Search Results (6,405)

Background: Vitamin K antagonist oral anticoagulant (VKA) therapy, using warfarin or acenocoumarol in our health system, is indicated, according to clinical guidelines, for the prophylaxis of thromboembolic events. In Málaga, the VKA patient management program currently includes a total of 856 patients. Hypothesis: The use of an AI-based application can enhance treatment adherence among VKA patients participating in self-monitoring and self-management programs. Furthermore, it can support the comprehensive implementation of the system, leading to reduced costs and fewer interventions for anticoagulated patients. Methods: The study will be conducted in several phases. The first phase involves the development of the application and the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second phase includes preliminary testing and validation of the developed application. The third phase consists of full implementation, along with an assessment of user-identified needs and potential quality improvements. Expected Results: The implementation of the AIto-Control app is expected to reduce healthcare-related costs by decreasing primary care visits and hospital admissions due to thromboembolic or bleeding events. Additionally, it aims to ease the workload on both primary care and hospital services. These outcomes will be achieved through the involvement of advanced practice nurses who will supervise app-based monitoring and patient education. Full article

The expansion of conventional agricultural models in the Colombian Amazon has caused deforestation, biodiversity loss, and socio-environmental degradation. In response, agroecology and bioeconomy are emerging as key strategies to regenerate landscapes and foster sustainable production systems. We evaluated the agroecological performance of 25 farms in the Andean–Amazon transition zone of Colombia using FAO’s Tool for Agroecology Performance Evaluation (TAPE). The analysis included land cover dynamics (2002–2024), characterization of the agroecological transition based on the 10 Elements of Agroecology, and 23 economic, environmental, and social indicators. Four farm typologies were identified; among them, Mixed Family Farms (MFF) achieved the highest transition score (CAET = 60.5%) and excelled in crop diversity (64%), soil health (SHI = 4.24), productive autonomy (VA/GVP = 0.69), and household empowerment (FMEF= 85%). Correlation analyses showed strong links between agroecological practices, economic efficiency, and social cohesion. Land cover dynamics revealed a continuous decline in forest cover (12.9% in 2002 to 7.1% in 2024) and an increase in secondary vegetation, underscoring the urgent need for restorative approaches. Overall, farms further along the agroecological transition were more productive, autonomous, and socially cohesive, strengthening territorial resilience. The application of TAPE proved robust multidimensional evidence to support agroecological monitoring and decision-making, with direct implications for land use planning, rural development strategies, and sustainability policies in the Amazon. At the same time, its sensitivity to high baseline biodiversity and to the complex socio-ecological dynamics of the Colombian Amazon underscores the need to refine the methodology in future applications. By addressing these challenges, the study contributes to the broader international debate on agroecological transitions, offering insights relevant for other tropical frontiers and biodiversity-rich regions facing similar pressures. Full article

This study presents a comprehensive Quantitative Microbial Risk Assessment (QMRA) for Escherichia coli in northeastern Greece’s riverine and deltaic aquatic systems, evaluating potential human health risks from recreational water exposure. The analysis integrates seasonal microbiological monitoring data—E. coli, total coliforms, enterococci, Salmonella spp., Clostridium perfringens (spores and vegetative forms), and physicochemical parameters (e.g., pH, temperature, BOD₅)—across multiple sites. A beta-Poisson dose–response model within a Monte Carlo simulation framework (10,000 iterations) was applied to five exposure scenarios, simulating varying ingestion volumes for different population groups. Median annual infection risks ranged from negligible to high, with several locations (e.g., Mandra River, Konsynthos South, and Delta Evros) surpassing the World Health Organization (WHO)’s benchmark of 10⁻⁴ infections per person per year. A Gradient Boosting Regressor (GBR) model was developed to enhance predictive capacity, demonstrating superior accuracy metrics. Permutation Importance analysis identified enterococci, total coliforms, BOD₅, temperature, pH, and seasons as critical predictors of E. coli concentrations. Additionally, sensitivity analysis highlighted the dominant role of ingestion volume and E. coli levels across all scenarios and sites. These findings support the integration of ML-based tools and probabilistic modelling in water quality risk governance, enabling proactive public health strategies in vulnerable or high-use recreational zones. Full article

