Search Results (8,473)

Antimicrobial resistance (AMR) in livestock production systems has emerged as a major global health concern, threatening not only animal welfare and agricultural productivity but also food safety and public health. The widespread, and often poorly regulated, use of antimicrobials for growth promotion, prophylaxis, and metaphylaxis has accelerated the emergence and dissemination of resistant bacteria and resistance genes. These elements circulate across interconnected animal, environmental, and human ecosystems, driven by mobile genetic elements and amplified through the food production chain. It is estimated that more than two-thirds of medically important antimicrobials are used in animals, and AMR could cause millions of human deaths annually by mid-century if unchecked. In some livestock systems, multidrug-resistant E. coli prevalence already exceeds half of isolates, particularly in poultry and swine in low- and middle-income countries (LMICs). This narrative review provides a comprehensive overview of the molecular epidemiology, ecological drivers, and One Health implications of AMR in food-producing animals. We highlight key zoonotic and foodborne bacterial pathogens—including Escherichia coli, Salmonella enterica, and Staphylococcus aureus—as well as underappreciated reservoirs in commensal microbiota and livestock environments. Diagnostic platforms spanning phenotypic assays, PCR, MALDI-TOF MS, whole-genome sequencing, and CRISPR-based tools are examined for their roles in AMR detection, surveillance, and resistance gene characterization. We also evaluate current antimicrobial stewardship practices, global and regional surveillance initiatives, and policy frameworks, identifying critical implementation gaps, especially in low- and middle-income countries. Emerging sectors such as aquaculture and insect farming are considered for their potential role as future AMR hotspots. Finally, we outline future directions including real-time genomic surveillance, AI-assisted resistance prediction, and integrated One Health data platforms as essential innovations to combat AMR. Mitigating the threat of AMR in animal agriculture will require coordinated scientific, regulatory, and cross-sectoral responses to ensure the long-term efficacy of antimicrobial agents for both human and veterinary medicine. Full article

Despite the close linkage between carbon markets and fossil fuel markets, minimal research has investigated their co-movement dynamics during times of heightened geopolitical instability and public health crises, including the COVID-19 pandemic, Israeli–Palestinian conflict, and the Russia–Ukraine war. Most studies use conditional mean regression models for testing linear Granger causality, which falls short in assessing time-varying causal relationships. This paper employs a time-varying Granger causality framework to examine the dynamic linkages between fossil fuel markets and carbon markets across multiple time horizons. This methodology enables the evaluation of causal relationships that evolve over time, providing deeper insights into how the carbon market interacts with traditional fossil fuel markets. The study examines causal linkages among carbon, coal, and oil prices from 2 January 2018 to 11 July 2025, using data from Wind Database. The findings reveal a short-lived yet highly significant bidirectional causality between the carbon and fossil fuel markets during the COVID-19 period, whereas a sustained and highly significant bidirectional causal relationship emerges after the onset of the Russia–Ukraine war. During the outbreak of the Israeli–Palestinian conflict, this linkage continued without major disruptions or directional shifts. Furthermore, the recursive evolution approach, based on variable sub-window sizes, detects additional evidence of significant bidirectional causal relationships among carbon, coal, and oil prices. These discoveries can serve as valuable inputs for investors and policymakers, enabling them to make informed decisions that protect their interests and ensure market stability. Additionally, coal prices showed greater persistence than oil prices in these bidirectional causal links. Full article

Background/Objectives: Effective clinical handover is a critical component of nursing care, particularly in mental health settings, where the transfer of clinical and behavioral information is essential for both patients’ and health personnel’s safety. Gamification has emerged as a promising strategy to enhance clinical education, yet few interventions have focused specifically on mental health care contexts. This study aimed to evaluate the effectiveness of a serious game designed to teach the SBAR (Situation, Background, Assessment, Recommendation) handover framework to undergraduate nursing students through a psychiatric care unit scenario. Methods: A quasi-experimental pre–post design was employed with a convenience sample of 48 nursing students from a Northern Italian university. Participants completed a test assessing their ability to organize clinical information according to the SBAR model before and after the game intervention. Students’ experience was assessed using the Player Experience Inventory. Results: A statistically significant improvement in SBAR application was observed post-intervention. The majority of students reported a positive experience across PXI domains such as Meaning, Challenge, Progress Feedback, and Enjoyment. Comparisons with a previously validated video-based nursing serious game showed a consistent overall pattern in response trends. Conclusions: The SG was an effective and engaging educational tool for improving structured handover skills in nursing students. Gamification may represent a valuable complement to traditional instruction in nursing education, especially in high-communication clinical areas such as mental health. Further research is needed to assess long-term retention and to explore more immersive formats. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

Introduction: Market access, pricing, and reimbursement of digital health technologies (DHTs) in Europe are significantly challenged by regulatory fragmentation and various assessment methodologies. Understanding the challenges and priorities of technology developers is essential for developing effective and relevant policy responses. This study explores perceived barriers and developer-driven priorities to inform the development of a harmonized health technology assessment (HTA) framework under the EDiHTA project. Methods: A mixed-methods approach was adopted, including a scoping review to identify key challenges, a survey of 20 DHT developers, and interviews and focus groups with 29 industry representatives from startups to multinational companies across 10 European countries during 2024. Results: Key challenges included a lack of transparency in reimbursement processes, fragmented HTA requirements, and misalignment between traditional evidence models and the agile development of DHTs. Developers highlighted the need to integrate real-world evidence, consider usability and implementation factors, and provide structured, lifecycle-based guidance. Financial barriers and procedural burdens were particularly significant for small and medium-sized enterprises. Conclusions: These findings highlight the need for an HTA framework that reflects the iterative nature of digital development, integrates real-world evidence, and reduces uncertainty for developers. The EDiHTA project aims to respond to these challenges by building a harmonized and flexible approach that aligns with the goals of the European HTA Regulation. Full article

This study integrates the health impacts of environmental burdens and dietary intake using disability-adjusted life years (DALYs) to inform a healthier, more sustainable Japanese diet. Climate change, air pollution, ozone depletion, photochemical oxidants, and water consumption were quantified with Life cycle Impact assessment Method based on Endpoint modeling (LIME), while eleven dietary risks were converted to DALYs using dietary risk factors. Recipes collected online on a per-serving basis were classified into staple, main, side, and soup dishes and stratified into quartiles based on a nutrient profiling system (NPS) tailored to Japanese well-consumed dishes—the Ajinomoto Group NPS (ANPS) for dishes. ANPS—a culturally adapted NPS emphasizing protein, vegetables, sodium, and saturated fatty acids—was regressed against total DALYs to test whether higher ANPS scores correspond to lower combined health impacts of environment and diet. The analysis identified dish groups and high-scoring quartiles that minimized environmental and nutrition-related DALYs, revealing practical dish combinations that balance reduced sodium and red meat with increased vegetables, seafood, and nuts. These findings demonstrate the utility of coupling nutrient profiling with life cycle assessment (LCA) and provide a scientific basis for dietary guidelines that jointly advance human and planetary health within the emerging nutritional LCA framework. Full article

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

Keratinocytes are the primary cells of the epidermis layer in our skin. They play a crucial role in maintaining skin health, responding to injuries, and counteracting disease progression. Understanding their behaviour is essential for advancing wound healing therapies, improving outcomes in regenerative medicine, and developing numerical models that accurately mimic skin deformation. To create physically representative models, it is essential to evaluate the nuanced ways in which keratinocytes deform, interact, and respond to mechanical and biochemical signals. This has prompted researchers to investigate various computational methods that capture these dynamics effectively. This review summarises the main mathematical and biomechanical modelling techniques (with particular focus on the literature published since 2010). It includes reaction–diffusion frameworks, finite element analysis, viscoelastic models, stochastic simulations, and agent-based approaches. We also highlight how machine learning is being integrated to accelerate model calibration, improve image-based analyses, and enhance predictive simulations. While these models have significantly improved our understanding of keratinocyte function, many approaches rely on idealised assumptions. These may be two-dimensional unicellular analysis, simplistic material properties, or uncoupled analyses between mechanical and biochemical factors. We discuss the need for multiscale, integrative modelling frameworks that bridge these computational and experimental approaches. A more holistic representation of keratinocyte behaviour could enhance the development of personalised therapies, improve disease modelling, and refine bioengineered skin substitutes for clinical applications. Full article

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies. Full article

Time series models are widely used to examine temporal dynamics and uncover patterns across diverse fields. A commonly employed approach for modeling such data is the (Vector) Autoregressive (AR/VAR) model, in which each variable is represented as a linear combination of its own and others’ lagged values. However, the traditional (V)AR framework relies on the key assumption of stationarity, that autoregressive coefficients remain constant over time, which is often violated in practice, especially in systems affected by structural breaks, seasonal fluctuations, or evolving causal mechanisms. To overcome this limitation, Time-Varying (Vector) Autoregressive (TV-AR/TV-VAR) models have been developed, enabling model parameters to evolve over time and thus better capturing non-stationary behavior. Conventional approaches to estimating such models, including generalized additive modeling and kernel smoothing techniques, often require strong assumptions about basis functions, which can restrict their flexibility and applicability. To address these challenges, we introduce a novel framework that leverages physics-informed neural networks (PINN) to model TV-AR/TV-VAR processes. The proposed method extends the PINN framework to time series analysis by reducing reliance on explicitly defined physical structures, thereby broadening its applicability. Its effectiveness is validated through simulations on synthetic data and an empirical study of real-world health-related time series. Full article

The hybrid work environment significantly undermines the social well-being of employees in the workplace. Existing research predominantly addresses academics’ well-being challenges without offering practical strategies to counter these issues. This study identifies strategies that higher education institutions must adopt to enhance the social well-being of their academics in hybrid work settings. It employs Demerouti’s Job Demands-Resources (JD-R) model and Baumeister and Leary’s theory of the need to belong as its theoretical framework. Using a cross-sectional qualitative approach, semi-structured interviews were guided by an interview schedule to gather data. The sample comprised 23 academics from three campuses of North-West University, and thematic analysis was utilized to analyse the data. The study revealed that growth strategies, such as training, development, and mentoring, are crucial for fostering a sense of belonging, strengthening work relationships, and helping academics connect in a hybrid work environment. Support strategies like providing peer support, management support, physical resources, effective communication, and improvements in job quality enhance academics’ social well-being in this setting. Relationship strategies, which entail organizing social events and promoting a positive organizational culture, are key to encouraging social well-being in the hybrid work environment. Additionally, reward strategies, such as recognition and direct compensation, are essential for reinforcing a sense of belonging, improving work relationships, and enhancing social connections in a hybrid work environment. Intentional, coach-oriented, sensible, and inclusive leadership is vital. The findings offer valuable insights for higher education institutions to adopt a more comprehensive approach to managing the well-being of academic employees. This highlights the need to focus not only on mental and psychological health but also on social well-being. Full article

The reclaimed water sector is poised for significant growth driven by urbanization, technological advancements, and increasing demand for alternative water sources, with an emphasis on improving treatment capacities and promoting water reuse for various applications. This study examines the challenges and market potential of reclaimed water as a strategic solution to address water scarcity in Europe, assessing the regulatory framework, associated risks, and reuse potential. A multi-phase analysis was conducted, including a review of the European directives, an analysis of water scarcity, an evaluation of wastewater reuse potential, identification of risks and technological challenges, and segmentation of the reclaimed water market across various European regions. Results highlight the significant underutilization of treated wastewater in Europe; only about 3% of urban wastewater is reused, equal to 1 billion m³/year (2.4% of effluent, <0.5% of freshwater withdrawals). Wastewater is often regarded as a pollutant rather than a resource; yet, advances in recycling and treatment technologies have increased safety and efficiency, making it a practical solution to water scarcity while strengthening climate resilience. At the strategic level, the study concludes that Europe holds strong potential for water recovery and a substantial opportunity to tackle water scarcity through innovative recovery solutions, thereby contributing to sustainability, fostering a circular economy, and promoting planetary health. Full article

The anomalous enrichment of arsenic (As) and fluoride (F) in groundwater in the oasis area at the southern margin of the Tarim Basin has become a critical environmental and sustainability challenge. It poses not only potential health risks but also profound socio-economic impacts on local communities, threatening the long-term security of water resources in arid regions. Therefore, an in-depth investigation of the hydrochemical characteristics of groundwater and the co-enrichment mechanism of As and F is essential for advancing sustainable groundwater management. In this study, 110 phreatic water samples and 50 confined water samples were collected, and mathematical and statistical methods were applied to analyze the hydrochemical characteristics, sources, and co-enrichment mechanisms of As and F. The results show that (1) the groundwater chemistry types are mainly Cl·SO₄-Na, SO₄·Cl-Na·Mg, Cl·SO₄-Na·Mg, and Cl-Na, and the chemistry is primarily controlled by evaporation and concentration processes, with additional influence from human activities and cation exchange; (2) As and F mainly originate from soils and minerals, and are released through dissolution; (3) As and F enrichment is positively correlated with pH, Na⁺, and HCO₃⁻, but negatively correlated with Ca²⁺, Mg²⁺, and SO₄²⁻, indicating that a weakly alkaline hydrochemical environment with high HCO₃⁻ and Na⁺, and low Ca²⁺ promotes their enrichment; (4) strong evaporative concentration in retention zones, combined with artificial groundwater extraction, further intensifies As and F accumulation. This study not only provides an innovative theoretical and methodological framework for exploring trace element enrichment mechanisms in groundwater under arid conditions but also delivers critical scientific evidence for developing sustainable water resource management strategies, mitigating water-related health risks, and supporting regional socio-economic resilience under global climate change. Full article

As society ages, there is a growing concern about the comfort and health of elderly people. Although populations around the world, including Slovenia, are rapidly aging, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. An increasing number of older adults live with chronic diseases, functional limitations, or frailty. In 2025, Slovenia launched the project Systemic Approach to Frailty with a Focus on Pre-Frailty for Healthy and Hight-Quality Ageing, within the European Cohesion Policy Programme 2021–2027, aiming to address frailty through multidimensional and community-based interventions. In addition to presenting the project framework, this paper provides an analytical preliminary review of existing literature, critically reflecting on research gaps in the field. The main aim of this paper is to explore the possibilities for creating healthy living environments that support the prevention and management of frailty. The project’s core innovation lies in the integration of public health principles into urban planning and design through a structured, community-based approach and the use of the Living Environmental Assessment (OBO) Tool. This tool enables urban planners, municipalities, and local communities to collaboratively evaluate and co-design living environments (e.g., optimizing walkability, green space access, barrier-free design, and social amenities) to build resilience and independence among older adults. Designing inclusive, accessible, and health-promoting environments can help to prevent frailty and improve well-being across all age groups. Full article

Bolted connections are extensively utilized in aerospace, civil, and mechanical systems for structural assembly. However, inevitable structural vibrations can induce bolt loosening, leading to preload reduction and potential structural failure. Early-stage preload degradation, particularly during initial loosening, is often undetectable by conventional monitoring methods due to limited sensitivity and poor noise resilience. To address these limitations, this study proposes an intelligent bolt preload monitoring framework that combines electromechanical impedance (EMI) signal analysis with a parallel deep learning architecture. A multiphysics-coupled model of flange joint connections is developed to reveal the nonlinear relationships between preload degradation and changes in EMI conductance spectra, specifically resonance peak shifts and amplitude attenuation. Based on this insight, a parallel convolutional neural network (P-CNN) is designed, employing dual branches with 1 × 3 and 1 × 7 convolutional kernels to extract local and global spectral features, respectively. The architecture integrates dilated convolution to expand frequency–domain receptive fields and an enhanced SENet-based channel attention mechanism to adaptively highlight informative frequency bands. Experimental validation on a flange-bolt platform demonstrates that the proposed P-CNN achieves 99.86% classification accuracy, outperforming traditional CNNs by 20.65%. Moreover, the model maintains over 95% accuracy with only 25% of the original training samples, confirming its robustness and data efficiency. The results demonstrate the feasibility and scalability of the proposed approach for real-time, small-sample, and noise-resilient structural health monitoring of bolted connections. Full article