This study ultimately included 90 patients with CSR for analysis. Baseline characteristics showed no statistically significant differences among the three groups (P > 0.05). (i) Symptom scores. After treatment, the MMS, VAS, and NPQ scores decreased significantly from baseline in all three groups (P < 0.001). At follow-up, there was no significant difference in MMS, VAS, and NPQ scores of the experimental group compared with those at the end of the treatment (P > 0.05). After treatment, the experimental group showed significantly greater reductions in MMS, VAS, and NPQ scores than oral control and patch control groups (P < 0.001). At follow-up, these differences remained significant (P < 0.001). (ii) Inflammatory and oxidative stress markers. After treatment, serum levels of TNF-α, IL-6, and IL-1β, and MDA activity decreased significantly from baseline in all three groups (P < 0.001), and SOD content and GSH-Px activity increased significantly from baseline (P < 0.05). After treatment, the experimental group had significantly lower serum levels of TNF-α, IL-6, and IL-1β than oral and patch control groups. Additionally, it exhibited lower MDA activity and higher SOD content and GSH-Px activity compared with the two control groups. (P < 0.05). (iii) Overall efficacy. The total effective rate was 93.33% in the experimental group, 86.66% in the oral control group, and 83.33% in the patch control group. (iv) Pharmacological substance analysis. A total of 152 compounds were identified in the blood-absorbed components of Manggari. Among them, the core compounds—4-hydroxycoumarin, N-methylanthranilic acid, genistein, and ginsenoside-Rk1—showed binding energies to the key target proteins TNF-α and IL-1β range from − 4.7 to − 7.1 kcal/mol, with the majority of the binding energies being below − 5.0 kcal/mol, suggesting that it generally has a good binding affinity. Conclusion: Mongolian medicine hot compress therapy effectively modulates inflammatory and oxidative stress responses through the combined action of its thermal effects and active pharmaceutical ingredients Manggari. It inhibits cervical nerve root inflammation and alleviates radicular pain, improving clinical symptoms, reducing pain severity, and alleviating neck functional disability.
The philosophy of “treating disease before its onset” is a fundamental concept of traditional Chinese medicine (TCM), permeating its diagnostic and therapeutic framework, and is central to clinical practice. However, current TCM diagnostic and treatment models for the “pre-disease to disease” window period face several limitations, including the lack of comprehensive clinical parameters, difficulties in characterizing and integrating heterogeneous multimodal data, and insufficient dynamic precision in interventions and efficacy evaluations. To address these issues, guided by Professor Candong Li’s theory of TCM stateology, this study focuses on integrating objective multimodal data. It proposes a new model for personalized TCM diagnosis and treatment targeting the “pre-disease to disease” window period. This approach first proposes the idea of restructuring the conceptual framework of “symptom” and integrating multi-source heterogeneous data at macroscopic, mesoscopic, and microscopic levels to form a three-dimensional assessment indicator system. By integrating graph neural networks, convolutional neural networks, attention mechanisms, and knowledge graph-guided weight allocation, this approach enables collaborative representation, alignment, and fusion of multi-source data. Subsequently, it plans to construct a multimodal fusion model at both feature and decision levels, in order to establish mappings between indicators and TCM state elements, and to screen key indicators characterizing pathological evolution during the window period. Furthermore, it proposes a technical path for enhancing model interpretability using methods such as SHapley Additive exPlanations (SHAP) and Ablation-CAM++. Finally, with state assessment as the core, it proposes the concept of constructing a dynamic evaluation method for individualized diagnosis and treatment based on time-series data analysis using algorithms such as long short-term memory (LSTM) networks and gated recurrent units (GRUs). Moreover, a causal inference framework and semi-supervised learning strategies are introduced to enable quantitative evaluation of individual intervention effects and to provide interpretable therapeutic feedback, forming a complete technical path from data representation and fusion, weight adjustment, and interpretability analysis, to dynamic diagnosis feedback. This study aims to address deficiencies in the current TCM diagnosis and treatment model during the “pre-disease to disease” window period and to provide an operational framework for the clinical practice of TCM’s “treating disease before its onset”.
