[摘 要] 目的：探究外周血1型辅助性T细胞（Th1）/Th2/Th17细胞相关细胞因子IL-2、IL-4、IL-6、IL-10、IFN-γ、TNF-α、IL-17A对Ⅲ~Ⅳ期肺鳞状细胞癌（LSCC）患者一线免疫治疗联合化疗疗效和预后的预测价值及其动态变化的意义。方法：回顾性分析2020年1月至2023年12月在内蒙古医科大学附属医院接受一线免疫治疗联合化疗的58例Ⅲ~Ⅳ期LSCC患者的临床资料，采集基线及治疗2、4、6周期后和疾病进展时的外周血，用流式细胞术检测Th1/Th2/Th17细胞分泌的细胞因子水平，用受试者工作特性曲线（ROC）确定各细胞因子基线的最佳截断值，据此将患者分为高、低表达组；根据RECIST 1.1标准，将患者分为客观缓解（ORR）[完全缓解（CR） + 部分缓解（PR）]组、非ORR[（疾病稳定（SD） + 疾病进展（PD）]组、疾病控制（DCR）（CR + PR + SD）组和非DCR（即PD）组；根据PD-L1表达评分将患者分为PD-L1 ≥ 1%组和PD-L1 < 1%或未知组。比较组间疗效的差异；分析临床病理特征与疗效的相关性；用广义估计方程（GEE）评估细胞因子动态变化与疗效的关系；用Kaplan-Meier法绘制生存曲线，Log-Rank检验比较组间差异，COX比例风险回归模型进行单因素及多因素预后分析。结果：IL-2和IFN-γ高表达组患者的客观缓解率（ORR）显著高于低表达组患者（P < 0.001）。IL-2、IFN-γ高表达组和IL-10、TNF-α低表达组患者的疾病控制率（DCR）均显著高于对应低/高表达组（P < 0.001）。PD-L1 ≥ 1%组DCR显著高于PD-L1 < 1%或未知组（P < 0.001）。动态分析显示，在4周期及6周期时，有效组患者血清中IL-6表达水平显著低于无效组（P < 0.05），控制组IL-6表达水平显著低于未控制组（P < 0.001）；治疗前及6周期时有效组IFN-γ表达水平显著高于无效组（P < 0.05），治疗前控制组IFN-γ表达水平显著高于未控制组（P < 0.05）。生存分析显示，IL-2低表达组、IL-10高表达组、TNF-α高表达组和IFN-γ低表达组患者的中位PFS显著缩短（均P < 0.05）。COX多因素分析证实，治疗前IL-2 < 2.45 pg/mL和IL-10 ≥ 3.52 pg/mL 是PFS的独立危险因素。结论：外周血Th1/Th2/Th17细胞相关细胞因子的基线水平及动态变化对Ⅲ~Ⅳ期LSCC患者一线免疫治疗联合化疗的疗效和预后具有预测价值。

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective To observe the value of MRI radiomics combined with ResNet101 deep learning for differentiating lumbar spine brucella spondylitis(BS)and spinal metastases(SM).Methods Seventy-one cases of lumbar spine BS and the same amount of lumbar spine SM patients were retrospectively enrolled in training set,while 33 cases of lumbar spine BS and the same amount of lumbar spine SM patients were enrolled in test set.Clinical features were screened with univariate and multivariate logistic analysis,and a clinical model(Mclinic)was constructed.ROI of lesions were drawn on lumbar sagittal T 2WI,then radiomics features were extracted to construct a radiomics model(Mradiomics).ResNet101 deep learning was integrated with radiomics,then deep learning radiomics features were extracted to construct deep learning radiomics model(MDL+R).Finally a combined model(Mcombined)was constructed through combining clinical features and deep learning radiomics features.The efficacy of the above models for differentiating BS and SM were analyzed.Results Significant differences of patients' age and proportion of fever and accessory involvement were found between BS and SM patients in training and test sets(all P＜0.05),and univariate and multivariate logistic analysis showed the latter two were clinical features(both P＜0.001).The area under the curve(AUC)of Mclinic for differentiating lumbar spine BS and SM was 0.794 and 0.773 in training set and test set,of Mradiomics was 0.895 and 0.791,of MDL+R was 0.926 and 0.882,while of Mcombined was 0.967 and 0.906,respectively.AUC of Mcombined was the highest in training set(all P＜0.05),while in test set,AUC of Mcombined was significantly higher than that of Mclinic and Mradiomics(both P＜0.05).Conclusion MRI radiomics combined with ResNet101 deep learning was helpful for differentiating lumbar spine BS and SM.Combining with clinical data could improve its diagnostic efficacy.

