Search Results (283)

In this paper, an in-depth characterization of the composition of extra-virgin olive oil (EVOO) from different cultivars was performed, with the aim of obtaining the fingerprint profile of bioactive constituents and studying the oxidative stability of the samples, both by an accelerated stability test and after four months of storage at room temperature. Among the investigated cultivars, some were typical of Umbria (Central Italy), namely Moraiolo, Frantoio, and Dolce Agogia, others of Apulia (Southern Italy), Coratina, Peranzana, and Bella di Cerignola, and others were typical Spanish cultivars cultivated in Umbria (Arbequina and Arbosana). The comparison of the chemical parameters among oils from the different cultivars allowed for their discrimination by multivariate statistical analysis. Some phenolic compounds were mainly responsible for the sample group’s differentiation, with the oils from the Spanish cultivars clearly distinguished from the Umbrian and Apulian sample groups. The processing of the results by chemometric analysis during oil storage and stability tests again allowed the discrimination of the samples analyzed at different storage times. This study contributes to increasing knowledge on olive oils—chemical and nutraceutical properties from specific cultivars, particularly some less studied so far, such as the Bella di Cerignola cultivar, and their changes in their nutraceutical properties during storage. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth cycle by integrating radar remote sensing data with a phenology-informed clustering approach. The research was conducted in three wheat fields in Umbria, Italy, from 30 January to 10 June 2024, using in-field height measurements, phenological observations, and Sentinel-1 acquisitions. Backscatter variables (VH, VV, and CR) were processed using two speckle filters (Lee 7 × 7 and Refined Lee), alongside additional radar-derived parameters (entropy, anisotropy, alpha, and RVI). Fuzzy C-means clustering enabled the classification of observations into two phenological groups, supporting the development of stage-specific linear regression models. Results demonstrated high accuracy during early growth stages (tillering to stem elongation), with R² values of 0.76 (RMSE = 6.88) for Lee 7 × 7 and 0.79 (RMSE = 6.35) for Refined Lee. In later stages (booting to maturity), model performance declined, with Lee 7 × 7 outperforming Refined Lee (R² = 0.51 vs. 0.33). These findings underscore the potential of phenology-based modeling approaches to enhance crop height estimation and improve radar-driven crop monitoring. Full article

Laminosioptes cysticola (Vizioli, 1870), a tissue-dwelling mite responsible for nodular acariasis in birds, was identified from two hens reared in a rural backyard flock in Umbria, Italy. Adult mites were found in the subcutaneous tissue and on the serosal surface of various internal organs. Larval and first- and second-stage nymphal forms were observed beneath the skin and near the trachea and esophageal serosa. By comparing the existing literature with that reported in the present study, we propose a hypothetical reconstruction of the parasite’s life cycle. It is postulated that the entry of L. cysticola occurs through the cervical skin, where adults mate and larviparous females give birth to larvae. These larvae migrate into the loose connective tissues surrounding the trachea and esophagus, where they develop into nymphs. The immature forms then progress along the esophagus and trachea to reach the thoracic and abdominal cavities, colonizing the serosal surfaces of visceral organs. It remains unclear whether, or how, the mites return to the subcutaneous tissues to complete their maturation. Senescent specimens degenerate within the subcutis, where they are encased by a granulomatous inflammatory reaction that leads to the formation of characteristic calcified nodules. Full article

This study presents a GIS-based Weighted Overlay Process (WOP) for Small Hill Reservoir Best Siting (SHRBS) in the Umbria region (central Italy), with a focus on supporting regional-scale planning rather than site-specific engineering design. The WOP incorporated commonly adopted SHRBS criteria, with suitability scores defined through two approaches: Model A, based on scoring scales from the literature, and Model B, which assigns scores based on the frequency distribution of the various attributes observed in a database of over 3000 existing SHRs in the region. The comparison between the models revealed significant differences, particularly in the scores assigned to texture, precipitation, and contributing area. Models A and B, tested on the existing SHRs, indicated quite different average suitability values (2.68 and 3.30, respectively, on a 5-point scale) and only a slight agreement (weighted Cohen’s kappa Kw ≤ 0.13). Both models also showed poor agreement (Kw < 0) when compared with a third suitability model based solely on sedimentation risk, which was developed using the Sediment Delivery Ratio from the InVEST suite. This indicates that many sites considered highly suitable by models A and B were also highly susceptible to sedimentation. Given the economic and environmental implications of sedimentation, this study recommends explicitly incorporating sedimentation risk criteria into SHRBS methodologies to enhance the effectiveness of siting decisions. Full article

