肺腺癌竞争内源性RNA网络的综合分析:基于肿瘤基因组图谱数据库

目的:基于肿瘤基因组图谱数据库（TCGA）架构肺腺癌（LUAD）竞争内源性RNA（ceRNA）网络,鉴定潜在的生存关联性生物标志物,并确定特异性治疗的小分子药物。方法:依据纳入研究标准,利用Edger软件筛选LUAD组织与正常组织(mRNA、lncRNA和miRNA)差异表达的基因。通过miRcode、miRDB、TargetScan和miRanda数据库对差异表达的RNA之间的关系进行分析,构建ceRNA网络。采用Kaplan-Meier方法分析ceRNA网络中的RNA表达量与生存预后的关系,通过富集分析对网络中的mRNA基因功能和调控通路进行分析。通过Cmap数据库筛选治疗LUAD的特异性小分子药物。利用D-lnc软件确定与关键的lncRNA相关的特异性小分子药物。结果:mRNAs(ELAVL2和PBK)、miRNAs(miR-13和miR-210)和lncRNAs(AP002478.1、DSCAM-AS1、LINC00269、LINC00470和LINC00483)与总生存期(OS)关系密切。喜树碱和甲萘醌有可能逆转LUAD的状态。卡铂、多西紫杉醇、帕比司他被确定为与关键lncRNA密切相关的药物。结论:ceRNA网络在LUAD中发挥重要作用,多种差异表达RNA与LUAD预后相关,可能成为潜在的肿瘤诊断标志物和治疗靶点。     

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

BackgroundThe innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.MethodsDifferentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.ResultsIn the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).ConclusionsThrough constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.     

Prediction of the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks and chest radiography

BackgroundWe aimed to predict the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks (CNNs) and chest radiography.MethodsMedical records were searched for eligible patients with culture-confirmed active pulmonary tuberculosis. The eligible patients were randomly assigned to the training dataset group (N = 180) and the validation dataset group (N = 59). Posteroanterior X-ray radiographs in the standing position were obtained at diagnosis. The image data were augmented by a factor of 10 by randomly shifting and rotating the original image. Thus, 1800 images (112 × 112 pixels, 8-bit grayscale) from 180 patients in the training dataset group were used for training the CNN model. The model performance was evaluated on the validation dataset.ResultsThe values predicted by the CNN model were significantly associated with the actual values (Pearson's correlation coefficient 0.392, p = 0.002). The mean absolute error was 18.0. The visualization of the layer outputs suggested that the CNN model recognized some of the chest radiographic findings that were useful in predicting the duration needed to achieve culture negativity.ConclusionsThe CNN model was useful for predicting the duration needed to achieve culture negativity in active pulmonary tuberculosis, although the accuracy was unsatisfactory. This study suggests that chest radiography findings are as important as other clinical factors for prediction and could be learned by the machine.     

基于网络药理学预测川芎嗪、芍药苷干预糖尿病血管并发症的作用机制

目的运用网络药理学方法分析川芎嗪和芍药苷治疗糖尿病血管并发症的潜在作用机制。方法检索SwissTargetPrediction ver 2019、Batman-TCM、PharmMapper ver 2017、ZINC、SEA、GeneCards、DrugBank 5.0、DisGenet、String v11数据库,筛选川芎嗪和芍药苷化学成分作用靶点以及糖尿病血管并发症的相关靶点;应用Cytoscape 3.2.1软件,构建川芎嗪和芍药苷成分靶点-疾病靶点网络;通过GO和KEGG富集分析预测川芎嗪和芍药苷治疗糖尿病血管并发症的潜在作用机制。结果从川芎嗪和芍药苷中分别筛选出成分靶点103个和168个,将这些成分靶点与4662个糖尿病血管并发症靶点取交集得到146个靶点,网络拓扑分析得到TP53、HSP90AA1、STAT3和ANXA14个核心靶点,可能为川芎嗪和芍药苷治疗糖尿病血管并发症的重要靶点;GO和KEGG富集分析显示川芎嗪和芍药苷治疗糖尿病血管并发症的可能通路是Neuroactive ligand-receptor interaction,Calcium signaling pathway,Serotonergic synapse,Inflammatory mediator regulation of TRP channels,cGMP-PKG,Dopaminergic synapse。结论初步揭示了川芎嗪和芍药苷治疗糖尿病血管并发症的作用通路及重要活性靶点,为开发更好地治疗糖尿病血管并发症候选药物的活性成分提供参考依据。     

