ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective To investigate the causal relationship between metabolic syndrome and breast cancer by using a bidirectional two-sample Mendelian randomization (MR) approach. Methods Genome-wide association study (GWAS) summary statistics for metabolic syndrome and breast cancer were acquired from the Integrative Epidemiology Unit GWAS database and the GWAS Catalog, with populations encompassing the United States and East Asia. A bidirectional causal design was employed: a forward analysis with metabolic syndrome as the exposure and breast cancer as the outcome, followed by a reverse analysis wherein their roles were interchanged. The inverse-variance weighting (IVW) method was primarily used for effect estimation, supplemented by MR-Egger regression, the weighted median method, the simple mode method, and the weighted mode method. Instrument variable strength was screened using the F-statistic (F>10). Robustness of the results was assessed through heterogeneity tests, horizontal pleiotropy tests, forest plots, and leave-one-out sensitivity analyses. Results The IVW analysis indicated no significant causal relationship between metabolic syndrome and breast cancer (OR=1.00, 95%CI: 0.97-1.03), P>0.05). Sensitivity analyses yielded consistent results, suggesting the good robustness of the study findings. Conclusion This study found no evidence to support a causal relationship, either positive or negative, between metabolic syndrome and breast cancer.

Neurological disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), cerebral ischemia, and multiple sclerosis (MS), impose an escalating global health burden and remain largely incurable. These disorders arise from multifactorial and interconnected pathological processes, such as chronic neuroinflammation, oxidative stress, protein misfolding and aggregation, demyelination, and neurovascular dysfunction. Despite substantial advances in elucidating disease-associated molecular mechanisms, current therapeutic strategies are predominantly symptomatic and fail to effectively halt or reverse disease progression. This limitation highlights the urgent need to identify endogenous regulatory molecules capable of coordinating neuronal survival, synaptic maintenance, inflammatory control, and tissue repair within the central nervous system (CNS). Pleiotrophin (PTN) is a heparin-binding, growth-associated cytokine that has emerged as a key regulator of neural development, plasticity, and regeneration. Structurally, PTN contains multiple high-affinity heparin-binding domains that facilitate interactions with extracellular matrix components and cell surface proteoglycans, enabling spatially restricted and context-dependent signaling. Through these molecular properties, PTN functions as a multifunctional organizer of neural growth, plasticity, and tissue remodeling across developmental and adult stages. Its diverse biological effects are executed through a multi-receptor signaling system that integrates extracellular cues with intracellular programs governing cellular survival, migration, and differentiation. Notably, PTN displays a highly dynamic and cell type-specific expression pattern in the central nervous system, being enriched in neural progenitor cells during development and later restricted to discrete neuronal populations, neural stem cells, and non-neuronal niche cells—including astrocytes, pericytes, and vascular endothelial cells—which serve as critical sources of PTN under physiological and pathological conditions. PTN expression is tightly regulated during development and exhibits pronounced plasticity in response to pathological stimuli. Under physiological conditions, PTN is transiently expressed during critical windows of neural growth and synaptogenesis, supporting neuron-glia interactions and myelin formation. In contrast, in pathological contexts such as amyloid β-protein (Aβ) accumulation in AD, dopaminergic neuron degeneration in PD, demyelination in MS, and ischemic brain injury, PTN expression is frequently dysregulated, suggesting an active role in disease-associated remodeling rather than a passive bystander effect. Importantly, accumulating evidence indicates that PTN exerts a dual and context-dependent influence on neurological disorders. On the one hand, aberrant PTN signaling may contribute to maladaptive responses, including sustained glial activation, dysregulated neuroinflammation, extracellular matrix remodeling, and enhanced Aβ deposition. On the other hand, PTN displays robust neuroprotective and reparative functions by promoting neuronal survival, enhancing oligodendrocyte maturation and remyelination, and stimulating post-injury angiogenesis, thereby facilitating tissue repair and functional recovery. At the mechanistic level, PTN signaling is characterized by extensive cross-talk among receptor-dependent pathways. Activation of anaplastic lymphoma kinase (ALK) triggers canonical PI3K-AKT-mTOR and MAPK cascades that support neuronal survival and axonal integrity. PTN binding to protein tyrosine phosphatase receptor type Z1 (PTPRZ1) induces conformational inhibition of its phosphatase activity, resulting in increased phosphorylation of downstream effectors such as β-catenin, Fyn, and Src, which regulate neuronal migration and synaptic stabilization. Syndecan-3 (SDC3) functions as both a co-receptor and an independent signaling mediator by capturing extracellular PTN, amplifying ALK- and PTPRZ1-dependent signaling, and directly modulating cytoskeletal dynamics through PKC and ERK pathways. In parallel, PTN interaction with αVβ3 integrin contributes to remodeling of the neurovascular niche, linking angiogenesis with neurogenesis and neural repair. From a translational perspective, therapeutic strategies targeting PTN can be broadly classified into 3 categories: direct enhancement of PTN signaling through exogenous protein supplementation or gene therapy-mediated upregulation, pharmacological modulation of PTN-associated receptor pathways and downstream signaling nodes, and exploitation of PTN as a dynamic biomarker to inform disease stratification and therapeutic responsiveness. These complementary approaches underscore the growing interest in PTN-centered interventions across a spectrum of neurological disorders. In summary, PTN functions not merely as a classical trophic factor but as a central signaling hub integrating inflammatory regulation, neural regeneration, and vascular remodeling within the CNS. This review aims to synthesize current insights into PTN’s molecular architecture, multi-receptor signaling mechanisms, and disease-specific functions, and to highlight emerging therapeutic strategies targeting PTN. By conceptualizing PTN as a dynamic modulator of neuronal resilience rather than a static biomarker, we propose that precise modulation of PTN signaling may offer promising avenues for therapeutic development in neurodegenerative and neuroinflammatory diseases.

Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

ObjectiveTo explore the mechanism by which Qidan Yifei Tongqiao granules （QDYF） alleviate nasal mucosal remodeling in allergic rhinitis （AR） via the interleukin-33 （IL-33）/growth stimulation expressed gene 2 （ST2）/interleukin-1 receptor accessory protein （IL-1RAP） signaling pathway from the perspective of Qi-replenishing and blood-activating therapy. MethodsFirst， according to the previous network pharmacology results， this study predicted the potential mechanisms of QDYF in treating AR by screening key pathways， components， and targets. Molecular docking was performed via AutoDock and PyMOL 2.5.5. Subsequently， a rat model of ovalbumin （OVA）-induced AR was used for validation through in vivo experiments. Forty-eight rats were assigned into 6 groups： Control， model， low-dose QDYF （QDYF-L， 4.04 g·kg^-1）， medium-dose QDYF （QDYF-M， 8.08 g·kg^-1）， high-dose QDYF （QDYF-H， 16.16 g·kg^-1）， and loratadine （0.9 mg·kg^-1）. After 14 days of intervention， behavioral scores of the rats were observed. The morphological changes of nasal mucosa tissue were observed by hematoxylin-eosin （HE） staining. Masson staining was used to observe collagen fiber deposition in the nasal mucosal tissue and to calculate the collagen volume fraction （CVF）. The expression of E-cadherin （E-cad） in the nasal mucosa tissue was detected by immunofluorescence. The serum levels of helper T cell 2 （Th2） cytokines interleukin-4 （IL-4）， interleukin-5 （IL-5）， and interleukin-13 （IL-13） as well as helper T cell 1 （Th1） cytokines interleukin-2 （IL-2） and interferon-γ （INF-γ） were quantified by enzyme-linked immunosorbent assay （ELISA）. The protein levels of transforming growth factor-beta 1 （TGF-β₁）， IL-33， ST2， and IL-1RAP in the nasal mucosa tissue were determined by Western blot. ResultsIL-33， ST2， and IL-1RAP had strong binding ability with the main active ingredients—wogonin， 7-methoxy-2-methylisoflavone， formononetin， naringenin， stigmasterol， and beta-sitosterol of QDYF， with the binding energy < -4.25 kcal⋅mol^-1（1 cal≈4.184 J）. The results of in vivo experiments showed that compared with the control group， the model group exhibited increased behavioral scores （P<0.05）， aggravated pathological damage of nasal mucosa， increased collagen fiber deposition and CVF （P<0.05）， elevated serum levels of IL-4， IL-5， and IL-13， up-regulated protein levels of TGF-β₁， IL-33， ST2， and IL-1RAP in the nasal mucosa （P<0.05）， down-regulated expression of E-cad， and declined serum levels of IL-2， IFN-γ， and IFN-γ/IL-4 ratio （P<0.05）. Compared with the model group， the QDYF groups and loratadine group showed reduced behavioral scores （P<0.05）， alleviated pathological damage of nasal mucosa， reduced collagen fiber deposition and CVF （P<0.05）， and up-regulated E-cad expression （P<0.05）. Compared with the model group， the QDYF-H group and the loratadine group showed raised levels of INF-γ and IFN-γ/IL-4 ratio （P<0.05）， declined serum levels of IL-4， IL-5， and IL-13， and down-regulated protein levels of TGF-β₁， IL-33， ST2， and IL-1RAP in the nasal mucosa （P<0.05）. In addition， the QDYF-H group exhibited an elevated serum IL-2 level （P<0.05）. The QDYF-M group showed down-regulated protein levels of TGF-β₁， IL-33 and IL-1RAP in the nasal mucosa （P<0.05）. The QDYF-L group demonstrated a down-regulated protein level of ST2 in the nasal mucosa （P<0.05）. ConclusionQDYF may regulate the Th1/Th2 balance through the IL-33/ST2/IL-1RAP signaling pathway， thereby ameliorating nasal mucosal tissue remodeling and alleviating AR.

