Erythrocytes as a Model for Heavy Metal-Related Vascular Dysfunction: The Protective Effect of Dietary Components.

Heavy metals are toxic environmental pollutants associated with severe ecological and human health risks. Among them is mercury (Hg), widespread in air, soil, and water, due to its peculiar geo-biochemical cycle. The clinical consequences of Hg exposure include neurotoxicity and nephrotoxicity. Furthermore, increased risk for cardiovascular diseases is also reported due to a direct effect on cardiovascular tissues, including endothelial cells, recently identified as important targets for the harmful action of heavy metals. In this review, we will discuss the rationale for the potential use of erythrocytes as a surrogate model to study Hg-related toxicity on the cardiovascular system. The toxic effects of Hg on erythrocytes have been amply investigated in the last few years. Among the observed alterations, phosphatidylserine exposure has been proposed as an underlying mechanism responsible for Hg-induced increased proatherogenic and prothrombotic activity of these cells. Furthermore, following Hg-exposure, a decrease in NOS activity has also been reported, with consequent lowering of NO bioavailability, thus impairing endothelial function. An additional mechanism that may induce a decrease in NO availability is the generation of an oxidative microenvironment. Finally, considering that chronic Hg exposure mainly occurs through contaminated foods, the protective effect of dietary components is also discussed

Helpless Mothers Dropping Out of the Workplace: The Italian Case of Voluntary Resignation

In the Italian social context difficulties in remaining in the labor market characterizes working mothers, leading them sometimes to resign from their jobs. The aim of this research is to explore narratives of those women dropping out of the workforce during pregnancy and soon after childbirth and their experiences in these circumstances. The study analysed 30 interviews with working mothers with an average age of 35.4 years, living in Naples, Italy, who “spontaneously” left their jobs. Grounded Theory Methodology allowed a deeper understanding of these women’s thoughts, feelings, and experiences. The content of the interviews was categorized into 4 macro-areas: (1) The role of the family and of the working contexts, (2) Mothering and caregiving (3) Identity conflicts, and (4) The consequences of resignation. A sense of helplessness towards fulfilling maternal expectations, role assignments, and employers’ requests ultimately led to the individuals’ response to the requirements of motherhood. The narratives highlighted how respondents feel powerless and oppressed by the burden of guilt and feelings of ambivalence towards both work and motherhood and how all these subjective feelings were supported and had been induced by external social factors (discriminatory business strategies, organizational time management, lack of support services, familial cultural models idealizing maternity)

Olive Oil Phenols Prevent Mercury-Induced Phosphatidylserine Exposure and Morphological Changes in Human Erythrocytes Regardless of Their Different Scavenging Activity

: Phosphatidylserine (PS) translocation to the external membrane leaflet represents a key mechanism in the pathophysiology of human erythrocytes (RBC) acting as an "eat me" signal for the removal of aged/stressed cells. Loss of physiological membrane asymmetry, however, can lead to adverse effects on the cardiovascular system, activating a prothrombotic activity. The data presented indicate that structurally related olive oil phenols prevent cell alterations induced in intact human RBC exposed to HgCl2 (5-40 µM) or Ca2+ ionophore (5 µM), as measured by hallmarks including PS exposure, reactive oxygen species generation, glutathione depletion and microvesicles formation. The protective effect is observed in a concentration range of 1-30 µM, hydroxytyrosol being the most effective; its in vivo metabolite homovanillic alcohol still retains the biological activity of its dietary precursor. Significant protection is also exerted by tyrosol, in spite of its weak scavenging activity, indicating that additional mechanisms are involved in the protective effect. When RBC alterations are mediated by an increase in intracellular calcium, the protective effect is observed at higher concentrations, indicating that the selected phenols mainly act on Ca2+-independent mechanisms, identified as protection of glutathione depletion. Our findings strengthen the nutritional relevance of olive oil bioactive compounds in the claimed health-promoting effects of the Mediterranean Diet

Novel Insights into Mercury Effects on Hemoglobin and Membrane Proteins in Human Erythrocytes

Mercury (Hg) is a global environmental pollutant that affects human and ecosystem health. With the aim of exploring the Hg-induced protein modifications, intact human erythrocytes were exposed to HgCl2 (1-60 µM) and cytosolic and membrane proteins were analyzed by SDS-PAGE and AU-PAGE. A spectrofluorimetric assay for quantification of Reactive Oxygen Species (ROS) generation was also performed. Hg2+ exposure induces alterations in the electrophoretic profile of cytosolic proteins with a significant decrease in the intensity of the hemoglobin monomer, associated with the appearance of a 64 kDa band, identified as a mercurized tetrameric form. This protein decreases with increasing HgCl2 concentrations and Hg-induced ROS formation. Moreover, it appears resistant to urea denaturation and it is only partially dissociated by exposure to dithiothreitol, likely due to additional protein-Hg interactions involved in aggregate formation. In addition, specific membrane proteins, including band 3 and cytoskeletal proteins 4.1 and 4.2, are affected by Hg2+-treatment. The findings reported provide new insights into the Hg-induced possible detrimental effects on erythrocyte physiology, mainly related to alterations in the oxygen binding capacity of hemoglobin as well as decreases in band 3-mediated anion exchange. Finally, modifications of cytoskeletal proteins 4.1 and 4.2 could contribute to the previously reported alteration in cell morphology

A retrospective analysis of 3 156 admissions with fever of unknown origin in a large Italian hospital

Background: fever of unknown origin (FUO) is defined as a fever with no etiologic diagnosis after standardized investigations performed during 3 days in hospital or after at least 3 ambulatory visits. Our study aims to describe the epidemiology of classic FUO through the retrospective analysis of 902 861 admissions to a large University Hospital in Italy, to investigate its temporal trend, and to evaluate differences between young and old patients. Methods: we retrieved data records of all the admissions between the 1st January 1988 and 31st December 2007. Proportional admission rate (PAR ) of FUO was calculated. Time trends of FUO admissions were analysed by joinpoint regression, with time changes expressed as Expected Annual Percent Change (EA PC). The ICD 9-CM code was used to identify the diagnosis on discharge of FUO cases. Results: in the study period 3 156 patients were admitted with a diagnosis of FUO (PAR=3.50 per 1 000). The time-trend analysis showed two joinpoints, the first in 1995 (EAPC of 307.80, 95% CI: 89.66-776.84, p=0.002), and the second in 1998 (EAPC=-8.57, 95% CI: -10.37-6.73; p<0.001). Around 22% of admissions remained without a definitive diagnosis of FUO, with this percentage being lower in patients ≥65 years compared with subjects aged 21-64. Conclusions: FUO is a leading cause of admission to hospitals, as well as of morbidity and mortality, thus representing a challenge for diagnostic medicine and hospital care. It is necessary to develop a diagnostic methodology for FUO , so as to reduce costs of preventable hospitalizations

Spiking Neural Networks for event-based action recognition: A new task to understand their advantage

Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood. In order to provide answers, in this work we demonstrate how Spiking neurons can enable temporal feature extraction in feed-forward neural networks without the need for recurrent synapses, showing how their bio-inspired computing principles can be successfully exploited beyond energy efficiency gains and evidencing their differences with respect to conventional neurons. This is demonstrated by proposing a new task, DVS-Gesture-Chain (DVS-GC), which allows, for the first time, to evaluate the perception of temporal dependencies in a real event-based action recognition dataset. Our study proves how the widely used DVS Gesture benchmark could be solved by networks without temporal feature extraction, unlike the new DVS-GC which demands an understanding of the ordering of the events. Furthermore, this setup allowed us to unveil the role of the leakage rate in spiking neurons for temporal processing tasks and demonstrated the benefits of "hard reset" mechanisms. Additionally, we also show how time-dependent weights and normalization can lead to understanding order by means of temporal attention.Comment: New article superseding the one in previous version

Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I&F) models are often adopted, with the simple Leaky I&F (LIF) being the most used. The reason for adopting such models is their efficiency and/or biological plausibility. Nevertheless, rigorous justification for adopting LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers various neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I&F neuron models, namely the LIF, the Quadratic I&F (QIF) and the Exponential I&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with Spike-Timing Dependent Plasticity (STDP) on a classification task on the N-MNIST and DVS Gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer Spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the whole system's performance. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available

Simple and complex spiking neurons : perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience, and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I\&F) models are often adopted as considered more suitable, with the simple Leaky I\&F (LIF) being the most used. The reason for adopting such models is their efficiency or biological plausibility. Nevertheless, rigorous justification for the adoption of LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers a variety of neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I\&F neuron models, namely the LIF, the Quadratic I\&F (QIF) and the Exponential I\&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with Spike-Timing Dependent Plasticity (STDP) on a classification task on the N-MNIST and DVS Gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the performance of the whole system. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available