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.

Objective: To investigate the association between brain and muscle Arnt like protein 1( BMAL 1) gene and ambulatory blood pressure (ABP) parameters in college students, and to explore the influence of the interaction between the gene and weight status on the ABP levels, so as to provide reference for early prevention of chronic cardiovascular diseases related to hypertension. Methods: During September 2022 to June 2024, a total of 510 college students were recruited from a university in Changsha, China for on site questionnaire investigation, physical examination, ABP monitoring and genotyping testing. Multiple linear regression method was used to explore the association between the BMAL 1 gene and ABP indicators. An interaction term was included in the general linear model of multiple linear regression to analyze the interactive effects of BMAL 1 gene polymorphisms and weight status on ABP levels. Results: The prevalence of abnormal 24 hour blood pressure, abnormal daytime blood pressure, and abnormal nighttime blood pressure were 3.3%, 3.1%, and 3.9%, respectively. Multiple linear regression analysis showed that the BMAL 1/rs7950226 polymorphism was positively correlated with the 24 hour diastolic blood pressure(DBP) and the daytime DBP level after adjusting for gender, age, and body mass index( β =0.87,0.99, both P <0.05). Also, interaction between BMAL 1/ rs 11022775 and weight status on the nighttime DBP level ( P interaction =0.03) was found. The CC genotype carriers had significantly higher nighttime DBP level ( β =3.52, P <0.05) among overweight/obesity college students, but no differences in nocturnal DBP levels were observed between CC genotype carriers and TT+CT genotype carriers among college students with normal body weight( P > 0.05). Conclusions The rs 7950226 polymorphism is associated with 24 hours DBP and daytime DBP levels. Significant interaction between rs 11022775 with weight status on the nighttime DBP level is found among college students.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

OBJECTIVE To investigate the prevalence and influencing factors of potentially inappropriate medication （PIM） among the elderly in nursing homes in Urumqi based on three PIM screening tools， and to compare the applicability of different tools. METHODS A cross-sectional study design was adopted. Elderly individuals from six nursing homes in Urumqi were selected as the research subjects from May 2021 to September 2023. Demographic characteristics， disease burden and medication records of the elderly were collected. PIM screening was performed using the Chinese PIM criteria （2024 edition）， the American Geriatrics Society （AGS） Beers criteria （2023 edition）， and the Screening Tool of Older Person’S Prescriptions（STOPP）/Screening Tool to Alert to Right Treatment（START） （2023 edition）. Fleiss’s Kappa test was used to assess the consistency among the three screening tools， and multivariate Logistic regression was used to analyze the influencing factors of PIM. RESULTS The prevalence of PIM screened by the Chinese PIM criteria was 47.4% （323/682）， by the AGS Beers criteria was 43.4% （296/682）， and by the STOPP criteria was 50.9% （347/682）. The consistency among the three screening tools was low （Fleiss’s Kappa＝0.12， P ＜0.001）. Multivariate Logistic regression analysis showed that all three screening tools indicated that polypharmacy， aspirin use and psychotropic drug use were independent risk factors for PIM among elderly in nursing houses （all OR＞1， P ＜0.05）. CONCLUSIONS The prevalence of PIM among the elderly in nursing homes in Urumqi is relatively high. Polypharmacy， aspirin use and psychotropic drug use are common independent risk factors for PIM. There are differences in screening results among the three tools with low consistency. The Chinese PIM criteria can serve as a first-line tool for PIM screening in nursing homes.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

