Search Results (2,373)

Background/Objectives: Ganoderma lucidum (Curtis) P. Karst. (commonly known as reishi mushroom), a well-characterized medicinal fungus, contains diverse bioactive metabolites. This study aimed to fractionate, characterize and identify the biologically active inhibitors present in G. lucidum and to evaluate their antioxidant, antimicrobial, and antidiabetic activities. Methods: The ethanol extract of G. lucidum was fractionated using column chromatography, yielding ten distinct fractions (designated as A, B, E, F, K, L, M, N, O, and P based on their elution order and visual characteristics). Liquid Chromatography–Mass Spectrometry (LC-MS) analysis identified 46 bioactive compounds, including terpenoids, alkaloids, flavonoids, and polysaccharides. Results: Among the fractions, Fraction L exhibited the strongest antioxidant activity, with an IC₅₀ of 1.59 mg/mL. Fraction O displayed significant antibacterial activity against Escherichia coli ATCC 25922 (24.4 ± 0.238 mm), Klebsiella pneumoniae ATCC 13883 (20.5 ± 0.035 mm), Bacillus subtilis ATCC 6633 (8 ± 0.176 mm), and Staphylococcus warneri ATCC 10209 (20 ± 0.080 mm). Regarding antidiabetic activity, Fraction B demonstrated the strongest inhibition of α-amylase (IC₅₀ 1.69 ± 0.03 mg/mL), while Fraction E showed the strongest α-glucosidase inhibition (IC₅₀ = 1.69 ± 0.02 mg/mL), demonstrating reciprocal selectivity between enzyme targets. Conclusions: These results establish that chromatographic fractionation concentrates specific bioactivities into distinct fractions, supporting its potential for the development of novel therapeutic agents with enhanced specificity and efficacy. Full article

Purpose: The purpose of this preliminary study was to investigate the prevalence of Charles Bonnet Syndrome (CBS) among patients attending the National Centre of Service and Research for the Prevention of Blindness and Vision Rehabilitation of the Visually Impaired, Rome, Italy. Furthermore, the research aimed to delineate the psychological profile of these individuals to determine whether significant differences exist compared with visually impaired patients who do not experience hallucinatory phenomena and to identify likely predictors. Methods: A preliminary cross-sectional analysis was conducted on a convenience sample of patients recruited between January 2025 and December 2025. Prevalence was calculated based on structured clinical interviews, while the psychological profile was assessed by comparing the CBS group with a control group (non-CBS) matched for visual acuity. Participants underwent comprehensive ophthalmological and psychological assessments, including best-corrected visual acuity (BCVA), reading acuity (RA), contrast sensitivity (CS), fixation stability, and retinal sensitivity (RS). Psychological status was evaluated using the Symptom Check List-90-Revised (SCL-90-R), the Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder Questionnaire (GAD-7). Patients experiencing CBS were further interviewed regarding the specific characteristics and patterns of their hallucinations. The association between CBS and both psychological profiles and visual function parameters was evaluated using regression analysis. Results: Out of 385 individuals screened, 120 participants (58% women; mean age 55.4 ± 18.8 years) were included; CBS was detected in 19%. No significant differences were observed between participants with and without CBS in demographic variables or psychological questionnaire scores (p > 0.05). Mean SCL-90-R, PHQ-9, and GAD-7 scores indicated mild psychological distress, depression, and anxiety, with no significant group differences (p > 0.05). Using standard cut-off values, depressive and anxiety symptoms were prevalent in 65% and 88% of participants, respectively, but were not significantly associated with CBS in chi-square or logistic regression analyses (p > 0.05). Logistic regression analysis of SCL-90 scores showed that only anxiety was significantly associated with hallucination occurrence among the visually impaired participants (OR = 0.27; 95% CI = 0.08–0.87; p < 0.05). Among the visual function parameters, poorer RA in the worse eye was significantly associated with CBS (p < 0.05). Conclusions: This study confirms that CBS is a prevalent, yet frequently under-reported, condition within rehabilitation settings. While overall visual function did not differ significantly between patients with and without CBS, reduced reading acuity (RA) in the worse eye emerged as a potential specific risk factor. Characterizing the psychological profile of these patients is essential to differentiate the syndrome from psychiatric disorders and to develop tailored support pathways. Despite its preliminary nature, this research underscores the necessity of systematic screening to enhance clinical management and the emotional well-being of visually impaired individuals. Consequently, integrating psychological support into visual rehabilitation programs is vital to addressing the high prevalence of comorbid anxiety and depression. Full article

