Unveiling the Hidden Therapeutic Potential of Carnosine,a Molecule with a Multimodal Mechanism of Action: A Position Paper

Carnosine (β-alanyl-L-histidine) is a naturally occurring endogenous dipeptide and an over-the-counter food supplement with a well-demonstrated multimodal mechanism of action that includes the detoxification of reactive oxygen and nitrogen species, the down-regulation of the production of pro-inflammatory mediators, the inhibition of aberrant protein formation, and the modulation of cells in the peripheral (macrophages) and brain (microglia) immune systems. Since its discovery more than 100 years ago, a plethora of in vivo preclinical studies have been carried out; however, there is still substantial heterogeneity regarding the route of administration, the dosage, the duration of the treatment, and the animal model selected, underlining the urgent need for “coordinated/aligned” preclinical studies laying the foundations for well-defined future clinical trials. The main aim of the present position paper is to critically and concisely consider these key points and open a discussion on the possible “alignment” for future studies, with the goal of validating the full therapeutic potential of this intriguing molecule.     

GO-SWCNT Buckypapers as an Enhanced Technology for Water Decontamination from Lead

Water decontamination is an important challenge resulting from the incorrect disposal of heavy metal waste into the environment. Among the different available techniques (e.g., filtration, coagulation, precipitation, and ion-exchange), adsorption is considered the cheapest and most effective procedure for the removal of water pollutants. In the last years, several materials have been tested for the removal of heavy metals from water, including metal-organic frameworks (MOFs), single-walled carbon nanotubes (SWCNTs), and graphene oxide (GO). Nevertheless, their powder consistency, which makes the recovery and reuse after adsorption difficult, is the main drawback for these materials. More recently, SWCNT buckypapers (SWCNT BPs) have been proposed as self-standing porous membranes for filtration and adsorption processes. In this paper, the adsorption capacity and selectivity of Pb²⁺ (both from neat solutions and in the presence of other interferents) by SWCNT BPs were evaluated as a function of the increasing amount of GO used in their preparation (GO-SWCNT buckypapers). The highest adsorption capacity, 479 ± 25 mg g⁻¹, achieved for GO-SWCNT buckypapers with 75 wt.% of graphene oxide confirmed the effective application of such materials for cheap and fast water decontamination from lead.     

Copper Binding and Oligomerization Studies of the Metal Resistance Determinant CrdA from Helicobacter pylori

Within this research, the CrdA protein from Helicobacter pylori (HpCrdA), a putative copper-binding protein important for the survival of bacterium, was biophysically characterized in a solution, and its binding affinity toward copper was experimentally determined. Incubation of HpCrdA with Cu(II) ions favors the formation of the monomeric species in the solution. The modeled HpCrdA structure shows a conserved methionine-rich region, a potential binding site for Cu(I), as in the structures of similar copper-binding proteins, CopC and PcoC, from Pseudomonas syringae and from Escherichia coli, respectively. Within the conserved amino acid motif, HpCrdA contains two additional methionines and two glutamic acid residues (MMXEMPGMXXMXEM) in comparison to CopC and PcoC but lacks the canonical Cu(II) binding site (two His) since the sequence has no His residues. The methionine-rich site is in a flexible loop and can adopt different geometries for the two copper oxidation states. It could bind copper in both oxidation states (I and II), but with different binding affinities, micromolar was found for Cu(II), and less than nanomolar is proposed for Cu(I). Considering that CrdA is a periplasmic protein involved in chaperoning copper export and delivery in the H. pylori cell and that the affinity of the interaction corresponds to a middle or strong metal–protein interaction depending on the copper oxidation state, we conclude that the interaction also occurs in vivo and is physiologically relevant for H. pylori.     

Surface anchoring,polarization fields and memory states in polymer dispersed liquid crystals

We have investigated the formation and development of memory states in polymer dispersed liquid crystals induced by the application of a strong electric field. Both the optical transmittance and polarization field have been followed as functions of time. We have been able to distinguish between the contributions to the memory states arising from the surface anchoring of the liquid crystal at the droplet interface and from the electrical reorientation of the mesogenic molecules. The dependence of both residual transmittance and polarization field on temperature is reported and a simple model is proposed.     

Field Study on Mobility and Persistence of Linuron and Monolinuron in Agricultural Soil

Abstract

A field site equipped with suction cup lysimeters was installed at Treviglio (BG) to assess the migration capacity of the herbicides linuron and monolinuron from topsoil to groundwater and to verify the appearance of their relevant transformation products in soil and water samples. A constant hydraulic head was applied in order to develop water saturation conditions in the upper layers. KCl was used as a tracer to evaluate water infiltration velocity through the vertical soil profile. The constant hydraulic head accelerated infiltration rates, while herbicide concentrations reached maximum contamination because soil adsorption capacity was underdeveloped. The results indicated two main processes of pesticide transport: firstly transport due mainly to water infiltration through macropores; secondly the transport driven by matrix flow. Linuron was found to be the most mobile herbicide, while chloroanilines were found to be the major transformation products of the herbicides considered.     

An interior estimate for a nonlinear parabolic equation

In this paper the derivatives of the solution of an initial boundary value problem for a nonlinear uniformly parabolic equation in the interior with the total variation of the boundary data and the L^∞-norm of the initial condition are estimated.     

On the “projective” magnetohydrodynamics

In the projective relativity based on the de Sitter universe, Maxwell's generalized theory gives us the magnetohydrodynamics, valid on a cosmic scale, for hyperdense matter and high energies. From the new theory can be deduced as limiting cases the hydrodynamics and thermohydrodynamics, the ideal magnetohydrodynamics, the electromagnetism, and the electrohydrodynamics, invariants for the projective Fantappié group.     

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.