Histamine and neuroinflammation: insights from murine experimental autoimmune encephalomyelitis

Multiple sclerosis (MS) is a chronic inflammatory, neurodegenerative disease of the CNS whose pathogenesis remains largely unknown, and available therapies are rarely successful in reversing neurological deficits or stopping disease progression. Ongoing studies on MS and the widely used murine model of experimental autoimmune encephalomyelitis (EAE) are focused on the many components of this complex and heterogeneous neurodegenerative disease in the hope of providing a mechanism-based characterization of MS that will afford successful strategies to limit and repair the neuronal damage. Recently, histamine has been postulated to have a key regulatory role in EAE and MS pathogenesis. Histamine is a mediator of inflammation and immune responses, exerting its many actions through four G protein-coupled receptors (H1,2,3,4R) that signal through distinct intracellular pathways and have different therapeutic potentials as they vary in expression, isoform distribution, signaling properties, and function. Immune cells involved in MS/EAE, including dendritic cells (DCs) and T lymphocytes, express H1R, H2R and H4R, and histamine may have varying and counteracting effects on a particular cell type, depending on the receptor subtypes being activated. Here, we review evidence of the complex and controversial role of histamine in the pathogenesis of MS and EAE and evaluate the therapeutic potential of histaminergic ligands in the treatment of autoimmune diseases

A Query-by-Example Content-Based Image Retrieval System of Non-melanoma Skin Lesions

Abstract. This paper proposes a content-based image retrieval system for skin lesion images as a diagnostic aid. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types are used. Colour and texture features are extracted from lesions. Feature selection is achieved by optimising a similarity matching function. Experiments on our database of 208 images are performed and results evaluated.

Fuzzy Description of Skin Lesions

We propose a system for describing skin lesions images based on a human perception model. Pigmented skin lesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works on classification of skin lesions already exist but they mainly concentrate on melanoma. The novelty of our work is that our system gives to skin lesion images a semantic label in a manner similar to humans. This work consists of two parts: first we capture they way users perceive each lesion, second we train a machine learning system that simulates how people describe images. For the first part, we choose 5 attributes: colour (light to dark), colour uniformity (uniform to non-uniform), symmetry (symmetric to non-symmetric), border (regular to irregular), texture (smooth to rough). Using a web based form we asked people to pick a value of each attribute for each lesion. In the second part, we extract 93 features from each lesions and we trained a machine learning algorithm using such features as input and the values of the human attributes as output. Results are quite promising, especially for the colour related attributes, where our system classifies over 80 % of the lesions into the same semantic classes as humans

Utility of Non-rule-based Visual Matching as a Strategy to Allow Novices to Achieve Skin Lesion Diagnosis

Non-analytical reasoning is thought to play a key role in dermatology diagnosis. Considering its potential importance, surprisingly little work has been done to research whether similar identification processes can be supported in non-experts. We describe here a prototype diagnostic support software, which we have used to examine the ability of medical students (at the beginning and end of a dermatology attachment) and lay volunteers, to diagnose 12 images of common skin lesions. Overall, the non-experts using the software had a diagnostic accuracy of 98% (923/936) compared with 33% for the control group (215/648) (Wilcoxon p < 0.0001). We have demonstrated, within the constraints of a simplified clinical model, that novices’ diagnostic scores are significantly increased by the use of a structured image database coupled with matching of index and referent images. The novices achieve this high degree of accuracy without any use of explicit definitions of likeness or rule-based strategies

Parking and the visual perception of space

Using measured data we demonstrate that there is an amazing correspondence among the statistical properties of spacings between parked cars and the distances between birds perching on a power line. We show that this observation is easily explained by the fact that birds and human use the same mechanism of distance estimation. We give a simple mathematical model of this phenomenon and prove its validity using measured data

Landing together: how flocks arrive at a coherent action in time and space in the presence of perturbations

Collective motion is abundant in nature, producing a vast amount of phenomena which have been studied in recent years, including the landing of flocks of birds. We investigate the collective decision making scenario where a flock of birds decides the optimal time of landing in the absence of a global leader. We introduce a simple phenomenological model in the spirit of the statistical mechanics-based self-propelled particles (SPP-s) approach to interpret this process. We expect that our model is applicable to a larger class of spatiotemporal decision making situations than just the landing of flocks (which process is used as a paradigmatic case). In the model birds are only influenced by observable variables, like position and velocity. Heterogeneity is introduced in the flock in terms of a depletion time after which a bird feels increasing bias to move towards the ground. Our model demonstrates a possible mechanism by which animals in a large group can arrive at an egalitarian decision about the time of switching from one activity to another in the absence of a leader. In particular, we show the existence of a paradoxical effect where noise enhances the coherence of the landing process.Comment: 15 pages, 7 figure

Influence of different heating systems on thermal comfort perception: a dynamic and CFD analysis

In this paper, we investigate the influence of different heating systems on the thermal comfort indexes, Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), for a residential apartment located in Bologna (Italy). The apartment has an area of 40 m2 and is located on the ground floor of 4 floors building. The envelop consists in horizontal perforated bricks with internal thermal insulation material and two windows. The analyses are performed employing Trnsys, a commercial dynamic simulation software and Simcenter STAR-CCM+, a multiphysics computational fluid dynamics (CFD) software. The CFD analysis regards a steady condition of a typical winter day in Bologna. Thermal comfort indexes and thermal energy demand are studied comparing two different heating generation systems existing in the considered apartment: a condensing gas boiler coupled with radiators as terminal emitters and an air-to-air heat pump. By crossing the results obtained by the dynamical approach and by the CFD simulations, a two-objective methodology where energy consumption is minimised while thermal comfort is obtained, is presented

Graphene oxide prevents lateral amygdala dysfunctional synaptic plasticity and reverts long lasting anxiety behavior in rats

Engineered small graphene oxide (s-GO) sheets were previously shown to reversibly down-regulate glutamatergic synapses in the hippocampus of juvenile rats, disclosing an unexpected translational potential of these nanomaterials to target selective synapses in vivo. Synapses are anatomical specializations acting in the Central Nervous System (CNS) as functional interfaces among neurons. Dynamic changes in synaptic function, named synaptic plasticity, are crucial to learning and memory. More recently, pathological mechanisms involving dysfunctional synaptic plasticity were implicated in several brain diseases, from dementia to anxiety disorders. Hyper-excitability of glutamatergic neurons in the lateral nucleus of the amygdala complex (LA) is substantially involved in the storage of aversive memory induced by stressful events enabling post-traumatic stress disorder (PTSD). Here we translated in PTSD animal model the ability of s-GO, when stereotaxically administered to hamper LA glutamatergic transmission and to prevent the behavioral response featured in long-term aversive memory. We propose that s-GO, by interference with glutamatergic plasticity, impair LA-dependent memory retrieval related to PTSD

Inhibition of Immune Synapse by Altered Dendritic Cell Actin Distribution: A New Pathway of Mesenchymal Stem Cell Immune Regulation

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