Search Results (5,468)

Liver fibrosis is driven by persistent oxidative stress and inflammatory signaling, with transforming growth factor-β (TGF-β) acting as a key profibrotic mediator. Rutin (Ru) is a plant-derived flavonoid with antioxidant and anti-inflammatory effects, but its low bioavailability limits therapeutic efficacy. This study investigated whether rutin-phytoreduced gold nanoparticles (RuAuNPs) enhanced rutin delivery leading to antifibrotic and anti-inflammatory effects in a rat model of liver fibrosis. Liver fibrosis was induced by oral administration of thioacetamide (TAA, 150 mg/kg body weight, p.o.) for six weeks. Following fibrosis induction, the animals were treated with free rutin (30 mg/kg body weight), RuAuNPs (0.3 mg/kg body weight), or AuNPs (0.3 mg/kg body weight), both expressed as nanoparticle mass, all administered orally for four weeks. RuAuNPs were synthesized by green rutin-mediated reduction and further characterized by TEM, DLS, and FTIR spectroscopy; they were spherical, showing an average hydrodynamic size of 104.1 nm (PDI 0.345). FTIR confirmed rutin capping. Biological effects were evaluated by liver morphology (H&E histology, TEM), biochemical assessment of liver aminotransferases and glico-lipidic status, ELISA and spectrophotometry measurement of redox biomarkers (lipid peroxidation, glutathione status, antioxidant enzymes), cytokines (TNF-α, IL-1β, IL-6), and TGF-β. TAA-induced hepatic injury and remodeling with increased profibrotic signaling, oxidative stress, and inflammation. Free rutin slightly ameliorated the liver damage, whereas RuAuNP improved histological features, reduced TGF-β and pro-inflammatory cytokines, decreased lipid peroxidation, and supported antioxidant defenses. Overall, RuAuNP may enhance rutin efficacy in TAA-induced liver fibrosis, with novelty stemming from the integrated in vivo evaluation of tissue changes and key profibrotic/oxidative/inflammatory pathway. Full article

Gallbladder diseases spanning cholelithiasis, cholecystitis, and gallbladder cancer represent a clinically heterogeneous continuum in which type 2 diabetes mellitus (T2DM) acts as a key metabolic modifier. Conventional models centered on bile supersaturation alone do not sufficiently account for the persistent inflammation and inter-individual variability frequently observed in practice. Here, we synthesize emerging evidence implicating the gut microbiota–bile acid (BA) axis as an integrative mechanism linking metabolic dysregulation, barrier dysfunction, and biliary pathobiology in the diabetic host. Hyperglycemia and insulin resistance, together with impaired mucosal resilience, are associated with shifts in microbial community structure and BA-transforming functions (e.g., bile salt hydrolase and 7α-dehydroxylation), favoring a more hydrophobic BA pool. These changes may disrupt BA receptor signaling, including FXR–FGF15/19 and TGR5-related pathways, thereby amplifying metabolic inflammation, promoting lithogenic bile formation, and impairing gallbladder motility. In parallel, barrier vulnerability may facilitate microbial translocation and LPS-driven immune activation, reinforcing a feed-forward loop that supports the gallstone–inflammation–carcinogenesis trajectory. Translationally, microbiome- and BA-oriented strategies (dietary patterns, bile acid therapeutics, and targeted microbiome modulation) are promising adjuncts, yet precision management should explicitly consider medication- and weight loss–related confounding—particularly with incretin-based therapies—to optimize biliary outcomes across disease stages. Full article

Osteoarthritis (OA) represents a degenerative joint disease which advances through cartilage breakdown, synovial inflammation, and subchondral bone transformation until it causes persistent pain and mobility loss. The scientific community lacks complete knowledge about OA disease mechanisms and post-operative healing processes despite arthroplasty surgery providing effective symptom relief. This study investigated plasma proteomic changes in OA patients before and after arthroplasty. The cohort included eight OA patients undergoing knee or hip arthroplasty and ten age-, sex-, and body mass index-matched healthy controls. Plasma proteins were analyzed using liquid chromatography–tandem mass spectrometry following enzymatic digestion and depletion of high-abundance components. The bioinformatic analysis together with quantitative methods showed that OA patients experienced changes in inflammatory pathways, extracellular matrix remodeling, immune system regulation and coagulation processes. A total of 93 proteins were differentially abundant in the pre-operative comparison. Among these, 63 proteins were consistently up-regulated and 23 were consistently down-regulated across both pre- and post-operative time points. In addition, 20 proteins exhibited post-operative-specific changes. These findings highlight both persistent disease-associated alterations and transient proteomic shifts linked to post-operative recovery. Overall, this study identifies candidate plasma proteomic signatures associated with OA and surgical intervention, providing exploratory insights into disease monitoring and potential personalized therapeutic strategies. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems. Full article

Amid the accelerating waves of global digitalization and the deepening interplay of cultural diversity, urban creative spaces have become pivotal arenas for the digital creative industry—yet a systematic, cross-culturally robust tool for assessing their sustainability remains conspicuously absent. Here, we address this gap by constructing a multi-dimensional evaluation framework derived from a systematic literature review, comprising five primary dimensions—AIGC technology integration, cultural heritage preservation, the economic benefits of the digital cultural industry, ecological synergy and social inclusiveness, and governance and policy support—along with 20 secondary indicators. To enhance methodological rigor, we integrate the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) to determine indicator weights while mitigating the subjective biases inherent in traditional approaches and employ the TOPSIS method to quantitatively assess and rank the creative spaces of five representative cities: London, Shanghai, Los Angeles, Tokyo, and Berlin. Our findings reveal that London leads in comprehensive sustainability, followed closely by Shanghai, with sensitivity analysis confirming the high robustness of the rankings. The originality of this work lies in reconceptualizing AIGC not as a conventional digital instrument but as a core transformative driver embedded within the evaluation architecture, while the application of IFAHP substantially enhances the scientific validity and methodological reliability of the assessment. This research provides an operational diagnostic tool and actionable optimization pathways for advancing the sustainability of urban creative spaces worldwide, offering practical implications for fostering cultural innovation, bridging the digital divide, promoting social inclusiveness, and informing evidence-based urban governance policies. Full article

Background/Objectives: Fabry disease (FD) is a lysosomal storage disorder characterized by progressive renal and cardiac involvement and an increased burden of cardiovascular and cerebrovascular events. While cardiac magnetic resonance imaging (CMR) has significantly advanced structural assessment, circulating biomarkers reflecting disease-related cardiac manifestations remain incompletely understood. We therefore investigated adiponectin and leptin, two adipokines involved in inflammatory, metabolic, and fibrotic pathways, in relation to cardiac involvement and analyzed long-term lipid trajectories in FD. Methods: This longitudinal observational study included 49 patients with FD with 149 study visits. Circulating adiponectin, leptin, NT-proBNP, and conventional lipid parameters were assessed longitudinally and stratified by FD-specific therapy status and sex. Multivariable linear regression was performed to evaluate independent associations with log-transformed NT-proBNP values. Results: Adiponectin was positively associated with NT-proBNP, reflecting cardiac involvement, independent of age, sex, BMI, and eGFR (p < 0.001). Higher adiponectin levels were observed in patients with left ventricular hypertrophy or low T1 and those with fibrosis, detected by CMR (p = 0.009 and p < 0.001, respectively). This association was mainly seen in patients receiving FD-specific therapy, raising the question of whether this reflects underlying organ involvement or treatment effects. Leptin demonstrated weaker, inverse associations. Adiponectin, leptin, Triglycerides, total cholesterol, and HDL- and LDL-cholesterol levels remained stable over long-term follow-up, irrespective of FD-specific therapy or sex. Conclusions: In FD, adiponectin appears to be associated with cardiac involvement, and conventional lipid parameters remained unchanged over time. These findings suggest that alterations in adipokines, rather than progressive dyslipidemia, may reflect disease-related cardiac manifestations. Full article

Environmental, social, and governance (ESG) information is increasingly vital in driving capital markets to promote sustainable development. However, significant barriers remain in effectively transforming ESG information into intelligence that supports investor decision-making. Drawing upon information chain theory and intelligence transformation theory, this study constructs an ESG intelligence transformation model tailored for investor decision-making, aiming to address relevant challenges within China’s unique capital market environment. Through a mixed deductive-inductive content analysis of analyst reports issued by Chinese securities firms, this study identifies underlying issues in current ESG information utilization: excessive focus on social dimensions at the expense of integrated consideration of environmental and governance issues; inadequate conversion of environmental and governance data into decision-relevant information; and incomplete pathways for transforming ESG knowledge into intelligence supporting investment decisions. These constraints significantly undermine the potential of ESG information to guide sustainable investment strategies and support green economic transformation. To bridge these gaps, this study proposes an integrated multi-stakeholder optimization strategy encompassing: enhanced disclosure standards by regulators; greater corporate emphasis on environmental and governance disclosures; more sophisticated assessment techniques by rating agencies; and optimized information channels between corporations and investors by financial institutions. This study provides a theoretical foundation and practical pathways for enhancing the quality and utility of ESG information, contributing to sustainable finance research. Full article

Metastatic colorectal cancer (mCRC) remains a major cause of cancer-related mortality worldwide. Advances in molecular profiling have transformed the therapeutic landscape, enabling biomarker-driven treatment strategies based on alterations in RAS, BRAF V600E, HER2 amplification, and mismatch repair status. