Functional dyspepsia （FD） is a prioritized disease category where traditional Chinese medicine （TCM） demonstrates distinct therapeutic advantages. The current western medicine treatment for FD is mainly based on proton pump inhibitors and prokinetic agents， with digestive enzymes， probiotics and antidepressants serving as adjuvant medication， yet such therapies still have certain limitations. TCM treatment for FD includes oral administration of Chinese herbal formulas and Chinese patent medicines， as well as external TCM therapies such as acupuncture and moxibustion， acupoint application， hot medicinal compress therapy， rubbing with ointment， medicinal iontophoresis， auricular acupoint therapy and tui na （Chinese medical massage）. The combined treatment of FD with integrated TCM and western medicine can significantly improve clinical effectiveness and reduce adverse reactions. The common mechanisms underlying the therapeutic effects of both TCM and western medicine revolve around the core pathological processes of FD， mainly focusing on restoring gastrointestinal motility， regulating the levels of brain-gut peptides， modulating intestinal microecology， and ameliorating inflammatory status. The differential mechanisms lie in the precise targeting feature of western medicine versus the holistic-regulating and multi-target characteristics of TCM， and the two approaches exert a synergistic effect to enhance efficacy. This paper proposes to leverage the advantages of TCM in holistic regulation and the strengths of western medicine in targeted treatment， so as to provide personalized and comprehensive treatment regimens for FD patients.

ObjectiveTo observe the effects of Dihuang Yinzi （DY） on motor dysfunction in rats with advanced Parkinson's disease （PD） and to investigate the mechanisms by which DY improves advanced PD symptoms through the "gut microbiota-short-chain fatty acids （SCFAs）-inflammation-neuroprotection pathway". MethodsAn advanced PD rat model was induced by rotenone. Rats were divided into a normal group， model group， positive drug group （levodopa， 50 mg·kg^-1）， and DY low-， medium-， and high-dose groups （5.2， 10.4， 20.8 g·kg^-1）. After 7 days of administration， motor function was evaluated using the open-field， pole-climbing， and inclined plate tests. Hematoxylin-eosin （HE） staining was used to observe pathological changes in the substantia nigra and colon， and immunohistochemistry was performed to detect α-Synuclein （α-Syn） and tyrosine hydroxylase （TH） expression in the substantia nigra. Enzyme-linked immunosorbent assay （ELISA） was used to measure levels of dopamine （DA）， 5-hydroxytryptamine （5-HT）， 3，4-dihydroxyphenylacetic acid （DOPAC）， Levodopa， homovanillic acid （HVA）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）. Western blot analysis was used to detect the expression of zonula occludens-1 （ZO-1） and occludin. Gut microbiota diversity was analyzed by 16S rRNA sequencing， and gas chromatography （GC） was used to determine the content of SCFAs in colonic contents. ResultsCompared with the normal group， the model group showed significantly decreased movement speed and distance in the open-field test， prolonged pole-climbing time， and reduced retention angle on the inclined plate （P<0.01）， accompanied by increased α-Syn expression （P<0.01） and decreased TH expression （P<0.01） in the brain. Compared with the model group， all DY dose groups improved motor dysfunction in advanced PD rats to varying degrees （P<0.05， P<0.01） and alleviated pathological damage in the brain and colon. High-dose DY significantly reduced α-Syn aggregation in the substantia nigra （P<0.01） and increased TH expression （P<0.01）. ELISA and Western blot results showed that， compared with the normal group， the model group exhibited decreased levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum （P<0.01）， increased levels of TNF-α， IL-6， and IL-1β in the colon and striatum （P<0.01）， and significantly reduced expression of ZO-1 （P<0.05） and occludin in the colon （P<0.01）. Compared with the model group， all DY dose groups increased the levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum to varying degrees （P<0.05， P<0.01）. In the high-dose DY group， the levels of TNF-α， IL-6， and IL-1β in the colon and striatum were reduced （P<0.01）， while the expression of ZO-1 （P<0.05） and occludin in the intestine was increased. The 16S rRNA sequencing results indicated that the relative abundances of Actinobacteriota， Enterobacteriaceae， and Erysipelotrichaceae were increased in the model group， whereas the relative abundances of Bacteroidota， class Clostridia， Lachnospiraceae， and Akkermansia muciniphila were decreased. These changes were effectively reversed after high-dose DY intervention. GC analysis showed that the content of SCFAs in the colonic contents of rats in the model group was decreased （P<0.05， P<0.01）， while after high-dose DY intervention， the levels of acetate， propionate， isobutyrate， and butyrate were significantly increased （P<0.05， P<0.01）. ConclusionDY may exert therapeutic effects in advanced PD by regulating the gut microbiota-SCFAs-inflammation pathway.

