Search Results (114,245)

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost. Full article

Background: The convergence of machine learning (ML), gamification, and critical thinking assessment within adaptive educational platforms has accelerated since 2020, driven by large language models (LLMs) and graph neural networks (GNNs). No prior systematic review has jointly addressed all three dimensions, and Central Asian educational contexts remain underrepresented. Methods: Following PRISMA 2020 guidelines, we searched Scopus (n  =  4396) and OpenAlex (n  =  4152) for publications from 2016 to 2026. Quality assessment used the Mixed Methods Appraisal Tool (MMAT 2018; threshold ≥  2), yielding 82 papers. Five research questions addressed ML personalization (RQ1), gamification and engagement (RQ2), critical thinking assessment tools (RQ3), recommendation algorithms (RQ4), and regional applicability in Kazakhstan and Central Asia (RQ5). Results: Transformer-based and GNN models dominate the recent literature (52% of corpus from 2025), with an accuracy of 91–97% for dropout prediction and learning path recommendation under single-institution conditions. Gamification studies report up to 90% student satisfaction; LLM-based critical thinking assessment shows promise but faces validity concerns. Thirteen papers address Central Asian contexts. Conclusions: Significant gaps persist: no integrated gamification–critical thinking framework exists, recommendation systems lack explainability, and Kazakh-language datasets are severely underrepresented. Future research should prioritize multilingual adaptive systems, explainable algorithms, and privacy-preserving federated learning for low-resource contexts. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

Recent advancements in structural engineering have driven the development of sophisticated damping mechanisms aimed at reducing the detrimental effects of structural vibrations. As a result, accurate numerical modeling and analytical evaluation have become essential for assessing structural stability and enhancing seismic resilience. This study introduces a model-updating framework to develop analytical constitutive models for structural damping systems. The proposed approach employs a genetic algorithm (GA) to calibrate model parameters by minimizing the discrepancy between analytical predictions and experimental responses. Experimental force–displacement hysteresis data and displacement time-history records are used at both the element and system levels for model calibration. The methodology is applied to a rubber isolator, a 10-story structure equipped with Pall friction dampers, and a 6-story structure with friction dampers to evaluate its performance under different dynamic characteristics and damping mechanisms. The results indicate that the proposed approach achieves very high accuracy, with prediction errors reduced to negligible levels for both force and displacement responses in all cases. Consistent performance is observed using both global and local displacement measures in friction-damped systems, indicating the robustness of the proposed method. Overall, the findings indicate that the GA-based model-updating framework provides an efficient and reliable tool for improving the predictive capability of analytical models of structures with nonlinear damping devices and is suitable for practical structural engineering applications. Full article

Loess slopes are prone to rapid infiltration, surface erosion, and shallow instability under intense rainfall, highlighting the need for eco-friendly shallow protection methods with enhanced anti-seepage and erosion resistance. To improve the applicability of microbially induced calcite precipitation (MICP) in loess slope protection, this study proposes a combined MICP–sesbania gum (SG) modification method. Permeability tests, surface hardness tests, and indoor artificial rainfall model tests were conducted to systematically evaluate its effects on seepage control and the erosion resistance of loess slopes. The results show that calcium chloride provides a stronger permeability-reducing effect than calcium acetate. Compared with the MICP-only treatment, the combined MICP-SG treatment significantly reduces the permeability coefficient and increases surface hardness. Based on the overall modification performance, a cementation solution concentration of 1.0 mol/L and a curing time of 7 d were selected as suitable treatment parameters. Rainfall model tests further demonstrate that the combined treatment delays erosion failure, reduces infiltration rate and soil loss, and suppresses wetting front migration and internal water content response. These findings indicate that MICP combined with SG can effectively improve the anti-seepage, erosion resistance and surface stability of shallow loess slopes, providing experimental support for eco-friendly shallow slope protection in loess regions. Full article

Clarifying the sources of disparity and the systemic structure of influencing factors behind China’s economic resilience is crucial for promoting regional coordinated development and ensuring national security. This study constructs an evaluation index system based on the DPSIR model and employs the entropy method to measure China’s economic resilience from 2008 to 2023, examining its temporal evolution and spatial distribution. A bi-dimensional decomposition method of Gini coefficient is applied to examine disparities from both spatial and structural perspectives. Furthermore, the DEMATEL-ISM model is employed to reveal the systemic structure of influencing factors. The findings reveal that: (1) China’s economic resilience steadily improved during the study period, showing a spatial gradient of “Eastern > Central > Northeastern > Western,” with its geographic center shifting southeastward, reflecting strong spatial dependence. (2) Disparities in economic resilience have generally widened. Inter-regional differences are the main source of spatial disparities, while variations in response dominate the structural disparities. Initially, disparities were mainly due to differences in influence between eastern and western regions, but by the end of the period, disparities in driving forces became the key contributor. (3) Influencing factors follow a four-level, three-stage hierarchical structure. Foreign capital withdrawal risks, innovation investment, technological progress, factor supply, and the output of opening-up constitute deep-level factors influencing economic resilience. This study refines the evaluation framework of economic resilience and provides important references for understanding the disparities in China’s economic resilience and developing targeted improvement strategies. Full article

Background/Objectives: The aim of current study was the significant improvement of both the flowability and the compressibility of mesoporous silica microparticles (MSMs), to enable the formulation a potential drug delivery system. MSMs are of emerging interest in the pharmaceutical industry, due to their numerous advantages and versatile applicability, such as improvement in aqueous solubility and epithelial permeability, thus enhancing the oral bioavailability of drugs. However, the formulation of these types of materials has been a major challenge. This problem originates from poor powder flow characteristics due to particle properties. Methods: A binder-free high-shear wet granulation (HSWG) process was performed to improve the flowability and compressibility of the model material, meanwhile preserving its porosity. The prepared granules were characterized by particle size, size distribution, yield percentage, particle morphology, porosity, powder flowability, crushing strength, and stability. Micro-CT measurements were performed to examine the structure of the granules and to see the internal segmentation resulted by the two-step granulation process. The granules were compressed into tablets to evaluate the compressibility behavior based on the models of Kawakita and Walker. The physical parameters of the compressed tablets, such as breaking hardness, tensile strength, and thickness, were tested. Results: The prepared granules were evaluated successfully according to the mentioned properties and found to be satisfactory compared to the raw materials. The binder-free method appeared to be effective, thus the use of binders may be avoided if the process is designed well and critical process parameters (CPPs) selected carefully. The granules showed good stability over a one-year testing period. The micro-CT test also verified the success of the initial concept of preparing core–shell structured granules, and enabled the determination of macropores. Nevertheless, the results were completed with BET measurements to determine specific surface area of the granules. Conclusions: The effect of the critical process parameters of the granulation process on all the mentioned attributes was investigated and since major differences were observed between the batches, the effect of the selected CPPs were also verified. Full article

Background: Gestational diabetes mellitus (GDM) affects between 1% and 14% of pregnancies worldwide. Major risk factors include advanced maternal age, excess adiposity, family history of type 2 diabetes, and unhealthy dietary habits. In Mexico, evidence on the association between dietary patterns and GDM remains scarce, particularly in socioeconomically vulnerable populations with limited access to specialized nutrition services. This study aimed to evaluate the association between dietary patterns and the presence of GDM in pregnant women attending the outpatient obstetrics clinic of a teaching public hospital in Guadalajara, México. Methods: We conducted a case–control study including 169 pregnant women: 71 with GDM confirmed by the ADA one-step 75 g oral glucose tolerance test OGTT criteria and 98 without GDM based on a negative OGTT, recruited consecutively from the same clinic during the same period. Dietary intake was assessed using a culturally adapted and validated Food Frequency Questionnaire. Dietary patterns were identified through Principal Component Analysis, and associations were examined using logistic regression adjusted for maternal age, pregestational BMI, and family history of type 2 diabetes. Results: Women with GDM had higher maternal age, greater pregestational BMI, and more frequent family history of type 2 diabetes compared with controls. Three dietary patterns were identified: Western, Healthy, and Dairy/Refined. High adherence to the Western pattern was inversely associated with GDM (aOR = 0.36; 95% CI: 0.16–0.78; p = 0.010); however, this finding most likely reflects post-diagnosis dietary modifications rather than a protective effect, while maternal age remained the strongest risk factor (OR = 1.09; 95% CI: 1.03–1.16; p = 0.002). The Healthy pattern (aOR = 1.25; 95% CI: 0.55–2.82; p = 0.593) and the Dairy/Refined pattern (aOR = 0.80; 95% CI: 0.39–1.66; p = 0.554) were not significantly associated with GDM in the adjusted model. Conclusions: GDM was associated with older maternal age, higher pregestational BMI, and family history of T2DM. The inverse association with the Western pattern may reflect post-diagnosis dietary changes rather than a protective effect. Due to the retrospective design, causal inference is not possible, highlighting the need for longitudinal studies. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Background: Effective strategies for delivering neuroprotective agents to the brain remain a major challenge due to the poor solubility, rapid metabolism, and low bioavailability of promising molecules, such as 7,8-dihydroxyflavone (7,8-DHF). This small-molecule TrkB receptor agonist exhibits significant antioxidant, neuroprotective properties, and additional effects on metabolic regulation, but its therapeutic potential is limited by unfavorable pharmacokinetic characteristics. Nanotechnology-based delivery systems are increasingly explored to improve drug stability, enhance bioavailability, and facilitate direct nose-to-brain transport following intranasal administration. In this study, lipid nanoparticles encapsulating 7,8-DHF were developed using a fish-oil-based lipid core enriched with ω-3 polyunsaturated fatty acids (DHA and EPA) and naturally derived excipients, including soybean lecithin and ε-polylysine. Methods: The formulation was optimized using a Design of Experiments (DoE) approach based on a 2³ full factorial design, evaluating drug concentration, lecithin concentration, and surfactant type (Pluronic^® F127 or Tween^® 80). The main formulation responses considered were particle size, polydispersity index (PDI), zeta potential, and encapsulation efficiency. Results: The optimized nanoparticles exhibited nanometric dimensions (<250 nm); spherical morphology, confirmed by TEM; low polydispersity (PDI < 0.3); and adequate encapsulation efficiency. Stability studies in simulated biological fluids indicated good physicochemical stability for up to 48 h, while interaction studies with mucin suggested a good interaction within the mucus environment. ROS scavenging capacity was confirmed through the DPPH chemical assay, and in vitro experiments on olfactory ensheathing cells, selected as a biologically relevant model for their anatomical localization along the olfactory pathway, showed reduced cytotoxicity of the encapsulated drug compared with the free form. Conclusions: Collectively, these results support the potential application of the developed nanoformulation in the intranasal delivery of 7,8-DHF. Full article

Background/Objectives: Exposure to particulate matter (PM) containing bacterial endotoxins triggers inflammation and oxidative stress in the respiratory epithelium. In this study, we investigated chitosan nanoparticle-loaded Cordyceps militaris grown on germinated Rhynchosia nulubilis (GCN) as a potential functional food-derived ingredient against PM- and lipopolysaccharide (LPS)-induced cellular damage in human lung epithelial cells. Methods: This study employed an integrative approach combining GCN analysis with bioinformatics methods using a PM- and LPS-induced pulmonary cellular inflammation model. Gene Expression Omnibus (GEO) transcriptomic datasets and Cytoscape-based network analysis were utilized to identify key hub genes and signaling pathways associated with PM- and LPS-induced pulmonary inflammation, which were subsequently validated by RT-PCR and Western blotting. Results: Nano-encapsulation significantly improved the antioxidant capacity and storage stability of the extract compared with non-encapsulated Cordyceps militaris grown on germinated Rhynchosia nulubilis (GRC). GCN markedly attenuated PM- and LPS-induced cytotoxicity and intracellular reactive oxygen species (ROS) production in a dose-dependent manner, resulting in a therapeutic index approximately 4.5-fold higher than that of GRC under PM and LPS co-exposure. Bioinformatics analysis identified inflammation-related genes and pathways associated with PM- and LPS-induced pulmonary responses, primarily enriched in tumor necrosis factor (TNF)-related inflammatory pathways, Toll-like receptor signaling, and cytokine signaling. Consistent with these findings, GCN suppressed the expression of C-X-C motif chemokine ligand 2 (CXCL-2) and tumor necrosis factor-alpha (TNF-α) mRNA and inhibited mitogen-activated protein kinase (MAPK)-mediated activator protein-1 (AP-1) and nuclear factor-kappa B (NF-κB) signaling pathways in human type II alveolar epithelial cells (A549). Conclusions: Collectively, nano-encapsulation enhanced the stability and bioactivity of Cordyceps militaris-based extracts, suggesting that GCN may have potential as a functional food-derived candidate ingredient to protect airway epithelial cells against inflammation and oxidative stress induced by PM and LPS. As this study was conducted using an in vitro A549 epithelial cell model, further validation in physiologically relevant systems is needed to confirm its translational applicability. Full article