Search Results (20,834)

Avian influenza A viruses (IAVs) pose a persistent threat to the poultry industry, causing substantial economic losses. Although traditional vaccines have helped reduce the disease burden, they typically rely on multivalent antigens, emphasize humoral immunity, and require intensive production. This study aimed to establish a genetically matched host–cell system to evaluate antigen-specific immune responses and identify conserved CD8+ T cell epitopes in avian influenza viruses. To this end, we developed an MHC class I genotype (B21)-matched host (Lohmann VALO SPF chicken) and cell vector (DF-1 cell line) model. DF-1 cells were engineered to express the hemagglutinin (HA) gene of clade 2.3.4.4b H5N1 either transiently or stably, and to stably express the matrix 1 (M1) and nucleoprotein (NP) genes of A/chicken/South Korea/SL20/2020 (H9N2, Y280-lineage). Following prime-boost immunization with HA-expressing DF-1 cells, only live cells induced strong hemagglutination inhibition (HI) and virus-neutralizing (VN) antibody titers in haplotype-matched chickens. Importantly, immunization with DF-1 cells transiently expressing NP induced stronger IFN-γ production than those expressing M1, demonstrating the platform’s potential for differentiating antigen-specific cellular responses. CD8+ T cell epitope mapping by mass spectrometry identified one distinct MHC class I-bound peptide from each of the HA-, M1-, and NP-expressing DF-1 cell lines. Notably, the identified HA epitope was conserved in 97.6% of H5-subtype IAVs, and the NP epitope in 98.5% of pan-subtype IAVs. These findings highlight the platform’s utility for antigen dissection and rational vaccine design. While limited by MHC compatibility, this approach enables identification of naturally presented epitopes and provides insight into conserved, functionally constrained viral targets. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

Microplastics are pervasive environmental pollutants that pose risks to human health through ingestion and inhalation. This review synthesizes current practices to reduce exposure and toxicity by examining major exposure routes and dietary interventions. More than 130 papers were analyzed to achieve this aim. The findings show that microplastics contaminate a wide range of food products, with particular concern over seafood, drinking water, plastic-packaged foods, paper cups, and tea filter bags. Inhalation exposure is mainly linked to indoor air quality and smoking, while dermal contact poses minimal risk, though the release of additives from plastics onto the skin remains an area of concern. Recommended strategies to reduce dietary exposure include consuming only muscle parts of seafood, moderating intake of high-risk items like anchovies and mollusks, limiting canned seafood liquids, and purging mussels in clean water before consumption. Avoiding plastic containers, especially for hot food or microwaving, using wooden cutting boards, paper tea bags, and opting for tap or filtered water over bottled water are also advised. To mitigate inhalation exposure, the use of air filters with HyperHEPA systems, improved ventilation, regular vacuuming, and the reduction of smoking are recommended. While antioxidant supplementation shows potential in reducing microplastic toxicity, further research is needed to confirm its effectiveness. This review provides practical, evidence-based recommendations for minimizing daily microplastic exposure. Full article

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands. Full article

This study was conducted to examine the moderating effect of size on the impact of human capital on bank performance, using data from 26 commercial banks in Vietnam from 2008 to 2023 through panel data regression methods. The results indicate that bank size and human capital are important resources for commercial banks to increase their performance, which is consistent with the resource-based view and economies of scale theory. However, bank size fails to exhibit a significant moderating effect on the impact of human capital on the bank performance in Vietnam. This phenomenon can be explained by the relatively limited influence of size effects on human capital, coupled with the fact that the majority of Vietnamese commercial banks place significant strategic emphasis on human capital development within their operational frameworks. In addition, this study highlights the impact of some internal factors and the macroeconomic conditions on bank performance. From these empirical findings, this paper recommends several critical policies. Full article

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building efforts. Grounded in participatory feminist research, this study employed a multi-method qualitative approach, including semi-structured interviews and oral history narratives, with 60 women in two climate-vulnerable provinces. Data were analyzed through thematic coding, CATWOE (Customers, Actors, Transformation, Worldview, Owners, Environmental Constraints) analysis, and descriptive statistics. The findings identify nine major climate-related events disrupting livelihoods and reveal a limited understanding of HLR as a long-term, transformative concept. Adaptation strategies remain short-term and focused on immediate survival. Barriers to HLR include financial constraints, limited access to agricultural resources and technology, and entrenched gender norms restricting women’s leadership and decision-making. While local governments, women’s associations, and community networks provide some support, gaps in accessibility and adequacy persist. Participants expressed the need for financial assistance, vocational training, agricultural technologies, and stronger peer networks. Strengthening HLR among agrarian women requires gender-sensitive policies, investment in local support systems, and community-led initiatives. Empowering agrarian women as agents of change is critical for fostering resilient rural livelihoods and achieving inclusive, sustainable development. Full article