Here, we propose an analytical approach to simulating MammoWave, a novel apparatus for breast cancer detection using microwave imaging. The approach is built upon the theory of cylindrical waves emitted by line sources. The sample is modelled as a cylinder with an inclusion. Our results indicate that when compared with phantom measurements, our approach gives an average relative error (between the image generated through measurement with phantoms and the image generated through the analytical simulation approach) of less than 6% when considering the full frequency band of 1–9 GHz. The procedure permits the simulation of the MammoWave imaging system loaded with multilayered eccentric cylinders; thus, it can be used to obtain an insight into MammoWave’s detection capability, without having to perform either time-consuming full-wave simulations or phantom measurements. Full article

The 1992 reform of the European Common Agricultural Policy (CAP) introduced the Land Parcel Identification System (LPIS), a geodatabase of land parcels used to monitor and regulate agricultural subsidies. Traditionally, the LPIS has relied on high-resolution aerial orthophotos; however, recent advancements in very-high-resolution (VHR) satellite imagery present new opportunities to enhance its effectiveness. This study explores the feasibility of utilizing PlanetScope, a commercial VHR optical satellite constellation, to map agricultural parcels within the LPIS. A test was conducted in Umbria, Italy, integrating existing datasets with a series of PlanetScope images from 2023. A segmentation workflow was designed, employing the Normalized difference Vegetation Index (NDVI) alongside the Edge segmentation method with varying sensitivity thresholds. An accuracy evaluation based on geometric metrics, comparing detected parcels with cadastral references, revealed that a 30% scale threshold yielded the most reliable results, achieving an accuracy rate of 83.3%. The results indicate that the short revisit time of PlanetScope compensates for its lower spatial resolution compared to traditional orthophotos, allowing accurate delineation of parcels. However, challenges remain in automating parcel matching and integrating alternative methods for accuracy assessment. Further research should focus on refining segmentation parameters and optimizing PlanetScope’s temporal and spectral resolution to strengthen LPIS performance, ultimately fostering more sustainable and data-driven agricultural management. Full article

Background/Objectives: with the aim of describing how students and their teachers perceive and define their hearing and auditory experience in the classroom, we present the results of a questionnaire that examined the listening challenges faced by students and teachers at the University of Perugia and in four secondary schools in Friuli-Venezia Giulia, Italy. Methods: A survey was developed as part of the A.Ba.Co. project (Overcoming Communication Barriers). Closed or open-ended questions were used to analyze the responses of students and teachers regarding diagnosed or only perceived hearing difficulties in daily life and the quality of listening in school classes. Results: Hearing difficulties, either clinically diagnosed or only perceived, were reported by 8–9% of students. Between teachers, the reported hearing difficulties were 27.1% in high school and 12% at university (p < 0.001). The most frequent reason for less-than-optimal ease of listening in class differed between the two educational levels; 45.8% of high school students blamed it on the noise in the room compared to 18.2% of university students (p < 0.001). Inversely, 40.9% of university students connected listening difficulty with their place in class compared to 9.5% (101/1065) of high school students (p < 0.001). Conclusions: Although the minimum acoustic requirements for educational facilities have been established by the UNI 11532-2 standard, it is speculated that the majority of high school and university classrooms in Italy do not meet optimal listening conditions. Furthermore, the reasons for students’ poor listening quality appear to not be fully understood, neither by students nor by teachers. In addition to the need for greater attention to physical learning spaces (advocating the universal design principles), effective change will also need to involve a greater awareness of what the barriers to listening are and how much they influence both teaching and learning quality and effectiveness. Full article