Objective: To explore whether digital facial and tongue diagnostic technologies can support the assessment of coronary heart disease (CHD) patients for coronary artery stenosis severity, and examine potential associations between digital tongue diagnosis features and myocardial biomarkers. Methods: The TFDA-1 face and tongue diagnosis instrument and the TDAS analysis system were used to perform intelligent visual examination and analysis of the facial and tongue in CHD patients who attended the Department of Cardiology at Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine between October 2, 2023 and July 31, 2024. Variables were screened using principal component analysis (PCA) and multicollinearity analysis to construct four machine learning models, including random forest, LightGBM, decision tree, and naive Bayes, for the early prediction of coronary artery stenosis severity. Model performance metrics, including sensitivity, specificity, precision, F1 score, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. Visual analyses were performed using the SHapley Additive exPlanations (SHAP) interpreter and decision curve analysis. For patients after percutaneous coronary intervention (PCI), a conceptual model linking cardiac biomarkers and tongue diagnosis was constructed using the partial least squares structural equation modeling (PLS-SEM), and its validity was assessed. Results: A total of 459 CHD patients were enrolled and assigned to a PCI group and a non-PCI group (which comprised two subgroups: mild stenosis or less group, moderate stenosis or greater group). For sublingual vein (SV) features, the PCI group had lower SV-a and SV-b than the other groups (P < 0.01 and P < 0.05, respectively). For tongue surface features, the PCI group had significantly higher tongue body (TB)-L, TB-a, and TB-b (P < 0.05, P < 0.01, and P < 0.001, respectively), as well as higher tongue coating (TC)-a and TC-b (P < 0.01 and P < 0.001, respectively). Age, SV-a, SV-b, creatine kinase-myocardial band (CK-MB), CK, TC-a, lip-L, and lip-b were incorporated in the machine learning models. The random forest model performed best, with an AUC of 0.924, an F1 score of 0.839, precision of 0.807, accuracy of 0.864, sensitivity of 0.873, and specificity of 0.839. Decision curve analysis indicated that both LightGBM and random forest had clinical utility. PLS-SEM confirmed the pathway relationships: myocardial biomarkers → TB and myocardial biomarkers → TC (coefficient = – 0.238, t = 2.239, P = 0.025, and coefficient = – 0.270, t = 2.522, P = 0.012, respectively). Conclusion: This study developed a noninvasive early warning model for coronary artery stenosis in patients with CHD. It applied PLS-SEM to investigate the association between post-PCI cardiac biomarkers and tongue diagnosis, and validated the proposed association chain. These findings suggest that intelligent traditional Chinese medicine (TCM) visual diagnosis integrated with modern digital technology may support CHD risk assessment and comprehensive health management.
