Search Results (515)

Background: Xanthohumol (XN), a prenylated chalcone flavonoid derived from hops (Humulus lupulus), is increasingly recognized as a highly pleiotropic natural compound. Objective: We aimed to structure XN’s mechanistic hierarchy with emerging translational relevance across disease areas. Methods: We performed a comprehensive and integrative literature review of XN for its biological and translational effects across cancer, metabolic, neurological, cardiovascular, hepatic, renal, and dermatological disorders. Results: Mechanistically, XN exerts diverse bioactivities by inhibiting major pro-oncogenic and pro-inflammatory pathways, such as NF-κB, PI3K/Akt/mTOR, STAT3, HIF-1α, and selective MAPK cascades, while activating cytoprotective signaling, such as the Nrf2/ARE and AMPK pathways. Through these coordinated actions, XN modulates redox homeostasis, mitochondrial integrity, apoptosis, autophagy, ferroptosis, and inflammatory responses. In oncology, XN demonstrates broad-spectrum anticancer activity in preclinical models by inhibiting proliferation; inducing cell cycle arrest and apoptosis; suppressing epithelial–mesenchymal transition, angiogenesis, and metastasis; and restoring chemosensitivity in resistant cancers, including breast, lung, gastric, liver, and head-and-neck carcinomas. Beyond cancer, XN exhibits multi-organ protective bioactivities through antioxidative, antimicrobial, antiviral, and anti-inflammatory activities; inhibition of ferroptosis and excitotoxicity; and preservation of mitochondrial integrity. It shows beneficial effects in preclinical models of Parkinson’s disease, Alzheimer’s disease, hepatic steatosis and fibrosis, renal ischemia–reperfusion injury, cardiovascular dysfunction, skin photoaging, and atopic dermatitis. Human subject studies demonstrate that XN is safe and well tolerated, with observed reductions in oxidative DNA damage and inflammatory cytokine release. Recent advances in micellar formulations have improved XN’s systemic bioavailability and thus its translational feasibility. Conclusions: In summary, XN is a safe, multifunctional natural compound with strong potential for modulating disease-relevant biological pathways associated with cancer, neurodegenerative diseases, metabolic disorders, and inflammatory skin conditions. Continued efforts to enhance its bioavailability and conduct rigorous clinical trials are essential to fully establish its clinical relevance in patient populations. Full article

Active methodologies place the student at the core of the teaching–learning process, with the teacher becoming a companion and guide. Among these methodologies, gamification is demonstrating great capacity to attract students and promote interest, being of particular relevance in STEM subjects. While gamification and Project-Based Learning (PBL) have been extensively studied independently, their integration into Vocational Education and Training (VET) computer science courses remains underexplored, particularly regarding approaches where students develop games themselves rather than merely incorporating game elements or playing serious games. This work presents a novel gamified PBL approach specifically designed for VET Programming education, with three distinctive features: (i) students develop a complete game based on graph theory and Object-Oriented Programming, with each student working under personalised conditions and constraints; (ii) a custom-developed software tool that simultaneously serves as a pedagogical scaffold for students to validate their solutions iteratively and as an automated evaluation platform for teachers; and (iii) empirical validation through action-research with first-year VET students, employing mixed-methods analysis including qualitative observations and descriptive quantitative comparisons. The approach was implemented with first-year Web Application Design students in the Programming subject, where students developed a game integrating graph theory algorithms, Object-Oriented Programming, and Markup Language. Despite the small sample size (10 students), qualitative observations and descriptive analysis indicated promising results, and grade distributions were comparable to those in more accessible subjects. Teacher diary observations, follow-ups, and questionnaires documented sustained engagement, peer collaboration, and strategic problem-solving throughout the project phase. These preliminary findings suggest that gamification through game development, particularly when supported by automated tools enabling personalised conditions and iterative validation, represents a promising approach for teaching and learning Programming in VET contexts. Full article

Global urbanization is driving a rising demand for sand and gravel, which has intensified riverbed mining. This threatens fluvial stability, flood safety, and ecological integrity. Although previous studies have documented localized geomorphic and hydrological impacts, systematic assessments that integrate long-term incision trends, embankment stability mechanisms, and resource optimization under multiple objectives remain limited. In this study, we investigate the Wengang section of the Fu River (Jiangxi, China), a sediment-deficient river reach subjected to decades of intensive mining. Through the application of hydrosediment analysis, hydrodynamic modeling, geotechnical–hydrological–mechanical (GHM) simulations, and a dynamic optimization model, the sustained impacts of mining are quantified, and science-based management strategies are proposed. The results indicate that extensive excavation has resulted in irreversible riverbed incision, with a net volume increase of 12.97 × 10⁶ m³ between 2003 and 2023, far exceeding the natural sediment deposition volume (0.853 × 10⁶ m³). Although the overall longitudinal profile remains stable, localized flow velocities in the primary mining area are increased by 0.22–0.39 m/s. A GHM analysis identifies a critical safe distance of 13–14 m between pit edge and embankment toe and demonstrates that wide-shallow pit morphology is associated with reduced stability risk compared to narrow-deep pits. Based on these constraints, an economic optimization model incorporating flood safety and market demand is developed, yielding an optimal extraction plan for 2024–2028 with a total volume of 4.4848 million tons and an estimated revenue of 50.03 million USD. This study provides an integrated framework for assessing mining impacts and offers actionable strategies to support sustainable sediment management in vulnerable river systems. Full article

Background/Objectives: Depressive disorder (DD) is a prevalent psychiatric condition often diagnosed through subjective self-reports, which can be time-consuming and lead to inaccurate assessments. To enhance diagnostic precision, integrating Electroencephalography (EEG) with machine learning (ML) has gained attention as an objective approach for DD diagnosis and severity assessment. Methods: We propose an interpretable EEG-based ML framework that integrates optimized functional connectivity features, including Coherence, Phase Lag Index (PLI), and Granger causality, to explore EEG-based functional connectivity patterns in individuals clinically diagnosed with depressive DD and to model symptom severity and cognitive vulnerability. The identified biomarkers provide a promising foundation for developing objective, clinically actionable decision-support tools in psychiatric care. Feature selection was performed using the Neighborhood Component Analysis (NCA) method, and biomarkers were identified through statistical tests. Results: The highest classification performance (97.66% ± 2.05%accuracy, 99.20% ± 1.10% sensitivity, 95.91% ± 4.66% specificity, 98.00% ± 1.02% f1-score, and 0.95 ± 0.48 MCC) was achieved using 21 NCA-selected features with a KNN (K = 9) classifier. The best severity assessment (r² = 0.89 ± 0.10, MSE = 3.96 ± 17.05) and cognitive impairment prediction (r² = 0.89 ± 0.06, MSE = 0.23 ± 0.45) were obtained using an ANN regressor with 20 and 17 NCA-selected features, respectively. Conclusions: Our approach outperforms previous EEG-based ML models in DD classification and severity prediction using fewer features. Notably, this is the first study to use EEG connectivity features to predict patients’ severity and cognitive impairment in DD. Coherence and PLI values from frontal and temporal pathways across the alpha, beta, and gamma sub-bands may serve as critical biomarkers for DD diagnosis, severity assessment, and prediction of cognitive impairment. Full article

Accurate upper-limb motor assessment is critical for post-stroke rehabilitation but relies on subjective clinical scales. This study proposes the Action-Aware Multimodal Wavelet Fusion Network (AMWFNet), integrating surface electromyography (sEMG) and robotic kinematics for automated Fugl-Meyer Assessment (FMA-UE)-aligned quantification. Continuous Wavelet Transform (CWT) converts heterogeneous signals into unified time-frequency scalograms. A learnable modality gating mechanism dynamically weights physiological and kinematic features, while action embeddings encode task contexts across 18 standardized reaching tasks. Validated on 40 participants (20 post-stroke, 20 healthy), AMWFNet achieved 94.68% accuracy in six-class classification, outperforming baselines by 9.17% (Random Forest: 85.51%, SVM: 85.30%, 1D-CNN: 91.21%). The lightweight architecture (1.27 M parameters, 922 ms inference) enables real-time assessment-training integration in rehabilitation robots, providing an objective, efficient solution. Full article

Background: Population aging has increased attention on the quality of life and successful aging of older adults. Objective: To examine urban–rural differences in subjective quality of life and self-rated successful aging, explore associations with psychosocial factors, and identify predictors of successful aging, including potential moderating effects of place of residence and chronic illness. Methods: A cross-sectional study was conducted among 403 adults aged ≥ 60 years in Eastern Croatia. Measures included a sociodemographic questionnaire, the Self-assessment of Successful Aging Scale (SSAS), and the Personal Wellbeing Index (PWI). Data were analyzed using nonparametric tests (Mann–Whitney U, Spearman’s correlation), linear regression, and moderation analyses. Significance was set at p < 0.05. Ethical approval was obtained (Class: 602-01/24-12/02; IRB: 2158/97-97-10-24-36). Results: Rural participants reported lower PWI scores (p = 0.005) and self-rated successful aging (p < 0.001) than urban participants. Active community involvement was positively associated with quality of life (Rho = 0.46; p < 0.001), whereas regret about missed opportunities and past actions was negatively associated (Rho = −0.20; p < 0.01). Regression analyses explained 48.3% of the variance in SSAS, with higher PWI scores being strongly associated with higher SSAS scores, and rural residence and chronic illness being associated with lower SSAS scores. Moderation analyses indicated that the association between PWI and SSAS was consistent across different environmental contexts and in the presence of illness. Conclusions: Older adults living in rural areas reported lower quality of life and self-rated successful aging compared with those in urban and suburban areas, with subjective wellbeing emerging as a key predictor. Promoting social engagement and addressing psychosocial barriers may enhance successful aging, particularly in rural populations. Findings suggest that social engagement and psychosocial support are associated with higher level of perceived successful aging, indicating potential areas for future community-based or healthcare interventions. Full article

Background/Objective: Current treatments for peripheral nerve injury (PNI) lack robust evidence to suggest complete recovery; hence, alternative therapeutics offer new opportunities to develop more effective protocols. Mushroom species and their related components are considered potential candidates for peripheral nerve repair, but their specific effects and underlying mechanisms are not fully understood. This systematic review presents the available evidence on the use of mushroom species for PNI therapy, including the bioactive components and mechanisms of action. Methodology: A comprehensive literature search in three databases (PubMed, Scopus, and Google Scholar) led to the synthesis of 11 records published between 2010 and 2024. Qualitative analysis revealed the neuroregenerative potential of four mushrooms: Amanita muscaria (n = 2), Hericium erinaceus (n = 5), Lignosus rhinocerotis (n = 3), and Flammulina velutipes (n = 1), with aqueous extracts as the most common type of ingredients used (n = 4) relative to specific components such as muscimol, polysaccharide, Erinacine S, and nerve-guided conduits (NGCs). Results: These mushroom-derived treatments enhanced the migration of Schwann cells mainly via the FGF-2 signalling and MAPK pathway. In vivo studies also revealed the ability of H. erinaceus, A. muscaria, and L. rhinocerotis to promote peripheral nerve repair and functional recovery, with evidence suggesting the role of neurotrophic factors, anti-apoptotic signalling, and pro-inflammatory substances. H. erinaceus was identified as the most promising for potential clinical applications, given the stronger evidence-based data and its relatively safer components compared to A. muscuria and other mushroom species. Conclusions: Despite presenting the potential use of mushrooms in managing PNIs, the existing approaches need to be subjected to clinical research to accelerate the development of future therapeutics and preventive measures for PNIs. Full article

Background and Objectives: This study set out to better understand how posture, spinal level, gender and muscle activation influence the biomechanical properties of the lumbar erector spinae (LES) in healthy young adults. We aimed to measure how these factors influence LES tone, stiffness, and damping using a myotonometry device. Materials and Methods: Thirty healthy young adults (14 males, 16 females; aged 20–25 years) were evaluated at bilateral L3–L5 levels in prone, unsupported sitting, and standing positions, both under relaxed conditions and during submaximal isometric lumbar extension. The myotonometer measured LES tone (Hz), stiffness (N/m), and damping (logarithmic decrement). For each outcome, a mixed-model repeated-measures ANOVA was conducted with Gender as a between-subject factor and Posture, Level, and Action (relaxed vs. contracted) as within-subject factors (Bonferroni-adjusted α = 0.0167). Results: Posture produced the most significant and consistent effects on all properties—stiffness, tone, and damping (p < 0.0167)—with sitting and standing generally increasing stiffness and tone compared to prone, and sitting showing the highest values. Gender significantly impacted stiffness and tone (p < 0.0167), with males showing higher values. Spinal level also significantly influenced damping, stiffness, and tone (all p < 0.0167), with differences more apparent in females. Significant interactions included the influence of Posture × Gender on tone and damping (p < 0.0167), and of Posture × Action on stiffness and tone (p < 0.0167), alongside a strong three-way interaction for Level × Action × Posture across all outcomes, suggesting posture-related responses depend on activation state and spinal level. Conclusions: LES biomechanical properties are strongly affected by posture and further modulated by muscle activation, gender, and spinal level. These results support the creation of posture- and gender-specific reference values and underscore the value of dynamic, posture-specific myotonometer-based assessments for paraspinal muscle evaluation and clinical planning. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

Background/Objectives: Psoriasis is a chronic, inflammatory, systemic skin disease. Although topical and systemic drugs with proven effectiveness are used in the treatment, ozone therapy is also applied as a treatment option based on clinical personal experience and with limited published knowledge. In this project, the aim was to evaluate the effectiveness of major ozone therapy in psoriasis patients together with biomarkers in serum. Methods: A total of 26 psoriasis patients and 19 healthy controls were included in the study. The disease severity was evaluated by the psoriasis area severity index score and grouped as mild, moderate/severe. Serum tumor necrosis factor alpha (TNF-α), interleukin 1-beta (IL-1β), high-sensitivity C-reactive protein (Hs-CRP), sialic acid, and Sialic acid binding Ig-like Lectin-14 (Siglec-14) levels were investigated in controls and psoriasis patients. Results: Psoriasis area severity index (PASI) score decreased significantly in psoriasis patients after ozone autohemotherapy application (p < 0.005). The values of IL-1β, sialic acid, and Siglec-14 after treatment in healthy subjects were statistically significantly higher than in psoriasis patients. It was found that Hs-CRP and Siglec-14 decreased in all patients after treatment, Hs-CRP decreased more significantly in mild psoriasis patients, and Siglec-14 decreased in both mild and moderate-severe groups (p < 0.05). Conclusions: Our research results suggest that ozone autohemotherapy has clinical efficacy in psoriasis patients, inflammation also has a role in the mechanism of action, and its effectiveness in treatment can be evaluated with inflammation markers. Full article

Chronic pain and sleep disturbances are frequently associated and profoundly affect the quality of life, creating intertwined physical, emotional, and social challenges. This narrative review synthesizes current evidence on the molecular mechanisms and pharmacological influences underlying this bidirectional relationship. Elevated pro-inflammatory cytokines (IL-1β, IL-6, IL-10, TNF-α), neurodegenerative markers (tau, β-amyloid 42), metabolic hormones, and fasting glucose have been consistently associated with both objective and subjective sleep impairments in chronic pain conditions. Pharmacological agents such as melatonin and opioids exhibit heterogeneous effects on neurophysiological pathways, reflecting differences in mechanisms of action and their modulation of biological processes. Rather than offering therapeutic recommendations, this review aims to clarify how these mediators and drugs shape the complex interplay between pain and sleep. Overall, the evidence suggests that persistent dysregulation of inflammatory, neurodegenerative, and metabolic pathways may drive the reciprocal and detrimental interaction between chronic pain and sleep disturbances, highlighting opportunities for targeted research and integrated clinical strategies. Full article

This review provides a systematic critique of the debate between two paradigms in the philosophy of mind—the Naturalist–Externalist Research Program (NERP) and the Phenomenal Intentionality Research Program (PIRP)—with particular focus on Uriah Kriegel’s reconciliation project. Following Kriegel’s view, attention is given to rational agents’ awareness of their mental states—a key issue since most current artificial intelligence systems aim to model rational thinking and action. Naturalist accounts derive mental content from brain activity and environmental interaction, emphasizing a constitutive dependence of the former on the latter. In contrast, phenomenological theories assert that the object of mental states is an internal semblance presented to the subject. Within this framework, I maintain that Kriegel attempts to naturalize mental content within the framework of a Same Order theory, but this limits his ability to demonstrate that intentionality is grounded in consciousness in the sense of the Phenomenal Intentionality Research Program. Compounding this issue, the idea that the mind arises from manipulating representations has been challenged by dynamical approaches to cognitive science, yet advanced representational models persist, often simulating phenomenological qualities through forms of internal data organization. Methodologically, the approach is primarily comparative and reconstructive, focusing on the structural tensions and theoretical commitments that distinguish NERP and PIRP. Full article

