Search Results (35,318)

Healthcare-associated infections (HAIs) remain a significant global challenge, affecting approximately 7% of patients in developed countries and over 10% in developing regions, according to the World Health Organization. Medical textiles, particularly hospital bed linens and pillowcases, play a critical role in the transmission of pathogenic microorganisms due to their porous structure and moisture-retaining properties, which support microbial survival and proliferation, including bacteria such as Staphylococcus aureus and Escherichia coli. Conventional disinfection methods, including laundering and thermal treatments, provide only temporary protection, leading to rapid recontamination during use. In recent years, various antimicrobial agents and functionalization techniques have been developed to impart long-lasting antiseptic properties to textile materials. However, these approaches differ significantly in terms of antimicrobial efficiency, durability, cost-effectiveness, and environmental impact, making the selection of optimal strategies challenging for practical healthcare applications. This review provides a comprehensive comparative analysis of antimicrobial agents used in healthcare textile functionalization, including metal-based nanoparticles, organic compounds, and bio-based materials. In addition, it evaluates key modification methods such as coating, padding, and in situ synthesis, with particular emphasis on their influence on antimicrobial performance, wash durability, and practical applicability. Furthermore, this review discusses major challenges associated with the use of antiseptic coatings, including toxicity, environmental concerns, and economic limitations. Based on the analysis, promising directions for the development of safer, cost-effective, and durable antimicrobial textile systems are highlighted, offering valuable insights for future research and real-world healthcare applications. Full article

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

The application of rheological modeling in polyolefin-based systems has gained increasing attention in the context of sustainable materials and circular economy strategies. In particular, the use of recycled polyolefins reinforced with lignocellulosic fillers presents significant opportunities, but also introduces challenges associated with structural heterogeneity, degradation, and variability in processing behavior. Despite rheology’s central role in linking structure, processing, and properties, its use as a predictive tool in recycled systems remains insufficiently systematized. This work presents a systematic review conducted according to PRISMA guidelines to analyze the use of rheological models in polyolefin-based systems, with particular emphasis on their applicability to recycled materials and composite formulations. We analyze 50 studies using a structured data extraction protocol. The results show that rheological modeling approaches can be organized into a hierarchical framework ranging from indirect flow parameters and generalized Newtonian fluid models to viscoelastic, structural, multiscale, and hybrid approaches. However, these approaches are not evenly distributed across system types. Advanced models are predominantly applied to compositionally controlled systems, whereas recycled and post-consumer polyolefins are mainly addressed using simplified models or experimental characterization. The analysis further indicates that rheology is primarily used for data fitting and process simulation, with limited application as a predictive tool for material formulation. Quantitative trends reported in the literature indicate that filler incorporation typically increases viscosity by approximately 20–200%, depending on filler content, dispersion quality, and interfacial interactions. However, variability in experimental conditions and material heterogeneity significantly limits cross-study comparability. From a mechanistic perspective, the main limitation lies not in the availability of rheological models but in their adaptability to heterogeneous systems characterized by variable composition, degradation, and limited experimental accessibility. This review identifies a gap between the development of rheological models and their application in recycled polyolefin systems. Future progress on eco-composite design will require further development of integrative approaches that balance physical insight, predictive capability, and experimental feasibility. In this context, rheology should be repositioned from a post-characterization technique to a central tool for the design and optimization of sustainable polymer composites. From an applied perspective, these findings support the use of rheological parameters as practical indicators for guiding formulation strategies and optimizing processing conditions in recycled polyolefin-based materials. Full article

Background: Gliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increasing availability of large-scale multi-omics datasets resulting from advancements in sequencing technologies and online repositories that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels. Methods: In this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types. Results: Our results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers associated with survival as well as a transcriptional profile associated with neural system development. Conclusions: These findings may contribute to more personalized therapeutic strategies, potentially improving treatment effectiveness and survival outcomes in this disease. Full article