Objective To investigate methods for constructing a high-quality instructional dataset for traditional Chinese medicine (TCM) mental disorders and to validate its efficacy. Methods We proposed the Fine-Med-Mental-T&P methodology for constructing high-quality instruction datasets in TCM mental disorders. This approach integrates theoretical knowledge and practical case studies through a dual-track strategy. (i) Theoretical track: textbooks and guidelines on TCM mental disorders were manually segmented. Initial responses were generated using DeepSeek-V3, followed by refinement by the Qwen3-32B model to align the expression with human preferences. A screening algorithm was then applied to select 16 000 high-quality instruction pairs. (ii) Practical track: starting from over 600 real clinical case seeds, diagnostic and therapeutic instruction pairs were generated using DeepSeek-V3 and subsequently screened through manual evaluation, resulting in 4 000 high-quality practice-oriented instruction pairs. The integration of both tracks yielded the Med-Mental-Instruct-T&P dataset, comprising a total of 20 000 instruction pairs. To validate the dataset’s effectiveness, three experimental evaluations (both manual and automated) were conducted: (i) comparative studies to compare the performance of models fine-tuned on different datasets; (ii) benchmarking to compare against mainstream TCM-specific large language models (LLMs); (iii) data ablation study to investigate the relationship between data volume and model performance. Results Experimental results demonstrate the superior performance of T&P-model fine-tuned on the Med-Mental-Instruct-T&P dataset. In the comparative study, the T&P-model significantly outperformed the baseline models trained solely on self-generated or purely human-curated baseline data. This superiority was evident in both automated metrics (ROUGE-L > 0.55) and expert manual evaluations (scoring above 7/10 across accuracy). In benchmark comparisons, the T&P-model also excelled against existing mainstream TCM LLMs (e.g., HuatuoGPT and ZuoyiGPT). It showed particularly strong capabilities in handling diverse clinical presentations, including challenging disorders such as insomnia and coma, showcasing its robustness and versatility. Data ablation studies showed that T&P-model performance had an overall upward trend with minor fluctuations when training data increased from 10% to 50%; beyond 50%, performance improvement slowed significantly, with metrics plateauing and approaching a saturation point. Conclusion This study has successfully constructed the specialized Med-Mental-Instruct-T&P instruction dataset for TCM mental disorders proposed the systematic Fine-Med-Mental-T&P methodology for its development, effectively addressing the critical challenge of high-quality, domain-specific data scarcity in TCM, and providing essential data support for developing intelligent TCM diagnostic and therapeutic systems.
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.
Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine (TCM) syndrome differentiation within real clinical settings, we propose a data-efficient learning framework enhanced by knowledge graphs. Methods We developed Agent-GNN, a three-stage decoupled learning framework, and validated it on the Traditional Chinese Medicine Syndrome Diagnosis (TCM-SD) dataset containing 54 152 clinical records across 148 syndrome categories. First, we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system. Second, we proposed a Functional Patient Profiling (FPP) method that utilizes large language models (LLMs) combined with Graph Retrieval-Augmented Generation (RAG) to extract structured symptom-etiology-pathogenesis subgraphs from medical records. Third, we employed heterogeneous graph neural networks to learn structured combination patterns explicitly. We compared our method against multiple baselines including BERT, ZY-BERT, ZY-BERT + Know, GAT, and GPT-4 Few-shot, using macro-F1 score as the primary evaluation metric. Additionally, ablation experiments were conducted to validate the contribution of each key component to model performance. Results Agent-GNN achieved an overall macro-F1 score of 72.4%, representing an 8.7 percentage points improvement over ZY-BERT + Know (63.7%), the strongest baseline among traditional methods. For long-tail syndromes with fewer than 10 samples, Agent-GNN reached a macro-F1 score of 58.6%, compared with 39.3% for ZY-BERT + Know and 41.2% for GPT-4 Few-shot, representing relative improvements of 49.2% and 42.2%, respectively. Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance. Conclusion This study proposes Agent-GNN, a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation. By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs, our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis.
