Esophagectomy with Ileocoloplasty,Last Treatment for a Leak After Laparoscopic Sleeve: “From Less to More”

Obesity Surgery - Laparoscopic sleeve gastrectomy (LSG) is increasingly playing a key role in obesity management. Such operations, however, carry complications sometimes including leaks. The...     

SEROPOSITIVITY FOR ASCARIOSIS AND TOXOCARIOSIS AND CYTOKINE EXPRESSION AMONG THE INDIGENOUS PEOPLE IN THE VENEZUELAN DELTA REGION

The present study aimed at measuring seropositivities for infection by Ascaris suum and Toxocara canis using the excretory/secretory (E/S) antigens from Ascaris suum (AES) and Toxocara canis (TES) within an indigenous population. In addition, quantification of cytokine expressions in peripheral blood cells was determined. A total of 50 Warao indigenous were included; of which 43 were adults and seven children. In adults, 44.1% were seropositive for both parasites; whereas children had only seropositivity to one or the other helminth. For ascariosis, the percentage of AES seropositivity in adults and children was high; 23.3% and 57.1%, respectively. While that for toxocariosis, the percentage of TES seropositivity in adults and children was low; 9.3% and 14.3%, respectively. The percentage of seronegativity was comparable for AES and TES antigens in adults (27.9%) and children (28.6%). When positive sera were analyzed by Western blotting technique using AES antigens; three bands of 97.2, 193.6 and 200.2 kDas were mostly recognized. When the TES antigens were used, nine major bands were mostly identified; 47.4, 52.2, 84.9, 98.2, 119.1, 131.3, 175.6, 184.4 and 193.6 kDas. Stool examinations showed that Blastocystis hominis, Hymenolepis nana and Entamoeba coli were the most commonly observed intestinal parasites. Quantification of cytokines IFN-γ, IL-2, IL-6, TGF-β, TNF-α, IL-10 and IL-4 expressions showed that there was only a significant increased expression of IL-4 in indigenous with TES seropositivity (p < 0.002). Ascaris and Toxocara seropositivity was prevalent among Warao indigenous.     

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.     

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   

顶天立地服务需求 注重内涵创建一流——首都医科大学建校60周年教育教学与人才培养成果回顾

首都医科大学通过60年辛勤耕耘、精益求精，秉承“顶天立地”的人才培养理念，为社会培养了大批高水平的学术型与应用型医药卫生人才。回顾学校60年来本专科教育、全科医学教育、国际教育、研究生教育的人才培养成果，以总结经验并鼓舞全体首医人进一步求真务实、凝心聚力、积极进取、追求卓越，努力将学校建设成为国际一流的研究型医科大学。