Efficacy and safety of transjugular intrahepatic portosystemic shunt (TIPS) created using covered stents of different diameters: A systematic review and meta-analysis

PurposeThe purpose of this study was to make a systematic review and meta-analysis to determine the stent diameter (8 mm vs. 10 mm) that conveys better safety and clinical efficacy for transjugular intrahepatic portosystemic shunt (TIPS).Materials and methodsFour databases were used to identify clinical trials published from inception until March 2020. Data were extracted to estimate and compare one-year and three-year overall survivals, hepatic encephalopathy, variceal rebleeding, and shunt dysfunction rates between patients with 8 mm covered stents and those with 10 mm covered stents.ResultsFive eligible studies were selected, which included 489 patients (316 men, 173 women). The 8 mm covered stent group had higher efficacy regarding one-year or three-year overall survival (odds ratio [OR], 2.88; P = 0.003) and (OR, 1.81; P = 0.04) and lower hepatic encephalopathy (OR, 0.69; P = 0.04) compared with 10 mm covered stent group. There were no significant differences in variceal rebleeding rate (OR 0.80; P = 0.67). However, shunt dysfunction was lower in 10 mm covered stent group (OR, 2.26; P = 0.003).ConclusionsOur results suggest that the use of 8 mm covered stents should be preferred to that of 10 mm covered stents for TIPS placement when portal pressure is frequently monitored.     

Dectin-2受体介导宿主抵抗烟曲霉菌感染的研究

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。     

基于网络药理学和分子对接技术探讨黄芪干预腹膜纤维化的机制

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。     

国内外安宁疗护准入标准的研究进展

安宁疗护可提高患者在生命终末阶段的生活质量，减轻患者和家属的身心痛苦。科学合理的安宁疗护准入标准可帮助医护人员识别出需要安宁疗护服务的患者，使其及时获得安宁疗护服务，因此明确安宁疗护的准入标准是推进安宁疗护发展的基础。本文就国内外安宁疗护准入标准的制定方法、具体内容及优缺点进行综述，以期为我国安宁疗护准入标准的构建提供参考。     

个体化健康教育对糖尿病抑郁症运动依从性影响

目的研究糖尿病并发抑郁症患者运动依从性的影响因素,探讨有效的干预措施。方法选择2018年11月-2019年8月天津市公安医院收治的糖尿病患者158例为研究对象,根据抑郁自评量表(CES-D)调查情况分为糖尿病抑郁(DDM)组和糖尿病非抑郁(NDDM)组各79例,采用问卷的形式分析个体化健康教育指导前后患者运动依从性影响因素。结果 NDDM组患者运动依从性量表评分为(78.4±3.3)分,高于DDM组的(52.7±4.1)分;重度抑郁患者较轻度抑郁患者运动治疗依从性明显降低;两组患者个体化健康教育后运动依从性量表评分较教育前均提高,差异均有统计学意义(P<0.01)。不同运动依从性的糖尿病患者性别、婚姻状况、文化程度、家庭支持、医患关系、并发症数量、住院次数、BMI、HbA1c比较差异有统计学意义(P<0.05)。结论抑郁症对糖尿病患者的运动依从性有明显影响,且抑郁程度越重运动依从性越差。个体化健康教育能有效改善糖尿病并发抑郁症患者的运动依从性,值得临床进一步研究。     

心脏瓣膜置换术后患者的出院准备度现状及其影响因素分析

目的:调查心脏瓣膜置换术后患者的出院准备度现状,并分析其影响因素。方法:于2018年9月至2019年3月用一般资料、出院准备度量表、出院指导质量量表对139例瓣膜置换术后患者进行调查。结果:心脏瓣膜置换术后患者的出院准备度总分为(89.51±8.53)分,与出院指导质量呈正相关;多元回归分析显示,主要照顾者、婚姻状况、工作状态、服药种类、出院指导质量为出院准备度影响因素。结论:心脏瓣膜置换术后患者的出院准备度有待提高,患者感知的个人状态得分不理想;护士应鼓励患者配合进行早期康复,强化出院指导,采取有效措施提供康复信息,满足患者及其家属需求,提高出院准备度。     

儿科诊断性X线扫描家属知情与接受现状调查

目的:了解建德市基层医院儿科门诊进行X线扫描治疗中,家属对X射线检查的知情同意与接受现状。方法:随机抽取建德市属3家公立综合性医院共60位接受X线扫描的儿童患者家属为调查对象,发放自制调查问卷并统计分析。结果:55位(91.67%)患者表示治疗前主治医生只告知了X射线是诊断性检查,没有具体告知X射线的危害性;18位(30%)的患者家属不清楚辐射会对人体有损害,42位(70%)不清楚或不注意辐射警示标志,更不会主动要求防护措施,学历水平较高者及有从事医务工作背景者接受X射线检查的为12人(20%),明显低于学历水平低者或没有从事医务工作背景者的46人(76.67%),不知可否接受的有2人(3.33%)(P<0.001)。结论:基层医院儿科诊断性X射线扫描前的知情同意告知仍需加强,儿科放射检查偏多,应避免并加强宣传和教育。     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

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.