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目的探讨C型凝集素受体Dectin-2在烟曲霉菌感染中的作用及机制。方法用紫外线灭活的烟曲霉菌膨胀态分生孢子刺激野生型(wild type, WT)和Dectin-2缺失(Clec4n-/-)小鼠骨髓来源的巨噬细胞(bone marrow derived macrophages, BMDMs)。刺激20min及40min后,利用Western印迹法检测BMDMs中脾酪氨酸激酶(spleen tyrosine kinase, Syk)和核转录因子κB抑制因子(inhibitor-κB, IκBα)的磷酸化水平。刺激16h后,利用酶联免疫吸附法(enzyme-linked immunosorbent assay, ELISA)检测BMDMs细胞上清液中IL-6、TNF-α和IL-12p40的水平。另一方面,利用压舌感染的方法在WT和Clec4n-/-小鼠中构建烟曲霉菌肺部感染模型。感染2d后,统计小鼠整个肺脏荷菌量,并检测肺脏匀浆液中IL-6和IL-12p40水平。结果体外试验提示,烟曲霉菌刺激后,Dectin-2缺失的BMDMs中Syk和IκBα的磷酸化水平及IL-6、TNF-α和IL-12p40水平显著下降(P<0.05)。体内试验发现,烟曲霉菌感染后,Dectin-2缺失小鼠中肺脏荷菌量显著升高(P<0.05),肺脏匀浆液内IL-6、IL-12p40水平显著降低(P<0.05)。结论C型凝集素受体Dectin-2激活烟曲霉菌诱导的NF-κB信号通路并介导促炎细胞因子的产生,可在小鼠肺烟曲霉菌病动物模型中发挥保护性作用。 相似文献
4.
目的 运用网络药理学方法及分子对接技术探讨黄芪干预腹膜纤维化的可能机制。方法 利用中药系统药理学数据库及分析平台(TCMSP)检索黄芪的主要化学成分及靶点,并补充文献报道相关药理作用的成分作为潜在活性成分。以"peritoneal fibrosis"为关键词分别在OMIM、Genecards获取目前已知的与腹膜纤维化相关的疾病靶点,后取两者的交集靶点;对交集基因通过STRING数据库与Cytoscape 3.7.2软件构建"药物-成分-靶点-疾病"网络及蛋白互作(PPI)网络并筛选核心网络。基于R软件使用Bioconductor生物信息软件对核心靶点进行GO及KEGG富集分析,最终采用AutoDock软件将主要有效成分与核心靶点进行分子对接,得出其结合能力。结果 筛选出20个黄芪活性成分及文献报道有相关药理作用4个, 457药物作用靶点,与674个腹膜纤维化病靶点取交集,得到86个共同靶点。GO功能富集分析提示黄芪拮抗腹膜纤维化主要参与了蛋白激酶B信号转导的调节、细胞对化学的应激反应、炎症反应的调节等通路; KEGG通路富集分析主要涉及调控肿瘤、磷脂酰肌醇-3-羟激酶-蛋白激酶B(PI3K-Akt)、晚期糖基化终末产物/晚期糖基化终末产物受体(AGE-RAGE)、人类巨细胞病毒感染、HIF-1信号通路等;分子对接结果显示关键靶点与活性成分具有较好的结合能力。结论 黄芪治疗腹膜纤维化的分子机制,可能与抑制炎症及氧化应激反应、调节多种信号通路等相关。 相似文献
5.
目的:探索嘌呤能受体X1(purinergic receptor,P2RX1)与肺腺癌(LUAD)患者预后及免疫细胞浸润的相关性。方法:利用生物信息学技术分析非小细胞肺癌中P2RX1的表达及其甲基化与患者预后的关系,对P2RX1共表达基因进行富集分析并筛选核心基因。利用TIMER 2.0数据库、R软件等分析P2RX1与免疫细胞、免疫检查点、免疫基质评分等的相关性。结果:P2RX1在LUAD中表达下调,低表达P2RX1的患者预后较差(P<0.05),且P2RX1与肿瘤纯度、分期等临床病理因素有关(P<0.05)。P2RX1的表达与肺鳞癌患者预后无明显相关。Cg06475633等P2RX1 CpG位点甲基化与患者预后相关。P2RX1共表达基因主要富集于免疫细胞活化、分化等通路和生物学进程,核心基因主要包括BTK、IKZF1等。P2RX1的表达与B细胞浸润、免疫/基质评分、PD-1、CTLA-4等多个免疫检查点显著相关(P<0.05)。结论:P2RX1有望成为LUAD诊断和免疫治疗的新靶点。 相似文献
6.
