Search Results (1,954)

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness driven by elevated intraocular pressure from compromised aqueous outflow. While genome-wide association studies have identified numerous risk loci, specific candidate proteins and their cellular mechanisms remain elusive. We employed a multi-omics framework integrating UK Biobank plasma proteomics (N = 53,022) and large-scale POAG GWAS summary statistics. We performed a Proteome-Wide Association Study, Mendelian Randomization, and Bayesian colocalization to infer causality. Identified candidates were mapped to human and mouse ocular scRNA-seq atlases to characterize cell-type specificity, followed by druggability assessments. We prioritized five putative causal proteins, with SEL1L and TFPI demonstrating the strongest evidence. Cross-species scRNA-seq revealed that SEL1L and SERPINF1 are robustly expressed in the trabecular meshwork (TM), particularly the juxtacanalicular tissue, implicating them in outflow resistance. Conversely, TFPI and SLC9A3R2 localize to Schlemm’s canal endothelium, suggesting a role in modulating barrier function. Pathway analyses highlighted endoplasmic reticulum protein processing and coagulation cascades. This study maps putative causal POAG proteins to conventional outflow pathway cells, highlighting SEL1L as a novel target for TM homeostasis and TFPI for drug repurposing, thereby providing data-driven hypotheses to facilitate precision glaucoma therapeutics. Full article

Sarcopenia is an age-related syndrome characterized by the progressive loss of skeletal muscle mass, strength, and function, significantly impairing older adults’ independence and quality of life. Given their anti-inflammatory, antioxidant, and metabolic regulatory properties, n-3 polyunsaturated fatty acids (n-3 PUFAs) have emerged as a promising nutritional strategy to mitigate this muscle degeneration. This review systematically synthesizes existing evidence regarding the association between n-3 PUFAs and sarcopenia. To capture the relevant literature, we searched PubMed, Web of Science, CNKI, and Wanfang Data using a combination of subject headings and free-text terms. We supplemented primary search terms—such as “n-3 polyunsaturated fatty acids,” “omega-3 fatty acids,” “sarcopenia,” and “muscle mass”—with mechanism-related keywords like “inflammation,” “muscle satellite cells,” and “oxidative stress.” We also manually screened the reference lists of the included literature. Our inclusion criteria encompassed interventional studies, observational studies, and high-quality reviews, while excluding conference abstracts, duplicate publications, and studies with incomplete data. This review first outlines the established biological mechanisms linking n-3 PUFAs to the pathological progression of sarcopenia, specifically detailing how these fatty acids improve muscle satellite cell function, suppress inflammation and oxidative stress, and ameliorate metabolic disorders. Next, we critically evaluate recent clinical studies and reviews, analyzing sources of study heterogeneity such as variations in sample size, intervention dose and duration, outcome measures, and baseline participant characteristics. We also highlight current research hotspots—including specialized pro-resolving mediators (SPMs), the gut–organ axis, combined interventions, and precision nutrition strategies—while emphasizing the functional differences between EPA and DHA to guide future intervention designs. Current evidence indicates that while n-3 PUFA supplementation can improve muscle strength and physical performance in older adults, its effects on muscle mass remain inconsistent. Addressing key research gaps, particularly the lack of standardized core outcome measures and unclear dose–response relationships, is critical. Ultimately, future research must prioritize developing high-bioavailability formulations, conducting personalized trials based on baseline n-3 PUFA status, and deepening investigations into inter-organ networks to translate these nutritional insights into effective sarcopenia prevention and management strategies. Full article

Background/Objectives: Atrial fibrillation (AF) is a complex polygenic disorder; its genetic architecture remains challenging to fully elucidate. Methods: In this study, we leveraged the extensive genetic overlap between AF and a spectrum of cardiometabolic and behavioral factors—collectively defined by Life’s Essential 8 (LE8)—to advance our understanding of its etiology. Results: We first estimated significant genetic correlations between AF and all LE8 components (r_g: −0.11 to 0.19) using LD score regression. We then applied conditional false discovery rate analysis and detected 970 pleiotropic loci associated with AF and at least one LE8 trait. Subsequent colocalization analysis identified 179 loci harboring shared causal variants between AF and one or more LE8 components, which were further refined into 137 distinct colocalized regions. Through region-based annotation and functional predictors, we finally prioritized 164 candidate genes from these colocalized loci, including 40 novel genes. These candidate genes were enriched in pathways related to heart development and regulation of cardiac contraction, and were also enriched among molecular targets of otological agents. Among all LE8 components, blood pressure demonstrated the most extensive shared genetic architecture with AF, supported by the strongest genetic correlation, highest pleiotropic enrichment, and the greatest number of colocalized loci with AF. Polygenic risk scores constructed from these colocalized loci demonstrated significant associations not only for AF but also for arrhythmia and heart failure. Conclusions: Our findings establish a genetic pleiotropy-informed framework that enhances the discovery of novel risk loci of AF and advances our understanding of the shared genetic architecture and potential biological mechanisms between AF and LE8 components. Full article