Urban trees are subjected to stressful conditions caused by anthropogenic, biotic, and abiotic factors. These stressors can cause structural changes, increasing the risks of branch failure or even complete uprooting. To mitigate the risks to people’s safety, administrators must assess and evaluate the health and structural stability of trees. Risk analysis typically takes into account environmental vulnerability and tree characteristics, assessed at a specific point in time. However, although dynamic tests play a crucial role in risk assessment in urban environments, the high cost of the sensors significantly limits their widespread application across large tree populations. For this reason, the present study aims to evaluate the effectiveness of low-cost sensors in monitoring tree dynamics. A low-cost micro-electro-mechanical systems (MEMS) sensor is tested in the laboratory and the field using a pull-and-release test, and its performance is compared with that of seismic reference accelerometers. The collected data are analyzed and compared in terms of both the frequency and time domains. To obtain reliable measurements, the accelerations must be generated by substantial dynamic excitations, such as high wind events or abrupt changes in loading conditions. The results show that the MEMS sensor has lower accuracy and higher noise compared to the seismic sensor; however, the MEMS can still identify the main peaks in the frequency domain compared to the seismic sensor, provided that the input amplitude is sufficiently high. Full article

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

This study quantifies shear and flexural stiffnesses and their changes over time to support structural health monitoring of in-service bridge superstructures across four girder types: reinforced concrete (RC) beams, prestressed concrete (PSC) girders, steel girders, and ultra-high-performance concrete (UHPC) sections, using field ambient vibration testing. A total of 20 bridges across Georgia and Iowa are assessed, involving over 100 hours of on-site data collection and traffic control strategies. Results show that field-measured natural frequencies differ from theoretical predictions by average of 30–35% for RC, and 20–25% for PSC, 15–25% for steel and 2% for UHPC, reflecting the complexity of in situ structural dynamics and challenges in estimating material properties. Site-placed RC beams showed stiffness reduction due to deterioration, whereas prefabricated PSC girders maintained consistent stiffness with predictable variations. UHPC sections exhibited the highest stiffness, reflecting superior performance. Steel girders matched theoretical values, but a span-level test revealed that deck damage can reduce frequencies undetected by localized measurements. Importantly, vibration-based measurements revealed reductions in structural stiffness that were not apparent through conventional visual inspection, particularly in RC beams. The research significance of this work lies in establishing a portfolio-based framework that enables cross-comparison of stiffness behavior across multiple girder types, providing a scalable and field-validated approach for system-level bridge health monitoring and serving as a quantitative metric to support bridge inspections and decision-making. Full article

Background/Objectives: Adequate sleep has a fundamental role in human health, mainly in cognitive and physiological functions. However, the daily demands of modern society have led to a constant pursuit of better living conditions, requiring more active hours at the expense of sleeping hours. This sleep deprivation has been associated with human health deterioration, namely an increase in Diabetes Mellitus incidence. This metabolic disease is a chronic pathology that imposes a big burden on health systems and is associated with the rise in insulin resistance. In this sense, the aim of this review is to analyze the relation between sleep deprivation and insulin resistance, emphasizing the metabolic parameters and hormones that may be involved in the subjacent mechanism. Methods: A literature review of the last 10 years was performed with specific terms related to “sleep deprivation” and “insulin resistance”. Results: Overall, the studies analyzed showed a decrease in insulin sensitivity in cases of sleep deprivation, even with different study protocols. In addition, an association between sleep deprivation and increased non-esterified fatty acids was also noticeable; however, other parameters such as cortisol, metanephrines, and normetanephrines showed no consistent results among the studies. Conclusions: This review allowed us to confirm the relationship between sleep deprivation and insulin resistance; however, despite the difficulties to monitor sleep, more research is needed to understand the related mechanisms that have not yet been clarified. Full article

The parallel global increase in obesity and Alzheimer’s disease (AD) underscores an urgent public health challenge, with converging evidence indicating that metabolic dysfunction strongly contributes to neurodegeneration. Obesity is now recognized not only as a systemic metabolic condition but also as a modifiable risk factor for AD, acting through mechanisms such as chronic low-grade inflammation, insulin resistance, and adipose tissue dysfunction. Among the molecular mediators at this interface, adipokines have emerged as pivotal regulators linking metabolic imbalance to cognitive decline. Adipokines are hormone-like proteins secreted by adipose tissue, including adiponectin, leptin, and resistin, that regulate metabolism, inflammation and can influence brain function. Resistin, frequently elevated in obesity, promotes neuroinflammation, disrupts insulin signaling, and accelerates β-amyloid (Aβ) deposition and tau pathology. Conversely, adiponectin enhances insulin sensitivity, suppresses oxidative stress, and supports mitochondrial and endothelial function, thereby exerting neuroprotective actions. The imbalance between resistin and adiponectin may shift the central nervous system toward a pro-inflammatory and metabolically compromised state that predisposes to neurodegeneration. Beyond their mechanistic relevance, adipokines hold translational promise as biomarkers for early risk stratification and therapeutic monitoring. Importantly, natural compounds, including polyphenols, alkaloids, and terpenoids, have shown the capacity to modulate adipokine signaling, restore metabolic homeostasis, and attenuate AD-related pathology in preclinical models. This positions adipokines not only as pathogenic mediators but also as therapeutic targets at the intersection of diabetes, obesity, and dementia. By integrating mechanistic, clinical, and pharmacological evidence, this review emphasizes adipokine signaling as a novel axis for intervention and highlights natural compound-based strategies as emerging therapeutic approaches in obesity-associated AD. Beyond nutraceuticals, antidiabetic agents also modulate adipokines and AD-relevant pathways. GLP-1 receptor agonists, metformin, and thiazolidinediones tend to increase adiponectin and reduce inflammatory tone, while SGLT2 and DPP-4 inhibitors exert systemic anti-inflammatory and hemodynamic benefits with emerging but still limited cognitive evidence. Together, these drug classes offer mechanistically grounded strategies to target the adipokine–inflammation–metabolism axis in obesity-associated AD. Full article

The increasing reliance on imported fish products in Kazakhstan raises concerns about the presence of fish-borne parasitic infections, particularly zoonotic helminths that pose risks to public health. This study aimed to assess the diversity and prevalence of helminths in commercially imported marine fish using both traditional and molecular diagnostic methods. A total of 670 specimens representing 17 fish species were collected from retail markets in Astana, Almaty, and Karaganda. Macroscopic inspection and muscle compression techniques were used to detect larval parasites, followed by DNA extraction and PCR amplification targeting the ITS-2, 5.8S, 18S rRNA, and mitochondrial COX gene regions. Sequencing and phylogenetic analysis confirmed the presence of cestodes (Eubothrium crassum, Hepatoxylon trichiuri, Nybelinia surmenicola), acanthocephalans (Echinorhynchus gadi), and nematodes, with a predominance of zoonotic species from the Anisakidae family, including Anisakis simplex, A. pegreffii, Pseudoterranova decipiens, and Contracaecum osculatum. The highest levels of infection were detected in Atka mackerel (97.1%), herring (96.0%), mackerel (92.0%), and blue whiting (88.1%), while the lowest rates were recorded in smelt (6.8%), flounder (10.2%), and haddock (16.0%). This is the first molecular-based survey of fish helminths in Kazakhstan and highlights the need to integrate genetic screening into food safety control systems to better protect consumers and improve parasite monitoring of imported seafood. Full article

Particulate matter (PM2.5) is a critical indicator of air quality and has significant health implications. This study presents the development and evaluation of a custom-built PM2.5 device, named the P-Tracker, designed to offer an accessible alternative to commercially available air quality monitors. This paper presents the design framework used to address the requirements of a low-cost, accessible device which meets the performance of existing commercial systems. Step-by step build instructions are provided for hardware and software development and connection to the P-tracker open access website which displays the data and interactive map. To demonstrate the performance, the P-Tracker was compared against leading consumer devices, including the AtmoTube Pro by AtmoTech Inc., Flow by Plume Labs, View Plus by Airthings, and the Smart Citizen Kit 2.1 by Fab Lab Barcelona, across four controlled tests. The tests included: (1) a controlled paper combustion test in which all devices were exposed to combustion aerosols in a sealed environment alongside the DustTrak 8530 (TSI Incorporated, Shoreview, MN, USA), used as the gold standard reference, where the P-Tracker achieved a Pearson correlation of 0.99 with DustTrak over the final measurement period; (2) an outdoor test comparing readings with a stationary reference sensor, Osiris (Turnkey Instruments Ltd., Rudheath, UK), where the P-Tracker recorded a mean PM2.5 concentration of 3.08 µg/m³, closely aligning with the Osiris measurement of 3.53 µg/m³ and achieving a Pearson correlation of 0.77; (3) a controlled indoor air quality assessment, where the P-Tracker displayed stable readings with a standard deviation of 0.11 µg/m³, comparable to the AtmoTube Pro; and (4) a real-world kitchen environment test, where the P-Tracker effectively captured fluctuations in PM2.5 levels due to cooking activities, maintaining a consistent response with the DustTrak reference. The results indicate varied degrees of agreement across devices in different conditions, with the P-Tracker demonstrating strong correlation and low error margins in high-pollution and controlled scenarios. This research underscores the potential of open-source, low-cost, custom-built air quality sensors which may be developed and deployed by communities to provide hyperlocal measurements of air pollution. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

Accurate assessment of sleep architecture is critical for diagnosing and managing sleep disorders, which significantly impact global health and well-being. While polysomnography (PSG) remains the clinical gold standard, its inherent intrusiveness, high cost, and logistical complexity limit its utility for routine or home-based monitoring. Recent advances highlight that subtle variations in respiratory dynamics, such as respiratory rate and cycle regularity, exhibit meaningful correlations with distinct sleep stages and could serve as valuable non-invasive biomarkers. In this work, we propose a framework for estimating sleep stage distribution—specifically Wake, Light (N1+N2), Deep (N3), and REM—based on respiratory audio captured over a single sleep episode. The framework comprises three principal components: (1) a segmentation module that identifies distinct respiratory cycles in respiratory sounds using a fine-tuned Transformer-based architecture; (2) a feature extraction module that derives a suite of statistical, spectral, and distributional descriptors from these segmented respiratory patterns; and (3) stage-specific regression models that predict the proportion of time spent in each sleep stage. Experiments on the public PSG-Audio dataset (287 subjects; mean 5.3 h per subject), using subject-wise cross-validation, demonstrate the efficacy of the proposed approach. The segmentation model achieved lower RMSE and MAE in predicting respiratory rate and cycle duration, outperforming classical signal-processing baselines. For sleep stage proportion prediction, the proposed method yielded favorable RMSE and MAE across all stages, with the TabPFN model consistently delivering the best results. By quantifying interpretable respiratory features and intentionally avoiding black-box end-to-end modeling, our system may support transparent, contact-free sleep monitoring using passive audio. Full article

Background/Objectives: Biofilms are key in spreading antibiotic resistance in various ecosystems. This study employed comparative genomics to examine the resistance and adaptability mechanisms of the Vibrio parahaemolyticus strain Vaw-5, isolated from a seafood market biofilm. Methods: A comparative examination of Vaw-5 and 32 publicly available V. parahaemolyticus genomes identified a distinct set of genetic resistance characteristics. Results: Unlike clinical strains, Vaw-5 lacks acquired antimicrobial resistance genes like the blaCARB and qnr variations. Instead, its resistance potential is based on chromosomal alterations, efflux pump systems (vmeAB, vcmD), and a unique repertoire of 16 strain-specific transposons, including Tn5501 and Tn5393, which are well-known vectors for antibiotic resistance gene (ARG) mobilization. Although not multidrug-resistant, Vaw-5 possesses unique genomic islands that share negligible homology with those of clinical strains, enriched with gene clusters for environmental adaptation, such as exopolysaccharide production and a fully functional Type VI Secretion System. Vaw-5 carries a distinctive plasmid with the resistance gene aac(2${}^{\prime }$)-Ia. Conclusions: Biofilm adaptation promotes structural integrity, inherent processes, and resistance above standard ARG acquisition. This study focuses on how biofilm communities in the food chain can operate as covert incubators for mobilizable resistance determinants, emphasizing the significance of ecological monitoring within a One Health paradigm to reduce possible public health hazards. Full article

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article