Objective To construct a surgical pharmaceutical risk management index system and then to enhance surgi-cal pharmaceutical service quality.Methods Literature research and consulting pharmaceutical experts were used to collect relevant data and construct initial scale items.The Delphi expert consultation method was used to revise and improve indicators with 20 experts,after 2 rounds of inquiry improvement.The analytic hierarchy process(AHP)was used to calculate the weight of each indicator,finally forming the surgical pharmaceutical risk management index system.Results The final construction of the scale entries includes four dimensions,14 second-grade indexes,and 71 third-grade indexes.The questionnaire response rates were 100.00％,while the authority coefficient of experts was 0.832.In the consultation,the harmony coefficients of the first,second,and third indicators were 0.743,0.491,and 0.277,respectively.The AHP was used to determine the weights of indicators,and the re-sult passed the consistency test.The professional factors and pharmacist factors of the first indicators are large in weight.Con-clusion The constructed surgical pharmaceutical risk management index system is scientific and practical,which can provide a reference for the clinical work of surgeons and maximize the risk of avoidance.

Objective:This study aimed to identify key genes in endothelial cell (EC) associated with the pathogenesis and progression of human venous malformations (VMs) through bioinformatics analysis, providing potential biomarkers for early screening and targeted therapy of VMs.Methods:A case-control study was conducted using surgically resected tissue specimens from VMs patients at the Third Affiliated Hospital of Zhengzhou University (from September 2021 to September 2023), with malformed venous tissues as the experimental group and distal normal venous tissues as controls. Single-nucleus RNA sequencing (snRNA-seq) was performed on paired experimental and control samples from four VM patients. High-dimensional weighted gene co-expression network analysis (hdWGCNA), combined with gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and protein-protein interaction (PPI) network analysis, identified critical genes. Validation experiments included 15 additional VM cases and controls using reverse transcription quantitative polymerase chain reaction (RT-qPCR), immunohistochemistry (IHC), and Western blot.Results:A total of 55 430 nuclei were captured using snRNA-seq, with 30 391 nuclei from the experimental group and 25 039 nuclei from the control group. Cluster analysis identified 22 distinct cell populations, which were annotated into 8 cell types. hdWGCNA revealed four modules associated with invasion, which were enriched in angiogenesis, integrin signaling, and cell adhesion according to GO analysis. KEGG pathway analysis indicated that the PI3K-AKT signaling pathway and focal adhesion are key regulatory mechanisms. PPI network analysis combined with cytoscape identified EGFL7, TEK, and FLT1 as key genes. RT-qPCR results demonstrated that the relative mRNA expression levels of these three genes in the experimental group (6.66±2.31, 1.86±0.62, 3.49±0.58) were significantly higher than those in the control group (1.05±0.14, 1.00±0.14, 1.06±0.25), with statistically significant differences ( t=9.37, 4.27, 11.20, P<0.05). Immunohistochemical analysis showed that the relative protein expression levels of these three genes in the cytoplasm of the experimental group (0.84±0.15, 0.68±0.14, 0.85±0.12) were also significantly higher than those in the control group (0.19±0.05, 0.23±0.06, 0.30±0.05), with statistically significant differences ( t=16.62, 5.93, 11.68, P<0.05). Western blot analysis confirmed that the relative protein expression levels of these three genes in the experimental group (0.35±0.04, 0.36±0.09, 0.31±0.04) were significantly higher than those in the control group (0.19±0.01, 0.13±0.02, 0.14±0.04), with statistically significant differences ( t=7.05, 4.61, 5.93, P<0.05). Conclusion:EGFL7, FLT1, and TEK in EC may play crucial roles in the occurrence and invasion of VMs.

Objective:To investigate the incidence, clinical characteristics, and impact of spontaneous portosystemic shunt (SPSS) in patients with liver cirrhosis combined with hepatic encephalopathy (HE).Methods:The basic clinical and follow-up data were retrospectively analyzed for patients diagnosed with cirrhosis combined with HE at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2017 to December 2022. The patients were divided into large and small SPSS groups and a control group based on the results of abdominal enhanced CT or MRI.The clinical characteristics and outcome differences were compared among the three groups. Kaplan-Meier survival curves were used to compare HE-free survival time and overall survival time among the three groups. The log-rank test was used to compare the differences between groups. Cox regression analysis was used to identify the relevant risk factors affecting HE-free survival time and overall survival time.Results:A total of 223 cases with liver cirrhosis combined with HE were enrolled, including 150 in the SPSS and 73 in the control groups. The incidence rate of SPSS was 67.3% (150/223). The group was divided into small SPSS (79/150, 52.7%) and large SPSS group (71/150, 47.3%) according to the cross-sectional area of the diversion channel. The HE-free survival was shorter in the small and large SPSS groups compared with the control group (35.5 months in the small SPSS group and 21.3 months in the large SPSS group; P<0.001). The HE-free survival time was shorter in the large SPSS than with small SPSS group ( P=0.003). The overall survival time in the small SPSS group and the large SPSS group was shorter compared with the control group (small SPSS group: 39.4 months, large SPSS group: 52.9 months; P<0.001). There was no statistically significant difference in overall survival time between the small SPSS and large SPSS groups ( P=0.700). Cox regression analysis showed that SPSS was an independent risk factor affecting patients' HE-free survival time and overall survival time ( P<0.05). Conclusion:SPSS is more common in patients with liver cirrhosis combined with HE. Patients who combined with SPSS showed significant reductions in both HE-free survival time and overall survival time, especially evident in those with combined large SPSS.

Objective To construct a surgical pharmaceutical risk management index system and then to enhance surgi-cal pharmaceutical service quality.Methods Literature research and consulting pharmaceutical experts were used to collect relevant data and construct initial scale items.The Delphi expert consultation method was used to revise and improve indicators with 20 experts,after 2 rounds of inquiry improvement.The analytic hierarchy process(AHP)was used to calculate the weight of each indicator,finally forming the surgical pharmaceutical risk management index system.Results The final construction of the scale entries includes four dimensions,14 second-grade indexes,and 71 third-grade indexes.The questionnaire response rates were 100.00％,while the authority coefficient of experts was 0.832.In the consultation,the harmony coefficients of the first,second,and third indicators were 0.743,0.491,and 0.277,respectively.The AHP was used to determine the weights of indicators,and the re-sult passed the consistency test.The professional factors and pharmacist factors of the first indicators are large in weight.Con-clusion The constructed surgical pharmaceutical risk management index system is scientific and practical,which can provide a reference for the clinical work of surgeons and maximize the risk of avoidance.

