Idiopathic noncirrhotic portal hypertension: current perspectives

The term idiopathic noncirrhotic portal hypertension (INCPH) has been recently proposed to replace terms, such as hepatoportal sclerosis, idiopathic portal hypertension, incomplete septal cirrhosis, and nodular regenerative hyperplasia, used to describe patients with a hepatic presinusoidal cause of portal hypertension of unknown etiology, characterized by features of portal hypertension (esophageal varices, nonmalignant ascites, porto-venous collaterals), splenomegaly, patent portal, and hepatic veins and no clinical and histological signs of cirrhosis. Physicians should learn to look for this condition in a number of clinical settings, including cryptogenic cirrhosis, a disease known to be associated with INCPH, drug administration, and even chronic alterations in liver function tests. Once INCPH is clinically suspected, liver histology becomes mandatory for the correct diagnosis. However, pathologists should be familiar with the histological features of INCPH, especially in cases in which histology is not only requested to exclude liver cirrhosis

PINK1 homozygous W437X mutation in a patient with apparent dominant transmission of parkinsonism.

We analyzed the PINK1 gene in 58 patients with early-onset Parkinsonism and detected the homozygous mutation W437X in 1 patient. The clinical phenotype was characterized by early onset (22 years of age), good re- sponse to levodopa, early fluctuations and dyskinesias, and psychiatric symptoms. The mother, heterozygote for W437X mutation, was affected by Parkinson’s disease and 3 further relatives were reported affected, according to an autosomal dominant transmission

Localized scleroderma unius lateri and Borrelia burgdoferi infection

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Gastric cancer is the leading cause of death in Italian adult patients with common variable immunodeficiency

An increased prevalence of malignant lymphoma and of gastric cancer has been observed in large cohorts of patients with common variable immunodeficiency (CVID), the most frequently symptomatic primary immunodeficiency. Surveillance strategies for cancers in CVID should be defined based on epidemiological data. Risks and mortality for cancers among 455 Italian patients with CVID were compared to cancer incidence data from the Italian Cancer Registry database. CVID patients showed an increased cancer incidence for all sites combined (Obs = 133, SIR = 2.4; 95%CI = 1.7\u20133.5), due to an excess of non-Hodgkin lymphoma (Obs = 33, SIR = 14.3; 95%CI = 8.4\u201322.6) and of gastric cancer (Obs = 25; SIR = 6.4; 95%CI = 3.2\u201312.5). CVID patients with gastric cancer and lymphoma had a worse survival in comparison to cancer-free CVID (HR: 4.8, 95%CI: 4.2\u201344.4 and HR: 4.2, 95%CI: 2.8\u201344.4). Similar to what observed in other series, CVID-associated lymphomas were more likely to be of B cell origin and often occurred at extra-nodal sites. We collected the largest case-series of gastric cancers in CVID subjects. In contrast to other reports, gastric cancer was the leading cause of death in CVID. Standardized mortality ratio indicated a 10.1-fold excess mortality among CVID patients with gastric cancer. CVID developed gastric cancer 15 years earlier than the normative population, but they had a similar overall survival. Only CVID diagnosed at early stage gastric cancer survived >24 months. Stomach histology from upper endoscopy performed before cancer onset showed areas of atrophic gastritis, intestinal metaplasia or dysplasia. CVID patients might progress rapidly to an advanced cancer stage as shown by patients developing a III-IV stage gastric cancer within 1 year from an endoscopy without signs of dysplasia. Based on high rate of mortality due to gastric cancer in Italian CVID patients, we hereby suggest a strategy aimed at early diagnosis, based on regular upper endoscopy and on Helicobacter pylori infection treatment, recommending an implementation of national guidelines

Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones

Accurate data on community structure is a priority issue in studying coastal habitats facing human pressures. The recent development of remote sensing tools has offered a ground-breaking way to collect ecological information at a very fine scale, especially using low-cost aerial photogrammetry. Although coastal mapping is carried out using Unmanned Aerial Vehicles (UAVs or drones), they can provide limited information regarding underwater benthic habitats. To achieve a precise characterisation of underwater habitat types and species assemblages, new imagery acquisition instruments become necessary to support accurate mapping programmes. Therefore, this study aims to evaluate an integrated approach based on Structure from Motion (SfM) photogrammetric acquisition using low-cost Unmanned Aerial (UAV) and Surface (USV) Vehicles to finely map shallow benthic communities, which determine the high complexity of coastal environments. The photogrammetric outputs, including both UAV-based high (sub-meter) and USV-based ultra-high (sub-centimetre) raster products such as orthophoto mosaics and Digital Surface Models (DSMs), were classified using Object-Based Image Analysis (OBIA) approach. The application of a supervised learning method based on Support Vector Machines (SVM) classification resulted in good overall classification accuracies > 70%, proving to be a practical and feasible tool for analysing both aerial and underwater ultra-high spatial resolution imagery. The detected seabed cover classes included above and below-water key coastal features of ecological interest such as seagrass beds, “banquettes” deposits and hard bottoms. Using USV-based imagery can considerably improve the identification of specific organisms with a critical role in benthic communities, such as photophilous macroalgal beds. We conclude that the integrated use of low-cost unmanned aerial and surface vehicles and GIS processing is an effective strategy for allowing fully remote detailed data on shallow water benthic communities

Segmented Hairpin Topology for Reduced Losses at High Frequency Operations

Nowadays, one of the key challenges in transport electrification is the reduction of components’ size and weight. The electrical machine plays a relevant role in this regard. Designing machines with higher rotational speeds and excitation frequencies is one of the most effective solutions to increase power densities, but this comes at the cost of increased losses in cores and windings. This challenge is even more pronounced in preformed windings, such as hairpins, which enable higher slot fill factors and shorten manufacturing cycle times. In this work an improved hairpin winding concept is proposed, aiming to minimize high-frequency losses while maintaining the benefits deriving from the implementation of hairpin windings onto electrical machines. Analytical and finite element models are first used to assess the high-frequency losses in the proposed winding concept, namely the segmented hairpin, proving the benefits compared to conventional layouts. Experimental tests are also performed on a number of motorettes comprising both conventional and proposed segmented hairpin configurations. Finally, these experimental results are compared against those collected from motorettes equipped with random windings, demonstrating the competitiveness of the segmented hairpin layout even at high-frequency operations. © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper reflects only the author's view. JU is not responsible for any use that may be made of the information it contains

Transfer learning of deep neural network representations for fMRI decoding

Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view

Lung magnetic resonance imaging with diffusion weighted imaging provides regional structural as well as functional information without radiation exposure in primary antibody deficiencies

PURPOSE: Primary antibody deficiency patients suffer from infectious and non-infectious pulmonary complications leading over time to chronic lung disease. The complexity of this pulmonary involvement poses significant challenge in differential diagnosis in patients with long life disease and increased radio sensitivity. We planned to verify the utility of chest Magnetic Resolution Imaging with Diffusion-Weighted Imaging as a radiation free technique. METHODS: Prospective evaluation of 18 patients with Common Variable Immunodeficiency and X-linked Agammaglobulinemia. On the same day, patients underwent Magnetic Resonance Imaging with Diffusion Weighted Imaging sequences, High Resolution Computerized Tomography and Pulmonary Function Tests, including diffusing capacity factor for carbon monoxide. Images were scored using a modified version of the Bhalla scoring system. RESULTS: Magnetic Resonance Imaging was non-inferior to High Resolution Computerized Tomography in the capacity to identify bronchial and parenchymal abnormalities. HRCT had a higher capacity to identify peripheral airways abnormalities, defined as an involvement of bronchial generation up to the fifth and distal (scores 2-3). Bronchial scores negatively related to pulmonary function tests. One third of consolidations and nodules had Diffusion Weighted Imaging restrictions associated with systemic granulomatous disease and systemic lymphadenopathy. Lung Magnetic Resolution Imaging detected an improvement of bronchial and parenchymal abnormalities, in recently diagnosed patients soon after starting Ig replacement. CONCLUSIONS: Magnetic Resonance Imaging with Diffusion Weighted Imaging was a reliable technique to detect lung alterations in patients with Primary Antibody Deficiencies