Search Results (295)

Background: Cigarette smoking remains one of the most important preventable causes of respiratory morbidity, exerting detrimental effects even in young adults. Medical students represent a particularly relevant population, as the lifestyle habits they adopt during their training years may influence both their personal health and professional credibility. Methods: We conducted a cross-sectional analysis of 264 medical students from the University of Medicine, Pharmacology, Science and Technology of Târgu-Mures, aged 18–30 years, stratified according to smoking status, type of tobacco product used, and lifestyle characteristics (athletic vs. sedentary). Standardized spirometry was performed to assess FVC, FEV₁, FEV₁/FVC ratio, PEF, and small airway flow parameters (MEF₂₅, MEF₅₀, MEF₇₅). Statistical comparisons between groups were performed using t-tests, Mann–Whitney U tests, chi-square tests, and correlation analyses, with p < 0.05 considered statistically significant. Results: Smokers demonstrated significantly lower values for FEV₁, PEF, and MEF parameters compared with non-smokers, confirming early functional impairment of both large and small airways. Within the smoking group, users of e-cigarettes or heated tobacco products exhibited more favorable FEV₁ and small airway flow values than conventional cigarette smokers. However, differences in FVC were less pronounced. Significantly, athletes consistently outperformed their sedentary peers across all respiratory parameters, regardless of smoking status, with markedly higher FEV₁, FVC, and MEF values and a lower prevalence of obstructive patterns. Cumulative smoking exposure (pack-years) was inversely associated with small airway function, whereas higher levels of physical activity were independently linked to a pronounced protective effect. Conclusions: Even in early adulthood, smoking is related to measurable declines in lung function, particularly affecting small airway dynamics. Although alternative products may appear less harmful than conventional cigarettes, they cannot be considered risk-free. Conversely, regular physical activity demonstrated a protective association in the case–control analysis, attenuating functional decline and supporting the preservation of long-term respiratory health. These findings underscore the importance of integrated prevention strategies in medical universities, combining smoking cessation initiatives with the systematic promotion of physical activity to safeguard the health of future physicians and reinforce their role as credible health advocates. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Harmful algal blooms (HABs) represent an escalating threat in marine and coastal ecosystems, posing increasing risks to ecological balance, public health, and blue economy industries including fisheries, aquaculture, and tourism. This review examines the impact of climate change and anthropogenic pressures on the escalation of HAB occurrences, focusing especially on vulnerable regions in Mexico, which are the primary case study for this investigation. The methodological framework integrates HAB risk assessment (RA) methods found in the literature. Progress in detection and monitoring technologies—such as sensing, in situ sensor networks, and prediction tools based on machine learning—are reviewed for their roles in enhancing early-warning systems and aiding decision support. The key findings emphasize four linked aspects: (i) patterns of HAB risk in coastal zones, (ii) deficiencies and prospects in HAB-related policy development, (iii) how governance structures facilitate or hinder effective actions, and (iv) the growing usefulness of online monitoring and evaluation tools for real-time environmental observation. The results emphasize the need for coupled technological and governance solutions to reduce HAB impacts, protect marine biodiversity, and enhance the resilience of coastal communities confronting increasingly frequent and severe bloom events. Full article

Migratory birds have been implicated in the spread of diverse emerging infectious pathogens, including West Nile virus, Usutu virus, Avian influenza viruses, Salmonella, Campylobacter, antimicrobial-resistant (AMR) bacteria, and antibiotic resistance genes (ARGs). Beyond their roles as vectors and reservoirs, migratory birds are also susceptible hosts whose own health may be compromised by these infections, reflecting their dual position in the ecology of pathogens. As facilitators of pathogen transmission during their long-distance migrations, often spanning thousands of kilometres and connecting ecosystems across continents, these birds can easily cross-national borders and circumvent traditional biosecurity measures, thereby acting as primary or secondary vectors in the transmission of cross-species diseases among wildlife, livestock, and humans. Africa occupies a pivotal position in global migratory bird networks, yet comprehensive data on pathogen carriage remain limited. Gaps in knowledge of pathogen diversity constrain current surveillance systems, resulting in insufficient genomic monitoring of pathogen evolution and a weak integration of avian ecology with veterinary and human health. These limitations hinder early detection of novel pathogens and reduce the continent’s preparedness to manage outbreaks. Therefore, this review provides a holistic assessment of these challenges by consolidating existing knowledge concerning the pathogens transmitted by migratory birds in Africa, while recognizing the adverse effect of pathogens, which potentiates population decline, extinction, and ecological imbalance. It further advocates for the adoption of a comprehensive One Health-omics approach that not only strengthens surveillance and technological capacity but also prioritizes the protection of avian health as an integral component of ecosystem and public health. Full article

Fishermen operating in pelagic fisheries often experience significant barriers to medical care due to geographic isolation, harsh environmental conditions, and the absence of onboard healthcare personnel. Telemedicine offers an effective approach to overcome these limitations by enabling remote diagnosis, monitoring, and treatment through satellite-based communication systems. This review summarizes the progress and applications of telemedicine in maritime and other austere environments, focusing on technological advancements, clinical implementations, and emerging trends in artificial intelligence-driven healthcare. Evidence from pilot and retrospective studies highlights the growing use of wearable devices, telementored ultrasound, digital photography, and cloud-based monitoring systems for managing acute and chronic medical conditions at sea. The integration of machine learning and deep learning algorithms has further improved fatigue, stress, and motion detection, enhancing early risk assessment among seafarers. Despite challenges such as limited connectivity, data privacy concerns, and training requirements, the adoption of telemedicine significantly improves health outcomes, reduces emergency evacuations, and promotes occupational safety. Future directions emphasize the development of 5G-enabled Internet of Medical Things networks and predictive AI tools to establish comprehensive maritime telehealth ecosystems for fishermen in pelagic operations. Full article

Patient access to innovative care for complex conditions like schizophrenia remains limited by systemic, clinical and policy-level barriers. Cognitive impairment associated with schizophrenia (CIAS) illustrates how critical symptom domains are often overlooked despite their impact on long-term outcomes. This study examines how systemic, infrastructural and economic factors shape access to CIAS care across eight mid-sized European countries to identify shared constraints and opportunities for improvement. Semi-structured interviews were conducted with 32 healthcare professionals and 9 health policy experts. Thematic analysis identified consistent barriers across countries, including fragmented care pathways, insufficient capacity for cognitive assessment, underdeveloped community-based rehabilitation services and reimbursement structures that favour pharmacological over psychosocial interventions. Variability across countries was shaped by differences in community infrastructure, professional training and the breadth of health technology assessment perspectives applied to non-pharmacological care. Countries with stronger community infrastructure and broader reimbursement frameworks were better positioned to deliver comprehensive care. These findings highlight that structural constraints, rather than clinical uncertainty, are the primary impediments to care in complex therapeutic areas. Addressing them will require coordinated reforms that strengthen early identification, expand multidisciplinary rehabilitation capacity and align reimbursement with functional and long-term outcomes. Full article

Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose a transparent deep learning model based on the YOLOv11 structure to not only enhance lesion detection but also give the visual support of the model’s predictions. Five versions of YOLOv11—nano, small, medium, large, and extra large—were trained and tested by us on a comprehensive dataset of hematoxylin and eosin-stained histopathology slides with the inflammation, urothelial cell carcinoma (UCC), and invalid tissue categories. The YOLOv11-large variant turned out to be the best-performing model at the forefront of technology, with an accuracy of 97.09%, precision and recall of 95.47% each, and balanced accuracy of 96.60%. Besides the precision–recall curves (AUPRC: inflammation = 0.935, invalid = 0.852, UCC = 0.958), ROC-AUC curves (overall AUC = 0.972) and risk–coverage analysis (AUC = 0.994) were also used for detailed assessment of the model to confirm its steadiness and trustworthiness. The confusion matrix displayed the highest true positive rates in all classes and a few misclassifications, which mainly happened between inflammation and invalid samples, indicating a possible morphological overlap. Moreover, as supported by a low Expected Calibration Error (ECE), the model was in great calibration. YOLOv11 reaches higher performance while still being computationally efficient by incorporating advanced architectural features like the C3k2 block and C2PSA spatial attention module. This is a step towards the realization of the AI-assisted bladder cancer diagnostic system that is not only reliable and transparent but also scalable, presented in this work. Full article

