Association of Early Kidney Allograft Failure with Preformed IgA Antibodies to β2-Glycoprotein I

In the current immunosuppressive therapy era, vessel thrombosis is the most common cause of early graft loss after renal transplantation. The prevalence of IgA anti–β₂-glycoprotein I antibodies (IgA-aB2GPI-ab) in patients on dialysis is elevated (>30%), and these antibodies correlate with mortality and cardiovascular morbidity. To evaluate the effect of IgA-aB2GPI-ab in patients with transplants, we followed all patients transplanted from 2000 to 2002 in the Hospital 12 de Octubre prospectively for 10 years. Presence of IgA-aB2GPI-ab in pretransplant serum was examined retrospectively. Of 269 patients, 89 patients were positive for IgA-aB2GPI-ab (33%; group 1), and the remaining patients were negative (67%; group 2). Graft loss at 6 months post-transplant was significantly higher in group 1 (10 of 89 versus 3 of 180 patients in group 2; P=0.002). The most frequent cause of graft loss was thrombosis of the vessels, which was observed only in group 1 (8 of 10 versus 0 of 3 patients in group 2; P=0.04). Multivariate analysis showed that the presence of IgA-aB2GPI-ab was an independent risk factor for early graft loss (P=0.04) and delayed graft function (P=0.04). There were no significant differences regarding patient survival between the two groups. Graft survival was similar in both groups after 6 months. In conclusion, patients with pretransplant IgA-aB2GPI-ab have a high risk of early graft loss caused by thrombosis and a high risk of delayed graft function. Therefore, pretransplant IgA-aB2GPI-ab may have a detrimental effect on early clinical outcomes after renal transplantation.     

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...     

甘肃省和政县农村老年性白内障防盲项目的卫生经济学评价研究

目的 对2014-2016年在甘肃省和政县开展的白内障综合防盲干预项目进行卫生经济学评价。设计 横断面调查。 研究对象 甘肃省和政县2014-2016年老年性白内障手术前407例患者及术后半年109例患者。方法 对所有调查对象进行卫生经济学问卷调查。通过净效益法、成本效益分析法和成本效果分析法评价项目产生的经济效益和总成本。主要指标 直接成本、间接成本、直接经济效益、间接经济效益、总成本、总效益、净效益、成本效益比、成本效果比。结果 2014-2016年项目期间甘肃省和政县白内障所致总体疾病经济负担为2142.28万元。白内障手术产生的总效益为3398424.98元，总成本为2939125.20元，净效益为459299.78元，效益成本比为1.16:1。项目每投入1万元可降低50岁以上白内障患者导致的0.027%的致盲率和0.164%致残率；项目每降低1%的50岁以上白内障患者的盲率，需投入36.47万元；每降低1%50岁以上白内障患者的残率，需投入6.11万元。结论 在甘肃省和政县开展的老年性白内障防盲综合干预项目具有较好的产出回报比和较高的防盲技术效率。（眼科，2020，29： 298-303）     

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.