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.     

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.

Symptoms During or Shortly After Isolated Carpal Tunnel Release and Problems Within 24?hours After Surgery

This study used the National Survey of Ambulatory Surgery (NSAS) database to measure the incidence of and risk factors for symptoms in the ambulatory surgery center and problems within 24 h after isolated carpal tunnel release (CTR). The NSAS contained records on 400,000 adult patients with carpal tunnel syndrome who were treated with CTR in 2006, based on ICD-9 codes. The type of anesthesia used and factors associated with symptoms and problems were sought in bivariate and multivariable statistical analyses. The mean duration of the procedure was 16 ± 8.8 min. Only 5 % were performed under local anesthesia without sedation, 45 % with IV sedation, 28 % regional anesthesia, and 19 % general anesthesia. Symptoms in the ambulatory surgery center or a problem within 24 h after discharge were recorded in 10 % of patients, all of them minor and transient, including difficulties with pain and its treatment. The strongest risk factors were male sex, age of 45 years and older, and participation of an anesthesiologist. Local anesthesia and regional anesthesia were associated with more perioperative symptoms and postoperative problems. Most CTR are performed with some sedation in the United States. CTR is a safe procedure: one in 10 patients will experience a minor issue in the perioperative or immediate postoperative period.     

Coats plus syndrome (cerebroretinal microangiopathy with calcifications and cysts‐1): A case report

We present a 6‐year‐old girl with skin hyperpigmentation, leukoplakia, and onychodystrophy, the classic mucocutaneous triad usually associated with dyskeratosis congenita. The patient also had premature graying of the hair, bone marrow failure, hepatitis, exudative retinopathy, osteopenia with multiple long bone fractures, and intracranial calcifications and brain cysts. Coats plus syndrome is a rare disease with a clinical and genetic overlap with dyskeratosis congenita. This disease is reviewed, with a focus on the pathogenesis of the genetic anomalies and its background as a telomere biology disorder.