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血清免疫球蛋白GN-糖基的高通量分析——一种消化道癌症的非侵入性生物标志物
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作者 刘鹏程 王小兵 +9 位作者 顿爱社 李昱潼 李厚强 王璐 张怡春 李灿灿 张金霞 张晓雨 马立兴 侯海峰 《Engineering》 SCIE EI CAS CSCD 2023年第7期44-53,I0002,I0003,共12页
免疫球蛋白G(Immunoglobulin G,IgG)的N-糖基化在炎症性疾病的发展中起着重要作用。本研究旨在评价IgG N-糖基在消化道癌症亚型中的诊断效能。从中国医学科学院肿瘤医院招募749名消化道癌症患者,包括食管癌(esophageal cancer,EC)、胃癌... 免疫球蛋白G(Immunoglobulin G,IgG)的N-糖基化在炎症性疾病的发展中起着重要作用。本研究旨在评价IgG N-糖基在消化道癌症亚型中的诊断效能。从中国医学科学院肿瘤医院招募749名消化道癌症患者,包括食管癌(esophageal cancer,EC)、胃癌(gastric cancer,GC)、结直肠癌(colorectal cancer,CRC)和胰腺癌(pancreatic cancer,PC)患者。采用亲水交互高效液相色谱-超高效液相色谱(hydrophilic interaction liquid chromatography using ultra-performance liquid chromatography,HILIC-UPLC)分析血浆中IgG的N-糖基构成。采用Bio-Plex悬液芯片系统检测方法(Bio-Rad)进行炎症因子检测。采用典型相关分析(canonical correlation analysis,CCA)探索糖基和炎症因子之间的相关性。采用LASSO回归和logistic回归模型,基于检测到的糖基谱建立可用于区分胃肠癌症患者和健康人群诊断模型。与健康对照组相比,EC、GC、CRC和PC患者的唾液酸化和半乳糖基化水平降低,而二等分乙酰葡萄糖胺基化水平在消化道癌症患者中升高。此外,只有胰腺癌患者具有低水平的岩藻糖基化。消化道癌症组的IL-1β、IL-31和sCD40L水平均高于对照组。IgG N-糖基的组成与炎症因子相关(r=0.556)。基于糖基的模型表现出良好的诊断效能,EC、GC、CRC和PC的受试者工作特征曲线下面积(AUC)分别为0.972、0.871、0.867和0.907。这些研究结果表明,IgG N-糖基在调节消化道肿瘤的发病机制中发挥了重要作用。血清IgG N-糖基可以作为潜在的非侵入性辅助消化道癌症临床诊断的方法。 展开更多
关键词 Gastrointestinal cancer GLYCOSYLATION Immunoglobulin G Diagnostic biomarker
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Prediction of surface settlement caused by synchronous grouting during shield tunneling in coarse-grained soils:A combined FEM and machine learning approach
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作者 Chao Liu Zepan Wang +4 位作者 Hai Liu Jie Cui Xiangyun Huang lixing ma Shuang Zheng 《Underground Space》 SCIE EI 2024年第3期206-223,共18页
This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process.The proposed method combines finite element simulations with ma... This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process.The proposed method combines finite element simulations with machine learning algorithms and introduces an intelligent optimization algorithm to invert geological parameters and synchronous grouting variables,thereby predicting ground surface settlement without conducting numerous finite element analyses.Two surrogate models based on the random forest algorithm are established.The first is a parameter inversion surrogate model that combines an artificial fish swarm algorithm with random forest,taking into account the actual number and distribution of complex soil layers.The second model predicts surface settlement during synchronous grouting by employing actual cover-diameter ratio,inverted soil parameters,and grouting variables.To avoid changes to input parameters caused by the number of overlying soil layers,the dataset of this model is generated by the finite element model of the homogeneous soil layer.The surrogate modeling approach is validated by the case history of a large-diameter shield tunnel in Beijing,providing an alternative to numerical computation that can efficiently predict surface settlement with acceptable accuracy. 展开更多
关键词 Shield tunnel Machine learning Synchronous grouting Surrogate modeling Surface settlement
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