Objective Patients with atherosclerotic cardiovascular disease (ASCVD) following percutaneous coronary intervention (PCI) are classified as very-high-risk individuals in cardiovascular disease (CVD) risk stratification. The distribution pattern of traditional Chinese medicine (TCM) syndromes in this patient population, as well as its association with blood lipid profiles and clinical prognosis, remains unclear. The present prospective cohort study aims to investigate these correlations, thereby providing insights to enrich the research fields. Methods We enrolled consecutive patients with ASCVD who underwent PCI at the Integrated Cardiology Unit of China-Japan Friendship Hospital between September 1, 2020 and December 31, 2022. Demographics and clinical characteristics, signs and symptoms defining each TCM syndrome, and fasting venous blood samples were collected at baseline and follow up or upon major adverse cardiovascular events (MACEs). We analyzed the correlation between TCM syndromes, blood lipid profiles, and MACEs, and developed a new joint prognostic model incorporating both TCM syndromes and blood lipids using logistic regression. The analyses were based on detailed baseline and one-year follow-up data. Results A per-protocol analysis was performed on 586 patients with complete data ultimately. During the one-year follow-up, 174 patients (29.69%) experienced a MACE. We performed statistical analyses on comorbidities, medication, and biochemical indicators across groups defined by TCM syndrome differentiation. When comparing different TCM syndromes, no significant differences were found in age, body mass index (BMI), history of revascularization, comorbidities, family history of CVD, smoking or drinking, or statin intensity (P > 0.05). Patients with intertwined phlegm and blood stasis syndrome exhibited significantly higher levels of total cholesterol (TC, 5.27 ± 1.18 mmol/L, P < 0.001), triglyceride (TG, 1.96 ± 1.33 mmol/L, P = 0.008), low-density lipoprotein cholesterol (LDL-C, 3.35 ± 0.79 mmol/L, P < 0.001), and high-density lipoprotein cholesterol (HDL-C, 1.24 ± 0.81 mmol/L, P < 0.001) compared with those with other TCM syndromes combined. A multivariable logistic regression model was constructed to predict MACEs. The model included TCM syndrome type [with intertwined phlegm and blood stasis as a predictor, adjusted odds ratio (OR) = 1.413, 95% confidence interval (CI): 0.517 – 3.864, P = 0.501], age (adjusted OR = 0.97, 95% CI: 0.955 – 1.001, P = 0.057), male gender (adjusted OR = 0.698, 95% CI: 0.416 – 1.170, P = 0.173), TC (adjusted OR = 1.004, 95% CI: 0.513 – 1.965, P = 0.990), and LDL-C (adjusted OR = 5.825, 95% CI: 2.214 – 15.326, P < 0.001). This model demonstrated good discriminatory ability for MACEs in post-PCI ASCVD patients [the area under the receiver operating characteristic (ROC) curve (AUC) = 0.865, 95% CI: 0.816 – 0.914]. Conclusion The intertwined phlegm and blood stasis TCM syndrome is associated with a distinct atherogenic lipid profile characterized by elevated levels of TC and LDL-C. The prognostic model that incorporates this TCM syndrome type along with conventional lipid parameters (TC and LDL-C) shows good discriminatory ability for predicting MACEs in ASCVD patients after PCI, underscoring the potential clinical utility of integrating TCM syndrome differentiation into CVD risk assessment.
Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM. Publications were retrieved from the China National Knowledge Infrastructure (CNKI), Wanfang Data, and China Science and Technology Journal Database (VIP), covering the period from database inception to July 3, 2025. A hybrid segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment and evolutionary analysis of topics across different stages. Results A total of 3 919 publications published between 2003 and 2025 were included, and the research trajectory was divided into five stages based on data-driven breakpoint detection. The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis (2006 – 2010), to machine-learning-based syndrome and prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-graph construction, multimodal integration, and intelligent clinical decision-support systems, with recent studies (2024 – 2025) showing the emergence of large language models and agent-based diagnostic frameworks. Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis, alongside the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes, our findings offer a structured reference framework for the design of intelligent diagnostic systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.
