BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

ObjectiveTo evaluate the therapeutic effects of modified Banxia Xiexintang（MBXT） on polycystic ovary syndrome with insulin resistance（PCOS-IR） rats and reveal its potential mechanisms based on the integrated analysis of transcriptomics and metabolomics. MethodsFemale SD rats were selected， and a PCOS-IR model was established by intragastric administration of letrozole combined with a high-fat diet for 21 days. The modeled rats were randomly divided into the model group， MBXT low-， medium-， and high-dose groups（6.62， 13.23， 26.46 g·kg^-1）， and metformin group（0.158 g·kg^-1）， with a normal group set up separately. After 14 days of administration， the estrous cycle was observed， ovarian morphology was examined by hematoxylin-eosin（HE） staining， and the levels of testosterone（T）， estradiol（E₂）， follicle-stimulating hormone（FSH）， and luteinizing hormone（LH） in serum were detected by enzyme-linked immunosorbent assay（ELISA）. Serum metabolites and ovarian tissue gene expression were detected using ultra-performance liquid chromatography-quadrupole-electrostatic orbitrap mass spectrometry（UPLC-Q-Orbitrap-MS） and RNA-Seq technology， respectively， followed by multi-omics integrated analysis. ResultsPharmacodynamic findings revealed that all MBXT dose groups could reversed abnormal estrous cycles in PCOS-IR rats， improve polycystic ovarian lesions， and normalize dysregulated serum hormone levels（T， LH， E₂， FS， P<0.05， P<0.01）. Metabolomic analysis revealed that compared with the model group， MBXT reversed 278 differential metabolites such as estrone and S-formylglutathione， mainly involving pathways such as steroid hormone biosynthesis， glutathione metabolism， and lipid peroxidation regulation. Transcriptomic analysis identified 434 differentially expressed genes， and enrichment analysis revealed that MBXT significantly regulated lipid peroxidation defense systems， including glutathione metabolism， peroxisome function， and fatty acid metabolism， thereby intervening in ferroptosis processes. It also engaged in inflammation-related pathways such as the chemokine signaling pathway. Integrated analysis revealed that both metabolomics and transcriptomics co-enriched metabolic pathways associated with ferroptosis and fatty acid metabolism. And key Hub genes［such as Ras-related C3 botulinum toxin substrate 2 gene（Rac2） and Fas ligand gene（Faslg）］ showed significant correlations with differential metabolites. ConclusionMBXT can effectively ameliorate reproductive dysfunction and metabolic disorders in PCOS-IR rats. Its mechanism may be related to remodeling the immune-metabolism network， particularly by regulating MHC-mediated immune responses， inhibiting local ovarian ferroptosis， and enhancing steroid hormone synthesis pathways.

ObjectiveTo investigate the mechanism of modified Banxia Xiexintang（MBXT） in improving ovarian dysfunction in polycystic ovary syndrome with insulin resistance（PCOS-IR） rats by inhibiting ferroptosis through the adenosine monophosphate（AMP）-activated protein kinase（AMPK）/fatty acid synthase（FASN）/glutathione peroxidase 4（GPX4） signaling pathway. MethodsSeventy-six female SD rats were randomly divided into a normal group（n=13） and a modeling group（n=63）. The modeling group established a PCOS-IR model by intragastric administration of letrozole combined with a high-fat diet for 21 days. After successful modeling， these rats were randomly divided into the model group， MBXT low-， medium-， and high-dose groups（6.62， 13.23， 26.46 g·kg^-1）， metformin group（0.158 g·kg^-1）， and high-dose of MBXT combined with ferroptosis inducer Erastin group（15 mg·kg^-1）， with 10 rats in each group. After 14 days of intervention， ovarian pathological morphology was observed by hematoxylin-eosin（HE） staining， the mitochondrial ultrastructure of granulosa cells was observed by transmission electron microscopy（TEM）， ovarian reactive oxygen species（ROS） levels were detected by dihydroethidium（DHE） probe， biochemical methods were used to detect Fe²⁺， malondialdehyde（MDA）， glutathione（GSH） and other indicators in ovarian tissues， serum sex hormone and insulin levels were measured by enzyme-linked immunosorbent assay（ELISA）， and the protein expressions of AMPK， FASN， acyl-CoA synthetase long-chain family member 4（ACSL4）， GPX4， and solute carrier family 7 member 11（SLC7A11） in ovarian tissues were detected by Western blot. ResultsCompared with the normal group， the model group showed polycystic changes in the ovaries， with atrophy of mitochondria in granulosa cells and increased membrane density. Serum levels of testosterone（T）， luteinizing hormone（LH）， and insulin were significantly increased（P<0.01）. The levels of ROS， MDA， 4-hydroxynonenal（4-HNE）， and Fe²⁺ in ovarian tissues were significantly elevated（P<0.01）， while adenosine triphosphate（ATP）， GSH， and reduced nicotinamide adenine dinucleotide phosphate （NADPH） levels were significantly decreased（P<0.01）. The phosphorylation levels of AMPK and acetyl-CoA carboxylase （ACC）， as well as the protein expressions of SLC7A11， GPX4， and ferroptosis suppressor protein 1（FSP1） were significantly downregulated（P<0.01）， whereas the expressions of FASN， ACSL4， and nuclear receptor coactivator 4（NCOA4） were significantly upregulated（P<0.01）. Compared with the model group， MBXT intervention at various doses improved the above pathological changes and biochemical indicators in a dose-dependent manner， with the high-dose group showing the most significant effect（P<0.01）. Compared with the MBXT high-dose group， the high-dose of MBXT combined with ferroptosis inducer Erastin group restored ovarian ferroptosis characteristics in rats， with increased ROS and lipid peroxidation products， and altered expressions of key proteins（P<0.05， P<0.01）. ConclusionMBXT can effectively improve ovarian function and metabolic disorders in PCOS-IR rats. Its mechanism may be related to activating the AMPK/ACC signaling pathway， downregulating FASN and ACSL4 to reduce lipid peroxidation substrates， and restoring glucose-6-phosphate dehydrogenase/phosphoglycerate dehydrogenase（G6PD/PHGDH） metabolic flux to enhance the GPX4/FSP1 antioxidant defense system， thereby inhibiting ferroptosis in ovarian granulosa cells.