ObjectiveTo investigate the association between metabolic associated fatty liver disease （MAFLD） and the risk of atherosclerotic cardiovascular disease （ASCVD）， and to provide data support for the prevention and treatment of such metabolic-associated diseases in clinical practice. MethodsAn observation cohort was established for the workers of Kailuan who underwent physical examination for the first time from June 2006 to October 2007 and had complete liver assessment data， without the history of malignant tumor， MAFLD or ASCVD. According to the presence or absence of MAFLD， the patients were divided into non-MAFLD group with 67 565 patients and MAFLD group with 29 004 patients， and according to the presence or absence of ASCVD， the patients were divided into non-ASCVD group with 69 141 patients and ASCVD group with 481 patients. The group t-test or the Wilcoxon rank-sum test was used for comparison of continuous data between the two groups. The χ² test was used for comparison of categorical data between the two groups. The life-table method was used to calculate the cumulative incidence rates of new-onset ASCVD and MAFLD； the Kaplan-Meier method was used to plot the survival curves of the cumulative incidence rates of ASCVD in the MAFLD group and the non-MAFLD group and the cumulative incidence rates of MAFLD in the ASCVD group and the non-ASCVD group， and the log-rank test was used for comparison of cumulative incidence rates between two groups. A multivariate Cox proportional-hazards regression model analysis was used to investigate the impact of MAFLD on the risk of ASCVD and the impact of ASCVD on the risk of MAFLD. ResultsCompared with the non-MAFLD group， the MAFLD group had significantly higher levels of body mass index （BMI）， waist circumference， systolic blood pressure （SBP）， diastolic blood pressure （DBP）， resting heart rate， alanine aminotransferase， uric acid （UA）， fasting blood glucose （FBG）， triglyceride （TG）， total cholesterol， low-density lipoprotein cholesterol， and high sensitivity C-reactive protein （hs-CRP）， as well as significanty lower levels of estimated glomerular filtration rate （eGFR） and high-density lipoprotein cholesterol （all P <0.05）. Compared with the non-ASCVD group， the ASCVD group had significantly higher levels of BMI， waist circumference， SBP， DBP， UA， FBG， TG， and hs-CRP and a significantly lower level of eGFR （all P<0.05）. The incidence rate of new-onset ASCVD continued to increase over time in the MAFLD group and the non-MAFLD group， while the incidence rate of new-onset MAFLD firstly increased and then remained stable over time in the ASCVD group and the non-ASCVD group. There were 4 263 cases （14.70%） of new-osnet ASCVD in the MAFLD group， with an incidence density of 12.90 per 1 000 person-years， while there were 6 529 cases （9.66%） of new-osnet ASCVD in the non-MAFLD group， with an incidence density of 8.24 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of ASCVD between the two groups （χ²=519.09 and 531.80， both P<0.05）. There were 148 cases （30.77%） of new-onset MAFLD in the ASCVD group， with an incidence density of 40.10 per 1 000 person-years， while there were 32 194 cases （46.56%） of new-onset MAFLD in the non-ASCVD group， with an incidence density of 57.59 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of MAFLD between the two groups （χ²=19.29 and 30.78， both P<0.05）. The corrected multivariate Cox proportional-hazards regression model analysis showed that MAFLD was a risk factor for new-onset ASCVD （hazard ratio ［HR］=1.11， 95% confidence interval ［CI］： 1.06 — 1.16， P<0.001）， while ASCVD was a protective factor against new-onset MAFLD （HR=0.72， 95%CI： 0.61 — 0.85， P<0.001）. ConclusionThere is a significant association between MAFLD and ASCVD， with an increase in the risk of ASCVD in the MAFLD population and a reduction in the risk of MAFLD in the ASCVD population.

BACKGROUND:Choline kinase alpha is a key enzyme in phospholipid metabolism,involved in the synthesis of phosphatidylcholine,and plays an important role in maintaining cell membrane integrity and signal transduction.Research has shown that choline kinase alpha is highly expressed in various tumors and is closely related to cell proliferation,metabolic reprogramming,and tumor progression.As a potential therapeutic target,the role of choline kinase alpha in tumor metabolism and mitochondrial function still needs further exploration.OBJECTIVE:To evaluate the effects and the underlying mechanisms of choline kinase alpha on the proliferation and apoptosis of glioma U87MG and U251 cells.METHODS:Short hairpin RNA of choline kinase alpha and its empty vector control were transfected into U87MG and U251 glioma cells.Mitochondrial morphology was observed by transmission electron microscopy.Mitochondrial structure and functional protein levels were assessed by western blot assay.Reactive oxygen species levels in cells were measured using a reactive oxygen species fluorescent probe.Mitochondrial membrane potential was assessed with a JC-1 assay.Intracellular adenosine triphosphate levels were measured by chemiluminescence.Cell proliferation was evaluated using a CCK-8 assay.Apoptosis levels were analyzed by flow cytometry.The mitochondrial fission inhibitor Mdivi-1 was used to protect the mitochondrial function of the choline kinase α-silenced lentiviral cells.Finally,U87MG cells were subcutaneously injected to construct a subcutaneous tumor model in nude mice.The tumor growth in nude mice was observed before and after choline kinase alpha silencing and after the use of the mitochondrial fission inhibitor Mdivi-1.RESULTS AND CONCLUSION:(1)Compared with the empty control group,the mitochondria of U87MG and U251 cells in the choline kinase alpha silencing lentivirus group exhibited significant structural abnormalities in mitochondria,such as vacuolization and cristae disruption.The expressions of mitochondrial structure and function-related proteins TOM20,ACO2,and ATP5A were significantly decreased(P＜0.01,P＜0.001),the expression of SOD2 was significantly increased(P＜0.01,P＜0.000 1),the fluorescence intensity of reactive oxygen species was significantly increased(P＜0.01),the mitochondrial membrane potential and adenosine triphosphate level were significantly decreased(P＜0.01,P＜0.001),the cell proliferation ability was reduced(P＜0.01),and the apoptosis level was increased(P＜0.001).(2)Following Mdivi-1 treatment,the fluorescence intensity of reactive oxygen species in U87MG and U251 cells decreased(P＜0.05,P＜0.01),mitochondrial membrane potential and adenosine triphosphate levels were significantly restored(P＜0.05,P＜0.01,P＜0.001),cell proliferation ability was improved(P＜0.05,P＜0.01),and apoptosis level was decreased(P＜0.05).(3)In addition,the in vitro subcutaneous tumor formation experiment of nude mice showed that compared with the empty control group,the mass and growth rate of subcutaneous tumors formed by U87MG cells in the choline kinase alpha silencing lentivirus group were significantly reduced(P＜0.000 1).After Mdivi-1 treatment,the mass and growth rate of tumors were significantly increased(P＜0.000 1).(4)The results show that choline kinase alpha silencing affects the proliferation and apoptosis of glioma cells by inducing mitochondrial dysfunction.

