ObjectiveTo establish a model of dry eye with lung Yin deficiency syndrome in mice. MethodsA total of 40 SPF C57BL/6J mice were assigned via the random number table method into 5 groups （n=8）： Normal control， model control， and high-， medium-， and low-dose （11.7， 5.85， and 2.925 g·kg^-1， respectively） Yangyin Qingfeitang. Mice in the normal control group were fed normally without any intervention. Mice in Yangyin Qingfeitang group and model control group were treated with 0.2% benzalkonium chloride eye drops （5 μL） twice a day and fed in a controlled drying system in a dry environment for 28 days. At the same time， the mice were administrated with thyroxine tablet solution by gavage and placed in a glass fumigation tank （SO₂ concentration： 0.5 g·m^-3） for 14 days. After 4 weeks， mice in Yangyin Qingfeitang groups were treated with Yangyin Qingfeitang by gavage and those in the normal control group and model control group were administrated with deionized water at 0.01 mL·g^-1. The body mass， anal temperature， four examination information （claw and nail appearance）， basic tear secretion test， tear film rupture time， corneal fluorescein staining， and lacrimal gland HE staining were compared among groups. Compound Yangyin Qingfeitang granules were used to measure the syndrome to verify the success of modeling. ResultsAfter 28 days of continuous modeling， compared with the normal control group， the model group exhibited listless and emaciated status， coughing， drowsiness， dry and dull hair， dry and hard stool， reduced food intake and water intake， red lip circumference， red tongue with reduced fluid， dry nose and teeth， red claws and nails， body mass gain， decreased anal mild tear secretion （P<0.05）， and shortened tear film rupture time （P<0.05）. After 28 days of modeling， the mice showed large corneal fluorescein staining range， severe corneal injury， and increased content of interleukin （IL）-18， IL-β， and tumor necrosis factor （TNF）-α in lacrimal gland， compared with those in the normal control group （P<0.05）. After the treatment with Yangyin Qingfeitang， the mice had good drinking and eating conditions， with lighter redness of the tongue， moist nose， moist and shiny teeth， and the claw and nail color close to that in the normal group. Compared with the model control group， Yangyin Qingfeitang groups showed increases in body mass and anal temperature （P<0.05）， tear secretion （P<0.05）， and tear film rupture time （P<0.05）， narrowed range of corneal fluorescein staining， and declined levels of IL-18， IL-β， and TNF-α in lacrimal glands （P<0.01）. The high-dose group had the best effect， with the indicators close to the levels in the normal control group. ConclusionThe animal model of dry eye with lung Yin deficiency syndrome can be established by culture in a controlled drying system， treatment with benzalkonium chloride eye drops for 28 days， and administration of thyroxine tablet solution combined with SO₂ fumigation for 14 days.

OBJECTIVE To systematically review medication-related risk factors for falls in older adults， to provide references for ensuring medication safety among older adults. METHODS A systematic search was conducted in PubMed， Embase， Web of Science， and CNKI for relevant literature published from database inception to November 1， 2025. Relevant studies on medication-related falls in older adults， both domestic and international， were included. Drug factors influencing falls in older adults were summarized and analyzed. RESULTS A total of 22 studies were included. Four major classes of fall-risk-increasing drugs were identified： psychotropic medications [12 studies， odds ratio （OR） range 1.500-5.790]， cardiovascular system drugs （5 studies， OR range 1.236-4.784）， analgesics （3 studies， OR range 1.500-4.490）， and hypoglycemic agents （3 studies， OR range 2.070-2.751）. Additionally， anticholinergic burden （1 study， OR was 2.610） and polypharmacy （7 studies， OR range 2.902-25.897 for the use of ≥4 medications） were identified as significant risk factors for falls. CONCLUSIONS Falls in older adults are significantly associated with psychotropic medications， cardiovascular system drugs， analgesics， and hypoglycemic agents， among which psychotropic medications pose the highest risk. Anticholinergic burden and polypharmacy are also important risk factors. In clinical practice， interventions should be implemented through deprescribing and risk monitoring to effectively reduce the risk of falls in older adults.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