Objective To explore the number of peripheral blood regulatory T cells (Treg) in patients with chronic kidney disease (CKD) and its correlation with vascular calcification. Methods This was a single-center, cross-sectional, and observational study. Non-dialysis patients with CKD treated at Zhongshan Hospital, Fudan University from March 2021 to March 2022 were enrolled. Abdominal aortic calcification (AAC) was assessed using lateral abdominal X-ray. Number of Treg and cytokine levels were measured by flow cytometry. Logistic regression analysis was performed to evaluate the related factors for AAC in CKD patients. Results A total of 83 patients were included, aged 17–86 years, with 57 males (68.7%). The distribution of CKD stages was as follows: stage G1 in 7 patients (8.4%), stage G2 in 17 patients (20.5%), stage G3 in 21 patients (25.3%), stage G4 in 19 patients (22.9%), and stage G5 in 19 patients (22.9%). No AAC was observed in patients with stages G1 and G2, while the prevalence of AAC in patients with stages G3, G4, and G5 was 23.8%, 21.1%, and 26.3%, respectively. Compared with stage G1 patients, those with stages G3–5 showed decreased number of peripheral blood Treg and elevated levels of interleukin (IL)-6 and IL-17F (P＜0.05). The area under the receiver operating characteristic curve for number of peripheral blood Treg in predicting AAC in CKD patients was 0.766 (95%CI 0.652–0.879, P＝0.002). Logistic regression analysis showed that decreased number of Treg was related factor for AAC in CKD patients (OR＝0.957, 95%CI 0.922–0.992, P＝0.018). Conclusion As CKD progresses, number of peripheral blood Treg significantly decreases, which is correlated with AAC in CKD patients.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Exercise-induced metabolic remodeling is a fundamental adaptive process whereby the body reorganizes systemic and cellular metabolism to meet the dynamic energy demands posed by physical activity. Emerging evidence reveals that such remodeling not only enhances energy homeostasis but also profoundly influences immune function through complex molecular interactions involving glucose, lipid, and protein metabolism. This review presents an in-depth synthesis of recent advances, elucidating how exercise modulates immune regulation via metabolic reprogramming, highlighting key molecular mechanisms, immune-metabolic signaling axes, and the authors’ academic perspective on the integrated “exercise-metabolism-immunity” network. In the domain of glucose metabolism, regular exercise improves insulin sensitivity and reduces hyperglycemia, thereby attenuating glucose toxicity-induced immune dysfunction. It suppresses the formation of advanced glycation end-products (AGEs) and interrupts the AGEs-RAGE-inflammation positive feedback loop in innate and adaptive immune cells. Importantly, exercise-induced lactate, traditionally viewed as a metabolic byproduct, is now recognized as an active immunomodulatory molecule. At high concentrations, lactate can suppress immune function through pH-mediated effects and GPR81 receptor activation. At physiological levels, it supports regulatory T cell survival, promotes macrophage M2 polarization, and modulates gene expression via histone lactylation. Additionally, key metabolic regulators such as AMPK and mTOR coordinate immune cell energy balance and phenotype; exercise activates the AMPK-mTOR axis to favor anti-inflammatory immune cell profiles. Simultaneously, hypoxia-inducible factor-1α (HIF-1α) is transiently activated during exercise, driving glycolytic reprogramming in T cells and macrophages, and shaping the immune landscape. In lipid metabolism, exercise alleviates adipose tissue inflammation by reducing fat mass and reshaping the immune microenvironment. It promotes the polarization of adipose tissue macrophages from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype. Moreover, exercise alters the secretion profile of adipokines—raising adiponectin levels while reducing leptin and resistin—thereby influencing systemic immune balance. At the circulatory level, exercise improves lipid profiles by lowering pro-inflammatory free fatty acids (particularly saturated fatty acids) and triglycerides, while enhancing high-density lipoprotein (HDL) function, which has immunoregulatory properties such as endotoxin neutralization and macrophage cholesterol efflux. Regarding protein metabolism, exercise triggers the expression of heat shock proteins (HSPs) that act as intracellular chaperones and extracellular immune signals. Exercise also promotes the secretion of myokines (e.g., IL-6, IL-15, irisin, FGF21) from skeletal muscle, which modulate immune responses, facilitate T cell and macrophage function, and support immunological memory. Furthermore, exercise reshapes amino acid metabolism, particularly of glutamine, arginine, and branched-chain amino acids (BCAAs), thereby influencing immune cell proliferation, biosynthesis, and signaling. Leucine-mTORC1 signaling plays a key role in T cell fate, while arginine metabolism governs macrophage polarization and T cell activation. In summary, this review underscores the complex, bidirectional relationship between exercise and immune function, orchestrated through metabolic remodeling. Future research should focus on causative links among specific metabolites, signaling pathways, and immune phenotypes, as well as explore the epigenetic consequences of exercise-induced metabolic shifts. This integrated perspective advances understanding of exercise as a non-pharmacological intervention for immune regulation and offers theoretical foundations for individualized exercise prescriptions in health and disease contexts.