柴胡加龙骨牡蛎汤“异病同治”冠心病和焦虑症的网络药理学作用机制

目的 应用网络药理学和分子对接方法探讨柴胡加龙骨牡蛎汤“异病同治”冠心病和焦虑症的作用机制。方法 利用TCMSP、BATMAN-TCM数据库获得柴胡加龙骨牡蛎汤的活性成分及对应靶点,结合GeneCards数据库获取冠心病和焦虑症相关的疾病靶点;对药物与疾病靶点映射得到的柴胡加龙骨牡蛎汤治疗冠心病和焦虑症的共有靶点,通过STRING平台构建蛋白互作网络;运用R语言对共有靶点进行基因本体(GO)富集分析与京都基因和基因组百科全书(KEGG)通路富集分析;采用 Cytoscape3.7.1软件构建“活性成分-靶点-通路”网络;应用Auto Dock vina软件对PPI网络中关键靶点与主要活性成分进行分子对接。结果 网络分析结果显示,柴胡加龙骨牡蛎汤中74个有效活性成分参与调控92个共有靶点,通过调节丝裂原活化蛋白激酶(MAPK)信号通路、白细胞介素17(IL-17)信号通路、5-羟色胺能突触、磷脂酰肌醇3-激酶(PI3K)、蛋白激酶B(Akt)等121条信号通路实现对冠心病和焦虑症“异病同治”的作用。结论 现有研究初步揭示了柴胡加龙骨牡蛎汤“异病同治”冠心病和焦虑症多成分、多靶点、多通路的作用机制,可能为后续深入研究提供新方向。     

川芎抗动脉粥样硬化作用机制的网络药理学与分子对接技术研究

目的使用网络药理学与分子对接技术探究川芎抗动脉粥样硬化的作用机制。方法运用中药系统药理(TCMSP)数据库筛选川芎的活性成分及质控成分,通过Swiss Target Prediction预测药物靶点。在DrugBank和DisGeNET数据库筛选出动脉粥样硬化的相关靶点。通过STRING构建靶点蛋白互作网络,运用Cytoscape绘制网络并进行拓扑学分析。使用Omicshare对相关靶点进行GO富集分析与KEGG富集分析。运用DockThor进行分子对接。结果获得川芎抗动脉粥样硬化的167个相关治疗靶点,通过网络拓扑分析发现钙敏感受体、丝裂原活化蛋白激酶3等46个靶点为核心靶点。GO富集分析发现川芎在生物过程、分子功能、细胞组成多方面影响动脉粥样硬化的发生发展。KEGG通路富集分析发现,川芎可能通过调节神经活性配体-受体相互作用、钙信号通路等多条代谢通路来发挥抗动脉粥样硬化的作用。结论运用网络药理学的方法证实了川芎抗动脉粥样硬化具有多途径、多靶点作用的特点。预测了川芎抗动脉粥样硬化的可能机制,为其后续基础研究提供了参考和理论依据。     

基于数据增强和混合神经网络的人工智能技术在上消化道内镜检查部位识别中的应用

目的评估利用深度卷积神经网络(deep convolutional neural network, DCNN)构建的人工智能技术在上消化道内镜检查部位识别中的应用价值。方法收集中国医学科学院肿瘤医院2019年1月—2021年6月间的21 310张上消化道内镜图片, 其中19 191张图片用于深度学习构建部位识别模型, 其余2 119张图片用于验证。比较两种DCCN网络构建的模型在上消化道30个部位识别上的性能差异, 一种是由Inception-ResNetV2(ResNetV2)构建的传统的ResNetV2模型, 另一种是由Inception-ResNetV2 and Squeeze-Excitation Networks(RESENet)构建的混合神经网络RESENet模型, 主要观察指标包括识别准确率、灵敏度、特异度、阳性预测值和阴性预测值。结果 ResNetV2模型识别上消化道30个部位的准确率、灵敏度、特异度、阳性预测值和阴性预测值分别为94.62%～99.10%、30.61%～100.00%、96.07%～99.56%、42.26%～86.44%和97.13%～99.75%,...     

用小波变换结合神经网络检测ECG信号的P波

通过小波变换对ＥＧＣ信号进行分解,然后采用神经网络检测ＥＣＧ信号的Ｐ波,该方法作为一种辅助检测手段,效果良好。将其用于心率变异性分析具有重要意义。