Search Results (41,282)

The ongoing emergence of New Psychoactive Substances represents a growing threat to public health, as newly synthesized compounds continuously enter the illicit drug market, evading standard detection methods and challenging regulatory frameworks. Among New Psychoactive Substances, nitazenes are potent non-fentanyl opioids associated with severe cases of intoxication. This study evaluated the genotoxic potential of metodesnitazene and etodesnitazene in the human TK6 cell line. Cells were exposed to increasing concentrations of studied compounds, with and without S9 metabolic activation system. Preliminary assessments and micronuclei frequency analyses were performed by flow cytometry in at least three independent experiments. Metodesnitazene induced an increase in micronuclei frequency starting from 12.5 μM (p < 0.05), whereas etodesnitazene induced an effect only at 50 μM. Metabolic activation increases micronuclei formation at higher concentrations of metodesnitazene 25 μM, but did not substantially affect the response to etodesnitazene. Both compounds also induced intracellular reactive oxygen species production, measured through a chemiluminescent-based bioassay, suggesting oxidative stress as a potential contributing mechanism. These findings highlight the need for compound-specific toxicological profiling to better anticipate the acute and long-term risks associated with nitazene consumption. Full article

Background: Biliary drainage is a key component of palliative management in patients with malignant biliary obstruction. In cases where endoscopic approaches are unsuccessful or cannot be performed, percutaneous transhepatic biliary drainage (PTBD) represents an established alternative for achieving biliary decompression. The C-reactive protein–albumin–lymphocyte (CALLY) index combines inflammatory, nutritional, and immune-related parameters into a single marker, while the modified Glasgow Prognostic Score (mGPS), based on C-reactive protein and albumin concentrations, reflects the systemic inflammatory status of the patient. This study aimed to evaluate the prognostic value of the preprocedural CALLY index and mGPS in predicting 30-day mortality among patients with advanced malignant biliary obstruction undergoing palliative PTBD. Methods: This single-center retrospective study was conducted in a total of 179 patients who underwent palliative PTBD for malignant biliary obstruction at Ankara Etlik City Hospital between December 2022 and June 2025. Results: The 30-day mortality rate was 25.1%. The cut-off value for CALLY was determined as 67 based on receiver operating characteristic (ROC) curve analysis, and mGPS was categorized as 0–1 versus 2. In univariable Cox regression analyses, pancreaticobiliary tumor type, mGPS = 2, and CALLY < 67 were associated with early mortality. In multivariable Cox analysis, CALLY ≥ 67 was independently associated with a reduced risk of 30-day mortality, whereas pancreaticobiliary tumor type was independently associated with an increased risk. In the CALLY–mGPS risk stratification, 30-day mortality rates were 8.0%, 13.5%, and 44.1% in the low-, intermediate-, and high-risk groups, respectively. Conclusions: In this retrospective cohort, preprocedural inflammation- and nutrition-based markers were found to be associated with early mortality in patients with malignant biliary obstruction undergoing PTBD. Accordingly, risk stratification using readily available parameters such as CALLY and mGPS appears feasible in the preprocedural setting. The CALLY–mGPS-based approach may provide a practical framework for clinical risk assessment; however, prospective multicenter validation, including tumor-specific subgroup analyses, is warranted. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Deaf people experience significant barriers to education, healthcare, employment, and information access, resulting in inequities across a myriad of contexts. To better understand these disparities, our all-deaf research team conducted semi-structured interviews with deaf and hearing (parents, caregivers, and educators) adults across Ecuador, exploring how structural, institutional, and social factors influence daily life and well-being. Participants (n = 36) described systemic exclusion from education and employment, limited access to interpreters and assistive technologies, and constrained autonomy due to insufficient family support and institutional resources. These barriers compound health risks by restricting access to care, information, and social participation. Participants’ narratives highlighted how political and economic instability, institutional neglect, and discrimination create structural vulnerabilities that extend beyond individual-level factors. Findings underscore the importance of public health interventions that address structural and communicative inequities, including inclusive education, accessible health services, and community-based support, to improve health equity and quality of life for deaf populations in Ecuador. Full article

To investigate the predictive factors for resistance to VEN combined with HMAs in the treatment of AML, construct a drug resistance prediction model, and visualize the model. A retrospective analysis was conducted on 74 AML patients. Multivariate logistic regression was used to identify independent predictors of primary resistance, based on which a nomogram model and a risk scoring system for drug resistance were constructed. The results showed that KIT (p = 0.012), TP53 (p = 0.010), and FAB-M5 (p = 0.059) were significantly associated with primary resistance to VEN. A nomogram prediction model incorporating FAB-M5, KIT, and TP53 was established. Based on the nomogram model, a drug resistance prediction scoring tool comprising three variables was developed, categorizing patients into high-risk (6–10 points), intermediate-risk (3–5 points), and low-risk (0–2 points) groups. Significant differences in NR rates were observed among the three risk groups (p < 0.001). KIT, TP53, and FAB-M5 are independent factors influencing VEN resistance. The constructed nomogram prediction model and scoring system may provide valuable references for predicting primary resistance to VEN. Full article

Pesticide-residue risk perceptions among agricultural professionals are shaped by factors that extend beyond knowledge gaps. This study examines how trust in regulatory systems and information sources jointly shape residue-related attitudes among Greek agronomists. We used principal components analysis (PCA) to identify coherent domains and then latent profile analysis (LPA) to derive person-centered profiles based on standardized component scores. Two dominant profiles emerged, differing in regulatory confidence, reliance on institutional/scientific information channels, and comparative risk framing. Residue-Concerned Skeptics expressed lower confidence in enforcement capacity (implementation and staffing) and in the system’s alignment with other EU Member States, together with concerns about chronic pesticide exposure. The System-Confident profile reported higher regulatory confidence and greater reliance on official and scientific channels, as well as stronger endorsement of IPM effectiveness and comparative risk rankings. External validation supported profile differences in perceived training adequacy, IPM beliefs, and organic avoidance behavior. Professional involvement in plant protection and older age were associated with membership in the System-Confident profile. These findings suggest that interventions should emphasize clear communication, capacity building, and address concerns about chronic exposure, beyond information provision alone. Full article

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

Background/Objectives: The increasing burden of non-communicable diseases, together with accelerating environmental degradation, highlights the urgent need for sustainable dietary patterns that promote both human and planetary health. The Mediterranean diet (MedDiet), traditionally followed in countries bordering the Mediterranean basin, has gained recognition as a model of sustainable nutrition due to its well-documented health benefits and relatively low environmental impact. However, its broader role within sustainable food systems requires comprehensive and interdisciplinary evaluation. The aim of this review is to provide a state-of-the-art synthesis of the evidence on the MedDiet as a sustainable dietary pattern, integrating its health, environmental, economic, and socio-cultural dimensions. Methods: This state-of-the-art narrative review synthesizes evidence from peer-reviewed literature on the MedDiet and sustainability. Relevant studies were identified through major scientific databases, focusing on publications addressing nutritional, environmental, economic, and socio-cultural dimensions. Both observational and interventional studies, as well as modeling and life cycle assessment analyses, were included. Additional sources from international organizations and policy reports were incorporated to contextualize global trends and challenges. Results: High adherence to the MedDiet is consistently associated with a reduced risk of cardiovascular disease, type 2 diabetes, cancer, and all-cause mortality. From an environmental perspective, the MedDiet is associated with lower greenhouse gas emissions, reduced land and water use, and enhanced biodiversity conservation compared with Western dietary patterns. Economically, it may represent a cost-effective dietary model and support local food systems when grounded in traditional practices, although affordability varies across contexts. Socio-culturally, the MedDiet promotes food heritage, culinary skills, and social cohesion. Nevertheless, globalization, urbanization, and the increasing consumption of ultra-processed foods have contributed to declining adherence, posing significant challenges to its sustainability and scalability. Moreover, the sustainability benefits of the MedDiet seem to be context-dependent rather than intrinsic, raising several challenges and limitations for its adoption. Conclusions: The MedDiet should be viewed not as a definitive solution to global food-system challenges but as a valuable reference model that illustrates how dietary practices can contribute simultaneously to human health, environmental sustainability, and cultural continuity. Modern sustainable dietary strategies should build upon the strengths of the MedDiet while recognizing its limitations, embracing contextual adaptation, and addressing the structural determinants that shape food choices. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Multiple sclerosis (MS) is a heterogeneous autoimmune disorder of the central nervous system of polygenic nature. Uncovering the genetic predictors of MS phenotype can help to explain the nature of the disease’s clinical heterogeneity, and contribute to the development of novel tools for precise disease prognosis. We conducted a retrospective genetic association study of 35 polymorphic variants in immune-related genes with MS severity assessed using the Multiple Sclerosis Severity Score (MSSS) in a sample of 548 Russian relapsing-onset MS patients who have not previously received immunomodulatory therapy. Variants in the CXCR5, EOMES, TNFRSF1A, IRF8, PVT1, CCR5, HLA-DRB1, IL6, TCF7, and CD40 genes were identified as MSSS-associated in at least two of the three models analyzed (MSSS > 3.5 versus ≤3.5; MSSS > 5.0 versus <2.5; MSSS as a continuous variable). Among them, variants in CCR5, HLA-DRB1 and IL6 genes were associated with MSSS only in women, while variants in the TCF7 and CD40 genes only in men. The variant in CXCR5 was MSSS-associated both in the total sample and in subgroups of female and male MS patients. Thus, we demonstrate that several GWAS-identified MS risk genes, along with other immunological loci, act as modifiers of the MS phenotype. Full article

Domoic acid (DA) is a well-known seafood toxin produced by some species of marine phytoplankton in the genus Pseudo-nitzschia during harmful algal blooms (HABs). Acute toxic exposures induce overt clinical signs of neuroexcitotoxicity, such as seizures in mammals due to overstimulation of glutamate receptors in the central nervous system (CNS). Acute DA excitotoxicity via the CNS has been well-studied in both field poisoning events and laboratory exposure studies with rodent models, but little is known about the impacts of low-level DA exposures below those that cause outward signs of neurotoxicity; the impacts on other potential target organs, including the heart; or age-related sensitivities. Here, low-level DA exposures in young adult (9 mo) and old (24 mo) mice were conducted over multiple weeks. Mortality, cardiac function, frailty, and protein expression were quantified to assess age-related DA sensitivity and potential impacts on heart function. Echocardiography and proteome data confirm that chronic low-level DA exposure causes irreversible functional cardiomyopathy and protein remodeling in young adult mice that mimics natural cardiac aging. In addition, old mice exhibit higher mortality and frailty than young adult mice with the same low-level DA exposures. These results provide critical information for assessing potential health risks to humans who regularly consume seafood with low levels of DA. Full article

Flooding has become increasingly severe due to rapid urbanization and changing hydrological conditions, necessitating effective and sustainable mitigation strategies. This study investigates the hydraulic performance of a hybrid flood defense system comprising a dike, a moat, and vegetation under varying surface roughness conditions. The results demonstrate that increasing roughness significantly enhances flood mitigation performance by improving energy dissipation and delaying the propagation of floodwater. A maximum energy reduction of approximately 75.56% and a delay in floodwater arrival of up to 65% were observed under higher roughness conditions. In contrast, increasing flow intensity reduced system efficiency, highlighting the importance of optimizing roughness under varying hydraulic conditions. The findings reveal that surface roughness is the dominant factor controlling flow resistance, turbulence generation, and hydraulic jump formation within the system. The novelty of this study lies in systematically quantifying the combined effect of roughness across structural and vegetative components within a hybrid defense framework. These results provide a practical basis for the design and optimization of eco-engineered flood defense systems, offering a cost-effective approach for reducing flood risk in riverine environments. Full article

Background/Objectives: Patients with chronic kidney disease (CKD) have an increased risk of ischemic heart disease (IHD). Some discrepancies exist between cardiological and nephrological guidelines regarding the extent of diagnostic procedures in CKD patients who are candidates for kidney transplantation. The aim of this study was to assess the cardiac status of these patients after cardiological checkup. Methods: The present study included all kidney transplant candidates referred to the Regional Qualification Center between January 2021 and February 2024. We characterized the group of patients in whom IHD was diagnosed during the cardiological checkup. Results: Among 346 patients, IHD was newly identified in 44 (12.7%) subjects. These patients were significantly older [median 62.9 (51.9–65.4) vs. 47.2 (36.8–57.9) years; p < 0.001], had longer dialysis vintage [median 20 (12.5–42) vs. 14 (6–31) months; p < 0.05] and were more frequently diabetic (29.6 vs. 16.9%, p < 0.05) than the rest of the study cohort. Of note, they were also characterized by significantly more frequent manifestation of atherosclerosis lesions visualized using routine imaging methods (i.e., chest X-ray and abdominal aorta and iliac artery visualization). The stepwise logistic regression analysis revealed that age [OR 1.05 (1.02–1.09); p <0.01] and the ad hoc atherosclerotic score [OR 1.88 (1.27–2.77); p < 0.001] independently predicted the diagnosis of IHD during the cardiological qualification of potential kidney transplant candidates. Conclusions: During the cardiological examination, IHD was diagnosed in a substantial number of kidney transplant candidates. The presence of atherosclerotic lesions detected by routine noninvasive vascular system imaging methods may suggest the need for extending IHD diagnostics even in relatively young patients without clinical symptoms. Full article

Salmonella primarily affects the gastrointestinal tract, causing local and systemic symptoms. Fucoidan exhibits therapeutic potential against Salmonella-induced pathology; however, the influence of its molecular weight on efficacy remains poorly understood. In this study, low-molecular-weight fucoidan from Undaria pinnatifida (LUPF) was prepared and characterized, and its protective effects against Salmonella infection were evaluated in a mouse model. LUPF effectively mitigated Salmonella-induced multiple organ damage by preserving mucin secretion and tight junction protein expression. Metabolomics analysis further demonstrated that LUPF normalized Salmonella-induced metabolic disturbances, thereby reducing systemic dysfunction. Mechanistically, LUPF suppressed inflammation by inhibiting mitogen-activated protein kinase (MAPK) and nuclear factor-κB (NF-κB) signaling pathways, while alleviating oxidative stress through activation of the Nrf2 pathway. In addition, LUPF restored gut microbiota homeostasis by reducing Proteobacteria levels, improving the Bacteroidota/Firmicutes ratio, enriching beneficial taxa, and enhancing short-chain fatty acid production. In vitro experiments further revealed that LUPF attenuated Salmonella-induced inflammation by modulating macrophage polarization. Collectively, these results suggest that LUPF has promising potential as a prebiotic candidate for reducing the risk of Salmonella-associated diseases. Full article