ObjectiveTo collate and compare the characteristics and differences in the methods for constructing animal models of different subtypes of gastroesophageal reflux disease （GERD） based on literature， providing a reference for researchers in this field regarding animal model construction. MethodsExperimental studies related to GERD including reflux esophagitis （RE）， nonerosive reflux disease （NERD） and Barrett's esophagus （BE） model construction from January 1， 2014 to January 27， 2024， were retrieved from databases such as CNKI， Wanfang， VIP， Web of Science， and Pubmed. Information on animal strains， genders， modeling methods including disease-syndrome combination models， modeling cycles were extracted； for studies with model evaluation， the methods of model evaluation were also extracted； then analyzing all those information. ResultsA total of 182 articles were included. SD rats were most frequently selected when inducing animal models of RE （88/148， 59.46%） and NERD （9/14， 64.29%）. For BE， C57BL/6 mice were most commonly used （11/20， 55.00%）. Male animals （RE： 111/135， 82.22%； NERD： 11/14， 78.57%； BE： 10/12， 83.33%） were the most common gender among the three subtypes. The key to constructing RE animal models lies in structural damage to the esophageal mucosal layer， gastric content reflux， or mixed reflux， among which forestomach ligation + incomplete pylorus ligation （42/158， 26.58%） was the most common modeling method； the key to constructing NERD animal models lies in micro-inflammation of the esophageal mucosa， visceral hypersensitivity， and emotional problems， and intraperitoneal injection of a mixed suspension of ovalbumin and aluminum hydroxide combined with acid perfusion in the lower esophagus （8/14， 57.14%） was the most common modeling method； the key to constructing BE animal models lies in long-term inflammatory stimulation of the esophageal mucosa and bile acid reflux， and constructing interleukin 2-interleukin 1β transgenic mice （7/25， 28.00%） was the most common modeling method. Adverse psychological stress was the most common method for inducing liver depression. ConclusionsThe construction key principles and methodologies for RE， NERD， and BE animal models exhibit significant differences. Researchers should select appropriate models based on subtype characteristics （e.g.， RE focusing on structural damage， NERD emphasizing visceral hypersensitivity）. Current studies show insufficient exploration of traditional Chinese medicine disease-syndrome combination models. Future research needs to optimize syndrome modeling approaches （e.g.， composite etiology simulation） and establish integrated Chinese-Western medicine evaluation systems to better support mechanistic investigations of traditional Chinese medicine.

Rheumatoid arthritis （RA） is a chronic autoimmune disease characterized by synovitis as its pathological basis. Although current therapeutic drugs can alleviate symptoms， they are often accompanied by a high risk of side effects. In recent years， the use of flavonoids from traditional Chinese medicine （TCM） in the treatment of RA has garnered significant attention. Studies have shown that the mechanisms by which flavonoids treat RA include inhibiting the release of pro-inflammatory factors， regulating multiple cellular signaling pathways， alleviating oxidative stress， modulating immune system functions， inhibiting bone destruction， and suppressing angiogenesis. Due to their notable anti-inflammatory， antioxidant， and immunomodulatory activities， flavonoids hold promise as potential therapeutic agents for RA. A substantial number of articles in this field have been published. By reviewing Chinese and international literature and applying bibliometric and visual analysis using CiteSpace， this paper explored research hotspots and frontiers in this field， systematically reviewed the structures and anti-RA mechanisms of TCM flavonoids， provided a theoretical basis for their use in RA treatment and clinical applications， and offered new perspectives and references for the discovery of novel TCM-based anti-RA drugs.