BackgroundPersistent cognitive impairment is prevalent among patients with stable schizophrenia. While serum total bile acid （TBA） level in acute-phase patients are known to be associated with cognitive dysfunction， the relationship between serum TBA and multi-dimensional cognitive functions in stable phase patients remains unclear. ObjectiveTo investigate the correlation between serum TBA level and cognitive function in patients with stable schizophrenia， and to evaluate its predictive value for cognitive impairment， thereby providing a serological biomarker for the timely identification and objective assessment of cognitive dysfunction. MethodsA cross-sectional study was conducted on 137 inpatients with stable schizophrenia at The Fourth People's Hospital of Yancheng from March to December 2024. All participants met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）. Cognitive function was evaluated using the Chinese Brief Cognitive Test （C-BCT）， patients were categorized into four groups： normal cognition （n=28）， mild impairment （n=28）， moderate impairment （n=47）， and severe impairment （n=34）. Fasting venous blood samples were collected， and serum TBA level was quantified using an enzymatic cycle assay. Spearman correlation analysis was ultilized to determine the relationship between serum TBA level， overall cognitive function， and specific cognitive domains. Binary Logistic regression model was used （adjusting for covariates such as age， gender， and disease duration） to analyze the impact of serum TBA level on overall and individual cognitive functions. The predictive value of serum TBA level for overall cognitive impairment was evaluated using receiver operating characteristic （ROC） curve. ResultsSerum TBA levels differed significantly among the four groups （H=18.677， P<0.01）. Specifically， serum TBA levels in both the moderate and severe cognitive impairment groups were significantly higher than those in the normal cognitive group （adjusted P<0.01）. Serum TBA level was positively correlated with the severity grading of overall cognitive impairment （r_s=0.354， P<0.05）， and negatively correlated with T-scores on the trail making test （r_s=-0.328， P<0.05）， continuous performance test （r_s=-0.247， P<0.05）， digit span （r_s=-0.265， P<0.05）， and symbol coding （r_s=-0.221， P<0.05）. Binary Logistic regression analysis identified serum TBA level as an independent risk factor for overall cognitive impairment （OR=1.322， 95% CI： 1.021 - 1.713， P=0.034）， with a particularly robust predictive ability for impaired information processing speed （OR=1.325， 95% CI： 1.057 - 1.661， P=0.015）. The area under ROC curve （AUC） for serum TBA level in predicting overall cognitive impairment was 0.738， with a sensitivity of 60.61% and a specificity of 78.64%. ConclusionIn patients with stable schizophrenia， elevated serum TBA levels are associated with worse overall cognitive function， as well as deficits in information processing speed， attention， working memory， and executive function. Serum TBA serves as an independent risk factor and exhibits moderate predictive value for overall cognitive impairmen，particularly in the domain of information processing speed. ［Funded by Yancheng Municipal Health Commission Medical Research Project （number， YK2024141）］

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.

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.

Low anterior resection syndrome （LARS） is a common complication of sphincter-preserving rectal cancer surgery， and its manifestations change dynamically at different stages. By analyzing and summarizing the clinical symptoms and pathomechanism evolution of LARS across different stages， this paper proposes a staged prevention and treatment strategy using acupuncture. During perioperative stage， the main principle is activating the transport function of the body， supplemented by regulating the mind， with the use of dredging the bowels as needed. During the stoma reversal stage， treatment focuses primarily on fortifying the spleen， with draining dampness as a supplementary method， to help consolidate the intestines. During the radiotherapy or chemotherapy stage， the main focus is reinfor-cing healthy qi， with reducing toxin as an adjunct， to achieve the effect of activating the transport of the pivot. During the survival management stage， treatment primarily focuses on tonifying the kidneys and secondarily on fortifying the spleen， with regulating the corporeal soul as the therapeutic emphasis. Acupoints are selected and combined in accordance with the treatment principles at each stage， and different stimulation methods such as electroacupuncture and moxibustion are applied. An analysis of the mechanisms underlying acupuncture for LARS is also provided， offering a theoretical basis and practical approach for the prevention and treatment of LARS with acupuncture.

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）.

