目的探讨药物性肝损伤(DILI)患者临床转归的影响因素,构建列线图模型并进行内部验证。方法回顾性分析哈尔滨工业大学附属黑龙江省医院2017年1月—2022年12月收治的188例DILI患者的一般资料和实验室数据,根据患者临床转归分为结局良好组(...目的探讨药物性肝损伤(DILI)患者临床转归的影响因素,构建列线图模型并进行内部验证。方法回顾性分析哈尔滨工业大学附属黑龙江省医院2017年1月—2022年12月收治的188例DILI患者的一般资料和实验室数据,根据患者临床转归分为结局良好组(n=146)和不良结局组(n=42)。正态分布计量资料两组间比较采用成组t检验;非正态分布计量资料两组间比较采用Mann-Whitney U检验。计数资料两组间比较采用χ^(2)检验。通过单因素和多因素Logistic回归分析筛选DILI患者临床转归相关的独立影响因素。R Studio 4.1.2软件构建列线图模型,通过校准曲线、受试者工作特征曲线(ROC曲线)和决策曲线分析(DCA)对模型进行内部验证。结果单因素Logistic回归分析结果显示,肝活检诊断DILI、PLT、ChE、Alb、PTA、IgM和IgG与DILI患者不良结局相关(P值均<0.05)。多因素Logistic回归分析结果显示,肝活检诊断DILI(OR=0.072,95%CI:0.022~0.213,P<0.001)、临床分型(OR=0.463,95%CI:0.213~0.926,P=0.039)、ALT(OR=0.999,95%CI:0.998~1.000,P=0.025)、PTA(OR=0.973,95%CI:0.952~0.993,P=0.011)和Ig M(OR=1.456,95%CI:1.082~2.021,P=0.015)是DILI患者临床转归的独立影响因素。构建列线图,经验证校准曲线接近参考曲线,ROC曲线下面积为0.829,决策曲线分析显示该模型具有良好的临床净收益。结论构建的列线图模型对评估DILI患者的临床转归具有较好的临床校准度、鉴别能力和应用价值。展开更多
目的对比分析单操作孔胸腔镜联合腹腔镜三切口食管切除食管癌根治术(sTEME)和三孔胸腔镜腹部开放三切口食管切除食管癌根治术(tTLME)治疗食管癌的效果及预后。方法选择2020年6月至2023年6月于某医院接受三切口食管切除术的104例食管癌...目的对比分析单操作孔胸腔镜联合腹腔镜三切口食管切除食管癌根治术(sTEME)和三孔胸腔镜腹部开放三切口食管切除食管癌根治术(tTLME)治疗食管癌的效果及预后。方法选择2020年6月至2023年6月于某医院接受三切口食管切除术的104例食管癌患者为研究对象,根据手术方式不同分为sTEME组和tTLME组,每组52例。比较两组手术相关指标及术后恢复指标、视觉模拟评分法(VAS)评分、并发症发生情况;比较两组术后1年复发及生存情况。结果sTEME组术后住院时间短于tTLME组(P<0.05);术后4、12、24 h sTEME组VAS评分均低于tTLME组(P<0.05);sTEME组术后并发症发生率低于tTLME组(P<0.05)。两组术后随访1年内均未出现死亡病例。两组术后1年内复发率比较,差异无统计学意义(P>0.05)。结论sTEME术和tTLME术治疗食管癌的效果均较好,但sTEME术更有利于缩短术后住院时间、减少术后并发症及降低术后疼痛感。展开更多
Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of...Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of this disease, providing a theoretical basis for finding new therapeutic targets. Methods: Gene microarray data were downloaded from the Gene Expression Profiling Integrated Database (GEO) and cross-calculated to identify differentially expressed genes (DEGs). Analysis of differentially expressed genes (DEGs) with gene ontology (GO) is a method used to study the differences in gene expression under different conditions as well as their functions and interrelationships, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is a tool used to explore the functions and pathways of genes in specific biological processes. By calculating the distribution of immune cell infiltration, the result of immune infiltration in the rejection group can be analysed as a trait in Weighted Gene Co-Expression Network Analysis (WGCNA) for genes associated with rejection. Then, protein-protein interaction networks (PPI) were constructed using the STRING database and Cytoscape software to identify hub gene markers. Results: A total of 60 integrated DEGs were obtained from 3 datasets (GSE7392, GSE181757, GSE222889). By GO and KEGG analysis, the GEDs were mainly concentrated in the regulation of immune response, defence response, regulation of immune system processes, and stimulation response. The pathways were mainly enriched in antigen processing and presentation, EBV infection, graft-versus-host, allograft rejection, and natural killer cell-mediated cytotoxicity. After further screening using WGCNA and PPI networks, HLA-A, HLA-B, HLA-F, and TYROBP were identified as hub genes (Hub genes). The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model plots of the four hub genes, and the results concluded that all four Hub genes had good diagnostic value (area under the curve in the range of 0.794-0.819). From the inference, it can be concluded that the four genes, HLA-A, HLA-B, HLA-F and TYROBP, may have an important role in the development and progression of chronic rejection after renal transplantation. Conclusion: DEGs play an important role in the study of the pathogenesis of chronic rejection after renal transplantation, and can provide theoretical support for further research on the pathogenesis of chronic rejection after renal transplantation and the discovery of new therapeutic targets through enrichment analysis and pivotal gene screening, as well as inferential analyses of related diagnostic efficacy and disease risk prediction.展开更多