The protection of landscape and biological diversity on small Mediterranean islands represents a significant challenge in the context of intensive anthropogenic pressure and land-use change. The aim of this study was to determine the composition of ornamental flora in private gardens on the island of Koločep (IPA, Natura 2000 site), the smallest inhabited island in the Croatian part of the Adriatic, with special emphasis on invasive (IAS) and potentially invasive (PIAS) plant species, and to analyse their relationship with landscape changes and property types. A total of 161 private gardens were analysed, representing all private gardens on the island. In total, 2095 plant records corresponded to 255 unique horticultural taxa from 82 families. Allochthonous species dominate in the gardens (73%). Private gardens represent the primary pathway for the introduction of IAS and PIAS taxa on the island. The taxa with the highest invasion intensity were Ailanthus altissima and Carpobrotus edulis, while among PIAS species, high invasive potential was observed for Mirabilis jalapa and Diospyros virginiana. The study highlights the need for systematic monitoring of ornamental flora and landscape transformation, and the promotion of horticultural practices focused on autochthonous species in gardens, in order to preserve island biological and landscape diversity. Full article

Background/Objectives: Maternal postpartum depressive symptoms and the COVID-19 pandemic have both been identified as potential risk factors for socioemotional difficulties in children. This study aimed to assess behavioral outcomes in young children born to mothers previously screened for postpartum depressive symptoms, comparing cohorts evaluated during and after the pandemic using the Child Behavior Checklist (CBCL 1½–5). Methods: An observational follow-up cohort study was conducted on 52 mother–child dyads derived from a previously established maternal cohort screened with the Edinburgh Postnatal Depression Scale (EPDS). Two cohorts were defined according to the child’s birth period: during-pandemic (January–April 2022) and post-pandemic (October–November 2023) groups. Behavioral outcomes were assessed using CBCL 1½–5. Group differences were tested using parametric or non-parametric methods for continuous variables and χ2 or Fisher’s exact tests for categorical variables. Exploratory regression models and sensitivity analyses were also performed. Results: Children assessed in the post-pandemic cohort showed a lower prevalence of non-normal internalizing scores than those assessed in the during-pandemic cohort, whereas externalizing outcomes and Total Problems did not significantly differ between groups. In exploratory models, a child’s age showed a near-significant association with internalizing outcomes, suggesting that developmental stage at assessment may have contributed to the observed cohort difference. Maternal SARS-CoV-2 infection at delivery was not associated with children’s behavioral outcomes. Conclusions: These findings suggest a possible difference in internalizing behavioral profiles between children assessed in during-pandemic and post-pandemic cohorts. However, this pattern should be interpreted cautiously because the cohorts differed substantially in age at follow-up, and age-related factors may have affected symptom detectability. Continued longitudinal follow-up will be important to clarify whether the observed differences persist over time. Full article

The rapid adoption of diffusion models in medical imaging has raised significant concerns regarding data privacy, especially their susceptibility to Membership Inference Attacks (MIAs). However, the privacy risks associated with diffusion models in the medical domain remain underexplored compared to natural images. In this study, we propose a novel grey-box attack framework, termed the Discrete Encoding-Based Membership Inference Attack (DEB), inspired by Denoising Diffusion Codebook Models (DDCM). DEB injects semantically meaningful noise via a discrete codebook strategy and identifies training samples by analyzing the model’s output trajectory under this discrete encoding, specifically measuring the average of intermediate predictions across selected time steps. We conduct an evaluation of MIAs across natural images and five representative datasets from the MedMNIST collection. Our experiments reveal that the susceptibility of diffusion models is highly dependent on the data modality; for instance, while certain datasets exhibit near-complete vulnerability, others like PathMNIST demonstrate strong inherent resistance to MIAs. Furthermore, DEB demonstrates superior performance compared to existing baselines (e.g., SecMI, PIA, SimA), particularly on challenging datasets. For example, DEB achieves a True Positive Rate at 1% False Positive Rate (TPR @ 1% FPR) of 60.3% on CIFAR-10, significantly outperforming the SimA baseline (35.9%). Notably, even on the highly resistant PathMNIST dataset, DEB attains a 10.2% TPR @ 1% FPR, establishing a substantial advantage over the PIA baseline (1.1%). This work provides practical insights into the privacy risks inherent in diffusion models and emphasizes that model providers should carefully assess these vulnerabilities when exposing intermediate generation APIs. Full article

Background/Objectives: Accurate and reproducible lymph node (LN) measurement is essential for oncologic staging and therapy monitoring but is subject to inter-reader variability. This study evaluated the accuracy and reproducibility of a fully automated artificial intelligence (AI)-based LN measurement workflow in contrast-enhanced chest CT, using multi-reader manual measurements as reference. Methods: Sixty thoracic LNs from seven patients were independently measured by 13 radiologists in two reading rounds. The median of all measurements served as the ground truth (GT). Automated short- and long-axis diameters were derived from fully automated 3D CNN-based segmentations. Agreement between AI and manual measurements was assessed using Friedman testing, intraclass correlation coefficients (ICCs), and concordance correlation coefficients (CCCs). Measurement stability was evaluated across repeated runs on different hardware systems. Results: A total of 2280 manual measurements were analyzed. Manual assessment showed significant inter-reader variability (p < 0.01), while intra-reader agreement was high. No significant differences were observed between AI-based measurements and the GT (all p > 0.01). Agreement was good, with CCC values of 0.86 (SAD) and 0.79 (LAD). AI-based measurements were numerically stable across hardware configurations. Conclusions: Fully automated AI-based LN measurements in chest CT scans provide strong agreement with multi-reader consensus and high numerical stability. Automated measurement may support more standardized and reproducible oncologic imaging assessment. Full article

The growing deployment of photovoltaic (PV) systems worldwide has amplified the need for efficient, non-invasive diagnostic techniques to monitor their performance and ensure long-term reliability. Infrared (IR) thermography has emerged as a powerful tool for detecting thermal anomalies such as hotspots, cell mismatches, shading effects, and degradation in PV modules under real operating conditions. This review presents a comprehensive overview of recent advancements in thermographic analysis applied to PV diagnostics. It discusses the principles of thermal imaging, imaging protocols, and data interpretation techniques, alongside common thermal defects encountered in field and laboratory settings. Furthermore, the integration of irradiance mapping, drone-assisted surveys, and AI-based image analysis is examined for enhancing detection accuracy and scalability. The review also highlights standardization challenges, environmental influences, and emerging trends in automation and predictive maintenance. By consolidating current research, this study underscores the critical role of thermography in optimizing PV performance, reducing maintenance costs, and supporting the transition to smarter, more resilient solar energy infrastructures. Full article

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES. Full article

Background and Aim: The correct identification of patients presenting with chest pain and the stratification of their risk for major adverse cardiovascular events (MACE) is essential. The aim of this study was to evaluate subjects who came to the ED for chest pain through the chest pain score, the HEART score and the TIMI risk score in order to assess their validity and prognostic accuracy and to compare their performance. Methods: Patients included in the study met the following criteria: age ≥18 years, reported atraumatic chest pain, and consent to participate in the clinical study. Subsequently, the final scores were calculated based on the information collected and a follow-up was performed to assess the occurrence of adverse cardiovascular events (MACEs) at 30 days. The MACEs considered were a composite endpoint of STEMI or NSTEMI myocardial infarction, positive coronary angiography for critical lesions, percutaneous coronary angioplasty, coronary artery bypass grafting, and death. Results: A total of 102 patients were included in the study sample, divided into 76 patients who did not develop MACEs and 26 patients who experienced MACEs. The AUC values of the ROC curves of the chest pain score, HEART score and TIMI risk score were 0.8312, 0.9757 and 0.9378 respectively. Conclusions: All three scores examined were considered excellent tools to predict the onset of MACEs in patients with chest pain at different points of clinical management, although the HEART score outperformed both the chest pain score and the TIMI risk score in terms of prognostic accuracy. Full article

Background/Objectives: Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to sustain proliferation, survive under metabolic stress, and develop therapeutic resistance. While oncogenic signaling pathways regulating cancer metabolism have been extensively studied, increasing evidence indicates that non-coding RNAs (ncRNAs) play essential roles in coordinating metabolic adaptation. This review aims to synthesize current knowledge on long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) as important but relatively less characterized regulators of cancer metabolic adaptation and discuss their potential as biomarkers and therapeutic targets. Methods: We analyzed their roles across multiple types of cancer, prioritizing studies that integrate ncRNA profiling with metabolomics and mechanistic investigations, with particular attention to their diagnostic, prognostic, and predictive value. Results: LncRNAs and circRNAs regulate major metabolic pathways, including glycolysis, mitochondrial function, glutaminolysis, lipid metabolism, and redox balance. They act through transcriptional and epigenetic mechanisms, protein scaffolding, peptide encoding, and miRNA sponging, frequently converging on key regulators such as HIF-1α, c-Myc, p53, AMPK, and mTOR. However, many reported associations remain largely correlative, with limited integration of quantitative metabolic flux analyses and insufficient validation in physiologically relevant models. Conclusions: Although lncRNAs and circRNAs constitute an important context-dependent regulatory layer linking oncogenic signaling to metabolic reprogramming, future studies should combine ncRNA perturbation with stable isotope tracing, fluxomics, spatial metabolomics, long-read sequencing, and single-cell approaches to define causal and spatially resolved metabolic functions. Such integrative strategies may improve biomarker development and support ncRNA-informed, metabolism-oriented therapeutic interventions. Full article