《本草图经》记载:"牛皮胶制作不甚精。"为求证原因,笔者以北魏《齐民要术》所载煮胶法为基础,分别用鲜牛皮与牛皮鞋制作牛皮胶。结果显示,鲜牛皮制作的牛皮胶出胶率高,胶呈琥珀色,色泽明亮,拍之即碎,断面有玻璃茬,无腥秽气味;而用牛皮鞋制作的牛皮胶出胶率低,胶呈黑褐色,色泽晦暗,不易干燥,拍之难碎,断面无玻璃茬,有轻度腥秽气味。由于北宋的皮革业发展水平远远高于北魏时期,后者鞋履等旧皮多为未经过鞣制的生皮,前者则为经过鞣制的熟皮。因此,《本草图经》记载的"牛皮胶制作不甚精"真实原因可能是煮制过程中使用了充分鞣制过的熟牛皮。 相似文献
7.
目的 利用频域光学相干断层扫描深度增强(enhanced depth imaging spectral domain optical coherence tomography,EDI SD-OCT)观察糖尿病黄斑水肿(diabetic macular edema,DME)患者脉络膜厚度(choroidal thickness,CT)的变化及结构特点,探讨DME病变程度与CT的关系。方法 纳入2型糖尿病患者共123例204眼,其中69眼诊断为DME(DME组),135眼无黄斑水肿为对照组。DME眼依据OCT形态学特点进一步分为视网膜弥漫性增厚(diffuse retinal thickness,DRT)型(34眼)、黄斑囊样水肿(cystoid macular edema,CME)型(19眼)和浆液性视网膜脱离(serous retinal detachment,SRD)型(16眼),利用EDI-OCT分别测量黄斑中心凹下CT和以黄斑为中心上、下、鼻、颞500 μm、1000 μm、1500 μm、2000 μm处CT。结果 DME组黄斑中心凹下CT为(326.72±90.15)μm,对照组为(320.17±106.46)μm,两组之间无统计学差异,但黄斑中心凹下CT与视网膜厚度间具有明显正相关关系(r=0.270,P=0.025)。DME亚型CT分别为:DRT型(303.94±81.47)μm、CME型(304.42±73.98)μm和SRD型(401.63±88.80)μm,SRD型CT明显高于其他亚型(P<0.05),此外,SRD型的周边CT同样呈现均匀一致的增厚;鼻侧CT从500 μm至2000 μm呈距离敏感性降低(P<0.05),但SRD型鼻侧CT降低幅度明显变缓(P=0.195)。结论 SRD型黄斑水肿患者CT在中心凹下及周边部均显著增厚,CT与DME病变程度之间有一定相关性。 相似文献
8.
目的探讨维生素D(VitD)联合鱼油对糖尿病前期(PDM)患者糖脂代谢、胰岛β细胞功能的影响。 方法选取PDM患者132例,随机均分为联合组(VitD+鱼油)、VitD组(VitD)和对照组(不干预)。比较各组糖脂代谢、胰岛β细胞功能、炎症反应、血管内皮功能等指标。 结果与干预前比较,干预后联合组甘油三酯降低,白细胞介素-10增高(P<0.05),联合组和VitD组低密度脂蛋白胆固醇、肿瘤坏死因子-α、胰岛素抵抗指数、前列腺素E2、瘦素、抵抗素降低(P<0.05),空腹胰岛素、胰岛β细胞功能指数、脂联素增高(P<0.05),且联合组改善更为明显(P<0.05)。 结论维生素D联合鱼油治疗PDM患者可改善其脂代谢和胰岛功能相关指标,具有一定临床应用价值。 相似文献
9.
L. Umapathy G.G. Perez-Carrillo M.B. Keerthivasan J.A. Rosado-Toro M.I. Altbach B. Winegar C. Weinkauf A. Bilgin for the Alzheimers Disease Neuroimaging Initiative 《AJNR. American journal of neuroradiology》2021,42(4):639
BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.1 Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.4,5 Although the extent of WMHs can be visually scored,6 the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.7 Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.8-11 More recently, a WMH segmentation challenge12 was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.12Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio13 and have shown promising results in MS lesion detection using 3D CNNs.14 Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.15 The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.16 The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.17 Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization18 framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques. 相似文献