Search Results (12,086)

Wound healing in cosmetological and aesthetic surgery extends beyond tissue closure to achieving rapid regeneration, minimal scarring, and restoration of functional skin architecture. However, conventional wound care strategies inadequately regulate the complex wound microenvironment required for optimal cosmetic outcomes, leading to prolonged healing times and suboptimal aesthetic results, which can negatively impact patient satisfaction and increase the risk of complications. Phytochemicals exhibit multifunctional bioactivities, such as antioxidant, anti-inflammatory, antimicrobial, and pro-regenerative effects, but their clinical translation faces obstacles due to poor solubility, stability, and bioavailability. Nanotechnology-based delivery systems have emerged as a critical enabling strategy to overcome these limitations. This narrative review provides an updated, mechanistically integrated synthesis of phytochemical-loaded nanotherapeutics, including polymeric nanoparticles, nanohydrogels, nanofibers, and lipid- and vesicle-based systems, with a specific focus on their roles in modulating key wound-healing pathways, such as inflammation resolution, angiogenesis, collagen remodelling, and re-epithelialization. Evidence from preclinical studies consistently demonstrates that nano-enabled phytochemicals enhance therapeutic efficacy, improve skin penetration, and contribute to superior cosmetic outcomes, particularly by reducing fibrosis and scar formation. However, critical gaps remain, including limited high-quality clinical evidence, a lack of standardized formulation design, variability in reported outcomes, and unresolved concerns regarding long-term safety and regulatory translation. Taken together, the key insight of this review is that phytochemical-loaded nanotherapeutics represent a promising but still transitional strategy, biologically compelling at the preclinical level yet clinically under-validated. Bridging this gap requires rigorously designed clinical trials, quantitative outcome reporting, and balanced regulatory frameworks. Advancing these areas will be essential to translate nano-enabled phytochemicals from experimental systems into reliable, evidence-based solutions for cosmetological wound management. Full article

Groundwater is a critical lifeline for ecosystems and human settlements in arid and semi-arid regions, yet it is increasingly vulnerable to the dual pressures of extreme climatic conditions and intensifying anthropogenic activities. This study investigated 24 groundwater and 4 river water samples to discuss the hydrogeochemical evolution and water quality suitability in the Tianjun Basin, a typical high-altitude arid basin on the northeastern Tibetan Plateau. The results indicate that groundwater is mildly alkaline (pH: 7.65–8.35) and predominantly fresh (TDS: 233.77–1061.42 mg/L). Hydrochemical facies evolve from HCO₃-Ca type in upstream areas to Mixed HCO₃-Na·Ca and Cl-Na types. Hydrochemical analysis suggests that silicate weathering and carbonate dissolution are the dominant natural processes, while cation exchange further modifies the ionic composition. Notably, anthropogenic nitrogen (NO₃⁻ and NH₄⁺) contamination, primarily from domestic sewage in the Tianjun Basin, has significantly impacted groundwater quality. Health risk assessment shows that infants are the most vulnerable group, with 16.67% of samples posing a non-carcinogenic risk via the oral pathway. Regarding irrigation suitability, while sodium hazards are generally low, a significant salinity hazard is identified due to elevated electrical conductivity in the arid environment. This poses a substantial risk of secondary soil salinization, necessitating strict salt management strategies to preserve long-term land productivity. These findings provide critical insights for the sustainable management of fragile groundwater resources in extreme arid environments. Full article

Emerging evidence highlights the gut microbiota as a critical modulator in the pathogenesis of heart failure (HF), particularly among patients with type 2 diabetes mellitus (T2DM). Dysbiosis contributes to systemic inflammation, endothelial dysfunction, and adverse cardiac remodeling via microbial metabolites such as trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs). However, the therapeutic intersection between the gut microbiota and pharmacological interventions remains insufficiently integrated. Sodium-glucose cotransporter-2 inhibitors (SGLT2is), a cornerstone of T2DM management, confer cardioprotective effects that may involve microbiota-mediated pathways. This review provides a novel synthesis of how SGLT2is influence gut ecology, specifically through altered glucose excretion and osmotic shifts, to potentially restore SCFA-producing taxa. By delineating the structural transitions from gut physiology to SGLT2i-modulated cardiac outcomes, we emphasize the gut–heart axis as a pivotal therapeutic target. This focused framework offers new insights into the triadic interplay between microbiome stability and cardiometabolic health, moving beyond traditional glucose-centric paradigms. Full article

Neurodegenerative diseases (NDDs) in children represent a heterogeneous group of rare but collectively significant disorders characterized by progressive neurological decline, developmental regression, and substantial morbidity and mortality. Unlike adult-onset neurodegeneration, pediatric conditions are predominantly genetic and frequently arise from defects in fundamental cellular pathways, including lysosomal degradation, mitochondrial oxidative phosphorylation, peroxisomal lipid metabolism, and myelin maintenance. This comprehensive review synthesizes current knowledge regarding the epidemiology, molecular classification, pathophysiology, and emerging therapeutic strategies of major pediatric neurodegenerative disorders. Epidemiological data indicate a “rare-but-many” landscape, where individually uncommon diseases collectively impose a measurable population burden. Mechanistically, disease progression reflects converging processes such as toxic substrate accumulation, impaired autophagy–lysosome flux, mitochondrial bioenergetic failure, oxidative stress, neuroinflammation, and glial dysfunction. Representative groups discussed include lysosomal storage disorders, leukodystrophies, mitochondrial encephalopathies, peroxisomal disorders, and other monogenic neurodegenerative syndromes. Advances in next-generation sequencing, metabolic profiling, and neuroimaging have substantially improved diagnostic accuracy and enabled earlier detection, including through newborn screening programs. Therapeutic paradigms are shifting from primarily supportive care toward mechanism-based interventions, including enzyme replacement therapy, hematopoietic stem cell transplantation, substrate reduction strategies, and gene therapy approaches. Early molecular diagnosis is increasingly recognized as critical for optimizing outcomes, particularly in disorders amenable to presymptomatic intervention. Continued integration of genomic medicine, standardized epidemiologic surveillance, and translational research will be essential to refine disease classification, improve prognostication, and expand access to targeted therapies. Collectively, pediatric neurodegenerative diseases exemplify the intersection of developmental neurobiology and inherited metabolic dysfunction, underscoring the need for multidisciplinary, precision-based clinical strategies. Full article

The accelerated processes of industrialization and urbanization have led to increasingly prominent environmental risks by emerging organic contaminants (EOCs) in wastewater. These contaminants are characterized by low concentrations, high toxicity, and complex composition, making their efficient removal crucial for safeguarding ecological security and human health. Advanced oxidation processes exhibit significant potential for the removal of EOCs due to their high degradation efficiency. However, current treatment paradigms remain constrained by several critical issues. Notably, the routine over-oxidation of low-toxicity small-molecule organics solely aims to satisfy chemical oxygen demand (COD) compliance standards. This unnecessary practice not only increases operational costs and carbon footprint but also leads to energy waste and reduced overall treatment efficiency. Based on the current technological landscape, this paper analyzes the core challenges in the removal of EOCs at present. In light of policy orientations and technological trends, it outlines future research directions and industrial development pathways, providing insights for achieving the synergistic goals of efficient removal of EOCs, low carbon emissions, and cost-effective operation. Full article

The operation of medical cyclotrons for PET radiopharmaceutical production presents significant radiological and environmental challenges that require systematic risk assessment and evidence-based mitigation strategies. In this study, an integrated framework combining Failure Mode and Effects Analysis (FMEA) with a quantitative Defense Effectiveness Factor (DEF) approach to evaluate and reduce residual risk in a real urban cyclotron facility. High-criticality failure modes (Risk Priority Number $\ge 120$) affecting HVAC systems, stack exhaust, and power supply were identified and validated through a Delphi expert consensus process. These modes were addressed with multi-layered defense-in-depth strategies: redundant systems (occurrence reduction, 60–80% effectiveness), real-time monitoring (detection reduction, 40–50% effectiveness), and design robustness (severity reduction, 70–85% effectiveness). The combined DEF yielded a 96–97% risk reduction. One-way sensitivity analysis confirmed the robustness of these results, with residual annual effective dose to the representative person remaining between 50–88 $\mathsf{\mu }$Sv/year (well below the IAEA 1 mSv/year public dose constraint) even under pessimistic scenarios. Primary exposure pathways were inhalation and cloud gamma from ${}^{18}$F and ${}^{41}$Ar during the early-morning production window, while secondary pathways were negligible due to the short half-lives of the radionuclides. These findings demonstrate that the integration of FMEA with DEF-based defense-in-depth and Gaussian plume modeling provides a transparent, robust, and regulatory-compliant framework for managing radioactive atmospheric emissions in urban medical cyclotron facilities. Full article

The expanding role of artificial intelligence (AI) in education raises important questions about how AI-supported learning may foster higher-order thinking and creative talent development. Guided by social cognitive theory, the current research examined how AI self-efficacy predicts creativity among second language (L2) learners through the mediating roles of AI literacy and critical thinking disposition. Two substudies were conducted. Study 1 (N = 72) tested a simple mediation model and demonstrated that AI self-efficacy positively predicted creativity both directly and indirectly through AI literacy. Study 2 (N = 135) extended these findings by incorporating critical thinking disposition and by using another measure of creativity. Results showed that AI self-efficacy positively predicted creativity, and this relationship was mediated independently by AI literacy and critical thinking disposition, as well as sequentially through both factors. The current study provides empirical evidence for pathways linking AI self-efficacy, AI literacy, critical thinking disposition, and creativity in AI-supported L2 learning. It highlights the importance of reflective and critical use of AI tools in language education. Full article

The automotive sector is the third-largest consumer of plastics in Europe, after packaging and construction, and its demand is expected to grow. Plastic recycling at the end of vehicle life remains low, with 80% of plastics ending up in energy recovery or landfills. Three vehicle models (SEAT Ibiza Gen. IV and SEAT Leon Gen. II and III) with two trim versions (Reference and Formula Racing) were examined to identify the most critical plastic components from an exergy perspective. Ecodesign measures were defined by considering both the disassemblability of vehicle components and their recyclability potential as key criteria to evaluate end-of-life recovery pathways and guide material and design optimization strategies. The proposed methodology classified the measures into three types: (1) substitution of high-exergy plastics with lower-impact alternatives; (2) use of recycled plastics instead of primary materials, with substitution rates depending on the material; and (3) reuse of components in new models, evaluated by disassemblability and end-of-life condition. Results show that Type 1 measures achieved savings up to 70 MJ, mainly in the floor covering and engine compartment insulator, while Type 2 measures provided larger reductions, up to 1.7 GJ, mainly in bumpers and carpets. Type 3 measures showed reuse potential for paddings and insulators but faced limitations in carpets and dashboards. Findings highlight the importance of material selection and implementing disassembly and recycling strategies to reduce the exergy of vehicle plastics. Full article

Molecular diagnosis has advanced rapidly in neurogenetic disorders, yet translating genotype into patient-specific predictions of brain network dysfunction and progression remains limited. Virtual brain models provide a structured solution by embedding individual anatomy and connectomics into biophysical whole-brain simulations. The critical step is to position genetics not as a diagnostic label, but as a constructive input to model design. This review outlines a genetics-centered framework for virtual brain modeling. First, atlas-derived transcriptomic and cell-type maps can define region-specific molecular priors, constraining vulnerability or excitability parameters and reducing model degeneracy. Second, when reproducible genotype-linked network phenotypes exist, mutation groups can inform stratified initialization and progression regimes. Third, at the patient level, exome and CNV data—summarized as pathway burdens and, where appropriate, calibrated polygenic modifiers—can be translated into individualized priors or regularizers, provided that mapping rules are explicit and externally validated. By integrating genetics at multiple levels of evidence, virtual brain models gain mechanistic plausibility, improved calibration, and explicit uncertainty quantification. The most realistic impact over the next few years is likely to be improved stratification, progression-aware forecasting, and scenario-based decision support in rare neurogenetic diseases, especially where longitudinal cohort infrastructure and validated biomarker inputs are already available, rather than deterministic individual prediction. Full article

Domain Generation Algorithms (DGAs) pose a persistent threat by enabling malware to dynamically generate numerous command-and-control domains, evading traditional blocklists. While machine learning-based detectors have achieved high accuracy, they operate as statistical pattern matchers and lack the human-like anomaly perception that enables security experts to intuitively recognize unnatural domains. This paper introduces CogNormDGA, a cognitive-driven framework that models normal domain characteristics from a defender’s perspective while also anticipating how attackers might exploit cognitive blind spots. Inspired by dual-process theory, CogNormDGA combines intuitive, pattern-based screening (System 1) with analytical, rule-based evaluation of phonotactic, morphological, and semantic violations (System 2). The cognitive principles of System 1 and System 2 are computationally realized as two distinct pathways: an Attentional Salience Network and a Linguistic Constraint Evaluator, respectively. The framework produces interpretable outputs via attention saliency maps and cognitive violation reports. Extensive experiments on 400,000 domains spanning 33 DGA families demonstrate that CogNormDGA achieves competitive detection performance (F1-score 0.941) while establishing a cognitive-driven detection paradigm that produces human-aligned explanations—a property critical for practical security. It shows promising results on low-entropy and novel DGA families. Human subject studies confirm strong alignment between the model’s internal explanations and expert reasoning. Furthermore, CogNormDGA is particularly effective against low-entropy DGA families that exploit cognitive blind spots. By bridging cognitive science and cybersecurity, our work offers an interpretable and human-aligned approach to threat detection, with promising resilience that requires further validation. Full article

