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Peritoneal adhesions (PA) are a common and severe complication after abdominal surgery, impacting millions of patients worldwide. The use of anti-adhesion materials as physical barriers is an effective strategy to prevent postoperative adhesions. However, the local inflammatory microenvironment exerts a significant impact on the efficacy of anti-adhesion therapies. In this study, an injectable hydrogel based on oxidized dextran/carboxymethyl chitosan (DCC) is designed and prepared. Furthermore, the DCC hydrogel is specifically engineered to load the adipose mesenchymal stem cells (ADSCs)-derived exosomes (Exos) for the treatment of PA. The prepared DCC hydrogel can act as the physical barrier via covering the irregular wound surface effectively. Moreover, it shows controlled degradation property, enabling the regulated release of Exos. The DCC hydrogel loaded Exos (DCC/Exo) system has high antioxidant capacity, and can effectively modulate the inflammatory microenvironments and diminish apoptosis. Notably, it promotes a polarization shift towards the M2-like phenotype in macrophages. The RNA-seq analysis confirms that the DCC/Exo system exhibits significant anti-inflammatory properties and promotes a reduction in collagen deposition. Consequently, the DCC/Exo system can inhibit peritoneal adhesions significantly in a mouse cecum-abdominal wall injury model. These results demonstrate the DCC/Exo is an ideal material for preventing postoperative adhesions.

Abstract Objective Immune checkpoints have emerged as promising therapeutic targets for autoimmune diseases. However, the specific roles of immune checkpoints in the pathophysiology of ankylosing spondylitis (AS) remain unclear. Methods Hip ligament samples were obtained from two patient groups: those with AS and femoral head deformity, and those with femoral head necrosis but without AS, undergoing hip arthroplasty. Label-Free Quantification (LFQ) Protein Park Analysis was used to identify the protein composition of the ligaments. Peripheral blood samples of 104 AS patients from public database were used to validate the expression of key proteins. KEGG, GO, and GSVA were employed to explore potential pathways regulated by immune checkpoints in AS progression. xCell was used to calculate cell infiltration levels, LASSO regression was applied to select key cells, and the correlation between immune checkpoints and immune cells was analyzed. Drug sensitivity analysis was conducted to identify potential therapeutic drugs targeting immune checkpoints in AS. The expression of key genes was validated through immunohistochemistry (IHC). Results HLA-DMB and HLA-DPA1 were downregulated in the ligaments of AS and this has been validated through peripheral blood datasets and IHC. Significant differences in expression were observed in CD8 + Tcm, CD8 + T cells, CD8 + Tem, osteoblasts, Th1 cells, and CD8 + naive T cells in AS. The infiltration levels of CD8 + Tcm and CD8 + naive T cells were significantly positively correlated with the expression levels of HLA-DMB and HLA-DPA1. Immune cell selection using LASSO regression showed good predictive ability for AS, with AUC values of 0.98, 0.81, and 0.75 for the three prediction models, respectively. Furthermore, this study found that HLA-DMB and HLA-DPA1 are involved in Th17 cell differentiation, and both Th17 cell differentiation and the NF-kappa B signaling pathway are activated in the AS group. Drug sensitivity analysis showed that AS patients are more sensitive to drugs such as doramapimod and GSK269962A. Conclusion Immune checkpoints and immune cells could serve as avenues for exploring diagnostic and therapeutic strategies for AS.

Abstract New two‐dimensional (2D) transition‐metal borides have attracted considerable interest in research on electrode materials for Li‐ion batteries (LIBs) owing to their promising properties. In this study, 2D molybdenum boride (Mo2B2) with and without transition metal (TM, TM=Mn, Fe, Co, Ni, Ru, and Pt) atom doping was investigated. Our results indicated that all TM‐doped Mo2B2 samples exhibited excellent electronic conductivity, similar to the intrinsic 2D Mo2B2 metal behavior, which is highly beneficial for application in LIBs. Moreover, we found that the diffusion energy barriers of Li along paths 1 and 2 for all TM‐doped Mo2B2 samples are smaller than 0.30 and 0.24 eV of the pristine Mo2B2. In particular, for 2D Co‐doped Mo2B2, the diffusion energy barriers of Li along paths 1 and 2 are reduced to 0.14 and 0.11 eV, respectively, making them the lowest Li diffusion barriers in both paths 1 and 2. This indicates that TM doping can improve the electrochemical performance of 2D Mo2B2 and that Co‐doped Mo2B2 is a promising electrode material for LIBs. Our work not only identifies electrode materials with promising electrochemical performance but also provides guidance for the design of high‐performance electrode materials for LIBs.

Abstract With advances in tissue engineering and bioelectronics, flexible electronic hydrogels that allow conformal tissue integration, online precision diagnosis, and simultaneous tissue regeneration are expected to be the next-generation platform for the treatment of myocardial infarction. Here, we report a functionalized polyaniline-based chronological adhesive hydrogel patch (CAHP) that achieves spatiotemporally selective and conformal embedded integration with a moist and dynamic epicardium surface. Significantly, CAHP has high adhesion toughness, rapid self-healing ability, and enhanced electrochemical performance, facilitating sensitive sensing of cardiac mechanophysiology-mediated microdeformations and simultaneous improvement of myocardial fibrosis-induced electrophysiology. As a result, the flexible CAHP platform monitors diastolic-systolic amplitude and rhythm in the infarcted myocardium online while effectively inhibiting ventricular remodeling, promoting vascular regeneration, and improving electrophysiological function through electrocoupling therapy. Therefore, this diagnostic and therapeutic integration provides a promising monitorable treatment protocol for cardiac disease.

Abstract Background HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. Methods This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. Results Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. Conclusion To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Abstract The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Abstract Background In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. Results The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. Conclusion In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Zwitterionic hydrogels are nonfouling and hemocompatibility but several key challenges such as weak mechanical strength and low adhesion hamper their application as coating materials for devices. Here, the authors report a microgel reinforced zwitterionic hydrogel with excellent mechanical robustness and anti-swelling properties.

Background Metastasis is one of the most significant prognostic factors in osteosarcoma (OS). The goal of this study was to construct a clinical prediction model for OS patients in a population cohort and to evaluate the factors influencing the occurrence of pulmonary metastasis. Methods We collected data from 612 patients with osteosarcoma (OS), and 103 clinical indicators were collected. After the data were filtered, the patients were randomly divided into training and validation cohorts by using random sampling. The training cohort included 191 patients with pulmonary metastasis in OS and 126 patients with non-pulmonary metastasis, and the validation cohort included 50 patients with pulmonary metastasis in OS and 57 patients with non-pulmonary metastasis. Univariate logistics regression analysis, LASSO regression analysis and multivariate logistic regression analysis were performed to identify potential risk factors for pulmonary metastasis in patients with osteosarcoma. A nomogram was developed that included risk influencing variables selected by multivariable analysis, and used the concordance index (C-index) and calibration curve to validate the model. Receiver operating characteristic curve (ROC), decision analysis curve (DCA) and clinical impact curve (CIC) were employed to assess the model. In addition, we used a predictive model on the validation cohort. Results Logistic regression analysis was used to identify independent predictors [N Stage + Alkaline phosphatase (ALP)+Thyroid stimulating hormone (TSH)+Free triiodothyronine (FT3)]. A nomogram was constructed to predict the risk of pulmonary metastasis in patients with osteosarcoma. The performance was evaluated by the concordance index (C-index) and calibration curve. The ROC curve provides the predictive power of the nomogram (AUC = 0.701 in the training cohort, AUC = 0.786 in the training cohort). Decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the clinical value of the nomogram and higher overall net benefits. Conclusions Our study can help clinicians effectively predict the risk of lung metastases in osteosarcoma with more readily available clinical indicators, provide more personalized diagnosis and treatment guidance, and improve the prognosis of patients. Mini Abstract A new risk model was constructed to predict the pulmonary metastasis in patients with osteosarcoma based on multiple machine learning.

