Association Study of Matrix Metalloproteinases Gene Polymorphisms with Susceptibility to Rheumatoid Arthritis: A Meta-Analysis

Objective: Association of matrix metalloproteinases (MMPs) gene polymorphisms with rheumatoid arthritis is controversial. We conduct a meta-analysis to clarify this dispute.

Methods: We systematically searched the electronic PUBMED, EMBASE and CNKI databases for research articles about MMPs (MMP-1, MMP-2, MMP-3, MMP-9) gene polymorphisms and rheumatoid arthritis (RA) up to January 2015. According to the heterogeneity, fixed-effects or random-effects models were used to calculate crude odds ratios (ORs) and 95% confidence intervals (95% CIs).

Results: A total of 11 articles involving 2143 cases and 2049 controls were included in this meta-analysis. Overall, no significant associations were observed between MMP-1-1607 1G/2G polymorphism and RA. Stratification by ethnicity, no significant associations were observed in Caucasian populations. Similarly, no significant associations were observed between MMP-3-1171 5A/6A, MMP-9-1562 C/T polymorphisms and RA in overall and Caucasian populations, respectively. However, a weak association was found between MMP-2-1306 C/T polymorphism and RA (C vs. T, OR?=?0.813, 95%CI?=?0.694–0.953, p?=?0.010) in overall populations.

Conclusions: The present meta-analysis suggests that MMP-1-1607 1G/2G, MMP-3-1171 5A/6A, MMP-9-1562 C/T polymorphisms are not associated with the susceptibility of RA, but MMP-2 -1306 C/T is weakly associated with susceptibility to RA. Further studies with more sample size are needed for definitive conclusions.     

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.     

腹股沟疝一侧术后对侧发病的时间间隔分析

目的探讨腹股沟疝一侧术后对侧发生腹股沟疝的时间间隔,并对其进行分析。方法回顾医院2012年5月—2019年9月收治的腹股沟疝患者37例,所有患者对侧均有腹股沟疝手术史,将2次发病的时间间隔分为3组,T≤2年为短期组,2相似文献   

中国X连锁视网膜色素变性家系RPGR新突变

目的 检测分析被诊断为X连锁视网膜色素变性（XLRP）的三个中国家系内的基因突变。设计 基因研究。研究对象 三个中国XLRP家系共27位受试者（其中18人为男性）。方法 由同一医生收集家系成员的详细临床资料并进行眼部检查，采集三个家系的先证者及有条件采血者的外周静脉血，提取基因组DNA。应用PCR技术扩增RPGR和RP2基因的全部外显子和内含子交界区序列，包括RPGR基因15号外显子开放阅读框，产物直接测序进行突变分析。主要指标 临床特征及基因测序结果。结果 基因筛查证实了两个RPGR基因的新型无义突变(c.1541C>G;p.S514X 和 c.2833G>T;p.E945X) 及一个错义突变(c.607G>C;p.A203P)。基因型-表型的相关性分析表明家系3患者在接近ORF15下游位置存在突变，这种突变导致视锥细胞功能的早期丧失。ORF15无义突变的女性携带者临床表型重，呈现出部分显性遗传的特点。结论 本研究证实了三种RPGR基因的新型突变，这一结果扩展了RPGR的突变谱及表型谱。     

Maîtrise des risques liés à la production des fluides de dialyse dans une unité de télédialyse

Objective

The “Centre Hospitalier Francois Dunan” is located on an isolated island and ensures patients care in hemodialysis thanks to telemedicine support. Many research studies have demonstrated the importance of hemodialysis fluids composition to reduce morbidity in patients on chronic hemodialysis. The aim of this study was to identify the risks inherent in the production of dialysis fluids in a particular context, in order to set up an improvement action plan to improve risk control on the production of dialysis fluids.

中日友好医院分型股骨头坏死腓骨植入治疗的三维有限元分析

文题释义：股骨头坏死中日友好医院分型的有限元分析：根据李子荣等提出的中日友好医院分型，建立股骨头坏死三维模型，分为 M型(内侧型)、C型(中央型)和 L型(外侧型)，其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析，为该分型的保髋治疗提供了一定力学依据，显示外侧柱的存留是精准预防塌陷的重要因素，为进一步实现个体化治疗提供力学基础。 腓骨支撑坏死股骨头保髋手术：是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压，清除一定坏死骨，空腔填塞松质骨(髂骨为主)，打压结实后植入腓骨(异体或自体)支撑，给坏死区的提供力学支撑及生物学修复，预防股骨头进一步坏死及塌陷。 背景：研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关，中日友好医院分型是根据三柱结构确立的，对股骨头塌陷的预测准确性高。 目的：建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型，通过有限元分析各分型腓骨植入的力学变化，探讨外侧柱存留对保髋疗效的意义，为该分型的塌陷精准预测提供基础。 方法：建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型，运用ANSYS软件进行有限元分析计算，观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。 结果与结论：①坏死组位移最大，应变最大，且因坏死分型不同而位移不同，位移变化如下：M型相似文献