Background/Objectives: Peristomal skin complications (PSCs) are common among patients with ostomies, significantly impacting quality of life and increasing healthcare utilization. This study aimed to evaluate the effects of the Dermamecum protocol, a risk-stratified educational intervention, on the prevention of PSCs, self-care improvements, health-related quality of life (HRQoL), and patient satisfaction over a 90-day follow-up period. Methods: This prospective cohort study included 305 patients stratified into three risk-based groups (green, yellow, and red paths) according to the Dermamecum protocol. Primary outcomes included PSC rates at 30, 60, and 90 days. Secondary outcomes included self-care scores, HRQoL, and patient satisfaction. Comparative analyses and trend assessments were performed across groups and time points. Temporal trends in PSCs were analyzed using Poisson regression. Results: Early PSC rates were 8.5% at 30 days, with late complications at 7.9% and 6.2% at 60 and 90 days, respectively. No significant differences in PSC rates were observed between paths. Self-care scores improved over time, with stability across groups and domains. HRQoL remained stable, with minor fluctuations in physical and mental components. Patient satisfaction was high across all paths. Poisson regression identified significant temporal trends in PSC rates, with higher risks at 30, 60, and 90 days compared to baseline. Age, BMI, and path assignment (lower risk for the green path) were significant predictors of PSCs. Conclusions: The Dermamecum protocol effectively maintained low PSC rates, supported self-care, and sustained HRQoL and patient satisfaction. These findings highlight the value of risk-stratified, patient-centered interventions in ostomy care. Further studies are needed to validate these results and explore long-term outcomes. Full article

Background: The present study aimed to investigate the clinical correlates of treatment response to mood stabilizers in patients with bipolar disorder (BD), with a specific focus on trait-related characteristics such as impulsivity and affective temperaments. Methods: In- and outpatients diagnosed with BD were recruited at the Section of Psychiatry of the General Hospital/University of Perugia. Socio-demographic, clinical, and current psychopathological characteristics were collected. The treatment response was retrospectively assessed using the Alda Scale. Trait characteristics were evaluated through the Barratt Impulsiveness Scale (BIS-11) and the Brief Temperament Evaluation of Memphis, Pisa, and San Diego—Münster version (briefTEMPS-M). Bivariate analyses and a general linear model were employed to analyze the correlates of treatment response to mood stabilizers. Results: Among the investigated variables, trait impulsivity showed a significant negative association with treatment response. A similar effect was observed for depressive temperament, while other affective temperaments were not significantly associated with treatment outcomes. Patients with good treatment responses exhibited higher illness duration and lower severity of BD, higher prevalence of comorbid anxiety disorders, lower diurnal variation in depressive symptoms, and lower functional impairment in autonomy and occupational domains. The main limitations of this study were represented by the small sample size, the retrospective assessment of treatment response, and the inclusion of patients from a single center. Conclusions: The present findings suggest that impulsivity and depressive temperament should be investigated as potential predictors of poor response to mood stabilizers in BD. These trait dimensions, together with other clinical markers, may serve as useful targets for patient stratification and the development of personalized treatment strategies. Full article

The interest in fish welfare within aquaculture facilities has significantly increased over the past decade, recognizing the fundamental role of animal welfare in the quality of aquaculture products. It has been shown that stress in fish can affect their health, causing pathologies and immune failures, while stress-free fish grow faster and healthier. This has prompted aquaculture farmers to adopt strategies that reduce stress, improve water quality, and optimize stocking densities, thereby enhancing fish welfare. A key area is the role of amino acids in improving fish welfare. Amino acids, such as histidine, isoleucine, leucine, and tryptophan, are essential for various physiological processes, including neurotransmitter formation, energy metabolism, and immune function. Amino acids like tryptophan, arginine, and methionine play a crucial role in mitigating the effects of stress, improving immune function, and reducing oxidative stress. In the present review, the main roles of those amino acids related to fish stress have been shown, analyzing the physiological pathways involved in the link between amino acid ingestion and metabolization and stress responses. Full article

