Search Results (1,479)

Background: Fixed-dose combinations of rosuvastatin and ezetimibe are increasingly used in clinical practice, but real-world data on their effectiveness and safety in large populations remain limited. Methods: This prospective, single-group, open-label, non-interventional observational study was conducted in the Republic of Korea to evaluate the effectiveness and safety of Rovazet^® (a fixed-dose combination of rosuvastatin and ezetimibe). Patients were prospectively enrolled from 235 institutions (50 general hospitals and 185 private clinics) as part of routine clinical practice over a five-year period. Lipid profiles and medication compliance questionnaire results were collected at baseline, 12 weeks, and 24 weeks of treatment. Results: A total of 5527 patients with dyslipidemia, the majority were men (53.0%), and the mean age was 60.4 years. Rovazet^® significantly reduced low-density lipoprotein cholesterol (LDL-C) by 23.5% at 12 weeks (from 117.47 ± 50.65 mg/dL to 81.14 ± 38.20 mg/dL; p < 0.0001) and by 27.4% at 24 weeks (from 117.47 ± 50.65 mg/dL to 74.52 ± 33.36 mg/dL; p < 0.0001). Total cholesterol was significantly reduced by 17.7% at 12 weeks and by 19.8% at 24 weeks. Rovazet^® treatment reduced triglycerides by 4.1% at 12 weeks and by 7.2% at 24 weeks. High-density lipoprotein cholesterol increased by 4.5% at 12 weeks and by 7.9% at 24 weeks following Rovazet^® treatment. These changes in lipid profiles were consistent, regardless of cardiovascular risk profiles. By 24 weeks of treatment with Rovazet^®, 91.8% of patients had reached their target LDL-C goals. Adverse drug reactions were reported in 2.81% of patients, most of which were minor, indicating that Rovazet^® was well tolerated. Conclusions: Rovazet^® was effective in improving lipid profiles and well tolerated in Korean adults with dyslipidemia. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

This study examines the contributions of renewable energy transition (RET) and environmental innovation (EI) to environmental performance in G7 countries from 2003 to 2021, with a focus on the transmission channels of green finance and environmental governance. Using the Augmented Mean Group (AMG) estimator and confirming robustness through the Dynamic Common Correlated Effects Mean Group (DCCE-MG) method, the study explores both direct and indirect effects of RET and EI on two key environmental indicators: the Environmental Performance Index and the Load Capacity Factor. The results reveal that both RET and EI have a significant impact on environmental performance. Moreover, green finance and environmental governance serve as crucial channels through which RET and EI exert their influence. These findings underscore the importance of developing effective financial instruments and robust regulatory frameworks to translate energy and innovation policies into tangible environmental benefits. By highlighting the interplay between technological advancement, financial capacity, and institutional quality, this study provides novel insights into the environmental policy landscape of advanced economies and offers guidance for designing integrated strategies to achieve long-term sustainability goals. Full article

Background/Objectives: In the era of treat-to-target strategies in inflammatory bowel disease (IBD), transmural healing (TH) is gaining recognition as a promising therapeutic goal. TH has been associated with significantly better long-term outcomes, including reduced rates of hospitalization, surgery, and the need for therapy escalation. Cross-sectional imaging techniques, such as intestinal ultrasound (IUS), magnetic resonance imaging (MRI), and computed tomography enterography (CTE), offer a comprehensive, non-invasive means to assess this deeper level of healing. This review explores how TH is currently defined across various imaging modalities and evaluates the feasibility and cost-effectiveness of achieving TH with available therapies. Methods: A literature search was conducted across PubMed, Scopus, and Embase using keywords, including “transmural healing”, “intestinal ultrasonography”, “magnetic resonance imaging”, “computed tomography enterography”, “Crohn’s disease”, “ulcerative colitis”, and “inflammatory bowel disease”. Only English-language studies were considered. Results: Despite growing interest, there is no standardized definition of TH across imaging platforms. Among the modalities, IUS emerges as the most feasible and cost-effective tool, owing to its accessibility, accuracy (sensitivity 62–95.2%, specificity 61.5–100%), and real-time capabilities, though it does have limitations. Current advanced therapies induce TH in roughly 20–40% of patients, with no consistent differences observed between biologics and small molecules. However, TH has only been evaluated as a formal endpoint in a single randomized controlled trial to date. Conclusions: A unified and validated definition of transmural healing is critically needed to harmonize research and guide clinical decision-making. While TH holds promise as a meaningful treatment target linked to improved outcomes, existing therapies often fall short of achieving complete transmural resolution. Further studies are essential to clarify its role and optimize strategies for deep healing in IBD. Full article

Background/Objectives: Most patients with non-small cell lung cancer (NSCLC) receive chemo- and/or immunotherapy, which can be associated with adverse events including fatigue. Affected patients may not be able to receive the complete chemo- and/or immunotherapy as planned. In this context, patients may benefit from maintaining their physical activity, which can be challenging. An app reminding patients to perform a certain number of steps may have a positive effect on physical activity during chemo- and/or immunotherapy. Such an app is under development and will be tested in a prospective trial. The current APACHIE-01 study (NCT06993896) is required for proper sample size calculation and design of the planned trial. Methods: The main goal of the APACHIE-01 study is to evaluate patterns and predictors of physical activity during chemo- and/or immunotherapy for locally advanced or metastatic NSCLC. The primary endpoint is the assessment of the mean number of steps per week during the first three cycles of chemo- and/or immunotherapy for lung cancer. The baseline value is represented by the mean number of steps during the last week prior to chemotherapy and/or immunotherapy. Secondary endpoints include associations between mean number of steps per week and a pain score, a distress score, and a fatigue score. The recruitment of the required 38 patients should be completed within 4 months and the treatment period will be 9–10 weeks (three cycles of chemo- and/or immunotherapy), resulting in a total running time of approximately 6 months. The APACHIE-01 study will contribute to the optimal design of a subsequent prospective trial. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

Understanding the effectiveness of suicide prevention measures for high-risk locations can often be challenging as many rely, at least to some degree, on psychological processes (e.g., engaging with help-seeking behaviours). Establishing how these measures may influence decision-making during a suicide attempt could be helpful for understanding how and when they may be most effective at preventing deaths. In the present work, we consider how suicide prevention measures may influence “goal pursuit” as it unfolds. Drawing on findings from across the suicide prevention literature, we apply the descriptive framework outlined in GOAL Architecture to consider how different measures may shape perceptions of “distance”, “time”, and “rate of progress” and, in turn, could influence levels of motivational drive associated with specific acts (e.g., “accessing means for suicide”). This is discussed in relation to real-time decisions around accessing means for suicide, avoiding intervention by a third party, and engaging in help-seeking behaviours. As well as the psychological processes that could encourage or prevent an individual from disengaging from a suicide attempt, we also consider potential risks and the influence of person-level factors. Full article

This study investigates farmers’ knowledge, perceptions, and adaptation strategies to climate change in Rwanda’s Eastern Province, integrating social and physical science approaches. Analyzing meteorological data (1981–2021) and surveys from 204 farmers across five districts, we assessed climate trends and adaptation behaviors using statistical methods (descriptive statistics, Chi-square, logistic regression, Regional Kendall test, dynamic linear state-space model). Results show that 85% of farmers acknowledge climate change, with 54% observing temperature increases and 37% noting rainfall declines. Climate data confirm significant rises in annual minimum (+0.76 °C/decade) and mean temperatures (+0.48 °C/decade), with the largest seasonal increase (+0.86 °C/decade) in June–August. Rainfall trends indicate a non-significant decrease in March–May and a slight increase in September–December. Farmers report crop failures, yield reductions, and food shortages as major climate impacts. Common adaptations include agroforestry, crop diversification, and fertilizer use, though financial limitations, information gaps, and input scarcity impede adoption. Despite limited formal education (53.9% primary, 22.3% no formal education), indigenous knowledge aids seasonal prediction. Farm location, group membership, and farming goal are key adaptation enablers. These findings emphasize the need for targeted policies and climate communication to enhance rural resilience by strengthening smallholder farmer support systems for effective climate adaptation. Full article

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article

Horticulture and agriculture are facing the challenge of growing healthy and high-quality crops. Plant extracts are currently being widely investigated as an alternative means of plant protection. Interest in these measures has increased in order to reduce the use of chemical pesticides, environmental pollution, and adverse effects on human health. Also, due to the goals of the European Green Deal and the decreasing use of chemical pesticides, it has become essential to look for safer alternatives. The aim of this study was to investigate the inhibitory effect of plant extracts of clove (Syzygium aromaticum L.) and rosemary (Rosmarinus officinalis L.) against Colletotrichum acutatum and Botrytis cinerea plant pathogens and to evaluate fungal pathogens recovery after the exposure to the extract. The plant extracts (PEs) were obtained by subcritical CO₂ extraction. The inhibitory effect of PEs was investigated in vitro at concentrations of 1200, 1600, 2000, 2400, 2800, and 3000 μL/L. Petri dishes were incubated at 25 ± 2 °C, and the mycelial growth of fungal pathogens was evaluated at 2, 4, and 7 days after inoculation (DAI). Reinoculation was then performed. The research showed that both plant extracts had an antifungal effect. However, clove PE was more effective. This allows us to say that plant-based measures can inhibit plant pathogens, but it is essential to determine the optimal concentrations and test them with different pathogens. Full article

The main goal of purebred cat breeding is to produce healthy offspring while maintaining breed purity. Pedigree cat breeders are affiliated with international federations that enforce similar breeding regulations, which helps prevent the overexploitation of cats in breeding. The minimum age for allowing a cat to breed is at least 10 months. This study aimed to analyze the breeding performance of three cat breeds: Maine Coon (MCO), British Shorthair (BSH), and Devon Rex (DRX). These breeds are classified as late-maturing (MCO), medium-late maturing (BSH), and early-maturing (DRX). The analysis was based on pedigree data obtained from the Polish Felinological Association, which operates under the auspices of the World Cat Federation. In total, data from 1016 litters (453 MCO, 453 BSH, and 110 DRX) were analyzed. Breeds differed significantly in age at first birth (p = 0.041), mean interval between litters (p < 0.01), and mean number of kittens per litter (p < 0.01). Breed effects were also noted for the mean interval between births (p < 0.01), mean number of kittens per litter (p < 0.01), and the total number of kittens sired by the mother (p = 0.007). Within each breed, differences were found in the sex ratio of litters, with a predominance of males in DRX (p = 0.049), MCO (p = 0.003), and overall breeds (p = 0.036). In contrast, the BSH breed showed no significant difference in the sex ratio of litters (p = 0.455). In both the MCO and DRX breeds, the lowest average interval between litters was observed in females that gave birth to their first litter early compared to those that gave birth later. The total number of kittens raised by the mother was highest in the MCO breed (p ≤ 0.05), while the shortest litter spacing was found in the DRX breed (p ≤ 0.05). Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

Background/Objectives: Early diagnosis of stroke is crucial for effective treatment with tissue plasminogen activator (tPA) and endovascular thrombectomy. Emergency medical services (EMSs) screening and the early activation of emergency department (ED) stroke protocols reduce treatment times and improve patient outcomes. This study aims to validate ED stroke protocol activation by EMSs in a large stroke center. Methods: This retrospective cross-sectional study was conducted at Magen David Adom and Shamir Medical Center between 1 January 2019 and 31 December 2019. Data were categorized into patients suspected by EMSs of having a stroke and those not suspected by EMSs but diagnosed as having a stroke in the ED. The primary outcome was the accuracy of EMSs in activating ED stroke protocols. Results: In this study, there were 23,061 patients, of which 11,841 (51.9%) were females. The mean age was 61.4 (SD = 22.72) years old. EMSs suspected 743 (3.22%) patients were having a stroke. In 587 (79%), EMSs activated ED stroke protocols. There were 88 cases where strokes were diagnosed in the ED when EMSs did not suspect a stroke. The overall EMSs negative predictive value (NPV) was 100% while the positive predictive value (PPV) was 20%. Conclusions: While Israeli EMSs over-activate the ED stroke protocol, stroke patients are almost never missed, achieving the goal of prehospital stroke screening. To prevent resource waste, all involved teams should be notified, and the actual activation of the stroke protocol should be carried out by an ED physician upon patient arrival. Communication between all levels regarding stroke protocol should also be increased to decrease the time to treatment. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article