Introduction: Promoting healthy habits in childhood is fundamental for fostering long-term well-being. This study aimed to develop and psychometrically validate an app-integrated instrument to assess knowledge, habits, and attitudes related to health in children aged 8–11, within the context of the MHealth intervention Healthy Jeart. Methods: A quantitative, cross-sectional design was used. An initial item pool underwent expert content validation before being administered to a sample of 623 children from primary education centers in Andalusia, Spain. Construct validity was examined through exploratory and confirmatory factor analyses. Results: The analyses supported a coherent four-factor structure comprising 21 items: (1) Use of technologies, (2) diet and growth, (3) psychological well-being, and (4) physical activity and well-being. The instrument demonstrated satisfactory model fit and internal consistency, providing a multidimensional assessment of children’s health-related behaviors. The sample was recruited from primary schools in Andalusia (Spain), which may limit the generalizability of the findings to other regions and cultural contexts. Conclusions: The validated instrument offers a reliable and efficient means of evaluating healthy habits in children aged 8–11, particularly when embedded within digital interventions such as Healthy Jeart. It represents a valuable tool for educators and pediatric nursing professionals working in school settings, enabling early identification of gaps in health literacy and supporting targeted interventions that promote holistic child well-being. Full article

In the process of determining whether a health technology should be covered by healthcare systems, patients and their representatives were initially excluded from both evaluations and decision-making. In Europe, direct dialogue between patient organisations and regulatory authorities—particularly in the pharmaceutical sector—began in the early 1990s. It was only decades later, as the high cost of medicines created new challenges, that authorities recognised the necessity of engaging with patients. Patients’ contributions to the assessment of a health technology begin with discussions about the need for the technology in question. Initially, these discussions involve the developer, and later—after research and development—regulators, HTA assessors, and payers. Given that multiple technologies may be under development, patients and their organisations often prioritise those that generate the most interest within the patient community. They can then share their perspectives with evaluators during the horizon-scanning phase. Another key contribution is the role patients play in guiding clinical research by participating in scientific advice. Finally, during the assessment and appraisal stages, various methods are used to gather their views. Full article

Background/Objectives: Young people are very susceptible to the marketing of technological devices and more frequently reach for heated tobacco products. There has been little research on how these products affect human health. The aim of the study was to assess the impact of heated tobacco use on hematological and biochemical parameters in young people. Methods: A case–control study was conducted in the years 2022–2025 among 200 healthy young individuals aged 18–30. The participants were divided into three groups, i.e., traditional cigarette smokers (DS), IQOS users (IQOS), and non-smokers (NS). Blood samples were collected from 111 subjects (38 IQOS, 28 DS and 45 NS), and morphological parameters were determined in the diagnostic laboratory at the Hospital of Brothers Hospitallers of St. John of God in Lodz. Results: Among the blood parameters analyzed, which did not follow a normal distribution, statistically significant differences in median values were identified between the NS, IQOS, and DS groups for uric acid (p < 0.01), hemoglobin (p < 0.05), mean corpuscular hemoglobin concentration (MCHC) (p < 0.05), and plateletcrit (PCT) (p < 0.01). Post hoc analysis revealed significant differences in uric acid levels between the NS and DS groups (4.3 vs. 5.2). For hemoglobin, statistically significant differences (p < 0.05) were found between the NS and IQOS groups (13.7 vs. 14.4). For MCHC, significant differences were also observed between the NS and IQOS groups (32.9 vs. 33.7). Among the multiple linear regression models, developed for variables with a normal distribution, only two models achieved an adjusted R² above 0.4. In the model predicting red blood cells (RBC) levels, the adjusted R² was 0.459. Two independent variables were significant, i.e., male sex (Beta = 0.703; p < 0.001) and DS compared to IQOS (Beta = −0.242; p < 0.01). The second model, predicting hematocrit levels, achieved an adjusted R² of 0.458. Significant effects were noted for male sex (Beta = 0.700; p < 0.001) and DS versus IQOS (Beta = −0.235; p < 0.01). Conclusions: Monitoring hematological parameters can be used as an early predictor of morbidity in IQOS users. Therefore, there is a need for long-term studies that follow users over an extended period. Full article

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites. Full article

Background: Autism spectrum disorder (ASD) and related neurodevelopmental conditions are a significant public health concern, with diagnostic delays hindering timely intervention. Traditional assessments often lead to waiting times exceeding a year. Advances in artificial intelligence (AI) and biomarker-based screening offer objective, efficient alternatives for early identification. Objective: This review synthesizes the latest evidence for AI-enabled technologies aimed at improving early ASD identification. Modalities covered include eye-tracking, acoustic analysis, video- and sensor-based behavioral screening, neuroimaging, molecular/genetic assays, electronic health record prediction, and home-based digital applications or apps. This manuscript critically evaluates their diagnostic accuracy, clinical feasibility, scalability, and implementation hurdles, while highlighting regulatory and ethical considerations. Findings: Across modalities, machine learning approaches demonstrate strong accuracy and specificity in ASD detection. Eye-tracking and voice-acoustic classifiers reliably differentiate for autistic children, while home-video analysis and Electronic Health Record (EHR)-based algorithms show promise for scalable screening. Multimodal integration significantly enhances predictive power. Several tools have received Food and Drug Administration clearance, signaling momentum for wider clinical deployment. Issues persist regarding equity, data privacy, algorithmic bias, and real-world performance. Conclusions: AI-enabled screeners and diagnostic aids have the potential to transform ASD detection and access to early intervention. Integrating these technologies into clinical workflows must safeguard equity, privacy, and clinician oversight. Ongoing longitudinal research and robust regulatory frameworks are essential to ensure these advances benefit diverse populations and deliver meaningful outcomes for children and families. Full article

Gynecologic cancers remain a major global health burden, particularly in low- and middle-income countries, with high incidence and mortality rates around 45–50%. The detection of minimal residual disease (MRD) is transforming the management of recurrence risk in gynecologic cancers through highly sensitive molecular technologies. MRD encompasses small populations of residual cancer cells or post-treatment molecular traces but remain undetectable by conventional methods. Its detection relies on circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and advanced next-generation sequencing (NGS), with ctDNA-based MRD assays having sensitivity levels between 85% and over 99%. Other technologies, such as liquid biopsies and digital PCR, are also in development. MRD status has demonstrated high predictors of recurrence and survival with positive MRD strongly associated with poor outcomes and negative MRD indicates sustained remission. However, MRD detection faces significant limitations, such as tumor heterogeneity, inconstant ctDNA levels, technical issues of false-negative results, and limited clinical accessibility. Therefore, this review presents current evidence regarding the molecular detection of MRD in gynecologic malignancies and assesses its prognostic and predictive relevance. Ultimately, MRD continuous integration into clinical practice offers a promising modality to enable early relapse detection, more precise therapeutic decision-making, and the improvement of personalized medicine access to gynecologic cancers worldwide. Full article

Major depressive disorder (MDD) represents a prevalent mental health condition characterized by prominent attentional biases, particularly toward negative stimuli. While extensive research has established the significance of negative attentional bias in depression, critical gaps remain in understanding the temporal dynamics and valence-specificity of these biases. This study employed eye-tracking technology to systematically examine the attentional processing of emotional faces (happy, fearful, sad) in MDD patients (n = 61) versus healthy controls (HC, n = 47), assessing both the initial orientation (initial gaze preference) and sustained attention (first dwell time). Key findings revealed the following: (1) while both groups showed an initial vigilance toward threatening faces (fearful/sad), only MDD patients displayed an additional attentional capture by happy faces; (2) a significant emotion main effect (F (2, 216) = 10.19, p < 0.001) indicated a stronger initial orientation to fearful versus happy faces, with Bayesian analyses (BF < 0.3) confirming the absence of group differences; and (3) no group disparities emerged in sustained attentional maintenance (all ps > 0.05). These results challenge conventional negativity-focused models by demonstrating valence-specific early-stage abnormalities in MDD, suggesting that depressive attentional dysfunction may be most pronounced during initial automatic processing rather than later strategic stages. The findings advance the theoretical understanding of attentional bias in depression while highlighting the need for stage-specific intervention approaches. Full article