Harnessing low-grade thermal energy from industrial processes and the environment represents an attractive route toward sustainable chemical production. In this work, we report a thermoelectrocatalytic (TE-Catal) system capable of converting small temperature gradients into chemical energy for hydrogen peroxide (H₂O₂) generation. A hybrid catalyst composed of nickel-based metal–organic frameworks (Ni-MOFs) nanoparticles integrated with carbon nanotubes (CNTs), Ni-MOFs/CNTs, was synthesized through a facile one-pot strategy. Under a temperature gradient, the thermoelectric response of the Ni-MOFs induces charge carrier generation through the Seebeck effect, enabling interfacial redox reactions that produce H₂O₂. However, rapid recombination of thermally generated carriers typically limits catalytic efficiency. By coupling Ni-MOFs with conductive CNTs networks, charge separation and transport are significantly enhanced due to the strong interfacial interaction and the high electrical conductivity of CNTs. As a result, the Ni-MOFs/CNTs nanohybrids exhibit greatly improved H₂O₂ generation rate of ~111.7 µmol g⁻¹ h⁻¹ compared with pristine Ni-MOFs (31.8 µmol g⁻¹ h⁻¹). Thermoelectric electrochemical measurements confirm that the CNT incorporation effectively promotes carrier migration and suppresses recombination. This study demonstrates the potential of MOF-based thermoelectric nanostructures for transforming waste heat into valuable chemical products. Full article

Fisheries wastewater contains high levels of suspended solids and organic matter, posing significant environmental risks and necessitating effective and sustainable treatment approaches. This study aims to determine the characteristics of the neem (Azadirachta indica) leaf biocoagulant, assess the interactions among research variables, and optimize its use to reduce total suspended solids (TSS) and chemical oxygen demand (COD) levels in fisheries wastewater. The method used is response surface methodology (RSM), specifically the Box–Behnken Design (BBD), which involves three variables (biocoagulant concentration, fast stirring speed, and sedimentation time) and two responses (TSS and COD removal). Characterization results (Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and zeta potential) indicated that the biocoagulant contains functional groups such as hydroxyl, carboxyl, and amine, contributing to coagulation–flocculation through adsorption and polymer bridging mechanisms. Statistical analysis confirmed that the developed quadratic models were significant (p-value < 0.05), with high F-values, non-significant lack of fit, and strong coefficients of determination (R² = 0.9111 for TSS and 0.9419 for COD), along with low coefficients of variation (CV < 5%), indicating good model reliability. Although the model generally has a significant effect on the response, the fast stirring speed does not, while the other two factors do. The optimal conditions (based on desirability) were determined to be a biocoagulant concentration of 79.8 mg/L, a fast stirring speed of 100 rpm, and a sedimentation time of 27.5 min. Under these conditions, TSS and COD removals of 88.72% and 79.98%, respectively, were achieved. These findings demonstrate the potential of neem leaf biocoagulant as a sustainable, environmentally friendly alternative to conventional chemical coagulation, supporting cleaner production in aquaculture systems. Full article

Conductive hydrogels have emerged as promising candidates for flexible strain sensors owing to their high water content, low elastic modulus, and intrinsic ionic conductivity. However, conventional hydrogel networks often suffer from an inherent trade-off among conductivity, mechanical robustness, and long-term stability, which limits their practical deployment in wearable sensing scenarios. The introduction of metal–ligand coordination bonds into hydrogel networks offers a versatile strategy to address these challenges: dynamic coordination cross-links can dissipate energy under deformation and reform upon unloading, thereby enhancing toughness, enabling self-healing, and contributing to ionic transport. This review focuses on metal-ion-coordinated conductive hydrogels designed for strain-sensing applications. Representative coordination systems based on Fe³⁺, Ca²⁺, Zn²⁺, Al³⁺, Cu²⁺, Ti⁴⁺, and Zr⁴⁺ are surveyed, with emphasis on their characteristic polymer matrices, ligand chemistries, and network-construction strategies. Key sensing-relevant properties—including ionic conductivity, mechanical stretchability, self-healing capability, interfacial adhesion, freezing resistance, and resistance to dehydration—are discussed in relation to coordination network design. Typical application demonstrations in large-deformation motion monitoring and subtle physiological signal detection are reviewed. Unlike existing reviews that survey conductive hydrogels broadly by conductive mechanism or sensor type, this review takes metal-ion coordination as the central organizing principle and systematically traces its influence across the full design chain—from ion–ligand coordination chemistry through network architecture to macroscopic sensing output. By comparatively analyzing seven representative metal-ion systems within a unified framework, this work aims to clarify how the choice of metal ion governs the interplay among conductivity, mechanical robustness, self-healing, and strain sensitivity—a perspective that has not yet been systematically addressed in prior reviews. Finally, current challenges—including the conductivity–mechanics coupling bottleneck, insufficient long-term stability, biosafety concerns for skin-contact deployment, the lack of standardized evaluation protocols, and device-integration barriers—are identified, and future directions for this field are outlined. Full article