Given their exceptional performance, plastic packaging products are widely used in daily life, and the dramatic expansion in plastic packaging waste (PPW) has exacerbated environmental problems. Many countries have enacted laws and developed recycling technologies to manage plastic packaging waste in consideration of the nature of PPW as both garbage and a resource. As the world’s largest producer and consumer of plastics, China has also taken measures to address this issue. This paper presents the latest management regulations and recycling strategies for PPW in China. Based on an analysis of the current management status of PPW and recycling technologies and their carbon emission impacts, some management suggestions and a comprehensive full-chain recycling process were put forward. We supposed that management challenges that need to be overcome in the future can be solved through the improvement of green designs for plastic packaging, manufacturing technology updates, consumption concept changes, and the high-value utilization of PPW. This paper aims to provide valuable references for government decisions on PPW management and, furthermore, to set up an economically sensible and industrially feasible PPW solution and boost the development of PPW recycling.

Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

BackgroundTuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.MethodsA total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group.ResultsThe results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644.ConclusionWe used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na+—of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

PurposeThe purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).MethodsClinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI).ResultsThe nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes.ConclusionLymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.

PurposeThis study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery.MethodsThe clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups.ResultsA total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05).ConclusionSeveral costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.

BackgroundThe pathogenesis of Ankylosing spondylitis (AS) has not been elucidated, especially involving hip joint disease. The purpose of this study was to analyze the proteome of diseased hip in AS and to identify key protein biomarkers.Material and MethodsWe used label-free quantification combined with liquid chromatography mass spectrometry (LC–MS/MS) to screen for differentially expressed proteins in hip ligament samples between AS and No-AS groups. Key protein was screened by Bioinformatics methods. and verified by in vitro experiments.ResultsThere were 3,755 identified proteins, of which 92.916% were quantified. A total of 193 DEPs (49 upregulated proteins and 144 downregulated proteins) were identified according to P < 0.01 and Log|FC| > 1. DEPs were mainly involved in cell compartment, including the vacuolar lumen, azurophil granule, primary lysosome, etc. The main KEGG pathway included Phagosome, Glycerophospholipid metabolism, Lysine degradation, Pentose phosphate pathway. Myeloperoxidase (MPO) was identified as a key protein involved in Phagosome pathway. The experiment of siRNA interfering with cells further confirmed that the upregulated MPO may promote the inflammatory response of fibroblasts.ConclusionsThe overexpression of MPO may contribute to the autoimmune inflammatory response of AS-affected hip joint through the phagosome pathway.

IntroductionThis study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.Materials and methodsGene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene’s protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.ResultsA total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.ConclusionFOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

Introduction. Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by symmetrical peripheral polyarthritis. A large number of studies have shown that RA is characterized by gender differences in clinical manifestations. The purpose of this study is to identify the key molecules of gender differences in patients with RA and to provide new molecular targets for personalized therapy. Material and Methods. The data from GSE55457 were downloaded from the comprehensive gene expression comprehensive database, and two groups (RA vs. No-RA groups, Male-RA vs. Female-RA groups) of differentially expressed genes (EDGs) were obtained by GEO2R. The GO function and KEGG pathway analyses of DEGs were carried out through the plug-in ClueGO in Cytoscape. Based on the STRING online, a protein-protein interaction (PPI) network was constructed. Hub genes were selected from CytoHubba. Through the intersection of the top 10 hub genes in two sets of EDGs, the key genes and related KEGG pathways were found. Quantitative Real-Time PCR and Western blotting analysis were performed for verification. Results. 1230 DEGs were screened between RA and No-RA groups, and 306 DEGs were screened between male and female RA groups. The common key gene of the two groups is IL-4. Between RA group and No-RA group, interleukin-4 (IL-4) participates in cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, etc. Conclusion. This study contributes to the molecular biological mechanism of gender differences in RA. IL-4 may have played an important role.