Objective To develop and validate a predictive model for autologous arteriovenous fistula(AVF)maturation in hemodialysis patients using conventional ultrasonography and three-dimensional speckle tracking imaging.Methods This case-control study enrolled 200 AVF patients from Chongqing Hospital of Traditional Chinese Medicine from July 2021 to June 2024.Clinical data,vascular ultrasound,and cardiac ultrasound parameters were systematically collected.After applying predefined inclusion criteria,186 patients were stratified into 2 cohorts based on arteriovenous fistula(AVF)maturation status:the spontaneous maturation group(n=111)and the assisted maturation requirement group(n=75).Comparative analysis between the 2 cohorts was conducted using univariate and multivariate logistic regression for variable selection,leading to the construction of a predictive model(model1)for spontaneous AVF maturation.A nomogram was subsequently developed based on model1.Internal validation was performed through 1 000 bootstrap resamples with calibration curve analysis.Model discrimination was quantified by the area under the receiver operating characteristic curve(AUC),while clinical utility was assessed via decision curve analysis(DCA).After excluding 104 patients lacking three-dimensional speckle tracking echocardiography data,the remaining 82 subjects were included in novel predictive model development.Three strain parameters,two-dimensional global longitudinal strain(2DGLS),three-dimensional global longitudinal strain(3DGLS),and three-dimensional left ventricular ejection fraction(3DEF),were independently incorporated into multivariable logistic regression analyses to establish three distinct models(designated as model2,model3 and model4 respectively).Model comparisons employed AUC,net reclassification improvement(NRI),and integrated discrimination improvement(IDI).Results Independent predictors for model1 included:2DEF(OR=1.133,95%CI:1.058～1.213),mid-cephalic vein depth(OR=1.453,95%CI:1.068～1.978),distal cephalic vein diameter(OR=2.141,95%CI:1.120～4.091),post-occlusive brachial artery resistance index(OR=0.004,95%CI:0.000～0.140),and postoperative brachial flow(OR=1.004,95%CI:1.002～1.007).model1 demonstrated excellent discrimination(AUC=0.869,95%CI:0.817～0.921)and calibration(mean absolute error=0.017).DCA showed superior net benefit at 0.1～1.0 threshold probabilities.Compared with model1,non-significant improvements in AUC and IDI,while model4 achieved significant NRI improvements(P<0.05).Conclusion The prediction performance of AVF natural maturity prediction models constructed with 2DGLS,3DGLS,3DEF,or 2DEF is relatively high;The NRI of the model involving 3DEF is better than that of the model involving 2DEF,indicating that it may have better clinical application value within a specific threshold probability range.