Polybrominated diphenyl ethers (PBDEs), widely used as brominated flame retardants, are known to exert persistent adverse effects on the immune systems of humans and other organisms. Previous studies have demonstrated that 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), a prevalent congener, induces apoptosis, impairs phagocytic function, and triggers aberrant immune-inflammatory reactions in RAW264.7 macrophages via the induction of elevated intracellular reactive oxygen species (ROS). However, the underlying regulatory mechanism remains unclear. The nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE) signaling pathway is a key cellular defense system against oxidative stress. In this study, we investigated the role of the Nrf2/ARE pathway in BDE-47-induced macrophage immunotoxicity. Network toxicology analysis identified Nrf2 as a hub gene within the BDE-47-associated immunotoxicity network. Molecular docking and molecular dynamics simulations suggested a potential interaction between BDE-47 and the Keap1-Nrf2 complex, with moderate binding affinity. Experimental studies in RAW264.7 cells showed that BDE-47 exposure activated the Nrf2/ARE pathway, as evidenced by Nrf2 nuclear translocation and the differential upregulation of downstream genes (GCLC, GCLM, HO-1, NQO1, SOD1, and CAT). Importantly, Nrf2 knockdown via lentiviral shRNA or pharmacological inhibition with brusatol significantly exacerbated BDE-47-induced apoptosis and immune dysfunction, including enhanced pro-inflammatory cytokine production and impaired phagocytosis. These results demonstrate that Nrf2/ARE pathway activation represents an adaptive antioxidant response and contributes to limiting BDE-47-induced cytotoxicity and immune impairment in macrophages. Full article

Efficient reproductive management is essential for optimizing productivity and sustainability in sheep and goat production systems. Estrus synchronization (ES) has emerged as a pivotal tool for coordinating mating, enhancing fertility, facilitating artificial insemination (AI), and supporting out-of-season breeding. Hormonal protocols, including progesterone devices, prostaglandins, and gonadotropin or gonadoliberine treatments, provide the highest precision in estrus and ovulation timing, with estrus response rates exceeding 90% and conception rates commonly between 65–85%. These methods are particularly effective in intensive or AI-based systems but are constrained by cost, labor, regulatory restrictions, and welfare considerations. Non-hormonal strategies, such as the ram effect, photoperiod manipulation, nutritional flushing, and management-based interventions, exploit natural physiological, socio sexual, and nutritional cues to partially synchronize estrus. While these approaches exhibit greater variability and lower precision than hormonal methods, they offer advantages in low input, organic, and extensive systems by improving reproductive clustering, ovulation, and lambing compactness. Among these, the ram effect is the most effective and widely applicable. Integrated reproductive management, combining hormonal or non-hormonal strategies with optimized nutrition, health, and flock management, is critical for achieving predictable and sustainable reproductive outcomes. Future research should focus on refining hormone-sparing protocols and enhancing the reliability of natural synchronization methods. Full article

