Managing alcoholic liver disease

Alcoholic liver disease continues to be a significant cause of liver-related morbidity and mortality throughout the world. A number of diagnostic and prognostic models have been developed in the management of this condition, although specific roles for liver biopsy still remain particularly in the setting of alcoholic hepatitis. Despite a large number of recent treatment trials, the ideal pharmacotherapy approach remains undefined. Most essential is the supportive care and focus on abstinence and nutrition. Owing in part to a great deal of attention from governmental funding sources, a number of new treatment approaches are undergoing rigorous evaluation, hopefully providing future treatment options in this very severe condition

AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, e.g. Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.Comment: To be published in IEEE Wireless Communications Magazin

Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks

The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning-based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.Comment: 6 pages, 7 figures, 3 table

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle's sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle's sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error

The role of gut-liver axis in the pathogenesis of liver cirrhosis and portal hypertension

Because of the anatomical position and its unique vascular system, the liver is susceptible to the exposure to the microbial products from the gut. Although large amount of microbes colonize in the gut, translocation of the microbes or microbial products into the liver and systemic circulation is prevented by gut epithelial barrier function and cleansing and detoxifying functions of the liver in healthy subjects. However, when the intestinal barrier function is disrupted, large amount of bacterial products can enter into the liver and systemic circulation and induce inflammation through their receptors. Nowadays, there have been various reports suggesting the role of gut flora and bacterial translocation in the pathogenesis of chronic liver disease and portal hypertension. This review summarizes the current knowledge about bacterial translocation and its contribution to the pathogenesis of chronic liver diseases and portal hypertension

Alcohol-related liver disease: Areas of consensus, unmet needs and opportunities for further study

A joint meeting of the European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD) was held in London on September 30 and October 1, 2017. The goals of the meeting were to identify areas of broad agreement and disagreement, develop consensus, and determine future directions to ultimately reduce the burden, morbidity, and mortality of alcohol-related liver disease (previously termed alcoholic liver disease). The specific aims of the meeting were to identify unmet needs and areas for future investigation, in order to reduce alcohol consumption, develop markers for diagnosis and prognosis of disease, and create a framework to test novel pharmacological agents with pre-specified treatment endpoints

Effect of Bleaching on Color Change and Surface Topography of Composite Restorations

This study was conducted to determine the effect of 15% carbamide peroxide bleaching agent on color change and surface topography of different composite veneering materials (Filtek Z350 (3M ESPE), Esthet X (Dentsply India), and Admira (Voco, Germany). Methods. 30 samples were fabricated for evaluation of color change using CIELAB color system and Gonioreflectometer (GK 311/M, ZEISS). 45 disc-shaped specimens were made for evaluation of surface topography after bleaching (Nupro White Gold; Dentsply) using SEM. Statistical analysis. One way ANOVA and Multiple comparison tests were used to analyze the data. Statistical significance was declared if the P value was .05 or less. Results and conclusion. All the specimens showed significant discoloration (ΔE > 3.3) after their immersion in solutions representing food and beverages. The total color change after bleaching as compared to baseline color was significant in Filtek Z350 (P = .000) and Esthet X (P = .002), while it was insignificant for Admira (P = .18). Esthet X showed maximum surface roughness followed by Admira and Filtek Z350. Bleaching was effective in reducing the discoloration to a clinically acceptable value in all the three groups (ΔE < 3.3)