Objective To develop a clinical decision and prescription generation system (CDPGS) specifically for diarrhea in traditional Chinese medicine (TCM), utilizing a specialized large language model (LLM), Qwen-TCM-Dia, to standardize diagnostic processes and prescription generation. Methods Two primary datasets were constructed: an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge, medical records, and chain-of-thought (CoT) reasoning datasets. After an initial evaluation of 16 open-source LLMs across inference time, accuracy, and output quality, Qwen2.5 was selected as the base model due to its superior overall performance. We then employed a two-stage low-rank adaptation (LoRA) fine-tuning strategy, integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records. This approach was designed to embed the clinical logic (symptoms → pathogenesis → therapeutic principles → prescriptions) into the model’s reasoning capabilities. The resulting fine-tuned model, specialized for TCM diarrhea, was designated as Qwen-TCM-Dia. Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy, precision, recall, and F1-score. Furthermore, the quality of the generated prescriptions was compared with that of established open-source TCM LLMs. Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs. It achieved 97.05% accuracy and 91.48% F1-score in disease diagnosis, and 74.54% accuracy and 74.21% F1-score in syndrome type differentiation. Compared with existing open-source TCM LLMs (BianCang, HuangDi, LingDan, TCMLLM-PR, and ZhongJing), Qwen-TCM-Dia exhibited higher fidelity in reconstructing the “symptoms → pathogenesis → therapeutic principles → prescriptions” logic chain. It provided complete prescriptions, whereas other models often omitted dosages or generated mismatched prescriptions. Conclusion By integrating continued pre-training, CoT reasoning, and a two-stage fine-tuning strategy, this study establishes a CDPGS for diarrhea in TCM. The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT. This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.
Objective To investigate the microbial mechanisms of Banxia Xiexin Decoction (半夏泻心汤, BXXXD) in the treatment of esophageal precancerous lesions. Methods A total of 30 specific pathogen-free (SPF) grade female C57BL/6J mice were randomly assigned to a control group (n = 6) and a 4-nitroquinoline 1-oxide (4-NQO)-exposed group (n = 24). Esophageal precancerous lesions were induced by providing the 4-NQO-exposed group with 4-NQO in drinking water (100 μg/mL) for 17 consecutive weeks, whereas control group received sterile drinking water. After model establishment, the mice in 4-NQO-exposed group were further randomized into model group and three BXXXD-treated groups: low-dose (BXXXD-L, 3.7 g/kg), medium-dose (BXXXD-M, 7.4 g/kg), and high-dose (BXXXD-H, 14.8 g/kg) groups (n = 6 per group). During the subsequent intervention period, mice in control and model groups were gavaged with sterile water, while mice in BXXXD groups were gavaged once daily with the corresponding dose of BXXXD aqueous extract for 4 weeks. Histopathological changes in esophageal tissues were observed by hematoxylin and eosin (HE) staining. The fecal and esophageal microbiota were profiled via 16S rDNA high-throughput sequencing to evaluate bacterial diversity, community structure, and co-occurrence networks. BXXXD chemical fingerprints were analyzed using ultra-high-performance liquid chromatography coupled with quadrupole QExactive Orbitrap mass spectrometry (UHPLC-QE-MS). Serum short-chain fatty acids (SCFA) level was quantified by targeted metabolomics using gas chromatography-mass spectrometry (GC-MS). Transcriptomic analysis of esophageal tissues was performed to assess gene expression profiles. Results Compared with model group, BXXXD-M group exhibited reduced mucosal hyperplasia and more orderly epithelial cell arrangement, with superior therapeutic effects in comparison with both BXXXD-L and BXXXD-H groups (P < 0.01). Microbiota analysis revealed that BXXXD increased the abundance of beneficial Enterococcus and reduced pathogenic Escherichia-Shigella in the esophagus. In the gut, BXXXD elevated the relative abundance of beneficial taxa, including Lactobacillus, Dubosiella, Bacteroides, and Faecalibacterium. Targeted metabolomics showed that BXXXD significantly reduced total serum SCFA level (P < 0.01). Transcriptomic analysis indicated that BXXXD downregulated the expression of genes associated with the progression, migration, and invasion of esophageal cancer, which were identified as kallikrein-related peptidase 6 (Klk6), defensin beta 4 (Defb4), family with sequence similarity 3 member B (Fam3b), carboxypeptidase A4 (Cpa4), serum amyloid A1 (Saa1), and chitinase-like 1 (Chil1) (P < 0.05). Conclusion BXXXD may reduce the expression levels of esophageal cancer-related genes and improve esophageal precancerous lesions through modulation of the gut microbiota and metabolites.