Objective:This study aimed to identify key genes in endothelial cell (EC) associated with the pathogenesis and progression of human venous malformations (VMs) through bioinformatics analysis, providing potential biomarkers for early screening and targeted therapy of VMs.Methods:A case-control study was conducted using surgically resected tissue specimens from VMs patients at the Third Affiliated Hospital of Zhengzhou University (from September 2021 to September 2023), with malformed venous tissues as the experimental group and distal normal venous tissues as controls. Single-nucleus RNA sequencing (snRNA-seq) was performed on paired experimental and control samples from four VM patients. High-dimensional weighted gene co-expression network analysis (hdWGCNA), combined with gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and protein-protein interaction (PPI) network analysis, identified critical genes. Validation experiments included 15 additional VM cases and controls using reverse transcription quantitative polymerase chain reaction (RT-qPCR), immunohistochemistry (IHC), and Western blot.Results:A total of 55 430 nuclei were captured using snRNA-seq, with 30 391 nuclei from the experimental group and 25 039 nuclei from the control group. Cluster analysis identified 22 distinct cell populations, which were annotated into 8 cell types. hdWGCNA revealed four modules associated with invasion, which were enriched in angiogenesis, integrin signaling, and cell adhesion according to GO analysis. KEGG pathway analysis indicated that the PI3K-AKT signaling pathway and focal adhesion are key regulatory mechanisms. PPI network analysis combined with cytoscape identified EGFL7, TEK, and FLT1 as key genes. RT-qPCR results demonstrated that the relative mRNA expression levels of these three genes in the experimental group (6.66±2.31, 1.86±0.62, 3.49±0.58) were significantly higher than those in the control group (1.05±0.14, 1.00±0.14, 1.06±0.25), with statistically significant differences ( t=9.37, 4.27, 11.20, P<0.05). Immunohistochemical analysis showed that the relative protein expression levels of these three genes in the cytoplasm of the experimental group (0.84±0.15, 0.68±0.14, 0.85±0.12) were also significantly higher than those in the control group (0.19±0.05, 0.23±0.06, 0.30±0.05), with statistically significant differences ( t=16.62, 5.93, 11.68, P<0.05). Western blot analysis confirmed that the relative protein expression levels of these three genes in the experimental group (0.35±0.04, 0.36±0.09, 0.31±0.04) were significantly higher than those in the control group (0.19±0.01, 0.13±0.02, 0.14±0.04), with statistically significant differences ( t=7.05, 4.61, 5.93, P<0.05). Conclusion:EGFL7, FLT1, and TEK in EC may play crucial roles in the occurrence and invasion of VMs.

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective To observe the value of MRI radiomics combined with ResNet101 deep learning for differentiating lumbar spine brucella spondylitis(BS)and spinal metastases(SM).Methods Seventy-one cases of lumbar spine BS and the same amount of lumbar spine SM patients were retrospectively enrolled in training set,while 33 cases of lumbar spine BS and the same amount of lumbar spine SM patients were enrolled in test set.Clinical features were screened with univariate and multivariate logistic analysis,and a clinical model(Mclinic)was constructed.ROI of lesions were drawn on lumbar sagittal T 2WI,then radiomics features were extracted to construct a radiomics model(Mradiomics).ResNet101 deep learning was integrated with radiomics,then deep learning radiomics features were extracted to construct deep learning radiomics model(MDL+R).Finally a combined model(Mcombined)was constructed through combining clinical features and deep learning radiomics features.The efficacy of the above models for differentiating BS and SM were analyzed.Results Significant differences of patients' age and proportion of fever and accessory involvement were found between BS and SM patients in training and test sets(all P＜0.05),and univariate and multivariate logistic analysis showed the latter two were clinical features(both P＜0.001).The area under the curve(AUC)of Mclinic for differentiating lumbar spine BS and SM was 0.794 and 0.773 in training set and test set,of Mradiomics was 0.895 and 0.791,of MDL+R was 0.926 and 0.882,while of Mcombined was 0.967 and 0.906,respectively.AUC of Mcombined was the highest in training set(all P＜0.05),while in test set,AUC of Mcombined was significantly higher than that of Mclinic and Mradiomics(both P＜0.05).Conclusion MRI radiomics combined with ResNet101 deep learning was helpful for differentiating lumbar spine BS and SM.Combining with clinical data could improve its diagnostic efficacy.