OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Objective:To explore the risk factors of adverse events during the perioperative period of transurethral resection of bladder tumor(TURBT)in bladder cancer patients with coronary atherosclerotic heart disease(CAD).Methods:We retrospectively analyzed the clinical data of bladder cancer patients who underwent TURBT in Beijing Anzhen Hospital from June 2022 to September 2024.All patients with bladder cancer and CAD underwent coronary computed tomography angiography(CCTA)for diagnosis and assessment of CAD before surgery.Based on the CCTA results,the patients with bladder cancer and CAD were divided into two groups:those with mild to moderate coronary stenosis and those with severe coronary stenosis.The severe coronary stenosis group was further divided into two subgroups based on whether they received low-molecular-weight heparin(LMWH)bridging therapy or continued their anti-platelet treatment before surgery.Perioperative anticoagulation and antiplatelet strategies were adjusted according to the opinions of the specialists.The incidence of adverse events within 30 days postoperative-ly was followed up and analyzed.Results:A total of 80 bladder cancer patients with CAD who underwent TURBT were included in the study.Among the 80 patients with CAD,55(68.8％)had mild to moder-ate coronary stenosis,and 25(31.2％)had severe coronary stenosis.Compared with those had mild to moderate coronary stenosis,the patients who had severe coronary stenosis had a higher incidence of post-operative bleeding and pulmonary embolism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the patients who had severe coronary stenosis(P=0.034).Among the patients with severe coronary stenosis,8(32.0％)received LMWH bridging therapy before TURBT,and 17(68.0％)continued their previous antiplatelet treatment.Compared with those who continued antiplatelet treatment,the patients who re-ceived LMWH bridging therapy had a higher incidence of postoperative bleeding and pulmonary embo-lism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the LMWH bridging group(P=0.032).Conclusion:Patients with mild-to-moderate coronary stenosis demonstrate relatively low perioperative risk during TURBT procedures and may safely undergo TURBT following antiplatelet therapy discontinuation.Conversely,those with severe coronary stenosis exhibit significantly higher perioperative risk and require intensive monitoring.In bladder cancer patients with concomitant severe coronary stenosis,perioperative LMWH bridging therapy is associated with increased myocardial infarction risk,whereas continued anti-platelet therapy does not elevate postoperative bleeding risk.Current evidence therefore supports maintai-ning antiplatelet therapy in these patients,with appropriate bleeding risk assessment.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Bone repair remains an important target in tissue engineering, making the development of bioactive scaffolds for effective bone defect repair a critical objective. In this study, β-tricalcium phosphate (β-TCP) scaffolds incorporated with processed pyritum decoction (PPD) were fabricated using three-dimensional (3D) printing-assisted freeze-casting. The produced composite scaffolds were evaluated for their mechanical strength, physicochemical properties, biocompatibility, in vitro pro-angiogenic activity, and in vivo efficacy in repairing rabbit femoral defects. They not only demonstrated excellent physicochemical properties, enhanced mechanical strength, and good biosafety but also significantly promoted the proliferation, migration, and aggregation of pro-angiogenic human umbilical vein endothelial cells (HUVECs). In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site, with the β-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1 (Notch1), vascular endothelial growth factor (VEGF), bone morphogenetic protein-2 (BMP-2), and osteopontin (OPN). Overall, the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo. The incorporation of PPD notably promoted the angiogenic-osteogenic coupling, thereby accelerating bone repair, which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.