Objective To investigate the correlation between serum monoamine oxidase(MAO),soluble growth stimulation expressed gene 2(sST2)and coronary slow flow(CSF),and to evaluate their value in pre-dicting CSF risk and guiding clinical decision-making.Methods A total of 118 patients with CSF confirmed by coronary angiography from January 2022 to September Dazhu Hospital Affiliated to North Sichuan Medical College 2023 were selected as the observation group,while 120 patients with normal coronary blood flow were selected as the control group.The coronary flow velocity was assessed using the TIMI frame count(TFC).Baseline characteristics,as well as serum MAO and sST2 levels,were compared between the two groups.Multi model calibration Logistic regression analysis was used to examine the relationship between the two markers and CSF.Pearson correlation analysis was conducted to evaluate the association between the markers and TFC.The predictive efficacy of MAO and sST2 for CSF and their benefit in clinical decision-making were as-sessed using receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results(1)The levels of body mass index(BMI),smoking proportion,C-reactive protein(CRP),uric acid(UA),creati-nine(Cr),MAO,and sST2 in the observation group were all higher than those in the control group(P＜0.05).(2)The TFC and mean TFC of LAD,LCX,and RCA in the observation group were higher than those in the control group(P＜0.05).(3)Multivariate Logistic regression analysis suggested that serum MAO and sST2 were independent risk factors for the occurrence of CSF(OR＞1,P＜0.05).(4)Pearson correlation a-nalysis showed that serum MAO and sST2 levels were positively correlated with the TFC of the LAD,LCX,and RCA,as well as the mean TFC(r=0.735,0.710,0.728,0.703,r=0.727,0.669,0.684,0.705,P＜0.05).(5)ROC curve revealed that the area under the curve(AUC)for predicting CSF was 0.761 of MAO,0.750 of sST2,and 0.807 of their combination.(6)DCA curve showed that the net benefit of serum MAO was greater than 0 when the threshold probability was between 0.05 and 0.70,the net benefit of serum sST2 was greater than 0 when the threshold probability was between 0.05 and 0.85,and their combined prediction yielded a greater net benefit within the threshold range of 0.40 to 0.90.Conclusion Elevated serum MAO and sST2 levels are independent risk factors for CSF.Both markers have good predictive value for CSF,and their combined detec-tion further improves predictive performance and clinical net benefit.

Diannan Su's bone-setting school is one of the orthopedic schools of traditional Chinese medicine(TCM),and Su Caichen,a famous bone-setting physician in Diannan,is the key figure of Diannan Su's bone-setting school.This paper systematically summarized Su Caichen's bone-setting academic thoughts of"adaptation","harmonization"and"recovery",presented his core bone-setting concepts of"original traumatic chamber","bone-setting prior to activating blood and vessles,bone-setting together with soothing tendons and then fracture healing naturally after the removal of stasis",and introduced his five kinds of bone-setting manipulations for treating the common upper limb fractures in detail,namely shaking and pushing manipulations for distal radius fracture,floating manipulations for fracture of both ulna and radius,five-step manipulations for supracondylar fractures of humerus,staging manipulations for humeral shaft fracture,and degloving manipulations for proximal humeral fractures complicated with shoulder dislocation.Su Caichen's bone-setting academic thoughts,bone-setting concepts and his specific TCM bone-setting manipulations have constructed the academic and theoretical system of Diannan Su's bone-setting school,which will provide an approach for TCM treatment of orthopedic diseases,and will promote the inheritance and development of the specific TCM orthopedic schools.

BACKGROUND:Rheumatoid arthritis is a chronic autoimmune disease.Early diagnosis is crucial for preventing disease progression and for effective treatment.Therefore,it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE:Based on the Gene Expression Omnibus(GEO)database,to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS:The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database.The data sets were merged using a batch effect removal method.Differential expression analysis and functional correlation analysis of genes were performed using R software.Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes,and key genes related to rheumatoid arthritis were screened.Furthermore,we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION:In both rheumatoid arthritis and normal synovial tissues,we identified 179 differentially expressed genes,with 124 genes up-regulated and 55 genes down-regulated.Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response.Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis.LRRC15(area under the curve=0.964,95%confidence interval:0.924-0.992)and MICB(area under the curve=0.961,95%confidence interval:0.923-0.990)demonstrated strong diagnostic performance on the validation dataset.The infiltration of 13 types of immune cells was altered,with macrophages being the most affected.In rheumatoid arthritis,the majority of proinflammatory pathways in immune cell function were activated.Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages.To conclude,this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis,with strong diagnostic performance and significant correlation with immune cell infiltration.Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis.