Publications by authors named "Matteo Garofalo"

We have recently shown that endogenous hydrogen sulfide (H₂S), an important cellular gaseous mediator, exerts an antiviral and anti-inflammatory activity in vitro and in vivo, and that exogenous H₂S delivered via the synthetic H₂S-releasing compound GYY4137 also has similar properties. In this study, we sought to extend our findings to a novel class of H₂S donors, thiol-activated gem-dithiol-based (TAGDDs). In an in vitro model of human respiratory syncytial virus (RSV) infection, TAGDD-1 treatment significantly reduced viral replication, even when added up to six hours after infection.

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Human metapneumovirus (hMPV) is a major cause of respiratory tract infections in children, elderly and immunocompromised hosts, for which no vaccine or treatment are currently available. Oxidative stress and inflammatory responses represent important pathogenic mechanism(s) of hMPV infection. Here, we explored the potential protective role of dietary antioxidants in hMPV infection.

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We used electrophysiological signals recorded by CMOS Micro Electrode Arrays (MEAs) at high spatial resolution to estimate the functional-effective connectivity of sparse hippocampal neuronal networks in vitro by applying a cross-correlation (CC) based method and ad hoc developed spatio-temporal filtering. Low-density cultures were recorded by a recently introduced CMOS-MEA device providing simultaneous multi-site acquisition at high-spatial (21 μm inter-electrode separation) as well as high-temporal resolution (8 kHz per channel). The method is applied to estimate functional connections in different cultures and it is refined by applying spatio-temporal filters that allow pruning of those functional connections not compatible with signal propagation.

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Multi-channel acquisition from neuronal networks, either in vivo or in vitro, is becoming a standard in modern neuroscience in order to infer how cell assemblies communicate. In spite of the large diffusion of micro-electrode-array-based systems, researchers usually find it difficult to manage the huge quantity of data routinely recorded during the experimental sessions. In fact, many of the available open-source toolboxes still lack two fundamental requirements for treating multi-channel recordings: (i) a rich repertoire of algorithms for extracting information both at a single channel and at the whole network level; (ii) the capability of autonomously repeating the same set of computational operations to 'multiple' recording streams (also from different experiments) and without a manual intervention.

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Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these "connectivity methods" on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs.

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