文摘目的探讨药物性肝损伤(DILI)患者临床转归的影响因素,构建列线图模型并进行内部验证。方法回顾性分析哈尔滨工业大学附属黑龙江省医院2017年1月—2022年12月收治的188例DILI患者的一般资料和实验室数据,根据患者临床转归分为结局良好组(n=146)和不良结局组(n=42)。正态分布计量资料两组间比较采用成组t检验;非正态分布计量资料两组间比较采用Mann-Whitney U检验。计数资料两组间比较采用χ^(2)检验。通过单因素和多因素Logistic回归分析筛选DILI患者临床转归相关的独立影响因素。R Studio 4.1.2软件构建列线图模型,通过校准曲线、受试者工作特征曲线(ROC曲线)和决策曲线分析(DCA)对模型进行内部验证。结果单因素Logistic回归分析结果显示,肝活检诊断DILI、PLT、ChE、Alb、PTA、IgM和IgG与DILI患者不良结局相关(P值均<0.05)。多因素Logistic回归分析结果显示,肝活检诊断DILI(OR=0.072,95%CI:0.022~0.213,P<0.001)、临床分型(OR=0.463,95%CI:0.213~0.926,P=0.039)、ALT(OR=0.999,95%CI:0.998~1.000,P=0.025)、PTA(OR=0.973,95%CI:0.952~0.993,P=0.011)和Ig M(OR=1.456,95%CI:1.082~2.021,P=0.015)是DILI患者临床转归的独立影响因素。构建列线图,经验证校准曲线接近参考曲线,ROC曲线下面积为0.829,决策曲线分析显示该模型具有良好的临床净收益。结论构建的列线图模型对评估DILI患者的临床转归具有较好的临床校准度、鉴别能力和应用价值。
文摘目的对比分析单操作孔胸腔镜联合腹腔镜三切口食管切除食管癌根治术(sTEME)和三孔胸腔镜腹部开放三切口食管切除食管癌根治术(tTLME)治疗食管癌的效果及预后。方法选择2020年6月至2023年6月于某医院接受三切口食管切除术的104例食管癌患者为研究对象,根据手术方式不同分为sTEME组和tTLME组,每组52例。比较两组手术相关指标及术后恢复指标、视觉模拟评分法(VAS)评分、并发症发生情况;比较两组术后1年复发及生存情况。结果sTEME组术后住院时间短于tTLME组(P<0.05);术后4、12、24 h sTEME组VAS评分均低于tTLME组(P<0.05);sTEME组术后并发症发生率低于tTLME组(P<0.05)。两组术后随访1年内均未出现死亡病例。两组术后1年内复发率比较,差异无统计学意义(P>0.05)。结论sTEME术和tTLME术治疗食管癌的效果均较好,但sTEME术更有利于缩短术后住院时间、减少术后并发症及降低术后疼痛感。
基金National Natural Science Foundation of China(No.82260154)。
文摘Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of this disease, providing a theoretical basis for finding new therapeutic targets. Methods: Gene microarray data were downloaded from the Gene Expression Profiling Integrated Database (GEO) and cross-calculated to identify differentially expressed genes (DEGs). Analysis of differentially expressed genes (DEGs) with gene ontology (GO) is a method used to study the differences in gene expression under different conditions as well as their functions and interrelationships, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is a tool used to explore the functions and pathways of genes in specific biological processes. By calculating the distribution of immune cell infiltration, the result of immune infiltration in the rejection group can be analysed as a trait in Weighted Gene Co-Expression Network Analysis (WGCNA) for genes associated with rejection. Then, protein-protein interaction networks (PPI) were constructed using the STRING database and Cytoscape software to identify hub gene markers. Results: A total of 60 integrated DEGs were obtained from 3 datasets (GSE7392, GSE181757, GSE222889). By GO and KEGG analysis, the GEDs were mainly concentrated in the regulation of immune response, defence response, regulation of immune system processes, and stimulation response. The pathways were mainly enriched in antigen processing and presentation, EBV infection, graft-versus-host, allograft rejection, and natural killer cell-mediated cytotoxicity. After further screening using WGCNA and PPI networks, HLA-A, HLA-B, HLA-F, and TYROBP were identified as hub genes (Hub genes). The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model plots of the four hub genes, and the results concluded that all four Hub genes had good diagnostic value (area under the curve in the range of 0.794-0.819). From the inference, it can be concluded that the four genes, HLA-A, HLA-B, HLA-F and TYROBP, may have an important role in the development and progression of chronic rejection after renal transplantation. Conclusion: DEGs play an important role in the study of the pathogenesis of chronic rejection after renal transplantation, and can provide theoretical support for further research on the pathogenesis of chronic rejection after renal transplantation and the discovery of new therapeutic targets through enrichment analysis and pivotal gene screening, as well as inferential analyses of related diagnostic efficacy and disease risk prediction.
