Pros and Cons of the Cannabinoid System in Cancer: Focus on Hematological Malignancies

The endocannabinoid system (ECS) is a composite cell-signaling system that allows endogenous cannabinoid ligands to control cell functions through the interaction with cannabinoid receptors. Modifications of the ECS might contribute to the pathogenesis of different diseases, including cancers. However, the use of these compounds as antitumor agents remains debatable. Pre-clinical experimental studies have shown that cannabinoids (CBs) might be effective for the treatment of hematological malignancies, such as leukemia and lymphoma. Specifically, CBs may activate programmed cell death mechanisms, thus blocking cancer cell growth, and may modulate both autophagy and angiogenesis. Therefore, CBs may have significant anti-tumor effects in hematologic diseases and may synergistically act with chemotherapeutic agents, possibly also reducing chemoresistance. Moreover, targeting ECS might be considered as a novel approach for the management of graft versus host disease, thus reducing some symptoms such as anorexia, cachexia, fatigue, anxiety, depression, and neuropathic pain. The aim of the present review is to collect the state of the art of CBs effects on hematological tumors, thus focusing on the essential topics that might be useful before moving into the clinical practice.     

Functional MRI changes in the central motor system in myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a multisystemic disease involving multiple organ systems including central nervous system (CNS) and muscles. Few studies have focused on the central motor system in DM1, pointing to a subclinical abnormality in the CNS. The aim of our study was to investigate patterns of cerebral activation in DM1 during a motor task using functional MRI (fMRI). Fifteen DM1 patients, aged 20 to 59 years, and 15 controls of comparable age were scanned during a self-paced sequential finger-to-thumb opposition task of their dominant right hand. Functional MRI images were analyzed using SPM99. Patients underwent clinical and genetic assessment; all subjects underwent a conventional MR study. Myotonic dystrophy type 1 patients showed greater activation than controls in bilateral sensorimotor areas and inferior parietal lobules, basal ganglia and thalami, in the ipsilateral premotor area, insula and supplementary motor area (corrected P<.05). Analysis of the interaction between disease and age showed that correlation with age was significantly greater in patients than in controls in bilateral sensorimotor areas and in contralateral parietal areas. Other clinical and MR characteristics did not correlate with fMRI. Functional changes in DM1 may represent compensatory mechanisms such as reorganization and redistribution of functional networks to compensate for ultrastructural and neurochemical changes occurring as part of the accelerated aging process.     

Theoretical-Computational Modeling of Gas-State Thermodynamics in Flexible Molecular Systems: Ionic Liquids in the Gas Phase as a Case Study

A theoretical-computational procedure based on the quasi-Gaussian entropy (QGE) theory and molecular dynamics (MD) simulations is proposed for the calculation of thermodynamic properties for molecular and supra-molecular species in the gas phase. The peculiarity of the methodology reported in this study is its ability to construct an analytical model of all the most relevant thermodynamic properties, even within a wide temperature range, based on a practically automatic sampling of the entire conformational repertoire of highly flexible systems, thereby bypassing the need for an explicit search for all possible conformers/rotamers deemed relevant. In this respect, the reliability of the presented method mainly depends on the quality of the force field used in the MD simulations and on the ability to discriminate in a physically coherent way between semi-classical and quantum degrees of freedom. The method was tested on six model systems (n-butane, n-butane, n-octanol, octadecane, 1-butyl-3-methylimidazolium hexafluorophosphate and 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic pairs), which, being experimentally characterized and already addressed by other theoretical-computational methods, were considered as particularly suitable to allow us to evaluate the method’s accuracy and efficiency, bringing out advantages and possible drawbacks. The results demonstrate that such a physically coherent yet relatively simple method can represent a further valid computational tool that is alternative and complementary to other extremely efficient computational methods, as it is particularly suited for addressing the thermodynamics of gaseous systems with a high conformational complexity over a large range of temperature.     