Traditional cardiopulmonary resuscitation (CPR) training faces limitations such as instructor dependency, low efficiency, and subjective assessment. To address these issues, this study proposes a novel computer vision-based method for the automation and objective evaluation of artificial respiration, shifting focus to the long-overlooked ventilation component. We developed an evaluation framework integrating human pose estimation and spatio-temporal graph convolution network (ST-GCN): first, OpenPose is utilized to extract skeletal keypoints of the rescuer, followed by action classification and recognition-including chest compressions, airway opening, and artificial breathing via a ST-GCN. Based on the American Heart Association (AHA) guidelines, this research defines and implements five quantitative metrics for ventilation quality, including CPR operation procedure, chin-frontal angle, interruption time, ventilation time, and ventilation frequency. An automated scoring model was established accordingly. Validated on a self-constructed dataset containing multi-source videos, the model achieved an accuracy of 87.64% in recognizing artificial respiration actions and 84.47% in evaluating action standardization. Experimental results demonstrate that the system can effectively and objectively evaluate the quality of artificial respiration. Compared with traditional instructor-dependent approaches, this study provides a low-cost, scalable technical solution, offering a new pathway for promoting high-quality CPR training. Full article

Background/Objectives: Diabetes mellitus is a serious global disease characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It represents a major health concern affecting millions of people worldwide. This condition can lead to severe complications significantly affecting patients’ quality of life. Due to the limitations and side effects of current therapies, the search for safer and more effective antidiabetic agents, particularly from natural sources, has gained considerable attention. This study investigates the antidiabetic potential of seaweed-derived compounds through structure-based virtual screening targeting α-glucosidase. Methods: A library of compounds derived from the Seaweed Metabolite Database was subjected to a hierarchical molecular docking protocol against α-glucosidase. Extra Precision (XP) docking was employed to identify the top-ranked ligands based on their binding affinities. Drug-likeness was assessed according to Lipinski’s Rule of Five, followed by pharmacokinetic and toxicity predictions to evaluate ADMET properties. Density Functional Theory (DFT) calculations were performed to analyze the electronic properties and chemical reactivity of the selected compounds. Furthermore, molecular dynamics simulations were carried out to examine the stability and dynamic behavior of the ligand–enzyme complexes. Results: Following XP docking and ADMET prediction, four promising compounds were selected: Colensolide A, Rhodomelol, Callophycin A, and 7-(2,3-dibromo-4,5-dihydroxybenzyl)-3,7-dihydro-1H-purine-2,6-dione. Molecular dynamics simulations further confirmed the structural stability and strong binding interactions of these compounds within the α-glucosidase active site. Conclusions: This investigation demonstrated the important role of seaweed-derived compounds in inhibiting α-glucosidase activity. Further experimental validation is warranted to confirm their biological activity and therapeutic potential. Full article

This paper investigates the behavior of PM-type polyurethane flexible joints connecting structural components. Although flexible polyurethanes are known for their energy dissipation capacity and ability to accommodate large deformations—particularly under seismic actions—research addressing their performance under shear loading remains limited. The primary objective of this work was to characterize these joints under varying levels of normal stress, identify failure modes, and estimate key mechanical parameters. Nine masonry triplet specimens, composed of concrete units and PM-type polyurethane, were subjected to shear testing using a procedure adapted from EN 1052-3. Tests were carried out at three precompression levels: 0.2, 0.6, and 1.0 N/mm². Tensile tests were further performed to calibrate material models. The results showed that increasing precompression led to higher ultimate shear loads. All specimens failed due to shear failure at the unit–flexible joint interface, with no damage observed in the masonry units. Based on linear regression following EN 1052-3, the initial shear strength was determined to be 0.729 N/mm², corresponding to a friction coefficient of 0.14. Full article