BACKGROUND:The Guilu Erxian Glue consists of Testudinis Plastrum,Cornu Cervi,Lycii Fructus,and Ginseng Radix.In earlier clinical observations,it is discovered that using Guilu Erxian Glue to treat patients with liver-kidney deficiency type knee osteoarthritis effectively alleviated knee pain,increased the range of motion,and improved walking ability.However,the exact mechanism by which oral administration of Guilu Erxian Glue can produce local therapeutic effects on the knee joint is still unclear.OBJECTIVE:To investigate the effects of Guilu Erxian Glue on gut microbiota in rats with knee osteoarthritis and to evaluate its mechanism using 16S rDNA sequencing and machine learning analysis.METHODS:Totally 18 female SD rats were randomly divided into three groups:blank group,model group,and Guilu Erxian Glue group,with 6 rats in each group.A knee osteoarthritis model was prepared using the destabilization of the medial meniscus surgical method.After successful modeling,the Guilu Erxian Glue group was given a decoction of Guilu Erxian Glue by gavage,while the blank and model groups were given an equal amount of distilled water.After 28 days of continuous intervention,high performance liquid chromatography was used to detect the active ingredients of Guilu Erxian Glue.MRI imaging was used to observe the condition of rat knee articular cartilage.Fecal samples were collected;DNA was extracted using a kit,amplified and purified by PCR,and an Illumina sequencing library was constructed.The Illumina MiSeq platform was used for high-throughput sequencing to generate raw sequence data.After obtaining the raw data,QIIME2 software was used to process the data.Linear Discriminant Analysis Effect Size analysis and random forest algorithm were used to screen for differential species in microbial data.KEGG and MetaCyc functional pathway analyses were used to explore the association between key microbial communities and experimental groups.Linear discriminant analysis effect values and random forest algorithm were used to screen for differential species.Association networks were used to analyze the interactions between microbial communities,and machine learning methods were used to analyze the composition and changes of gut microbiota.RESULTS AND CONCLUSION:(1)LC-MS component identification was conducted on the traditional Chinese medicine formula of Guilu Erxian Glue,and a total of 7 effective ingredients were identified.(2)MRI imaging showed that synovitis scope of high-density shadows in rats of the Guilu Erxian Glue group was reduced,and the degeneration of medial femoral condyle cartilage was less than that in the model group.(3)16S rDNA sequencing showed that the model group rats exhibited significant microbial imbalance,with a significant decrease in the abundance of Firmicutes and Bacteroidetes at the phylum level,while the proportion of Proteobacteria increased significantly(P＜0.05).The gut microbiota structure of rats in the Guilu Erxian Glue group was significantly improved,and the proportion of Firmicutes and Bacteroidetes increased,restoring a more diverse microbiota composition,approaching that of the blank group(P＜0.05).(4)KEGG and MetaCyc functional pathway analysis showed that the Guilu Erxian Glue group significantly activated multiple metabolic pathways,including amino acid metabolism,lipid metabolism,and biotin synthesis pathways(P＜0.05).(5)The results indicate that Guilu Erxian Glue contains seven active ingredients,and the changes in gut microbiota of knee osteoarthritis rats were analyzed using 16S rDNA sequencing.Guilu Erxian Glue can significantly improve the imbalance of gut microbiota,restore the abundance of beneficial bacteria,and have a significant impact on the composition of gut microbiota,providing scientific basis for the efficacy and mechanism of Guilu Erxian Glue.