Ultrasensitive Detection of Cu(II) and Pb(II) Using a Water-Soluble Perylene Probe

The selective detection of metal ions in water, using sustainable detection systems, is of crescent importance for monitoring water environments and drinking water safety. One of the key elements of future chemical sciences is the use of sustainable approaches in the design of new materials. In this study, we design and synthesize a low-cost, water-soluble potassium salt of 3,4,9,10-perylene tetracarboxylic acid (PTAS), which shows a selective optical response on the addition of Cu²⁺ and Pb²⁺ ions in aqueous solutions. By using a water-soluble chromophore, the interactions with the metal ions are definitely more intimate and efficient, with respect to standard methods employing cosolvents. The detection limits of PTAS for both Cu²⁺ and Pb²⁺ are found to be 2 µM by using a simple absorbance mode, and even lower (1 μM) with NMR experiments, indicating that this analyte–probe system is sensitive enough for the detection of copper ions in drinking water and lead ions in waste water. The complexation of PTAS with both ions is supported with NMR studies, which reveal the formation of new species between PTAS and analytes. By combining a low-cost water-soluble chromophore with efficient analyte–probe interactions due to the use of aqueous solutions, the results here obtained provide a basis for designing sustainable sensing systems.     

Molecular versus Silica-Supported Iridium Water Oxidation Catalysts

A stringent comparison between two pairs of molecular/immobilized water oxidation catalysts ([Cp^*Ir(Me-pica)Cl], 1 , versus 1_SiO₂ , Me-pica=κ²-N-methyl-picolinamide; [Cp^*Ir(pysa)NO₃], 2 , versus 2_SiO₂ , pysa=κ²-pyridine-2-sulfonamide]) reveals distinctive catalytic trends. While the molecular compound 1 exhibits a substantial higher activity than the analogous immobilized system 1_SiO₂ , under all the experimental conditions explored, the contrary is found with 2 that is far less active than its immobilized counterpart 2_SiO₂ . This is explained by the unique tendency of 2 to form dimeric complexes [Cp^*Ir-(κ²-μ₂-Hpysa)(κ²-μ₂-pysa)IrCp^*], 2 a , in phosphate buffered solution at pH 7, and [Cp^*Ir-(κ²-μ₂-Hpysa)₂IrCp^*], 2 b , in water. 2 a and 2 b have been completely characterized in solution by multinuclear and multidimensional NMR spectroscopy. They have been also isolated as single crystals and their structure in solid state determined by X-Ray diffractometry. 2 a and 2 b appear to be off-cycle species, whose formation is detrimental for water oxidation activity, as indicated by the observation of a long induction period when 2 a is used as catalytic precursor. In addition, TOF versus ΔE (E−E⁰=−RT/nF ln([IO₄⁻]/[IO₃⁻]) trends for the first two runs do not overlap for catalyst 2 and TOF_max is remarkably higher in the second run upon the addition of fresh NaIO₄. In the immobilized system 2_SiO₂ the detrimental associative processes are likely inhibited leading to an activity higher than that of 2 .     

Lost in a black-box? Interpretable machine learning for assessing Italian SMEs default

Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black-box type of models. In order to overcome this drawback and maintain the high performances of black-boxes, this paper has chosen a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Networks) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm still maintaining a rich interpretation framework to support decisions.     

Protonated Porphyrins: Bifunctional Catalysts for the Metal-Free Synthesis of N-Alkyl-Oxazolidinones

The protonation of commercially available porphyrin ligands yields a class of bifunctional catalysts able to promote the synthesis of N-alkyl oxazolidinones by CO₂ cycloaddition to corresponding aziridines. The catalytic system does not require the presence of any Lewis base or additive, and shows interesting features both in terms of cost effectiveness and eco-compatibility. The metal-free methodology is active even with a low catalytic loading of 1 % mol, and the chemical stability of the protonated porphyrin allowed it to be recycled three times without any decrease in performance. In addition, a DFT study was performed in order to suggest how a simple protonated porphyrin can mediate CO₂ cycloaddition to aziridines to yield oxazolidinones.     

Engineered Green Fluorescence Proteins for Proteomics and Biomolecular Electronic Applications

The discovery of the photochromic characteristics of engineered green fluorescent proteins (GFPs) allows new proteomics and biomolecular electronic applications. In particular, photoreversibility among two distinct optical states can lead to the realization of a bio-optical high density storage memory. Here we review our recent work on an optically bistable GFP and we report the recent developments of self-assembly methods for spatial immobilization of proteins into well-definite 2D patterns.