The study proposes and investigates a seismic retrofitting strategy based on an external reinforced concrete exoskeleton, grounded in the analysis of the actual force transfer mechanisms between the existing structure and the added system. The three-dimensional numerical model was developed in ETABS, employing linear response spectrum analysis in accordance with EN 1998-1 and P100-1/2013. The internal forces transmitted at the structural interface were determined using the Section Cut method, enabling the identification of integrated resultants and the prioritization of critical connections. Three types of connections are examined—slab-to-slab, column-to-wall, and beam-to-joint—while the distribution of stresses within the anchor groups is assessed based on an elastic model under combined axial force and bending action. The results indicate that the global structural response is governed by diaphragm coupling, whereas the vertical interfaces ensure kinematic compatibility and the redistribution of axial and bending effects. The proposed methodology provides a coherent framework for the rational design of interface connections in retrofit interventions carried out without interrupting building operation. Full article

Background/Objectives: Sport participation and nutrition are increasingly recognized as key determinants of cognitive function and academic achievement in student populations. However, the biological mechanisms underpinning these associations remain only partially understood. This scoping review aimed to map and synthesize the current evidence on neuroendocrine and autonomic mechanisms linking physical activity, sport participation, and nutrition to cognitive and academic outcomes in students. Methods: A systematic search of electronic databases was performed following PRISMA-ScR guidelines. Studies involving student populations that examined physical activity, sport participation, or dietary patterns in relation to cognitive function and/or academic performance were included. Particular attention was given to studies reporting biological or physiological indicators of underlying mechanisms, including neuroendocrine, autonomic, and brain-based measures. Data were extracted and synthesized qualitatively, with studies categorized according to the type of mechanistic evidence. Results: A total of 76 studies met the inclusion criteria. The available evidence was more extensive for physical activity, sport participation, and fitness-related exposures than for nutrition-related variables or integrated lifestyle models. Cognitive outcomes, particularly executive function, attention, working memory, and memory performance, were assessed more frequently and showed more consistent associations with lifestyle behaviors than academic outcomes, which were less commonly and more heterogeneously evaluated. Mechanistic evidence was unevenly distributed: only a limited subset of studies included direct biological or psychophysiological measures, mainly neuroimaging, brain-derived neurotrophic factors, cortisol-related indices, or heart rate variability. In contrast, inflammatory, metabolic, and gut microbiota-related mechanisms were mostly discussed at a conceptual or indirect level. Overall, the findings indicate a broad associative literature but a relatively small body of studies directly testing biological pathways linking physical activity, nutrition, cognition, and academic performance. Conclusions: Current evidence indicates potential associations between sport participation, nutrition, cognitive outcomes, and multiple biological pathways. However, the scoping nature of the review, the predominance of observational designs, and the limited use of direct mechanistic assessments prevent firm causal conclusions. Future research should prioritize longitudinal and intervention studies integrating behavioral, nutritional, cognitive, academic, and biological measures within the same design. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

This translational synthesis highlights the potential role of obesity-induced low-grade chronic inflammation in modulating clinical outcomes among patients with spondyloarthritis (SpA). Obesity transforms adipose tissue into a pro-inflammatory endocrine organ, where hypertrophic adipocytes release adipokines such as leptin alongside cytokines including TNF-α and IL-6, potentially contributing to macrophage polarization toward an M1 phenotype and activating NF-κB signaling pathways. This systemic immunometabolic priming may lower activation thresholds at the enthesis—the primary pathological site in SpA—potentially amplifying IL-23/IL-17 axis activity via Th17 bias, innate-like lymphocyte responses, and stromal–immune crosstalk under mechanical stress. Clinically, patients with SpA and obesity have been reported to demonstrate heightened disease activity (BASDAI, ASDAS), impaired function (BASFI), accelerated radiographic progression (syndesmophytes, enthesophytes), and diminished biologic response rates, potentially attributable to pharmacokinetic alterations (e.g., subtherapeutic TNF inhibitor levels) and pharmacodynamic resistance. Multisystem comorbidities, including non-alcoholic fatty liver disease, cardiovascular events, metabolic syndrome, sleep disturbances, and depression, further exacerbate morbidity and diminish quality of life. Therapeutic implications emphasize obesity as a modifiable disease modifier. Weight loss interventions, including hypocaloric diets, anti-inflammatory regimens (e.g., Mediterranean diet), multicomponent exercise, GLP-1 receptor agonists, and bariatric surgery, have been associated with reductions in inflammatory biomarkers, improved remission rates (MDA, DAPSA), and prolonged drug survival by restoring adipokine balance and disrupting mechano-inflammatory loops. Future randomized controlled trials should prioritize long-term evaluations of integrated multidisciplinary strategies that combine metabolic optimization with immunomodulatory therapies, addressing adherence challenges through psychological support and patient-tailored protocols, while elucidating dose–response relationships for GLP-1RAs and exercise in diverse SpA subtypes to establish precision management paradigms that mitigate cardiometabolic burden and improve holistic outcomes. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

Considering rising global uncertainties and intensifying resource and environmental pressures, it has become an inevitable trend to add more ecologically green factors to the traditional industrial chain resilience system and build a system of green industrial chain resilience (GICR). To address the inherent tension between security and green goals, this study develops a novel two-dimensional analytical framework encompassing fracture repair capacity and development regeneration capacity. This framework provides the theoretical foundation for constructing a pioneering city-level evaluation system for GICR. Employing this system and a suite of spatial econometric methods, we empirically analyze the spatiotemporal evolution, convergence, and driving mechanisms of GICR across 245 Chinese cities. The main findings are threefold. First, the proposed framework effectively captures the complexity of GICR, revealing an overall upward trend but significantly widening regional disparities, with a persistent core-periphery spatial pattern. Second, convergence analysis uncovers a club convergence dynamic nationwide, characterized by a notable “high-level equilibrium lock-in” in the advanced eastern region, in contrast to the catch-up convergence observed in central, western, and northeastern China. Third, geographical detector analysis identifies talent agglomeration as the paramount driver, with its interaction with other factors producing nonlinear enhancement effects. These findings underscore that enhancing GICR requires regionally differentiated strategies: policies must break the innovation lock-in in the east, embed resilience standards into industrial transfer in the central and western regions, and prioritize talent as the core lever for synergistic capacity building. Full article

Artemether, a derivative of the natural compound artemisinin, is increasingly recognized for its multi-target anti-inflammatory and antioxidant properties. This review systematically elucidates the molecular mechanisms underlying these effects, focusing on artemether’s dual modulation of the MAPK/NF-κB and Nrf2 signaling pathways. We detail how artemether concurrently inhibits the MAPK/NF-κB axis—suppressing IKKβ phosphorylation and IκBα degradation to block NF-κB nuclear translocation—and downregulates p38/contextually modulates ERK phosphorylation. This leads to a significant reduction in key inflammatory mediators, including TNF-α, IL-6, and COX-2. Simultaneously, artemether activates the Nrf2 antioxidant pathway, upregulating HO-1 expression and enhancing the activity of SOD and GSH-Px, which effectively scavenges free radicals and reduces markers of oxidative damage such as MDA and 8-OHdG. The core therapeutic synergy arises from artemether’s disruption of the ROS-NF-κB positive feedback loop, which inhibits neutrophil infiltration and lipid peroxidation, thereby ameliorating tissue injury in experimental models of arthritis and neurodegenerative diseases. Compared to conventional NSAIDs and glucocorticoids, artemether exhibits a favorable safety profile, particularly regarding gastrointestinal effects, and demonstrates unique immunomodulatory potential. Future research directions should prioritize the development of nano-targeted delivery systems and the elucidation of pathway crosstalk at the single-cell level to advance the clinical translation of artemether for chronic inflammatory diseases. Full article

