A State of the Art of Antioxidant Properties of Curcuminoids in Neurodegenerative Diseases

Neurodegenerative diseases represent a set of pathologies characterized by an irreversible and progressive, and a loss of neuronal cells in specific areas of the brain. Oxidative phosphorylation is a source of energy production by which many cells, such as the neuronal cells, meet their energy needs. Dysregulations of oxidative phosphorylation induce oxidative stress, which plays a key role in the onset of neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). To date, for most neurodegenerative diseases, there are no resolute treatments, but only interventions capable of alleviating the symptoms or slowing the course of the disease. Therefore, effective neuroprotection strategies are needed. In recent years, natural products, such as curcuminoids, have been intensively explored and studied for their therapeutic potentials in several neurodegenerative diseases. Curcuminoids are, nutraceutical compouns, that owen several therapeutic properties such as anti-oxidant, anti-inflammatory and neuroprotective effects. In this context, the aim of this review was to provide an overview of preclinical and clinical evidence aimed to illustrate the antioxidant effects of curcuminoids in neurodegenerative diseases. Promising results from preclinical studies encourage the use of curcuminoids for neurodegeneration prevention and treatment.     

The behaviour of consolidated Neapolitan yellow Tuff against salt weathering

Bulletin of Engineering Geology and the Environment - Salt crystallization is a strong weathering agent in porous building materials. The crystallization pressure exerted by salt crystals, growing...     

Multifunctional TiO2 coatings for Cultural Heritage

Environmental pollution arising from industrial implants and urban factors is constantly increasing, causing aesthetical and durability concerns to urban structures exposed to the atmosphere.     

Hybrid Bionic Systems for the Replacement of Hand Function

In recent years, thanks to the advancement of robotics and mechatronics, new and more effective devices for the restoration and replacement of sensory-motor function in disabled people have been developed. In all these systems, user acceptability is strictly connected to several issues such as the residual abilities of the subject, the mechatronic characteristics of the robot, and also the interface chosen to link them. It is possible to figure out different "human-interface-device" combinations [also defined as "hybrid bionic systems" (HBSs)] characterized by different properties in terms of level of hybridness, connection, and augmentation. In particular, in HBSs the interface has to be customized according to the characteristics of the robotic artefact to be controlled and to the desires and needs of the final users. In this paper, our attention has been focused on the problem of the replacement of hand function after amputation. Three HBSs characterized by different levels of complexity, dexterity, and sensorization are presented in order to show the possibility of developing acceptable and effective systems by choosing different levels of connection and hybridness (i.e., different interfaces) for different devices and applications. The following case studies are presented: 1) the use of invasive interfaces to the peripheral nervous system to control a dexterous and highly sensorized hand prosthesis; 2) the use of electromyographic signals recorded using surface electrodes to control a compliant adaptive prosthesis; and 3) the use of a foot interface to control a two-degrees-of-freedom prosthesis. The preliminary results achieved so far seem to confirm the idea that the correct choice of the proper interface while developing an HBS can increase effectiveness and usability     

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.     

Neuro-fuzzy extraction of angular information from muscle afferentsfor ankle control during standing in paraplegic subjects: an animalmodel

This paper is part of a project whose aim is the implementation of closed-loop control of ankle angular position during functional electrical stimulation (FES) assisted standing in paraplegic subjects using natural sensory information. In this paper, a neural fuzzy (NF) model is implemented to extract angular position information from the electroneurographic signals recorded from muscle afferents using cuff electrodes in an animal model. The NF model, named dynamic nonsingleton fuzzy logic system is a Mamdani-like fuzzy system, implemented in the framework of recurrent neural networks. The fuzzification procedure implemented was the nonsingleton technique which has been shown in previous works to be able to take into account the uncertainty in the data. The proposed algorithm was tested in different situations and was able to predict reasonably well the ankle angular trajectories especially for small excursions (as during standing) and when the stimulation sites are far from the registration sites. This suggests it may be possible to use activity from muscle afferents recorded with cuff electrodes for FES closed-loop control of ankle position during quite standing.     

Temperature wave‐trains of periodically forced networks of catalytic reactors

Networks of N identical catalytic reactors with periodically switched inlet and outlet sections are studied for first‐order irreversible exothermic reactions. Switching strategies with inlet and outlet sections periodically jumping a fixed number n_s of reactors are considered and the mechanisms governing the formation of traveling temperature wave‐trains are analyzed as n_s and N are varied. To this aim, a geometric approach to the analysis of the network energy balance is developed. Based on this approach, infinite domains of traveling temperature wave‐trains are predicted for any n_s and N. Analytical approximations are derived for the stability limits and the spatiotemporal patterns of these regimes. Stability boundaries predicted analytically include for any solution the largest part of the stability region computed by numerical simulation. Moreover, good agreement is found between the structure of the spatiotemporal patterns computed numerically and that predicted based on the proposed approach. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Oligophenylenevinylenes in Spatially Confined Nanochannels: Monitoring Intermolecular Interactions by UV/Vis and Raman Spectroscopy

Perhydrotriphenylene‐based channel‐forming inclusion compounds (ICs) and thin films made of polyphenylenevinylene (PPV)‐type oligomers with terminal alkoxy groups are investigated and compared in a combined experimental and theoretical approach. Interchromophore interactions and host‐guest interactions are elucidated by UV/Vis and Raman spectroscopy. The impact of the local environment of the chromophore on the optical and photophysical properties is discussed in light of quantum‐chemical calculations. In stark contrast to thin films where preferential side‐by‐side orientation leads to quenching of photoluminescence (PL) via non‐emissive traps, the ICs are found to be attractive materials for opto‐electronic applications: they offer high chromophore concentrations, but at the same time behave as quasi‐isolated entities of tightly packed, well‐oriented objects with high PL quantum yields and the possibility of color tuning.     

Preparation and characterization of thin film ZnCuTe semiconductors

Thin film p-ZnCuTe semiconductors were synthesized by electrodeposition. The deposition mechanism was investigated by cyclic voltammetry; formation of the ternary compound, having the cubic structure of ZnTe, was obtained upon annealing at 400°C, as confirmed by X-ray diffraction. A direct energy gap of 1.7 eV was determined by optical absorption experiments. Measurements of the Hall effect and Van der Pauw conductivity showed an increase of carrier concentration and a decrease of sheet resistance for ZnCuTe with respect to zinc telluride.