Amid concerns over the unequally distributed long-term consequences of the COVID-19 pandemic for children and schools, there is a renewed focus on mechanisms to promote positive wellbeing and restore social connections among Black students. As teachers are lauded as critical in supporting student well-being, their perspectives may help to better understand the impact of the COVID-19 pandemic for Black students and leverage the contributions of families and communities to support students’ needs. Using interviews with teachers, we examined the following aims: (1) Investigate the mental health challenges Black students experienced following the COVID-19 pandemic, and (2) Identify school-wide efforts to support resilience. Participants in this study included 15 teachers nominated by their principals and colleagues for demonstrating excellence in supporting Black students. Teachers described their Black students as experiencing (1) ongoing mental health problems, (2) disengagement from school, and (3) relearning how to socialize. To promote social connections and wellbeing, teachers described how their schools reinvested in connection and prioritized equitable access and use of technology. Understanding the unique mental health challenges Black youth face is critical for cultivating pathways towards resilience following the COVID-19 pandemic. Results contribute to the continued investigation and intentional promotion of equity and cultural responsivity when supporting Black students’ wellbeing. Researchers and educational shareholders can work to create and maintain socially and emotionally supportive environments that promote mental health by learning from the experiences of Black students and the teachers that support them. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

Neuroinflammation plays a critical role in the pathogenesis of various neurological diseases. Therefore, alleviating neuroinflammation has become a core therapeutic strategy for these disorders. In recent years, neural stem/progenitor cell (NSPC) transplantation has shown remarkable advantages and broad prospects in the treatment of neurological diseases. This narrative review systematically summarizes research progress over the past decade on how NSPC transplantation ameliorates neurological deficits by regulating neuroinflammation-related signaling pathways, and compares the shared mechanisms and disease-specific differences. In addition, we discuss the key bottlenecks limiting the clinical translation of NSPC transplantation and propose future research directions. Accumulating preclinical evidence highlights NSPC transplantation as a compelling candidate intervention for multiple neurological disorders. Full article

In the context of global carbon neutrality goals, substituting hydrogen for carbon as a reductant represents a critical pathway for mitigating emissions in the iron and steel industry. Hydrogen-based molten reduction technology, characterized by its rapid reaction kinetics and high feedstock flexibility, has emerged as a pivotal direction for the industry’s low-carbon transition. This article systematically reviews research progress on the hydrogen-based reduction of molten iron oxides. The thermodynamic behavior of molten systems is discussed, confirming the feasibility of reducing molten FeO with hydrogen at elevated temperatures. Furthermore, discrepancies and nonlinear characteristics within current mainstream thermodynamic databases regarding the high-temperature molten region are identified. Kinetic studies demonstrate that reduction rates in the molten state significantly exceed those in the solid state. The rate-limiting step is shown to vary with reaction conditions, primarily shifting between interfacial chemical reaction and liquid-phase mass transfer control. Additionally, the influence mechanisms of key parameters—including temperature, reaction time, gas flow rate, gas composition, and slag composition—on the reduction process are comprehensively reviewed. By synthesizing existing methodologies and theoretical advancements, this review aims to provide a theoretical reference for optimizing hydrogen-based molten reduction processes for iron oxides. Full article

Background/Objectives: Preservation of critical white matter (WM) pathways is essential for maximizing surgical safety in neuro-oncology and functional neurosurgery. Constrained spherical deconvolution (CSD) offers superior modeling of complex fiber architecture compared to diffusion tensor imaging (DTI). This case series evaluates the clinical utility of CSD in surgical planning and intraoperative navigation. Methods: A retrospective review of 20 patients (15 brain tumors, 5 functional disorders) treated between September 2022, and September 2024 was performed. All patients underwent preoperative MRI with CSD-based reconstruction of eloquent WM tracts. Clinical presentation, tract involvement, surgical strategy, and postoperative outcomes were analyzed. Results: CSD reliably reconstructed CST, AF, IFOF, OT, and DRTT depending on tumor location or DBS target. Compared with standard DTI, CSD provided improved delineation of tract extent and tumor–tract interfaces. Gross total resection (GTR) was achieved in all tumor patients without new neurological deficits. DBS cases showed precise correlation between stimulation thresholds, side effects, and CSD-predicted distances to critical WM tracts. DRTT targeting resulted in marked clinical improvement in Holmes tremor. Conclusions: CSD enhances anatomical accuracy in WM tract visualization, supporting safer resections in eloquent areas and improving DBS targeting. Its integration into routine workflow may optimize neurosurgical outcomes. Full article