Calcium Phosphates/chemistry* ; Animals ; Bone Regeneration ; Rabbits ; Tissue Scaffolds ; Printing, Three-Dimensional ; Humans ; Human Umbilical Vein Endothelial Cells ; Neovascularization, Physiologic ; Osteogenesis ; Tissue Engineering/methods* ; Biomimetic Materials ; Cell Proliferation ; Angiogenesis

Objective:To explore the impacts of Yiqi Wenfei Tongqiao Formula on inflammatory response and T helper cell(Th)1/Th2 immune balance in rats with lung Qi deficiency cold type allergic rhinitis.Methods:Sixty SD rats were randomly divided into model group,low dose Yiqi Wenfei Tongqiao Formula group,medium dose Yiqi Wenfei Tongqiao Formula group,high dose Yiqi Wenfei Tongqiao Formula group,and desloratadine group,with 12 rats in each group,using a combination of smoking,cold stimula-tion combined with ovalbumin(OVA)sensitization and stimulation construct lung Qi deficiency cold type allergic rhinitis model.An-other 12 normal SD rats were selected as control group.Each group of rats was treated with corresponding drugs once a day for 14 days.Model group and control group rats were given physiological saline.Scoring method was used to analyze the behavioral score and lung index of rats with allergic rhinitis.HE staining was applied to detect pathological changes in rat nasal mucosa tissue.ELISA was ap-plied to detect levels of IFN-γ,IL-4 and TNF-α in rats serum;flow cytometry was applied to detect Th1/Th2.Western blot was applied to detect expressions of T-bet and GATA-3 protein in nasal mucosa tissues of rats in each group.Results:Compared with control group,pathological damage to nasal mucosa tissue of model group rats was severe,allergic rhinitis behavior score(days 41,48,56),lung index,levels of IL-4 and TNF-α,Th2 and protein expression of GATA-3 were increased,while levels of IFN-γ,Th1,Th1/Th2,and protein expression of T-bet were decreased(P<0.05);compared with model group,pathological damage to nasal mucosa tissue of rats in low,medium,high doses Yiqi Wenfei Tongqiao Formula groups and desloratadine group were alleviated,the allergic rhinitis behavior score(days 48,56),lung index,levels of IL-4 and TNF-α,Th2 and protein expression of GATA-3 were decreased,while levels of IFN-γ,Th1,Th1/Th2,and protein expression of T-bet were increased(P<0.05).Conclusion:Yiqi Wenfei Tongqiao Formula can inhibit inflammatory response of lung Qi deficiency cold type allergic rhinitis rats By regulating Th1/Th2 immune balance.

Objective To investigate the effect of Yiqi Wenfei Tongqiao decoction on the inflammatory response in rats with allergic rhi-nitis(AR)of lung qi deficiency and cold type based on the Janus kinase 1(JAK1)/signal transducer and activator of transcription 3(STAT3)signaling pathway.Methods A AR model of lung qi deficiency and cold type was established in rats using ovalbumin(OVA)combined with smoking and cold stimulation.After modeling,the rats were randomly divided into model,loratadine,low-dose Chinese medicine,high-dose Chinese medicine,and high-dose Chinese medicine+RO8191(JAK1/STAT3 signaling pathway activator)groups,with 12 rats per group.Twelve healthy rats were used as controls.After 14 days of drug intervention,the changes in the symptoms of lung qi deficiency and cold syndrome and AR were observed in the rats.Enzyme-linked immunosorbent assay was used to detect the serum levels of tumor necrosis factor α(TNF-α),interleukin(IL)-4,interferon-γ(IFN--γ),and ovalbumin-specific immunoglobulin E(OVA-sIgE).The number of inflammatory cells in the nasal lavage fluid was determined and the eosinophil(EOS)percentage was calculated.Pathological changes in the nasal mucosal tissue were observed after hematoxylin and eosin(HE)staining.Western blotting was used to detect the expression of JAK1/STAT3 signaling pathway-related proteins in nasal mucosal tissue.Results Compared with those in the model group,the AR symptom score,serum IL-4,TNF-α,and OVA-sIgE levels,EOS percentage in nasal lavage fluid,and levels of p-JAK1/JAK1 and p-STAT3/STAT3 in the nasal mucosal tissue of rats in the loratadine,low-dose Chinese medicine,and high-dose Chinese medicine groups were lower,whereas the serum IFN-γ level was higher(P＜0.05).RO8191 weakened the inhibitory effect of Yiqi Wenfei Tongqiao decoction on the inflammatory response in rats with AR of lung qi deficiency and cold type.Conclusion Overall,Yiqi Wenfei Tongqiao decoction can alleviate the inflammatory response in rats with AR of lung qi deficiency and cold type by inhibiting JAK1/STAT3 signaling pathway acti-vation and regulating the balance of Th1/Th2 cytokines.