ObjectiveTo explore the effect of oral sodium butyrate on skeletal muscle atrophy in CT26 tumor mice through the gut microbiota-skeletal muscle axis and its potential mechanism. MethodsSixty SPF BALB/c male mice aged 8 weeks were randomly divided into a normal control group (NC, n=18) and a ABX-depleted group (ABX, n=42). The ABX mice were pretreated with a quadruple antibiotic cocktail via oral gavage (0.2 ml per administration, once daily, 6 d per week, for 2 weeks), whereas NC received an equal volume of sterile water. The quadruple antibiotic cocktail consisted of metronidazole (1 g/L), vancomycin (0.5 g/L), ampicillin (1 g/L), and gentamicin (1 g/L). Following successful pretreatment, six mice from each group were randomly selected for gut microbiota sequencing analysis and designated as the Abx group and the NC0 group, respectively. Theremaining mice in ABX were subcutaneously inoculated in the dorsum with 0.2 ml of CT26 cell suspension (at a cell density of 1×10⁷/ml). Then these mice were randomly allocated into three subgroups: a control tumor bearing model group (0_NaB, n=12), a tumor-bearing model group receiving low-dose oral sodium butyrate (L_NaB, n=12), a tumor-bearing model group receiving high-dose oral sodium butyrate (H_NaB, n=12). And mice in NC were inoculated at the same site with 0.2 ml of normal saline. The administration dose for L_NaB was 0.3 g/(kg·d), that for H_NaB was 0.5 g/(kg·d), while NC and 0_NaB were given the same volume of normal saline (0.2ml per time, once daily, 6 d per week, for 4 weeks). The general condition of mice was monitored, and forelimb grip strength gastrocnemius muscle mass and its muscle fiber cross-sectional area were measured for each group. The structural changes in gut microbiota were assessed by 16S rRNA sequencing of cecal contents. Pathological alterations in the intestinal wall were examined via HE staining. Serum and gastrocnemius muscle levels of TNF‑α, IL-6, IL-1β, and LPS were quantified using ELISA. The protein expression of ZO-1 and occludin in the small intestine, as well as proteins associated with the TLR4/MyD88/NF-κB signaling pathway in the gastrocnemius muscle, were detected by Western blot analysis. Results(1) The alpha-diversity in Abx was significantly lower than that in NC0 (P<0.01), a significant decrease of the mass and muscle fiber cross-sectional area of the gastrocnemius (P<0.01), with the majority of gut microbiota being effectively depleted. (2) Compared with NC, the subcutaneous tumors of mice in 0_NaB were prominent, a significant increase of the mass and muscle fiber cross-sectional area of the gastrocnemius, accompanied by a significant decrease in body weight at the end of the 3th and 4th week (P<0.05), and a significant weakening of the forelimb grasping strength at the 5th and 6th week (P<0.01). Compared with 0_NaB, the tumor mass of mice in L_NaB and H_NaB showed a significant decreasing trend, and the grip strength of the forelimbs significantly increased at the 5th and 6th week (P<0.05, P<0.01). (3) Compared with 0_NaB, the Shannon and Observed species indices in α diversity of L_NaB and H_NaB were significantly increased (P<0.05). At the genus level, compared with 0_NaB, L_NaB exhibited a significant decrease in the relative abundance of Parasutterella (P< 0.01), while H_NaB showed significant reductions in the relative abundances of both Escherichia-Shigella and Parasutterella (P < 0.01). (4) Compared with 0_NaB, the small intestinal tissue structure in L_NaB and H_NaB was more intact, the infiltration of inflammatory cells was significantly reduced, and the capillaries were slightly dilated. The expression levels of ZO-1 and occludin proteins in L_NaB were significantly increased (P<0.01). (5) The LPS concentration in the gastrocnemius muscle and the protein expression levels of TLR4, MyD88, p-IκBα, and p-NF‑κB p65 in L_NaB and H_NaB were significantly lower than those in 0_NaB (P<0.05). The serum TNF‑α concentration in H_NaB and TNF-α concentration in the gastrocnemius muscle of the L_NaB and H_NaB were significantly lower than those in 0_NaB (P<0.05, P<0.01, P<0.01). ConclusionOral administration of NaB can improve gut microbiota α diversity, adjusting its composition, improving intestinal mucosal barrier function, reducing the LPS-induced pro-inflammatory response, and delaying skeletal muscle atrophy. The underlying mechanism may involve down regulation of TLR4/MyD88/NF-κB signaling in skeletal muscle.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