Digital governance in higher education represents a complex systemic challenge, shaped by the intricate interplay of socio–economic–political contexts, technological infrastructures, and multiple stakeholders. Yet existing scholarship tends to examine these factors in isolation, lacking an integrated theoretical lens capable of capturing their systemic interdependencies and dynamic interactions. This study addresses this gap by drawing on the Social–Ecological Systems (SES) framework—a well-established systems theory for analyzing coupled social and ecological dynamics—to construct an integrated analytical framework for university digital governance. The framework organizes governance into three interconnected dimensions: external contexts, internal systems, and interaction effects. External contexts—including technological ecosystems and socio–economic–political factors—shape opportunities and constraints for universities. Internal systems, comprising resource systems, resource units, governance structures, and actors, form a complex network through information flows, resource flows, and institutional arrangements. Interaction effects emerge from these networks and are observed in both social outcomes and ecological outcomes, encompassing both positive and negative dimensions. The framework advances theory by extending the SES perspective to higher education, integrating multiple governance elements, and operationalizing core variables for measurement. Practically, it provides universities with a systematic tool for diagnosing digital governance performance, identifying gaps, and guiding optimization, while also supporting cross-institutional benchmarking and longitudinal monitoring. Future research should empirically test the framework, refine the operational indicators, and explore its applicability across diverse institutional and cultural contexts. Full article

Fluorosis, a systemic condition caused by chronic excessive fluoride intake, poses significant threats to livestock health and agricultural productivity worldwide. This systematic review synthesizes current evidence on the modulatory effects of exercise against fluorosis, integrating human studies, animal experiments, and methodological considerations. Human studies indicate negative associations between fluoride exposure and cognitive development, muscle function, and exercise capacity, with exercise influencing fluoride pharmacokinetics in an exercise-intensity-dependent manner. Animal experiments consistently demonstrate that regular moderate-intensity exercise attenuates fluoride-induced damage across multiple organ systems through activation of the Nrf2/ARE antioxidant pathway, modulation of BMP-2/Smads and OPG/RANKL/RANK signaling, suppression of inflammatory responses, and preservation of intestinal barrier integrity. Substantial heterogeneity exists among current fluorosis models regarding exposure dosages, durations, and exercise protocols, underscoring the need for standardization and consideration of genetic background. Overall, exercise shows promise for mitigating fluorosis-induced multi-organ damage, although human evidence remains limited. Future research should prioritize model optimization, elucidation of molecular targets, and exploration of synergistic interventions to provide a foundation for veterinary clinical management. Full article

Over the past 200 years (1820–2020), global life expectancy has nearly tripled, increasing from 26 to 72.91 years, due to factors such as poverty reduction and public health initiatives. Today, society faces different challenges than it did centuries ago. In patient care and healthcare system priorities, the goal is to develop smart, feasible, long-lasting, cost-effective, readily available, adverse-reaction-free, adaptable, and personalized solutions that minimize patient discomfort, reduce caregiver effort, and decrease hospitalization duration and costs. In this context, biomaterials serve as versatile tools capable of performing a wide range of diagnostic, therapeutic, and theragnostic functions. Thanks to their biocompatibility, biodegradability, surface chemistry, and responsiveness, biomaterials are currently addressing issues such as patient compliance (through controlled drug-delivery systems and smart wound dressings), long transplant waiting lists, transplant rejection, non-adaptable prosthetics (artificial organs), oncology treatment efficacy (nano-formulations for theragnostics and multiple tumor targeting), and inconsistent in vitro drug-testing models (organs-on-a-chip). In this review, we focus on biomaterials’ smartness, then explore databases for efficient product design, and finally highlight their applications in the biomedical field, especially in drug delivery, tissue engineering, and regenerative medicine. Full article

