Search Results (4,682)

Objectives: In the absence of massive screening programs, it is imperative to develop and validate a candidate selection strategy for opportunistic endoscopic screening (OES) targeting the early detection of gastric cancer. Methods: A hospital-based cross-sectional study was conducted, enrolling both health check-up controls and gastric cancer patients. Data collection included two components: (1) a questionnaire including demography, self-reported comorbidities, and family history of cancers; (2) serology including hemoglobin, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9). Associations between potential variables and gastric cancer risk were assessed and the predictive efficacy of these risk factors was quantified. Sequentially, risk stratification scoring systems were constructed and their cost-effectiveness profiles were evaluated. Results: A total of 58,218 participants were included in the analysis, among whom 619 (1.1%) were gastric cancer patients. Multivariate analyses identified male, age >40 years, family history of gastric cancer, comorbidities of upper digestive tract benign diseases (UDTBDs), anemia, and elevated serum CEA and/or CA19-9 as independent risk factors of increasing gastric cancer risk. Cost-effectiveness analysis demonstrated that individuals, especially those symptomatic, presenting any of following conditions should be recommended for OES: (1) age ≥50 years, (2) family history of gastric cancer, and/or (3) comorbid UDTBDs. Elsewise, unclear anemia and/or elevated serum CEA and/or CA19-9 presenting among males and/or persons 41–50 years of age should be considered for OES. Notably, this selection strategy achieved a detection rate comparable to that of alternative protocols while yielding superior cost-effectiveness outcomes. Conclusions: The integrated strategy combining questionnaire and sequential serology represents an effective and cost-effective approach to identifying high-risk candidates for gastric cancer OES. Further investigations are warranted to develop more precise and tailored screening and surveillance protocols, with the aim of optimizing both detection rates and cost-effectiveness in clinical practice. Full article

Background/Objective: Hypertension is a major global health concern and a leading cause of cardiovascular morbidity and mortality. Lifestyle behaviors, such as diet, physical activity, stress management, and self-confidence, markedly influence hypertension control. Exploring these behaviors can inform culturally relevant interventions for improving the prevention and management of hypertension and health outcomes of affected individuals. This study aimed to determine the effects of lifestyle behaviors, including dietary habits, physical activity, stress management, and self-confidence, on optimizing hypertension control among individuals in Saudi Arabia. Methods: A cross-sectional, descriptive–correlational design was used. Data were collected from 136 patients with hypertension attending primary healthcare centers in Saudi Arabia using validated scales for dietary habits, physical activity, perceived stress, and self-confidence, alongside blood pressure measurements. Data were analyzed using Statistical Package for the Social Sciences version 26. Results: The study revealed that most participants reported reasonably healthy dietary practices, low physical activity, and moderate stress and self-confidence. Significant sex differences (p < 0.05) were observed, with men and women reporting higher physical activity and stress, respectively. Education and age influenced dietary habits and self-confidence. Regression analysis identified age, education, and urban residence as predictors (p < 0.05) of blood pressure status, while stress, diet, and physical activity affected self-confidence and perceived stress levels. Conclusions: Hypertension management is influenced by interconnected lifestyle and psychosocial factors, and improving dietary habits, physical activity, stress management, and self-confidence is essential. Tailored interventions addressing demographic differences can enhance self-care behaviors and facilitate better hypertension control among Saudi individuals. Full article

Graphene oxide (GO), with its high surface area, tunable chemistry, and exceptional mechanical, thermal, and electrical properties, is rapidly advancing as a transformative material in both composite engineering and membrane technology. In composite systems, GO serves as a multifunctional reinforcement, significantly improving strength, stiffness, thermal stability, and conductivity when integrated into polymeric, ceramic, or metallic matrices. These enhancements are enabling high-performance solutions across electronics, aerospace, automotive, and construction sectors, where lightweight yet durable materials are in demand. In addition, GO-based membranes are revolutionizing water purification, desalination, and other high-end separation technologies. The layered structure, adjustable interlayer spacing, and abundant oxygen-containing functional groups of GO allow precise control over permeability and selectivity, enabling efficient transport of desired molecules while blocking contaminants. Tailoring GO morphology and surface chemistry offers a pathway to optimized membrane performance for both industrial and environmental applications. This paper gives a comprehensive overview of the latest developments in GO-based composites and membranes, highlighting the interplay between structure, morphology, and functionality. Future research directions toward scalable fabrication, performance optimization, and integration into sustainable technologies are discussed, underscoring GO’s pivotal role in shaping next-generation advanced materials. Full article

Background and Objectives: Adrenal gland tumors are frequently discovered incidentally. They remain challenging to evaluate because of their heterogeneous nature and overlapping imaging characteristics. Surgical resection continues to represent the primary treatment option for both benign and malignant lesions. This study aimed to characterize the clinical, demographic, and pathological features of adrenal tumors and to assess surgical management patterns in a tertiary referral center. Materials and Methods: A retrospective analysis was conducted on 112 patients who underwent adrenalectomy between 2015 and 2022. Demographic, clinical, radiological, and surgical data were reviewed. Histopathological findings were classified as benign tumors, primary adrenal malignancies, or adrenal metastases. Both laparoscopic adrenalectomy and open surgery were performed. The operative approach was determined by tumor characteristics and oncologic considerations. Results: Among the 112 patients, 48% had benign adrenal tumors, 32% had adrenal metastases, and 19.6% were diagnosed with primary adrenal malignancies. Most patients with adrenocortical carcinoma were women over 55 years of age. Benign lesions were predominantly managed with simple adrenalectomy and minimally invasive techniques, while malignant tumors frequently required complex oncologic resections and open surgical approaches. Distinct metastatic patterns were observed, with renal cell carcinoma representing the most common primary source of adrenal metastasis. Conclusions: Adrenal tumors demonstrate marked demographic and pathological variability. Surgical resection remains essential for definitive diagnosis and treatment, underscoring the importance of tailoring the operative approach. Minimally invasive surgery is appropriate for benign lesions, whereas open adrenalectomy is preferred for malignant or advanced tumors, where surgical expertise is critical to achieving optimal oncologic outcomes. Full article

Background: Acute coronary syndromes (ACSs) remain a leading cause of death and disability. Since the publication of the 2023 ESC ACS guidelines, multiple studies and an ESC/EAS dyslipidaemia update have refined how clinicians diagnose, revascularize, and treat ACS across the care continuum. Content: This state-of-the-art review synthesizes advances from 2023 to 2025 across five domains. Diagnosis: High-sensitivity troponin-based accelerated pathways remain foundational; GRACE 3.0 improves calibration for early vs. delayed angiography, while selective use of CCTA and routine use of intracoronary imaging/physiology help define the mechanism and optimize PCI. Revascularization: complete revascularization continues to underpin care in multivessel disease, with recent data favouring culprit-only PCI acutely and staged non-culprit treatment during the index stay in most STEMI presentations, particularly with heart-failure physiology. Antithrombotic therapy: Aspirin remains critical early after ACS-PCI; emerging evidence supports shorter DAPT and aspirin withdrawal after 1 month in carefully selected, low-ischaemic-risk patients, whereas day-0 aspirin-free strategies in unselected ACS are not non-inferior. Secondary prevention: A “strike early and strong” approach to LDL-cholesterol—often with combination therapy in hospital—is emphasized, alongside nuanced roles for SGLT2 inhibitors and GLP-1 receptor agonists. Special populations and implementation: Sex- and age-aware tailoring (including MINOCA/SCAD evaluation), pragmatic bleeding-risk mitigation, digitally enabled cardiac rehabilitation, and registry-driven quality improvement translate evidence into practice. Summary: Contemporary ACS care is moving from uniform protocols toward risk-stratified, mechanism-informed pathways. We offer practical algorithms and checklists to align interventional timing, antithrombotic intensity/duration, and secondary prevention with individual patient risk—bridging new evidence to bedside decisions. Full article

The integration of solar photovoltaic (PV) systems into smart grids necessitates robust, real-time fault detection mechanisms, particularly in resource-constrained environments like the Solar–Hydrogen AIoT microgrid framework at a university. This study conducts a comparative analysis of four prominent Convolutional Neural Network (CNN) architectures VGG16, ResNet-50, DenseNet121, and EfficientNetB0 to determine the optimal model for low-latency, edge-based fault diagnosis. The models were trained and validated on a dataset of solar panel images featuring multiple fault types. Quantitatively, DenseNet121 achieved the highest classification accuracy at 86.00%, demonstrating superior generalization and feature extraction capabilities. However, when considering the stringent requirements of an AIoT system, computational efficiency became the decisive factor. EfficientNetB0 emerged as the most suitable architecture, delivering an acceptable accuracy of 80.00% while featuring the smallest model size (5.3 M parameters) and a fast inference time (approx. 26 ms/step). This efficiency-to-accuracy balance makes EfficientNetB0 ideal for deployment on edge computing nodes where memory and real-time processing are critical limitations. DenseNet121 achieved 86% accuracy, while EfficientNetB0 achieved 80% accuracy with lowest model size and fastest inference time. This research provides a validated methodology for implementing efficient deep learning solutions in sustainable, intelligent energy management systems. The novelty of this work lies in its deployment-focused comparison of CNN architectures tailored for real-time inference on resource-constrained Solar–Hydrogen AIoT systems. Full article

The increasing volume of data from IoT sensors has made manual inspection time-consuming and prone to bias, particularly for spatiotemporal air pollution maps. While rule-based methods are adequate for simple datasets or individual maps, they are insufficient for interpreting multi-year time series data with 1 h timestamps, which require both domain-specific expertise and significant time investment. This limitation is especially critical in environmental monitoring, where analyzing long-term spatiotemporal PM_2.5 maps derived from 52 low-cost sensors remains labor-intensive and susceptible to human error. This study investigates the potential of generative artificial intelligence, specifically multi-modal large language models (MLLMs), for interpreting spatiotemporal PM_2.5 maps. Both open-source models (Janus-Pro and LLaVA-1.5) and commercial large language models (GPT-4o and Gemini 2.5 Pro) were evaluated. The initial results showed a limited performance, highlighting the difficulty of extracting meaningful information directly from raw sensor-derived maps. To address this, a visual context engineering framework was introduced, comprising systematic optimization of colormaps, normalization of intensity ranges, and refinement of map layers and legends to improve clarity and interpretability for AI models. Evaluation using the GEval metric demonstrated that visual context engineering increased interpretation accuracy (defined as the detection of PM_2.5 spatial extrema) by over 32.3% (relative improvement). These findings provide strong evidence that tailored visual preprocessing enables MLLMs to effectively interpret complex environmental time series data, representing a novel approach that bridges data-driven modeling with ecological monitoring and offers a scalable solution for automated, reliable, and reproducible analysis of high-resolution air quality datasets. Full article

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations. Full article

With the large-scale integration of new energy and power electronic devices into power systems, frequency stability has become an increasingly critical concern. To maintain frequency stability while mitigating the high capital expenditure of energy storage systems (ESSs), this paper develops a control framework centered on edge energy management terminals (EEMTs). The design is based on a demonstration project in which distributed energy resources (DERs) and flexible loads collaboratively provide frequency regulation. A monitoring station is implemented to make fast frequency response (FFR) resources dispatchable, detectable, measurable, and tradable. Furthermore, a control strategy tailored for building- and factory-level applications is proposed. This strategy enables real-time optimal scheduling of DERs and flexible loads through coordinated communication between EEMTs and net load units (NLUs). Two field tests further demonstrate the effectiveness and advantages of the proposed approach. In addition, this paper proposes a coordinated scheme in which wind farms and NLUs jointly participate in frequency regulation, aiming to mitigate the response delay of NLUs and the secondary frequency drop observed in wind farms. The feasibility and benefits of this scheme are validated through experimental tests. Full article

Optimizing window insulation is crucial for reducing heat loss and energy use in industrial buildings in Northeast China’s severe cold regions. Based on six typical building prototypes identified via cluster analysis of field survey data, this study used DesignBuilder (Version 6.1.0.006) to simulate the influence of key parameters for insulation materials (type, thickness, emissivity) and installation methods (position, air cavity, operation). Simulations reveal that the energy-saving potential is inversely proportional to a building’s existing thermal performance, reaching a maximum of 10.3%. Regarding material selection, results indicate that reducing surface emissivity from 0.92 to 0.05 effectively substitutes for approximately 20 mm of physical insulation thickness. Transparent films prioritize daytime comfort, raising nighttime temperatures by 1.5 °C, whereas opaque panels excel at nighttime insulation with a 2.28 °C increase. Techno-economic analysis identifies low-emissivity foil combined with EPS or XPS as the most cost-effective strategy, achieving rapid payback periods of 0.6–3.2 years. Regarding installation, an external configuration with a 20 mm air cavity and vertical operation was identified as optimal, yielding 1.5–2.0% greater energy savings than an internal setup. This study provides tailored retrofitting strategies for industrial building windows in these regions. Full article

Laser-driven ion acceleration in dense, hydrogen-rich media can be significantly enhanced by embedding metallic nanoantennas that support localized surface plasmon (LSP) resonances. Using large-scale particle-in-cell (PIC) simulations with the EPOCH code, we investigate how nanoantenna geometry and laser pulse parameters influence proton acceleration in gold-doped polymer targets. The study covers dipole, crossed, and advanced 3D-cross antenna configurations under laser intensities of 10¹⁷–10¹⁹ W/cm² and pulse durations from 2.5 to 500 fs, corresponding to experimental conditions at the ELI laser facility. Results show that the dipole antennas exhibit resonance-limited proton energies of ~0.12 MeV, with optimal acceleration at the intensities 4 × 10¹⁷–1 × 10¹⁸ W/cm² and pulse durations around 100–150 fs. This energy is higher by roughly three orders of magnitude than the proton energy for the same field and same polymer without dopes: ~1–2 × 10⁻⁴ MeV. Crossed antennas achieve higher energies (~0.2 MeV) due to dual-mode plasmonic coupling that sustains local fields longer. Advanced 3D and Yagi-like geometries further enhance field localization, yielding proton energies up to 0.4 MeV and larger high-energy proton populations. For dipole antennas, experimental data from ELI exists and our results agree with it. We find that moderate pulses preserve plasmonic resonance for longer and improve energy transfer efficiency, while overly intense pulses disrupt the resonance early. These findings reveal that plasmonic field enhancement and its lifetime govern energy transfer efficiency in laser–matter interaction. Crossed and 3D geometries with optimized spacing enable multimode resonance and sequential proton acceleration, overcoming the saturation limitations of simple dipoles. The results establish clear design principles for tailoring nanoantenna geometry and pulse characteristics to optimize compact, high-energy proton sources for inertial confinement fusion and high-energy-density applications. Full article

Background: Obesity during pregnancy increases the risk of adverse maternal and neonatal outcomes, and excessive gestational weight gain (GWG) remains highly prevalent worldwide. Although physical activity (PA) interventions have shown potential benefits, evidence on the optimal type, intensity, and duration of exercise for overweight or obese pregnant women remains limited. Methods: Electronic searches of EBSCOhost, Embase, PubMed and Web of Science were performed through August 2025 to identify randomized controlled trials comparing PA interventions versus usual prenatal care in overweight or obese pregnant women. Two reviewers independently screened studies, extracted data, and assessed risk of bias using Cochrane ROB domains. Continuous outcomes were pooled using inverse-variance meta-analytic methods and heterogeneity was quantified by I². Results: Ten randomized trials (twelve intervention arms) comprising 1150 participants met the inclusion criteria. In the domain of blinding of participants and personnel, three studies (30%) were judged as low risk, while seven (70%) were unclear. PA interventions varied in modality (aerobic, resistance, endurance, walking), setting (clinic, community, home/mHealth), and the intervention period ranges from 10 to 34 weeks. Most interventions (80%) employed moderate intensity, and 30% combined aerobic and resistance training. Results of the meta-analysis showed that the pooled mean GWG was 9.93 ± 5.48 kg in the treatment group and 10.65 ± 5.70 kg in the control group. Overall, PA interventions produced a modest but statistically significant reduction in GWG compared with controls, with negligible between-study heterogeneity (I² = 0%). Conclusions: Tailored, moderate-intensity PA may have the potential to modestly reduce GWG. Although 30% included trials employed combined aerobic and resistance training, current evidence is insufficient to establish whether combined modalities are more effective than aerobic-only or resistance-only interventions. However, the current evidence is limited by small trial sizes, methodological variability and geographic concentration in higher-income settings. Larger, rigorously designed RCTs, including evaluations of digital delivery platforms and carefully supervised higher-intensity protocols, are needed to refine exercise prescriptions and inform clinical guidelines. Full article

Marine mammals have been successfully maintained under human care; however, the media, public, and professionals within the field frequently voice welfare concerns. This study systematically surveyed peer-reviewed (PR) literature from 1948 to 2024 (n = 1308) and included an opportunistic sample of non-peer-reviewed (NPR) literature from the past 40 years (n = 756) to evaluate research efforts associated with species housed in zoos and aquariums. The current study updates and extends previous efforts to assess research categories. The findings indicate that the volume of research published mirrors the species abundance in human care. Across taxa, PR papers concentrate on science that enhances the understanding of biological functions (Acoustics, Biology, Breeding, Behaviour, Health) but is not necessarily tailored to improve management or optimal care. In contrast, a substantial portion of the NPR literature focuses on daily handling and management, highlighting Environment and Management and Enrichment-related activities. While welfare-related research has increased in both PR and NPR literature, this review underscores the need for additional welfare-related empirical studies to further enhance animal care and wellbeing. We encourage those involved in the practical care of such taxa to empirically evaluate these interventions and disseminate their findings in the PR literature. Full article

Background: Radiation-induced pulmonary fibrosis (RPF) remains a major burden of successful lung cancer radiotherapy. Clinically validated drugs targeting RPF remains scarce. Methods: We employed a transcriptome-based drug repurposing approach using REMEDY, a computational platform built on the Library of Integrated Network-Based Cellular Signatures (LINCS). Differentially expressed genes (DEGs) derived from radiation-induced lung injury (RILI) models were used as a query to identify compounds capable of reversing pro-fibrotic expression profile. Among top-ranked candidates, homoharringtonine (HHT), an FDA-approved protein synthesis inhibitor, was selected for experimental validation. Anti-fibrotic effects of HHT were assessed using an optimized in vitro fibrotic model based on activation of MRC-5 human lung fibroblasts. Complementary in silico molecular docking analyses were also conducted to explore the mechanistic basis of HHT’s actions. This represents the first transcriptome-guided, LINCS-based drug repurposing study applied specifically to radiation-induced pulmonary fibrosis, utilizing RPF-derived molecular signatures rather than general fibrosis-related datasets. Results: HHT significantly attenuated key fibrotic phenotypes, including fibroblast proliferation, myofibroblast differentiation, and extracellular matrix (ECM) production. Notably, HHT suppressed expression of cyclin D1 and α-smooth muscle actin (α-SMA), and reduced collagen deposition. Mechanistic investigations revealed that HHT modulates two pro-fibrotic pathways: RhoA/ROCK and Wnt/β-catenin signaling. Molecular docking further suggested that HHT may directly interact with fibrosis-related receptors such as integrins and Frizzled, providing structural insight into its anti-fibrotic potential. These findings underscore the novelty of reassigning HHT to a radiation-specific fibrotic context using a signature-reversal strategy uniquely tailored to RPF biology. Conclusions: Our findings identify HHT as a promising treatment of RPF, offering a dual mechanism of action—interruption of protein synthesis and targeted inhibition of fibrotic signaling pathways. This study highlights the value of computational drug repurposing platforms for accelerating therapeutic discovery. Further preclinical investigations are warranted to evaluate HHT’s in vivo efficacy and clinical applicability in RPF. Full article

Crosstalk between elements in ultrasonic transducer arrays significantly degrades image quality in medical ultrasound systems by introducing noise and reducing spatial resolution. This review provides a comprehensive overview of the origins of crosstalk—acoustic, mechanical, and electrical—and the main characterization methods used to analyze it, including direct measurements, impedance analysis, finite element modeling, and equivalent circuit approaches. Emphasis is placed on recent advances in passive and active mitigation strategies, such as material coatings, structural decoupling, phononic crystals, adaptive filtering, and impedance matching. A key finding is that the optimal crosstalk reduction method depends strongly on the transducer technology employed—whether CMUT, PMUT, or bulk PZT. The review highlights the importance of tailoring mitigation techniques to the physical properties and operating conditions of each technology. By synthesizing current knowledge and identifying remaining challenges—particularly the role of filler material losses—this work offers a solid foundation for the development of next-generation ultrasound arrays with enhanced imaging performance. Full article