Objective To explore the ^“contribution^” of different exposures to cardiovascular diseases at the population level and to construct a risk prediction model for the effective allocation of prevention resources. Methods The CHNS (China Health and Nutrition Survey) database was used. In 2009, 2011 and 2015, 9 899 permanent residents aged 35 to 75 years in 10 provinces and cities in the central and eastern regions (Beijing, Liaoning, Heilongjiang, Shanghai, Shandong, Henan, Hubei, Hunan, Guangxi and Jiangsu) were selected as the research subjects. A single-factor analysis was conducted to examine the risk factors including sex, age, BMI, marital status, urban/rural area, sleep time, smoking, alcohol consumption, diabetes, education, and health insurance. The multifactor-adjusted population-attributable risk of certain risk factors was also estimated based on logistic regression analysis. The cardiovascular disease (CVD) risk prediction model was developed using a modeling group of 6 927 randomly selected individuals (70%) and a validation group of 2 974 individuals (30%). The model's differentiation and calibration were assessed using the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness-of-fit test. Results The results showed that the adjusted population attributable risk and 95% confidence interval for BMI, sleep time, smoking, drinking and diabetes were 32.20% (27.67%-36.89%), 7.90% (1.68%-16.58%), 18.56% (11.35%-26.24%), 6.47% (0.11%-13.25%) and 5.73% (4.42%-7.03%). The results of multivariate adjusted population attributable risk percentage showed that BMI was the dominant cause of cardiovascular diseases, followed by smoking, sleep time, drinking and diabetes. The low-risk prevalence rate was 18.44%, the higher-risk prevalence rate was 14.19%, and the high-risk prevalence rate was 42.52%. The area under ROC curve AUC was 0.711, P<0.001, and Hosmer-Lemeshow goodness of fit test showed P=0.257. Conclusion In the future, it is important to focus on high-risk groups , control body mass index to the normal range, and reduce smoking , which is of great significance for the prevention of cardiovascular diseases. The risk prediction model has the value of good differentiation and practicability , and can provide certain prediction ability for the prevention of cardiovascular diseases.

BACKGROUND:Knee osteoarthritis(KOA)is a common chronic inflammatory disease that causes damage to joint cartilage and surrounding tissues.Immune cells play an important role in the immune-inflammatory response in knee osteoarthritis,but the specific mechanisms involved are still not fully understood. OBJECTIVE:To evaluate the potential causal relationship between 731 immune cell phenotypes and the risk of knee osteoarthritis using Mendelian randomization. METHODS:Summary statistics of genome-wide association studies(GWAS)for 731 immune cell phenotypes(from GCST0001391 to GCST0002121)obtained from the GWAS catalog and GWAS data for knee osteoarthritis from the IEUGWAS database(ebi-a-GCST007090)were used.Inverse variance-weighted method,MR-Egger regression,weighted median method,weighted mode method,and simple mode method were employed to investigate the causal relationship between immune cells and knee osteoarthritis.Sensitivity analyses were conducted to assess the reliability of the Mendelian randomization results.Reverse Mendelian randomization analysis was also performed using the same methods. RESULTS AND CONCLUSION:The forward MR analysis indicated significant causal relationships(FDR<0.20)between knee osteoarthritis and four immune cell phenotypes,namely CD27 on CD24+CD27+in B cells(OR=1.026,P=0.000 26,Pfdr=0.18),CD33 on CD33dim HLA DR-in myeloid cells(OR=1.014,P=0.000 50,Pfdr=0.18),and CD45RA+CD28-CD8br%CD8br in Treg cells(OR=1.001,P=0.000 78,Pfdr=0.18),and PDL-1 on monocytes in mononuclear cells(OR=0.952,P=0.000 98,Pfdr=0.18).These immune cell phenotypes showed direct positive or negative causal associations with the risk of knee osteoarthritis.Reverse Mendelian randomization analysis revealed no significant causal relationships(FDR<0.20)between knee osteoarthritis as exposure and any of the 731 immune cell phenotypes.The results of sensitivity analysis show that the P-values of the Cochran's Q test and the MR-Egger regression method for bidirectional Mendelian randomization were both greater than 0.05,indicating that there is no significant heterogeneity and pleiotropy in the causal effect analysis between immune cell phenotypes and knee osteoarthritis.To conclude,there may be four potential causal relationships between immune cell phenotypes,such as CD27 on CD24+CD27+cells,CD33 on CD33dim HLA DR-cells,CD45RA+CD28-CD8br%CD8br cells,and PDL-1 on monocytes,and knee osteoarthritis.These findings provide valuable clues for studying the biological mechanisms of knee osteoarthritis and exploring early prevention and treatment strategies.They also offer new directions for the development of intervention drugs.