Background: Percutaneous epidural neuroplasty (PEN) and related adhesiolysis procedures are widely used for managing chronic spinal pain. Although generally safe, complications—ranging from minor to life-threatening—have been reported. This review aimed to estimate the incidence and characteristics of complications following PEN and to evaluate the medical rationale for post-procedural inpatient monitoring. Methods: We systematically searched PubMed, Embase, and the Cochrane Library for studies published from January 2000 to April 2025 reporting complications associated with PEN. We performed a random-effects meta-analysis on five eligible cohort studies to estimate the pooled complication rate and evaluated heterogeneity. Risk of bias was assessed using the Newcastle–Ottawa Scale. Results: Five cohort studies (n = 1740) were included in the meta-analysis, with a pooled complication rate of 9.0% (95% CI: 4.8–13.1%, I² = 97.5%). A total of 133 complications were identified from cohort studies and case reports. Mechanical and neurological complications were most common. Serious complications, including hematoma, meningitis, and cardiopulmonary arrest, were concentrated within the first 6 h post-procedure. Conclusions: This meta-analysis highlights a quantifiable risk of complications associated with PEN. Our findings support structured inpatient monitoring during the immediate post-procedural period to enhance safety and outcomes. Full article

Faecal microbiota transplantation (FMT) is an emerging therapy for gastrointestinal and neurological disorders, acting via the microbiota–gut–brain axis. Altering gut microbial composition may influence cognitive function, but this has not been tested in cognitively healthy adults. This randomised, double-blinded, placebo-controlled pilot trial investigates whether FMT is feasible and improves cognition in adults with irritable bowel syndrome (IBS). Participants receive a single dose of FMT or placebo via rectal retention enema. Cognitive performance is the primary outcome, assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). Secondary outcomes include IBS symptom severity and mood. Tertiary outcomes include microbiome composition and plasma biomarkers related to inflammation, short-chain fatty acids, and tryptophan metabolism. Outcomes are assessed at baseline and at one, three, six, and twelve months following treatment. We hypothesise that FMT will lead to greater improvements in cognitive performance than placebo, with benefits extending beyond practice effects, emerging at one month and persisting in the long term. The findings will contribute to evaluating the safety and efficacy of FMT and enhance our understanding of gut–brain interactions. Full article

Background/Objectives: Antibiotic use is common among hospitalized pediatric patients. However, inappropriate use, including excessive use of Watch antibiotics, can contribute to antimicrobial resistance, adverse events, and increased healthcare costs. Consequently, there is a need to continually assess their usage among this vulnerable population. This was the objective behind this study. Methods: The medical records of all pediatric patients (under 12 years) admitted and treated with antibiotics at a Ghanaian Teaching Hospital between January 2022 and March 2022 were extracted from the hospital’s electronic database. The prevalence and appropriateness of antibiotic use were based on antibiotic choices compared with current guidelines. Influencing factors were also assessed. Results: Of the 410 admitted patients, 319 (77.80%) received at least one antibiotic. The majority (68.65%; n = 219/319) were between 0 and 2 years, and males (54.55%; n = 174/319). Ceftriaxone was the most commonly prescribed antibiotic (20.69%; n = 66/319), and most of the systemic antibiotics used belonged to the WHO Access and Watch groups, including a combination of Access and Watch groups (42.90%; n = 136/319). Neonatal sepsis (24.14%; n = 77/319) and pneumonia (14.42%; n = 46/319) were the most common diagnoses treated with antibiotics. Antibiotic appropriateness was 42.32% (n = 135/319). Multivariate analysis revealed ceftriaxone prescriptions (aOR = 0.12; CI = 0.02–0.95; p-value = 0.044) and surgical prophylaxis (aOR = 0.07; CI = 0.01–0.42; p-value = 0.004) were associated with reduced antibiotic appropriateness, while a pneumonia diagnosis appreciably increased this (aOR = 15.38; CI = 3.30–71.62; p-value < 0.001). Conclusions: There was high and suboptimal usage of antibiotics among hospitalized pediatric patients in this leading hospital. Antibiotic appropriateness was influenced by antibiotic type, diagnosis, and surgical prophylaxis. Targeted interventions, including education, are needed to improve antibiotic utilization in this setting in Ghana and, subsequently, in ambulatory care. Full article

Objectives: This study assessed the effectiveness of a cognitive behavioral therapy (CBT)-based fine dust education program, grounded in the Health Belief Model (HBM), on elementary students’ fine dust knowledge, related behaviors, and mental health (depression, anxiety, stress, sleep quality). Methods: From September to November 2024, 95 students (grades 4–6) living near a coal-fired power plant in midwestern South Korea were assigned to either an intervention group (n = 44) or a control group (n = 51). The intervention group completed a three-session CBT-based education program; the control group received stress management education. Assessments were conducted at weeks 1, 2, 4, and 8 using standardized mental health and behavior scales (PHQ: Patient Health Questionnaire, GAD: Generalized Anxiety Disorder Assessment, PSS: Perceived Stress Scale, ISI: Insomnia Severity Index). Results: A chi-square test was conducted to compare pre- and post-test changes in knowledge and behavior related to PM2.5. The intervention group showed significant improvements in seven fine dust-related knowledge and behavior items (e.g., PM2.5 awareness rose from 33.3% to 75.0%; p < 0.05). The control group showed limited gains. Regarding mental health, based on a mixed-design ANCOVA, anxiety scores significantly declined over time in the intervention group, with group and interaction effects also significant (p < 0.05). Depression scores showed time effects, but group and interaction effects were not significant. No significant changes were observed for stress, sleep, or group × PM2.5 interactions. Conclusions: The CBT-based education program effectively enhanced fine dust knowledge, health behaviors, and reduced anxiety among students. It presents a promising, evidence-based strategy to promote environmental and mental health in school-aged children. Full article

The rapid emergence of generative AI technologies has sparked significant transformation across educational landscapes worldwide. This study presents a comprehensive bibliometric analysis of GAI in education, mapping scholarly trends from 2022 to 2025. Drawing on 3808 peer-reviewed journal articles indexed in Scopus, the analysis reveals exponential growth in publications, with dominant contributions from the United States, China, and Hong Kong. Using VOSviewer, the study identifies six major thematic clusters, including GAI in higher education, ethics, technological foundations, writing support, and assessment. Prominent tools, especially ChatGPT, are shown to influence pedagogical design, academic integrity, and learner engagement. The study highlights interdisciplinary integration, regional research ecosystems, and evolving keyword patterns reflecting the field’s transition from tool-based inquiry to learner-centered concerns. This review offers strategic insights for educators, researchers, and policymakers navigating AI’s transformative role in education. Full article

In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected using color difference signal amplification and the plane-orthogonal-to-the-skin method. Additionally, the SpO2 is detected using the HR frequency and the absorption ratio of the G and B color channels based on oxyhemoglobin absorption and reflectance theory. After this, the respiratory frequency is detected using the PSD of rPPG through respiratory frequency band filtering. For the image sequences recorded under various imaging conditions, the proposed method demonstrated superior HR detection accuracy compared to existing methods. The confidence intervals for HR and SpO2 detection were analyzed using Bland–Altman plots. Furthermore, the proposed RR detection method was also verified to be reliable. Full article

Coronavirus disease 2019 (COVID-19) causes cardiovascular complications, which contributes to the high mortality rate of the disease. Emerging evidence indicates that aberrant vascular smooth muscle cell (SMC) function is a key driver of vascular disease in COVID-19. While antivirals alleviate the symptoms of COVID-19, it is not known whether these drugs directly affect SMCs. Accordingly, the present study investigated the ability of three approved COVID-19 antiviral drugs to influence SMC function. Treatment of SMCs with remdesivir (RDV), but not molnupiravir or nirmatrelvir, inhibited cell proliferation, DNA synthesis, and migration without affecting cell viability. RDV also stimulated an increase in heme oxygenase-1 (HO-1) expression that was not observed with molnupiravir or nirmatrelvir. The induction of HO-1 by RDV was abolished by mutating the antioxidant responsive element of the promoter, overexpressing dominant-negative NF-E2-related factor-2 (Nrf2), or treating cells with an antioxidant. Finally, silencing HO-1 partly rescued the proliferative and migratory response of RDV-treated SMCs, and this was reversed by carbon monoxide and bilirubin. In conclusion, the induction of HO-1 via the oxidant-sensitive Nrf2 signaling pathway contributes to the antiproliferative and antimigratory actions of RDV by generating carbon monoxide and bilirubin. These pleiotropic actions of RDV may prevent occlusive vascular disease in COVID-19. Full article

This article addresses the path planning problem for complete structural inspection using an unmanned aerial vehicle (UAV). The proposed method emphasizes the scalability of the viewpoints and aims to provide practical solutions to different inspection distance requirements, eliminating the need for extra view-planning procedures. First, the mixed-viewpoint generation is proposed. Then, the Multi-Layered Angle-Distance Traveling Salesman Problem (ML-ADTSP) is solved, which aims to reduce overall energy consumption and inspection path complexity. A two-step Genetic Algorithm (GA) is used to solve the combinatorial optimization problem. The performance of different crossover functions is also discussed. By solving the ML-ADTSP, the simulation results demonstrate that the mean accelerations of the UAV throughout the inspection path are flattened significantly, improving the overall path smoothness and reducing traversal difficulty. With minor low-level optimization, the proposed framework can be applied to inspect different structures. Full article