Ultra-high dose rate (UHDR) beams for FLASH radiotherapy present significant dosimetric challenges. Although novel approaches for decreasing or correcting ion recombination in ionization chambers are being proposed, applicability of ionimetric dosimetry to UHDR beams is still under investigation. Solid-state sensors have been recently investigated as a valuable alternative for real-time measurements, especially for relative dosimetry and beam monitoring. Among them, Silicon Carbide (SiC) represents a very promising candidate, compromising between the maturity of Silicon and the robustness of diamond. Its features allow for large area sensors and high electric fields, required to avoid ion recombination in UHDR beams. In this study, we present simulations and experimental measurements with the low energy UHDR electron beams accelerated with the ElectronFLASH machine developed by the SIT Sordina company (IT). The response of a newly developed 1 × 1 cm² SiC sensor in charge as a function of the dose-per-pulse and its radiation hardness up to a total delivered dose of 90 kGy, was investigated during a dedicated experimental campaign, which is, to our knowledge, the first characterization ever done of SiC with UHDR-pulsed beams accelerated by a dedicated ElectronFLASH LINAC. Results are encouraging and show a linear response of the SiC detector up to 2 Gy/pulse and a variation in the charge per pulse measured for a cumulative delivered dose of 90 kGy, within ±0.75%.

Immediate implant placement is a single-stage restorative approach for missing teeth widely used to overcome the ridge remodeling process occurring after dental extractions. The success of this procedure relies on opportune osseointegration in the surrounding tissues. To support this process, a multifunctional nanocomposite, to be applied in the fresh post-extraction socket, was here designed, prepared, and characterized. This formulation consists of quercetin (QRC)-loaded nanostructured lipid carriers (NLCs) entrapped in a chitosan-based solid matrix containing ciprofloxacin (CPX). QRC-NLCs were prepared by homogenization followed by high-frequency sonication, and thereafter this dispersion was trapped in a chitosan-based CPX-loaded gel, obtaining the nanocomposite powder (BioQ-CPX) by lyophilization. BioQ-CPX displayed desirable properties such as high porosity (94.1 ± 0.5%), drug amounts (2.1% QRC and 3.5% CPX). and low swelling index (100%). Moreover, the mechanism of drug release from BioQ-CPX and their ability to be accumulated in the target tissue were in vitro and ex vivo elucidated, also by applying mathematical models. When trapped into the nanocomposite, QRC stressed under UV light exposure (50 W) was shown to maintain its antioxidant power, and CPX and QRC under natural light were stable over nine months. Finally, both the measured antioxidant power and the antimicrobial and antibiofilm properties on Staphylococcus aureus demonstrated that BioQ-CPX could be a promising platform to support the single-stage dental restorative treatment.

Studying the sensory profile and chemical composition of monovarietal extra-virgin olive oils (EVOOs) is important to define and manage their quality and uniqueness. Chemical and sensory traits of olive oils from 14 minor Sicilian olive genotypes in comparison with oils from six major Sicilian and three international cultivars were analysed. Oils were extracted in 2015 from fruit of the 23 genotypes grown in an experimental orchard at a planting density of 1140 trees ha⁻¹. Fatty acid composition, phenol composition, carotenoid content and antioxidant power were determined and analysed using univariate and multivariate procedures, in particular Nocellara Etnea along with carotenoid, phenol content and good sensory attributes, producing the best quality EVOO among the genotypes in trial. These results show that some Sicilian accessions used in this study may represent valid alternatives to produce high-quality EVOOs in modern, hedgerow planting systems.

The development of efficacious means of delivering antioxidant polyphenols from natural sources for the treatment of skin diseases is of great interest for many cosmetic and pharmaceutical companies. Resveratrol (RSV) and Limonene (LIM) have been shown to possess good anti-inflammatory and antibacterial properties against Staphylococcus aureus infections responsible for many skin disorders, such as acne vulgaris. In this study, solid lipid microparticles are designed as composite vehicles capable of encapsulating a high amount of trans-RSV and enhancing its absorption through the stratum corneum. A microparticulate system based on mixture of PEGylate lipids, long-chain alcohols and LIM is able to entrap RSV in an amorphous state, increasing its half-life and avoiding inactivation due to isomerization phenomena, which represents the main drawback in topical formulations. Particles have been characterized in term of shape, size distribution and drug loading. Antimicrobial tests against S. aureus have highlighted that empty microspheres possess per se antimicrobial activity, which is enhanced by the presence of LIM, demonstrating that they can represent an interesting bactericide vehicle for RSV administration on the skin.

This study shows the frequency of seeds samples contaminated by Salmonella spp. collected randomly from local markets; on 30 black pepper sample no contaminated sample was found while Salmonella spp. was detected in 3 of 36 (8.3%) analyzed sesame samples; three different serotypes were identified: S. Montevideo, S. Stanleyville e S. Tilene. The efficacy of gamma irradiation to inactivate Salmonella Montevideo in black pepper and sesame irradiated between 1 and 5 kGy was evaluated. 3 kGy is sufficient to reduce of 3-4 log CFU/g; whereas 5 kGy have been need to reduce 5.5-6 log CFU/g for samples of black pepper and sesame. No statistically significant differences were found between black pepper and sesame.

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range $1\times {10}^{-6}$–$1\times {10}^{-4}$, while the gamma analysis results in a 90–100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters.