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目的通过生物信息学分析筛选脓毒症诱导急性肺损伤(ALI)的关键基因。 方法从基因表达谱(GEO)数据库中下载GSE10474数据集,该数据集包含13例脓毒症ALI患者样本(ALI组)和21例脓毒症患者样本(脓毒症组)基因数据。使用limma包筛选两组样本的差异表达基因,并对筛选出的差异表达基因进行基因本体论(GO)功能分析及京都基因与基因组百科全书(KEGG)富集分析。通过STRING数据库构建蛋白质相互作用(PPI)网络并确定前10位hub基因。 结果从GSE10474数据集中共筛选出115个差异表达基因,其中65个上调基因,50个下调基因。GO分析显示,生物过程的基因主要富集在金属离子稳态、氧化应激、电离辐射等;细胞组分主要富集在液泡膜、高尔基体膜、内质网膜、溶酶体膜等生物膜;这些基因主要与生物跨膜、泛素结合酶活性、蛋白络氨酸、丝氨酸和苏氨酸激酶结合蛋白活性以及蛋白激酶抑制活性等分子功能相关。KEGG富集分析显示,差异表达基因主要富集在磷脂酶信号通路、胰岛素信号通路、T细胞介导的免疫反应以及免疫相关的信号通路。PPI网络图筛选出了前10位hub基因,分别为CD4、CD74、髓细胞核分化抗原(MNDA)、髓细胞触发受体1(TREM1)、人白细胞抗原DRA(HLA-DRA)、细胞附着蛋白1相互作用蛋白(CYTIP)、凝血因子XⅢA链(F13A1)、血清胱抑素F(CST7)、丝裂原激活蛋白激酶1(MAPK1)、细胞周期蛋白依赖性激酶抑制剂1A(CDKN1A)。 结论CD4、CD74、MNDA、TREM1、HLA-DRA、CYTIP、F13A1、CST7、MAPK1及CDKN1A是脓毒症诱导ALI的关键基因,可作为临床治疗和新药开发的新靶点。 相似文献
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随着人类基因组测序、生物大数据信息分析、分子病理检测和人工智能辅助病理诊断等技术进步及其应用, 临床医学发展迈向精准诊疗时代。这一时代背景下, 传统诊断病理学迎来前所未有的历史机遇, 正在向"下一代诊断病理学(next-generation diagnostic pathology)"迈进。下一代诊断病理学以病理形态和临床信息为诊断基础, 以分子检测与生物信息分析、智慧制样与流程质控、智能诊断与远程会诊、病灶活体可视化与"无创"病理诊断等创新前沿交叉技术为主要特征, 以多组学和跨尺度整合诊断为病理报告内容, 实现对疾病的"最后诊断", 并预测疾病演进和结局、建议治疗方案和评估治疗反应, 形成新的疾病诊断"金标准"。未来, 需要激发病理学科创新活力, 加快下一代诊断病理学成熟和应用, 重塑病理学科理论和技术体系, 发挥诊断病理学在疾病"防、诊、治、养"等过程中的重要作用, 促进临床医学进一步发展, 服务健康中国战略。 相似文献
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《Arab Journal Of Gastroenterology》2022,23(3):172-187
Background and Study AimsDubin–Johnson syndrome (DJS) is a benevolent genetic disorder of the liver with autosomal inheritance. It is a rare disorder characterized by an increase in conjugated bilirubin and anomaly in coproporphyrin clearance. DJS is caused by deleterious mutations in the ABCC2 gene. A polymorphism in the ABCC2 gene causes malfunctions in its ability to regulate the efflux of different organic anions, such as bilirubin, from hepatocytes to the canaliculi. Multidrug resistance protein 2 (MRP2) encoded by the ABCC2 gene is one of the main regulators of the export of bilirubin to respective sites. ABCC2 gene mutations have widely drawn attention in the pathology of DJS in various populations.Patients and MethodsThe ABCC2 gene was subjected to the National Center for Biotechnology Information (NCBI) database in 2020, and non-synonymous single-nucleotide polymorphisms (nsSNPs) and variants in untranslated regions were studied using different computational servers. SIFT, Protein variation effect analyzer, and PolyPhen-2 were used to retrieve the damaging Single-nucleotide polymorphisms (SNPs); PhD-SNP, SNPs&GO, and Protein Analysis Through Evolutionary Relationships were used to predict the association of nsSNPs with DJS; Mutation3D illustrated the location of variants in the protein; SNAP2, MutPred2, ELASPIC, and HOPE were used to predict the structural and functional effects of these mutations on MRP2; and I-mutant 3.0 and MuPro were used to determine the effects of polymorphism on the function of MRP2.ResultsIn this study, 18,947 SNPs were screened from the NCBI database, followed by a series of refinement of variants using online available servers. We concluded that 41 ABCC2 gene variants are vital etiological candidates for DJS in humans. These 41 variants had highly damaging effects on the MRP2 protein, which may lead to deficient transportation capacity, thereby affecting the efflux of bilirubin across the canalicular membrane.ConclusionIn silico tools are an alternative approach for predicting the target SNPs. Hence, previously unreported variants can be considered strong etiological candidates for diseases related to MRP2. 相似文献
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目的探讨颅内动脉瘤Enterprise支架辅助弹簧圈栓塞后瘤腔和载瘤动脉的血流动力学改变。方法回顾性分析2021年3~8月Enterprise支架辅助弹簧圈栓塞治疗的24例颅内动脉瘤的临床资料。收集术前、术后即刻动脉瘤瘤腔和载瘤动脉处DSA数据的DICOM格式,导入OpenFOAM软件,应用计算流体力学数值模拟技术进行血流动力学分析,参数包括壁面剪切力(WSS)、均一化WSS(NWSS)、WSS分布梯度(WSSG)、流线方向WSSG(SWSSG)、归一化压力(NP)、剪切力震荡指数(OSI)。结果24例(24个动脉瘤)应用24枚Enterprise支架,术后即刻造影显示Raymond分级Ⅰ级18例,Ⅱ级6例。栓塞后WSS、NWSS、NWSS最小值、WSSG、WSSG最小值均明显升高(P<0.05),WSSG最大值明显降低(P<0.05)。结论Enterprise支架辅助弹簧圈栓塞治疗明显改变颅内动脉瘤的血流动力学,使载瘤动脉被重建、血流被重构,瘤腔内涡流减少或消失,血流形式由复杂转向简单。这些作用有助于瘤腔内血栓形成。 相似文献
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Bioinformatics Analysis of Key Genes and Pathways for Medulloblastoma as a Therapeutic Target 下载免费PDF全文
Fateme Shaanbanpour AghamalekiBehrouz MollashahiNika AghamohammadiNematollah RostamiZeinab MazloumiHamidreza MirzaeiAfshin MoradiMojgan SheikhpourAbolfazl Movafagh 《Asian Pacific journal of cancer prevention》2019,20(1):221-227
Introduction: One of the major challenges in cancer treatment is the lack of specific and accurate treatment incancer. Data analysis can help to understand the underlying molecular mechanism that leads to better treatment.Increasing availability and reliability of DNA microarray data leads to increase the use of these data in a variety ofcancers. This study aimed at applying and evaluating microarray data analyzing, identification of important pathwaysand gene network for medulloblastoma patients to improve treatment approaches especially target therapy. Methods:In the current study, Microarray gene expression data (GSE50161) were extracted from Geo datasets and then analyzedby the affylmGUI package to predict and investigate upregulated and downregulated genes in medulloblastoma. Then,the important pathways were determined by using software and gene enrichment analyses. Pathways visualizationand network analyses were performed by Cytoscape. Results: A total number of 249 differentially expressed genes(DEGs) were identified in medulloblastoma compared to normal samples. Cell cycle, p53, and FoxO signaling pathwayswere indicated in medulloblastoma, and CDK1, CCNB1, CDK2, and WEE1 were identified as some of the importantgenes in the medulloblastoma. Conclusion: Identification of critical and specific pathway in any disease, in our casemedulloblastoma, can lead us to better clinical management and accurate treatment and target therapy. 相似文献
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Peter M. Krafft 《Topics in Cognitive Science》2019,11(2):374-392
Researchers have recently demonstrated that group performance across tasks tends to be correlated, motivating the use of a single metric for the general collective intelligence of groups akin to general intelligence metrics for individuals. High general collective intelligence is achieved when a group performs well across a wide variety of tasks. A number of factors have been shown to be predictive of general collective intelligence, but there is sparse formal theory explaining the presence of correlations across tasks, betraying a fundamental gap in our understanding of what general collective intelligence is measuring. Here, we formally argue that general collective intelligence arises from groups achieving commitment to group goals, accurate shared beliefs, and coordinated actions. We then argue for the existence of generic mechanisms that help groups achieve these cognitive alignment conditions. The presence or absence of such mechanisms can potentially explain observed correlations in group performance across tasks. Under our view, general collective intelligence can be conceived as measuring group performance on classes of tasks that have particular combinations of cognitive alignment requirements. 相似文献