Agro-industrial waste-derived materials are promising candidates for short-cycle packaging applications. Here, we report a proof-of-concept for biodegradable biocomposites formulated with cassava residue (CR), sugarcane bagasse (SCB), and bacterial cellulose (BC) produced by symbiotic fermentation (SCOBY). This approach addresses the mechanical limitations typically associated with cassava starch-based matrices by introducing natural reinforcements to improve structural integrity and cohesion. A set of formulations with varying CR/BC/SCB ratios was processed and assessed through tensile and flexural testing, elongation at break, thermal analysis, and water-related behavior (sorption, absorption, and contact angle). Among the evaluated blends, formulation F1 (80% CR, 5% BC, 15% SCB) delivered the best overall balance between performance and moldability, achieving a tensile strength of 11.97 MPa and showing good dimensional stability. Biodegradability was confirmed by composting, reaching 72.74% mass loss after 84 days. Overall, BC incorporation improved matrix cohesion and enabled control of mechanical integrity and wettability in the blends, as highlighted for F1 (tensile strength 11.97 MPa; peak force 560.32 N; contact angle 65°; water absorption rate, WAR, 58.68%; sorption time 5.4 s). Given the abundance of sugarcane and cassava residues in Northeast Brazil, this low-complexity route leverages locally available feedstocks to add value to regional waste streams and support the partial replacement of synthetic polymers. Full article

The immune microenvironment critically influences bone healing, particularly in the oral cavity where inflammation and microbial biofilms can compromise regeneration. Plant-derived extracellular vesicles (PDEVs) offer a biocompatible means to modulate immune responses, and apple-derived extracellular vesicles (ADEVs) have shown antioxidant and anti-inflammatory activity, although their osteoregenerative potential remains unclear. Here, we investigate the indirect effects of ADEVs on bone regeneration by assessing how their immunomodulatory action on macrophages influences the osteogenic commitment of human dental pulp stem cells (DPSCs). ADEVs were isolated, characterized, and applied to THP-1-derived macrophages to evaluate polarization via morphology and immunofluorescence for M1 (iNOS) and M2 (ARG1) markers. Then, the extracellular vesicles (EVs) from untreated and ADEV-treated macrophages were isolated and applied to DPSCs. All EVs were efficiently internalized by both macrophages and DPSCs. Treated macrophages shifted toward an M2-like phenotype, and macrophage-derived EVs (MDEVs) promoted stem cell morphological features consistent with osteogenic activation. These findings suggest that ADEVs promote osteoregeneration indirectly by influencing macrophage polarization and modifying the osteoactive cargo of MDEVs, thereby supporting their potential in cell-free, immunomodulatory approaches for oral bone regeneration. Full article

The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector is a prototype of a new modular design for a liquid argon time-projection chamber (LArTPC), comprising a two-by-two array of four modules, each further segmented into two optically isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4-tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume—the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon antineutrino events. Full article

This study focuses on the spatial variation in seed germination characteristics of Picea glauca and P. mariana, prominent and widespread species within the Canadian boreal forest. The main objective was to determine seed germination requirements of geographically distinct seed collections of P. glauca and P. mariana. A total of 73 collections of P. glauca and 62 collections of P. mariana were selected across Canada and tested for germination under various temperatures. Base temperature (T_b) and thermal time required to reach 50% germination (TH₅₀) were derived from thermal model parameters for all seed collections. Correlation analyses between seed germination traits, geographic, and climatic variables were conducted. Base temperatures for germination of P. glauca ranged from 5.2 to 11.9 °C while P. mariana had base temperatures ranging from 6.2 to 12.8 °C, indicating a broader temperature range for the former to initiate germination. Optimal germination temperatures ranged from 15 to 20 °C for P. glauca and from 17.5 to 30 °C for P. mariana. Thermal time requirements for 50% germination were higher for P. glauca than for P. mariana, indicating that the former takes longer to germinate under the same temperature conditions. Latitudinal-related variables such as temperature of sites had a stronger influence on germination relative to precipitation or potential evaporation and affected seed viability, final germination and germination capacity of all seed sources. Seed viability was lower in northern seed collections and germination capacity was diminished at lower temperatures for both species. The results from this study can be built into models predicting shifts in boreal forest species under climate change. Full article