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The use of single-use or disposable plastic objects has massively increased during the last few decades, and plastic has become the main type of litter found in marine environments. The Adriatic Sea is seriously prone to marine litter pollution, and it collects about one-third of all the freshwater flowing into the Mediterranean, mainly via the river Po. This study investigated the type and composition of large microplastic debris collected in different sites in the Po Delta area. Visual classification was performed by relevant criteria, while chemical composition was assessed by infrared spectroscopy. The main plastic fraction is composed of polyolefin (76%), followed by polystyrene (19%). This proportion roughly matches global plastic production, rescaled after excluding plastics with negative buoyancy: all the identified compounds have a specific gravity lower than that of the seawater. Fragments (irregularly shaped debris) represent the most abundant category fraction (85%), followed by pellets, which represent roughly 10% of the total. Overall, the results provided an insight into large microplastic pollution in beach sediments in the Po delta area.

(1) Background: Self-adhesive systems have been proposed for the orthodontic bonding with the intention to reduce the traditional three-component system. (2) Methods: The sample consisted of 32 extracted intact permanent premolars randomly divided into two groups (n = 16). In Group I the metal brackets were bonded with Transbond XT Primer and Transbond XT Paste. In Group II the metal brackets were bonded with GC Ortho connect. The resin was polymerized for 20 s from two directions (mesial and occlusal) using a Bluephase light-curing unit. The shear bond strength (SBS) was measured using a universal testing machine. Immediately after SBS testing, Raman microspectrometry was performed for each sample to calculate the degree of conversion (DC). (3) Results: There was no statistically significant difference in the SBS between the two groups. A significantly higher DC (p < 0.001) value was recorded in Group II, in which the brackets were bonded with GC. Very weak or no correlation (0.01) was recorded between SBS and DC in Group I and moderate positive correlation was recorded in Group II (0.33). (4) Conclusions: No difference was found in SBS between the conventional and two-step systems used in orthodontics. The two-step system demonstrated higher DC compared to the conventional system. There is a very weak or moderate correlation between DC and SBS.

The advent of precision therapies against specific gene alterations characterizing different neoplasms is revolutionizing the oncology field, opening novel treatment scenarios. However, the onset of resistance mechanisms put in place by the tumor is increasingly emerging, making the use of these drugs ineffective over time. Therefore, the search for indicators that can monitor the development of resistance mechanisms and above all ways to overcome it, is increasingly important. In this scenario, microRNAs are ideal candidate biomarkers, being crucial post-transcriptional regulators of gene expression with a well-known role in mediating mechanisms of drug resistance. Moreover, as microRNAs are stable molecules, easily detectable in tissues and biofluids, they are the ideal candidate biomarker to identify patients with primary resistance to a specific targeted therapy and those who have developed acquired resistance. The aim of this review is to summarize the major studies that have investigated the role of microRNAs as mediators of resistance to targeted therapies currently in use in gastro-intestinal neoplasms, namely anti-EGFR, anti-HER2 and anti-VEGF antibodies, small-molecule tyrosine kinase inhibitors and immune checkpoint inhibitors. For every microRNA and microRNA signature analyzed, the putative mechanisms underlying drug resistance were outlined and the potential to be translated in clinical practice was evaluated.

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic.