Search Results (5,017)

Background: Celiac disease (CD) is a chronic, immune-mediated condition requiring lifelong adherence to a gluten-free diet. In children, CD can negatively impact not only physical health but also psychological well-being and quality of life. The burden of dietary restrictions, social limitations, and emotional stress may lead to increased anxiety and depressive symptoms. This study aims to compare the quality of life, anxiety, and depression levels in children with celiac disease to those of healthy peers without chronic illness. Methods: The research involved a total of 129 individuals aged 8–18 years (64 with celiac disease and 65 healthy volunteers) and their parents. To assess children with celiac disease and healthy children, we used a sociodemographic form that we created, along with the State-Trait Anxiety Inventory (STAI), Trait Anxiety Inventory (TAI), Children’s Depression Inventory (CDI), Pediatric Quality of Life Inventory (PedsQL), and Parent Quality of Life Inventory tests. Results: Celiac patients’ diet adherence, parental education level, and family income were found to be significantly associated with quality of life, as well as levels of depression and anxiety. (p < 0.037, p < 0.04, p < 0.004, respectively). Celiac patients had significantly lower BMI SDS (mean −0.55 ± 1.13, p < 0.001) and height SDS scores (mean −0.49 ± 1.28, p < 0.017). Key factors negatively affecting the quality of life in individuals with celiac disease were difficulty adhering to the diet and low family income levels. Conclusions: Elevated anxiety with reduced quality of life highlights the importance of integrating psychosocial support into the routine care of children with celiac disease. A holistic treatment approach that considers the psychosocial well-being of children can significantly improve their quality of life. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous monitoring and real-time data collection through interconnected medical devices such as wearable sensors and smart health monitors. These devices generate sensitive physiological data, including cardiac signals, glucose levels, and vital signs, that are integrated into electronic health records (EHRs). Machine Learning (ML) and Deep Learning (DL) techniques have shown significant potential for predictive diagnostics and decision support based on such data. However, traditional centralized ML approaches raise significant privacy concerns due to the transmission and aggregation of sensitive health information. Additionally, compliance with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), restricts centralized data sharing and analytics. To address these challenges, this study introduces FED-EHR, a privacy-preserving Federated Learning (FL) framework that enables collaborative model training on distributed EHR datasets without transferring raw data from its source. The framework is implemented using Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models and was evaluated using two publicly available clinical datasets: the UCI Breast Cancer Wisconsin (Diagnostic) dataset and the Pima Indians Diabetes dataset. The experimental results demonstrate that FED-EHR achieves a classification performance comparable to centralized learning, with ROC-AUC scores of 0.83 for the Diabetes dataset and 0.98 for the Breast Cancer dataset using MLP while preserving data privacy by ensuring data locality. These findings highlight the practical feasibility and effectiveness of applying the proposed FL approach in real-world IoMT scenarios, offering a secure, scalable, and regulation-compliant solution for intelligent healthcare analytics. Full article

Vibration-based structural health monitoring methods have been widely applied in the field of damage identification. This paper proposes an intelligent diagnostic approach that integrates a multi-scale convolutional neural network with a multi-head attention mechanism (MSCNN-MHA) for the structural health monitoring of jacket-type offshore platforms. Through numerical simulations, acceleration response signals of three-pile and four-pile jacket platforms under random wave excitation are analyzed. Damage localization studies are conducted under simulated crack and pitting corrosion cases. Unlike previous studies that often idealize damage by weakening structural parameters or removing components, this study focuses on small-scale damage forms to better reflect real engineering conditions. To verify the noise resistance of the proposed method, noise is added to the original signals for further testing. Finally, experiments are conducted on the basic structure of the jacket-type offshore platform, simulating small-scale crack and pitting damage under sinusoidal and pulse excitation, to further evaluate the applicability of the method. Compared to previous CNN and MSCNN-based approaches, the results of this study demonstrate that the MSCNN-MHA method achieves higher accuracy in identifying and locating minor damage in jacket-type offshore platforms. Full article

Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. This scoping review explores the current applications of these technologies in placental development and the diagnosis of pregnancy complications, identifying research gaps and providing recommendations for future studies. Methods: This review adhered to PRISMA-ScR guidelines. Studies were selected based on their focus on liquid biopsy or single-cell analysis in perinatal research, particularly related to placental development and pregnancy complications such as preeclampsia, preterm birth, and fetal growth restriction. A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published in the last ten years. Data extraction and thematic synthesis were performed to identify diagnostic applications, monitoring strategies, and biomarker identification. Results: Twelve studies were included, highlighting the transformative potential of liquid biopsy and single-cell analysis in perinatal research. Liquid biopsy technologies, such as cfDNA and cfRNA analysis, provided non-invasive methods for real-time monitoring of placental function and early identification of complications. Extracellular vesicles (EVs) emerged as biomarkers for conditions like preeclampsia. Single-cell RNA sequencing (scRNA-seq) revealed cellular diversity and pathways critical to placental health, offering insights into processes such as vascular remodeling and trophoblast invasion. While promising, challenges such as high costs, technical complexity, and the need for standardization limit their clinical integration. Conclusion: Liquid biopsy and single-cell analysis are revolutionizing perinatal research, offering non-invasive tools to understand and manage complications like preeclampsia. Overcoming challenges in accessibility and standardization will be key to unlocking their potential for personalized care, enabling better outcomes for mothers and children worldwide. Full article

Hand hygiene is a key measure to prevent healthcare-associated infections (HAIs), yet compliance remains low in Sub-Saharan Africa (SSA), due to limited resources, insufficient training, and behavioral challenges. Simulation-based education offers a promising approach to enhance technical and non-technical skills in safe learning environments, promoting behavioral change and patient safety. This study aimed to develop and pilot a contextually adapted hand hygiene simulation-based learning scenario for nursing students in SSA. Grounded in the Medical Research Council (MRC) Framework and Design-Based Research principles, a multidisciplinary team from European and African higher education institutions (HEIs) co-created this scenario, integrating international and regional hand hygiene guidelines. Two iterative pilot cycles were conducted with expert panels, educators, and students. Data from structured observation and post-simulation questionnaires were analyzed using descriptive statistics. The results confirm the scenario’s feasibility, relevance, and educational value. The participants rated highly the clarity of learning objectives (M = 5.0, SD = 0.0) and preparatory materials (M = 4.6, SD = 0.548), reporting increased knowledge/skills and confidence and emphasizing the importance of clear roles, structured facilitation, and real-time feedback. These findings suggest that integrating simulation in health curricula could strengthen HAI prevention and control in SSA. Further research is needed to evaluate long-term outcomes and the potential for wider implementation. Full article

The growing number of older adults with cancer confront practical and organizational limitations that hinder their ability to obtain care that is adapted to their health status, needs, expectations, and life choices. The integration into practice of evidence-based and institutional recommendations for a geriatric approach and person-centered high-quality care remains incomplete. This study uses an action research design to explore stakeholders’ perspectives of the challenges involved in translating the established care priorities into a compassionate geriatric approach in oncology and identify promising pathways to improvement. Fifty-three stakeholders participated in focus groups to create cognitive maps representing perceived relationships between concepts related to compassionate care of older adults with cancer. Combining maps results in a single model constructed in Mental Modeler software to weigh relationships and calculate concept centrality (importance in the model). The model represents stakeholders’ collective perspective of the determinants of compassionate care that need to be addressed at different decision-making levels. The results reveal pathways to improvement at systemic, organizational, practice, and societal levels. These include connecting policies on ageing and national cancer programs, addressing fragmented care through interdisciplinary teamwork, promoting person-centered care, cultivating relational proximity, and combatting ageism. Translating evidence-based practices and priority orientations into compassionate care rests on collective capacities across multiple providers to address the whole person and their unique trajectory. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

There is growing interest in the use of natural plant-derived compounds, such as polyphenols (including curcumin), flavonoids, silymarin, anthocyanins, lutein, and zeaxanthin, for the treatment of age-related macular degeneration (AMD). These substances exhibit antioxidant, anti-inflammatory, and protective effects on retinal cells, contributing to the preservation of retinal integrity by modulating the key pathogenic mechanisms of AMD, including oxidative stress, chronic inflammation, and pathological neovascularization. Consequently, they hold potential to support conventional therapeutic approaches and slow disease progression. Current studies highlight their promising role as adjunctive agents in AMD management. This literature review provides a comprehensive analysis of the potential role of the aforementioned natural plant-derived compounds in the prevention and supportive treatment of age-related macular degeneration. It also discusses their natural sources, modes of administration and supplementation, and highlights the importance of a nutrient-rich diet as a key factor in maintaining ocular health. Furthermore, the review synthesizes current scientific knowledge on the ability of natural antioxidants to slow the progression of AMD and outlines future research directions aimed at improving diagnostic methods and developing more effective preventive and therapeutic strategies. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article

Background: Tuberculosis (TB) remains a major global health challenge, including in Thailand, where both asymptomatic and symptomatic cases sustain transmission. The disease burden increases treatment complexity and mortality, requiring integrated care and coordinated policies. Methods: We developed a deterministic compartmental model to examine the transmission dynamics of TB in Thailand, incorporating both latent and active stages of infection, as well as vaccination coverage. The model was calibrated using national TB incidence data, and sensitivity analysis revealed that the TB transmission rate was the most influential parameter affecting the basic reproduction number (R₀). We evaluated the impact of several intervention strategies, including increased treatment coverage for latent and active TB infections and improved vaccination rates. Results: Our analysis indicates that among the single interventions, scaling up effective treatment for latent TB infections produced the greatest reduction in asymptomatic and symptomatic cases, while enhanced treatment for active TB cases was second most effective for reducing both asymptomatic and symptomatic cases. Importantly, our results indicate that combining multiple interventions yields significantly greater reductions in overall TB incidence than any single approach alone. Our findings suggest that a modest investment in integrated TB control can substantially reduce TB transmission and disease burden in Thailand. However, complete eradication of TB would require a comprehensive and sustained investment to achieve near-universal coverage of both preventive and curative strategies. Conclusions: TB remains a significant public health threat in Thailand. Targeted interventions and integrated strategies are key to reducing disease burden and improving treatment outcomes. Full article

Innovative and reliable structural health monitoring (SHM) is indispensable for ensuring the safety, dependability, and longevity of urban infrastructure. However, conventional methods lack full efficiency, remain labor-intensive, and are susceptible to errors, particularly in detecting subtle structural anomalies such as micro-cracks. To address this issue, this study proposes a novel deep-learning framework based on a modified Detection Transformer (DETR) architecture. The framework is enhanced by integrating a Vision Transformer (ViT) backbone and a specially designed Local Feature Extractor (LFE) module. The proposed ViT-based DETR model leverages ViT’s capability to capture global contextual information through its self-attention mechanism. The introduced LFE module significantly enhances the extraction and clarification of complex local spatial features in images. The LFE employs convolutional layers with residual connections and non-linear activations, facilitating efficient gradient propagation and reliable identification of micro-level defects. Thorough experimental validation conducted on the benchmark SDNET2018 dataset and a custom dataset of damaged bridge images demonstrates that the proposed Vision-Local Feature Detector (ViLFD) model outperforms existing approaches, including DETR variants and YOLO-based models (versions 5–9), thereby establishing a new state-of-the-art performance. The proposed model achieves superior accuracy (95.0%), precision (0.94), recall (0.93), F1-score (0.93), and mean Average Precision (mAP@0.5 = 0.89), confirming its capability to accurately and reliably detect subtle structural defects. The introduced architecture represents a significant advancement toward automated, precise, and reliable SHM solutions applicable in complex urban environments. Full article

Scorpionism is a growing public health concern in Brazil, with the Amazon region presenting the highest mortality rates but remaining understudied, especially regarding local scorpion venoms composition. This study presents the first comprehensive biochemical characterization of venoms from three Amazonian species—Tityus metuendus (TmetuV), Tityus silvestris (TsilvV), and Brotheas amazonicus (BamazV)—using an integrated approach combining Multi-Enzymatic Limited Digestion (MELD)-based bottom-up proteomics, high-resolution LC-MS/MS, chromatography, zymography, and enzymatic assays. Tityus serrulatus venom was included as a reference. Significant biochemical differences were observed: TsilvV was rich in 20–30 kDa proteins and showed strong metalloprotease activity; BamazV exhibited high molecular weight proteins and potent phospholipase A₂ (PLA₂) activity but lacked proteolytic and fibrinogenolytic activities; TmetuV showed the highest hyaluronidase activity and abundance of α-KTx neurotoxins. Zymography revealed a conserved ~45 kDa hyaluronidase in all species. Three novel components were partially characterized: BamazPLA₂ (Group III PLA₂), Tmetu1 (37-residue α-KTx), and TsilvMP_A (a metalloprotease homologous to antarease). This is the first application of MELD-based proteomics to Amazonian scorpion venoms, revealing molecular diversity and functional divergence within Tityus and Brotheas, emphasizing the need for region-specific antivenoms. These findings provide a foundation for future pharmacological studies and the discovery of bioactive peptides with therapeutic potential. Full article

Lower respiratory infections predominantly affect children under five and the elderly, with influenza viruses and respiratory syncytial viruses (including SARS-CoV-2) being the most common pathogens. The COVID-19 pandemic has posed significant global public health challenges. While vaccination remains crucial, its efficacy is limited, highlighting the need for complementary approaches to mitigate immune hyperactivation in severe COVID-19 cases. Medicinal plants like Cannabis sativa show therapeutic potential, with over 85% of SARS-CoV-2-infected patients in China receiving traditional herbal treatments. This review explores the antiviral applications of cannabis and its bioactive compounds, particularly against SARS-CoV-2, while evaluating their pharmacological and food industry potential. Cannabis contains over 100 cannabinoids, terpenes, flavonoids, and fatty acids. Cannabinoids may block viral entry, modulate immune responses (e.g., suppressing pro-inflammatory cytokines via CB2/PPARγ activation), and alleviate COVID-19-related psychological stress. There are several challenges with pharmacological and food applications of cannabinoids, including clinical validation of cannabinoids for COVID-19 treatment and optimizing cannabinoid solubility/bioavailability for functional foods. However, rising demand for health-focused products presents market opportunities. Genetic engineering to enhance cannabinoid yields and integrated pharmacological studies are needed to unlock cannabis’s full potential in drug discovery and nutraceuticals. Cannabis-derived compounds hold promise for antiviral therapies and functional ingredients, though further research is essential to ensure safety and efficacy. Full article

Fertilization strategies are pivotal in sustainable agriculture, affecting both soil health and crop quality. This study investigated the impact of a circular fertilization approach based on agro-industrial residues—specifically, a blend of sulfur bentonite and orange processing waste (RecOrgFert PLUS)—on soil physicochemical and biological properties, as well as the nutritional and nutraceutical quality of broccoli (Brassica oleracea var. italica) grown in Mediterranean conditions (Condofuri, Southern Italy). The effects of RecOrgFert PLUS were compared with those of a synthetic NPK fertilizer, an organic fertilizer (horse manure), and an unfertilized control. Results demonstrated that RecOrgFert PLUS significantly improved soil organic carbon (3.37%), microbial biomass carbon (791 μg C g⁻¹), and key enzymatic activities, indicating enhanced soil biological functioning. Broccoli cultivated under RecOrgFert PLUS also exhibited the highest concentrations of health-promoting compounds, including total phenols (48.87 mg GAE g⁻¹), vitamin C (51.93 mg ASA 100 g⁻¹), and total proteins (82.45 mg BSA g⁻¹). This work provides novel evidence that combining elemental sulphur with orange processing waste not only restores soil fertility but also boosts the nutraceutical and nutritional value of food crops. Unlike previous studies focusing on soil or plant yield alone, this study uniquely integrates soil health indicators with bioactive compound accumulation in broccoli, highlighting the potential of circular bio-based fertilization in functional food production and Mediterranean agroecosystem sustainability. Full article

Psychological distress remains a significant public health concern, particularly among youth. With the growing integration of mobile and wearable technologies into daily life, digital phenotyping has emerged as a promising approach for early self-detection and intervention in psychological distress. Objectives: The study aims to determine how behavioral and device-derived data can be used to identify early signs of emotional distress and to develop and evaluate a prototype system that enables users to self-detect these early warning signs, ultimately supporting early intervention and improved mental health outcomes. Method: To achieve this, this study involved a multi-phase, mixed-method approach, combining literature review, system design, and user evaluation. It started with a scoping review to guide system design, followed by the design and development of a prototype system (ESFY) and a mixed-method evaluation to assess its feasibility and utility in detecting early signs of psychological distress through digital phenotyping. Results: The results demonstrate the potential of digital phenotyping to support early self-detection for psychological distress while highlighting practical considerations for future deployment. Conclusions: The findings highlight the value of integrating active and passive data streams, prioritizing transparency and user empowerment, and designing adaptable systems that respond to the diverse needs and concerns of end users. The recommendations outlined in this study serve as a foundation for the continued development of scalable, trustworthy, and effective digital mental health solutions. Full article