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The clinical course of prion diseases is accurately predictable despite long latency periods, suggesting that prion pathogenesis is driven by precisely timed molecular events. We constructed a searchable genome-wide atlas of mRNA abundance and splicing alterations during the course of disease in prion-inoculated mice. Prion infection induced PrP-dependent transient changes in mRNA abundance and processing already at eight weeks post inoculation, well ahead of any neuropathological and clinical signs. In contrast, microglia-enriched genes displayed an increase simultaneous with the appearance of clinical signs, whereas neuronal-enriched transcripts remained unchanged until the very terminal stage of disease. This suggests that glial pathophysiology, rather than neuronal demise, could be the final driver of disease. The administration of young plasma attenuated the occurrence of early mRNA abundance alterations and delayed signs in the terminal phase of the disease. The early onset of prion-induced molecular changes might thus point to novel biomarkers and potential interventional targets. © 2020 Sorce et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Negli ultimi anni abbiamo assistito ad una grande diffusione dei cosiddetti “Kinect-like devices”, ovvero dispositivi basati su un insieme di sensori a basso costo, che consentono di ottenere un’immagine di profondità della scena ripresa. L’alta accessibilità di questi dispositivi, principalmente in termini di costi, ne ha facilitato la diffusione nell’ambito del riconoscimento dei gesti in numerose applicazioni, sia commerciali che di ricerca. In questo articolo saranno inizialmente illustrati i principi generali su cui si fondano le principali tecniche utilizzate per riconoscere i gesti, sfruttando i dati ottenibili dai dispositivi “Kinect-like”. Successivamente, saranno presentati alcuni ambiti applicativi, spaziando dal settore educativoricreativo a quello più scientifico (domotica, robotica ed ingegneria biomedica). In appendice, verranno elencati i principali prodotti disponibili in commercio e ne verrà presentata una sintetica analisi comparativa. Verrà inoltre descritto uno dei più noti e usati algoritmi di skeletal tracking, su cui si fonda la maggior parte delle soluzioni per il riconoscimento dei gesti. In the last decades, we have witnessed to the increase of the socalled Kinect-like devices, which are based on a set of low-cost sensors to acquire RGB and depth data of a scene. The high accessibility of such devices, mainly in terms of costs, has pushed their adoption as fundamental tool for gesture recognition in a large number of applications, both commercial and research-related ones. In this paper, we first discuss some of the general principles adopted by most of the main gesture recognition techniques described in literature. Then we present some application fields in which Kinect-like devices and gesture recognition algorithms have been used, ranging from educational-recreational examples to more complex and scientific fields (e.g. domotics, robotics and biomedical engineering). In two annexes, we list and shortly compare the main features of the Kinect-like devices available on the market, and we describe one of the most popular algorithm for skeletal tracking, which is the basis for the gesture recognition.

Neurodegenerative disease are frequently characterized by microglia activation and/or leukocyte infiltration in the parenchyma of the central nervous system and at the molecular level by increased oxidative modifications of proteins, lipids and nucleic acids. NADPH oxidases (NOX) emerged as a novel promising class of pharmacological targets for the treatment of neurodegeneration due to their role in oxidant generation and presumably in regulating microglia activation. The unique function of NOX is the generation of superoxide anion (O2•–) and hydrogen peroxide (H2O2). However in the context of neuroinflammation, they present paradoxical features since O2•–/H2O2 generated by NOX and/or secondary reactive oxygen species (ROS) derived from O2•–/H2O2 can either lead to neuronal oxidative damage or resolution of inflammation. The role of NOX enzymes has been investigated in many models of neurodegenerative diseases by using either genetic or pharmacological approaches. In the present review we provide a critical assessment of recent findings related to the role of NOX in the CNS as well as how the field has advanced over the last 5 years. In particular, we focus on the data derived from the work of a consortium (Neurinox) funded by the European Commission's Programme 7 (FP7). We discuss the evidence gathered from animal models and human samples linking NOX expression/activity with neuroinflammation in neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Creutzfeldt–Jakob disease as well as autoimmune demyelinating diseases like multiple sclerosis (MS) and chronic inflammatory demyelinating polyneuropathy (CIDP). We address the possibility to use measurement of the activity of the NOX2 isoform in blood samples as biomarker of disease severity and treatment efficacy in neurodegenerative disease. Finally we clarify key controversial aspects in the field of NOX, such as NOX cellular expression in the brain, measurement of NOX activity, impact of genetic deletion of NOX in animal models of neurodegeneration and specificity of NOX inhibitors. © 2017 The Authors

Although its involvement in prion replication and neurotoxicity during transmissible spongiform encephalopathies is undisputed, the physiological role of the cellular prion protein (PrPC) remains enigmatic. A plethora of functions have been ascribed to PrPC based on phenotypes of Prnp-/- mice. However, all currently available Prnp-/- lines were generated in embryonic stem cells from the 129 strain of the laboratory mouse and mostly crossed to non-129 strains. Therefore, Prnp-linked loci polymorphic between 129 and the backcrossing strain resulted in systematic genetic confounders and led to erroneous conclusions. We used TAL EN-mediated genome editing in fertilized mouse oocytes to create the Zurich-3 (ZH3) Prnp-ablated allele on a pure C57BL/6J genetic background. Genomic, transcriptional, and phenotypic characterization of PrnpZH3/ZH3 mice failed to identify phenotypes previously described in non-co-isogenic Prnp-/- mice. However, aged PrnpZH3/ZH3 mice developed a chronic demyelinating peripheral neuropathy, confirming the crucial involvement of PrPC in peripheral myelin maintenance. This new line represents a rigorous genetic resource for studying the role of PrPC in physiology and disease. © 2016 Nuvolone et al.

Prions induce lethal neurodegeneration and consist of PrPSc, an aggregated conformer of the cellular prion protein PrPC. Antibody-derived ligands to the globular domain of PrPC (collectively termed GDL) are also neurotoxic. Here we show that GDL and prion infections activate the same pathways. Firstly, both GDL and prion infection of cerebellar organotypic cultured slices (COCS) induced the production of reactive oxygen species (ROS). Accordingly, ROS scavenging, which counteracts GDL toxicity in vitro and in vivo, prolonged the lifespan of prion-infected mice and protected prion-infected COCS from neurodegeneration. Instead, neither glutamate receptor antagonists nor inhibitors of endoplasmic reticulum calcium channels abolished neurotoxicity in either model. Secondly, antibodies against the flexible tail (FT) of PrPC reduced neurotoxicity in both GDL-exposed and prion-infected COCS, suggesting that the FT executes toxicity in both paradigms. Thirdly, the PERK pathway of the unfolded protein response was activated in both models. Finally, 80% of transcriptionally downregulated genes overlapped between prion-infected and GDL-treated COCS. We conclude that GDL mimic the interaction of PrPSc with PrPC, thereby triggering the downstream events characteristic of prion infection. © 2015 Herrmann et al.

n this work we propose a smart, human-like PDA-based personal shopper assistant. The system is able to understand the user needs through a spoken natural language interaction and then stores the preferences of the potential customer. Subsequently the personal shopper suggests the most suitable items and shops that match the user profile. The interaction is given by automatic speech recognition and text-to-speech technologies; localization is allowed by the use of Wireless technologies, while the interaction is performed by an Alice-based chat-bot endowed with reasoning capabilities. Besides, being implemented on a PDA, the personal shopper satisfies the user needs of mobility and it is also usable on different mobile devices.

Coeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most frequent food intolerance and affects about 1% of the population, but it is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples to diagnose CD in adults. In paediatric CD, but recently in adults also, non-invasive diagnostic strategies have become increasingly popular. In order to increase the rates of correct diagnosis of the disease without the use of biopsy, researchers have recently been using approaches based on artificial intelligence techniques. In this work, we present a Clinical Decision Support System (CDSS)system for supporting CD diagnosis, developed in the context of the Italy-Malta cross-border project ITAMA. The implemented CDSS has been based on a neural-network-based fuzzy classifier. The system was developed and tested using a Virtual Database and a Real Database acquired during the ITAMA project. Analysis on 10,000 virtual patients shows that the system achieved an accuracy of 99% and a sensitivity of 99%. On 19,415 real patients, of which 109 with a confirmed diagnosis of coeliac disease, the system achieved 99.6% accuracy, 85.7% sensitivity, 99.6% specificity and 96% precision. Such results show that the developed system can be used effectively to support the diagnosis of the CD by reducing the appeal to invasive techniques such as biopsy.

Coeliac disease (CD) is frequently underdiagnosed with a consequent heavy burden in terms of morbidity and health care costs. Diagnosis of CD is based on the evaluation of symptoms and anti-transglutaminase antibodies IgA (TGA-IgA) levels, with values above a tenfold increase being the basis of the biopsy-free diagnostic approach suggested by present guidelines. This study showcased the largest screening project for CD carried out to date in school children (n=20,000) aimed at assessing the diagnostic accuracy of minimally invasive finger prick point-of-care tests (POCT) which, combined with conventional celiac serology and the aid of an artificial intelligence-based system, may eliminate the need for intestinal biopsy. Moreover, this study delves deeper into the “coeliac iceberg” in an attempt to identify people with disorders who may benefit from a gluten-free diet, even in the absence of gastrointestinal symptoms, abnormal serology and histology. This was achieved by looking for TGA-IgA mucosal deposits in duodenal biopsy. This large European multidisciplinary health project paves the way to an improved quality of life for patients by reducing the costs for diagnosis due to delayed findings of CD and to offer business opportunities in terms of diagnostic tools and support.

Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users’ interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.