The impact of breast cancer on public health is serious, and due to risk/benefit assessment, screening programs are usually restricted to women older than 49 years. Microwave imaging devices offer advantages such as non-ionizing radiation, low cost, and the ability to distinguish between cancerous and healthy tissues due to their electrical properties. Ensuring the safety of this technology is vital for its potential clinical application. To estimate the temperature increase in breast tissues from a microwave imaging scanner, cases of healthy, benign, and malignant breast tissues were analyzed using three digital models and adding two healthy breast models with varying densities. Virtual experiments were conducted using the Sim4Life software (version 7.2) with a system consisting of a horn antenna in transmission and a Vivaldi antenna in reception. Temperature increases were estimated based on the Specific Absorption Rate distributions computed for different configurations and frequencies. The highest temperature increase obtained in this analysis is lower than 60 μK in fibroglandular tissue or skin, depending on the frequency and breast density. The presence of a receiving antenna acting as a scatterer modifies the temperature increase, which is almost negligible. Microwave examination can be performed without harmful thermal effects due to electromagnetic field exposure. Full article

Background/Objectives: The increasing incidence of cancer during pregnancy is a growing public health concern, driven by delayed parenthood and rising maternal age. Pregnancy-associated cancer (PAC) presents complex clinical challenges, necessitating a balance between maternal cancer treatment and fetal safety. Historically considered incompatible with favorable pregnancy outcomes, evidence now suggests that pregnancy can often proceed without affecting cancer prognosis. A 2022 study in Italy provided the first population-based PAC estimates by linking cancer registries (CRs) and hospital discharge records (HDRs). This study aimed to update PAC estimates to 2019, covering 30% of the Italian population and addressing prior data limitations. Methods: A retrospective longitudinal analysis was conducted on women aged 15–49 diagnosed with malignant cancers between 2003 and 2019. Data from 21 Italian CRs were linked with HDRs to identify PAC cases, defined as obstetric hospitalizations occurring for women diagnosed with cancer in our study cohort in the period spanning from one year before to two years after a cancer diagnosis. All malignant cancers, excluding non-melanoma skin cancers, were analyzed. PAC rates were calculated per 1000 pregnancies, and trends were assessed using log-linear and JoinPoint regression models. Results: Among 131,774 women diagnosed with cancer, 6329 PAC cases were identified, with a PAC rate of 1.43 per 1000 pregnancies, consistent with global estimates. Thyroid (24.4%) and breast cancer (23.2%) were the most common. Analyzing the PAC rate by pregnancy outcome, in the period 2015–2019, this increased for both childbirths and miscarriages but decreased for voluntary terminations. Most hospitalizations (54%) occurred pre-diagnosis, peaking at diagnosis, especially for breast cancer (69%). Conclusions: PAC incidence is rising, particularly for live births and miscarriages, underscoring the need for multidisciplinary care and robust epidemiological insights to guide clinical management. Full article

The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field. Full article

Background and Objectives: HCV infection represents a main risk factor for type 2 diabetes (T2D). This real-world analysis investigated the HCV-positive (HCV+) population with a T2D co-diagnosis in Italy. Methods: From 2017 to 2021, HCV+ patients were identified from administrative databases and stratified into T2D-HCV+ and HCV+-only cohorts in the presence/absence of a T2D diagnosis. Both cohorts were further divided by treatment with direct-acting antivirals (DAAs). The subgroups were compared for demographic variables, comorbidity profiles, most frequent hospitalizations, and drug prescriptions before inclusion. A sensitivity analysis was performed on patients included after 2019, the year of widespread use of pangenotypic DAAs. Results: Considering HCV+ patients aged ≥55 years, T2D-HCV+ patients (N = 1277) were significantly (p < 0.001) older than HCV+-only (N = 6576) ones and burdened by a worse comorbidity profile (average Charlson index: 1.4 vs. 0.3, p < 0.05). Moreover, regardless of T2D presence, DAA-treated patients were older (p < 0.001) and had a worse Charlson index than the untreated ones. T2D-HCV+ patients showed tendentially higher hospitalization rates and co-medication prescriptions compared to the HCV+-only patients. After 2019, a trend towards reduced co-medication use in DAA-treated patients was noticed, especially antibiotics and cardiovascular drugs. Conclusions: The co-presence of T2D in HCV+ patients resulted in a worse clinical status, as confirmed by the more frequent requirement of hospitalizations and complex polypharmacy regimens. Full article