Dexamethasone induces systemic toxicity, including oxidative stress, inflammation, hematological disturbances, and organ damage, particularly in the lungs and thyroid. Taurine exhibits antioxidant and anti-inflammatory properties, but poor bioavailability limits its efficacy. Nanoparticle delivery may enhance stability and tissue targeting. This study aimed to evaluate the protective effects of taurine-loaded chitosan nanoparticles (Tau–CS NPs) against dexamethasone-induced tissue injury in rats. Forty-eight male Wistar rats were allocated into control, DEXA, DEXA + silymarin, DEXA + taurine, and DEXA + Tau–CS NPs groups. Tau–CS NPs were characterized by TEM, UV–vis, FTIR, encapsulation efficiency, and drug loading. Hematology, oxidative stress markers (CAT, SOD, GSH, MDA), thyroid hormones (T3, T4, TSH, calcitonin), protein profile, lung and thyroid histopathology, and MPO expression were assessed. Tau–CS NPs showed uniform spherical morphology (11–60 nm), high encapsulation (98.2%), and substantial loading (50.36%). Dexamethasone caused hematological, oxidative, thyroidal, and histological disturbances. Tau–CS NPs markedly restored hematological indices, antioxidant defenses, thyroid function, protein profile, and tissue architecture, outperforming free taurine and silymarin. MPO expression was significantly reduced, indicating decreased inflammation. Taurine nanoparticles effectively mitigate dexamethasone-induced systemic and organ-specific toxicity, offering improved bioavailability and targeted delivery, highlighting their therapeutic potential. Full article

Currently, the number of people who have been investigated for skin cancer has increased significantly worldwide. For prior diagnosis, dermatologists can typically visually inspect skin lesions for abnormalities. However, an expert is required, and the similarity of some skin lesions remains challenging. This study aimed to address the challenge of classifying multiple images of skin conditions, including both Benign and Malignant groups, using the hierarchical method. Instead of directly performing multi-class classification using a single model, multiple binary classification models were organized to reduce task complexity and improve overall performance. In the methodology, four convolutional neural network (CNN) models, namely MobileNetV2, EfficientNet-B0, ResNet-18, and ResNet-50, were selected as candidates for this problem. The proposed hierarchical binary classification model was evaluated against conventional multi-class classification methods. As a result, various evaluation metrics were used to assess model performance, with recall as the primary metric in this study, given the emphasis on minimizing false negatives. However, some results revealed discrepancies between the highest recall and other performance metrics. Further analysis demonstrated the potential of using recall as a selection criterion for identifying the most suitable CNN models. The single model-based classification of six classes of skin lesion images achieves the highest recall of 60.27% with MobileNetV2. Meanwhile, the proposed hierarchical model achieves a higher recall of 82.62%, representing a significant increase of 22.35%. Additionally, improvements were observed across all other evaluation metrics, including accuracy (+25.46%), precision (+17.21%), F1-score (+21.34%), balanced accuracy (+12.69%), specificity (+3.03%), and G-mean (+14.25%). These improvements indicate enhanced performance in correctly identifying both positive and negative cases, while reducing misclassification rates. This outcome demonstrates the potential to improve the model’s generalizability, thereby increasing its applicability across various clinical decision-support systems. Full article

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger (IML) framework, which augments a Markov game with monitoring, evidence logging, delayed settlement, and review while leaving the base dynamics unchanged. We derive conservative incentive checks that clarify how detection quality, review accuracy, settlement delay, and sanction size jointly shape deterrence and wrongful-penalty risk. We then provide pilot evidence in two canonical sequential social dilemmas, Harvest and Cleanup, using five agents, PPO training, five training seeds per condition, and comparisons against PPO, inequity aversion, social influence, and IML ablations. In these settings, IML avoided some of the optimization instability observed in the representative internalization baselines tested here, made monitoring error directly visible through ledger records, and showed how false positives can accumulate into a persistent welfare cost. Agent-level analyses in these symmetric environments found nearly uniform measured enforcement burden, while temporal analyses showed that late-stage enforcement is increasingly dominated by residual false positives. These results do not establish legitimacy in human-facing settings or deployment readiness. They instead position IML as a framework with pilot evidence for studying accountability mechanisms in cooperative human–AI systems and highlight measurement error, review design, and due process as central design constraints. Full article