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems. Full article

Background/Objectives: The Naxi people in Northwest Yunnan of China have used alcohol-soaked Pleione Pseudobulbus, which is the Pseudobulbus of Pleione bulbocodioides Rolfe (PBR), for lung diseases and tumors for a long period of time. This study aims to explore underlying mechanisms of bioactive ingredients in PBR, as well as to underscore the synergy between traditional medicine and modern pharmacological research. Methods: We verified the anti-tumor effects of the PBR extract through in vitro cell experiments, and explored its underlying mechanisms by combining untargeted metabolomics with network pharmacology to predict the related targets. Results: The anti-tumor potential of PBR extracts was systematically evaluated by integrating chemical profiling with in vitro cell-based assays. Untargeted metabolomics tentatively annotated metabolites spanning 12 major chemical classes, several of which have been previously reported to possess anti-tumor activity. To validate these annotations, prioritized candidates were further examined by LC-MS/MS against authentic reference standards at the nanogram scale, which confirmed the presence of sclareol—a naturally occurring diterpene with documented anti-tumor properties—as a constituent of PBR. Consistent with this chemical evidence, the PBR extract exerted multi-faceted anti-tumor effects in A549 lung cancer cells: it significantly suppressed proliferation, migration, and invasion; induced G0/G1-phase cell-cycle arrest; disrupted mitochondrial membrane potential; and modulated the expression of apoptosis-related proteins. Conclusions: By highlighting the pharmacological properties of cultivated PBR, we identified 118 overlapping targets between PBR compounds and lung disease-related targets, and we further selected 25 core lung cancer targets with high topological importance. This study suggests that the primary active compounds of Pleione bulbocodioides (Franch.) Rolfe may exert anti-lung cancer activity, potentially through targeting the EGFR tyrosine kinase inhibitor resistance pathway and the PI3K-Akt signaling pathway. Furthermore, in silico molecular docking suggested that the two major active compounds exhibited favorable predicted binding affinities with four core targets, particularly EGFR and AKT1, providing a basis for further experimental validation. These results support the potential value of Naxi traditional medicine and the need to further research onthese medicinal plants, thereby promoting Chinese herb medicine conservation efforts in the Naxi region. Full article

Marine invertebrates are characterized by high species diversity, a wide distribution, ease of culture, low cost, short life cycles and high sensitivity to pollutants, which makes them excellent models for observing toxic effects and elucidating underlying mechanisms. This paper reviews representative species from three phyla—Arthropoda, Mollusca, and Echinodermata—under both single emerging contaminant exposure and combined exposure scenarios, and analyzes the reproductive and neurotoxic impacts of these contaminants on marine invertebrates. Neurotoxicity is mediated by several key mechanisms: inhibition of acetylcholinesterase activity; disruption of neurotransmitter balance, oxidative stress; and cellular damage, interference with embryonic neural development and axis specification, and impairment of neural cell differentiation and migration. Reproductive toxicity impairs reproductive development by disrupting endocrine signaling, inducing oxidative stress, downregulating reproduction-related genes and damaging gonadal structure. Studies have shown that, besides environmental factors, contaminant concentration is closely correlated with toxic potency and differing concentration ratios can lead to either antagonistic or synergistic effects in combined toxicity. Current research has largely focused on single or binary contaminant systems, whereas studies on multi-contaminant mixtures and their interactions with multiple environmental factors remain limited. Future research should prioritize combined exposure to multiple contaminants, long-term multigenerational observations and the development of comprehensive ecological risk assessment models and monitoring standards, thereby providing a scientific basis for marine ecological conservation. Full article

Improving customer satisfaction (CS) and gaining competitive advantages are central goals of product improvement, both of which rely on accurate classification of product attributes. Online reviews on e-commerce platforms provide firms with abundant customer feedback, but accurately classifying and prioritizing product attributes remains challenging. To address this issue, we propose an interpretable Kano model. In this method, the Biterm Topic Model (BTM) is first used to identify product attributes from reviews. Then, the Enhanced BERT Model with Attribute-Aware and Convolutional Mechanisms (BERT-A-Conv) is employed to classify the sentiment categories of these attributes. Given the critical role of neutral sentiment, it is incorporated into the Light Gradient Boosting Machine (LightGBM) model to quantify the impact of AS on CS. The Shapley additive explanations (SHAP) method is then adopted to construct the marginal contribution difference (MCD) between adjacent categories, which this study uses to classify product attributes into five Kano categories. On this basis, we calculate the attribute improvement priority score (AIPS) by combining each attribute’s MCD and improvement potential, thereby offering firms a systematic analytical framework to support product iteration and improvement. A case study on smartwatches demonstrates the applicability and feasibility of the proposed method. Full article