ObjectiveTo observe the intervention effect of Fufang Kangjiaolv capsules on anxiety-like behaviors in the rat model of chronic restraint stress （CRS） and explore the mechanism underlying the anti-anxiety effect via the microbiota-gut-brain axis. MethodsRats were assigned into blank， model， positive drug （diazepam， 1 mg·kg^-1）， and low-， medium-， and high-dose （0.75， 1.5， 3 g·kg^-1， respectively） Fufang Kangjiaolv capsules groups. After 14 days of administration， the elevated plus maze test， open field test， light and dark box test， and marble burying test were performed. Hematoxylin-eosin staining was employed to observe the pathological changes in the hippocampus and colon of rats， and Nissl staining was conducted to observe the damage of hippocampal neurons. The gut microbiota was analyzed by 16S rRNA gene sequencing. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the mRNA levels of zonula occludens-1 （ZO-1） and occludin in the colon of rats. The levels of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β） in the colon， serum， and hippocampus were determined by enzyme-linked immunosorbent assay. Western blot was employed to determine the protein levels of ZO-1， occludin， nuclear factor-κB p65 （NF-κB p65） in the colon tissue and NF-κB p65 and brain-derived neurotrophic factor （BDNF） in the hippocampal tissue. ResultsCompared with the blank group， the model group showed reductions in the time and frequency ratio of rats entering the elevated plus maze， the time and frequency of rats entering the central area of the open field， the time of entering the open box， the times of passing through the light and dark box， and the number of unburied beads （P<0.05， P<0.01）. Compared with the model group， Fufang Kangjiaolv capsules ameliorated the anxiety of the model rats to varying degrees， and the high-dose group had the best effect， with increases in the proportions of time and frequency of rats entering the open arm in the elevated plus maze （P<0.05）， the number of rats entering the central area in the open field （P<0.05）， the time of entering the open box， the times of passing through the light and dark boxes， and the number of unburied beads （P<0.01）. Moreover， the high-dose group showed alleviated pathological damage of hippocampal neurons and colon. The results of 16S rRNA gene sequencing showed that the model group had increased relative abundance of Firmicutes， Deferribacterota， Romboutsia， and Phascolarctobacterium， while it had decreased relative abundance of Bavcteroidota and Lactobacillus. The drug administration groups showed increased relative abundance of Bavcteroidota， Bacteroides， norank f norank o Clostridia UCG-014， and Blautia and decreased relative abundance of Firmicutes and Deferribacterota. Compared with the blank group， the model group showed down-regulated protein and mRNA levels of ZO-1 and occludin in the colon （P<0.01）， elevated levels of TNF-α， IL-6， and IL-β in the colon， serum， and hippocampus （P<0.01）， up-regulated protein level of NF-κB p65 in the colon and hippocampus （P<0.01）， and down-regulated protein level of BDNF in the hippocampus （P<0.05）. Compared with the model group， high-dose Fufang Kangjiaolv capsules up-regulated the mRNA levels of ZO-1 and occludin in the colon （P<0.01）， lowered the levels of TNF-α， IL-6， and IL-β in the colon， serum， and hippocampus （P<0.01）， up-regulated the protein levels of ZO-1 （P<0.01） and occludin （P<0.05） in the colon， down-regulated the protein level of NF-κB p65 in the colon and hippocampus （P<0.05）， and up-regulated the protein level of BDNF in the hippocampus. ConclusionFufang Kangjiaolv capsules can reduce the anxiety-like behaviors in the rat model of CRS by regulating the gut microbiota disturbance， up-regulating the expression of tight junction proteins in the colon， repairing intestinal mucosal mechanical barrier， and down-regulating NF-κB/BDNF signaling pathway， thereby reducing peripheral and central inflammation. This study proves the hypothesis that Fufang Kangjiaolv capsules play an anti-anxiety role via the microbiota-gut-brain axis， providing a new idea for further research.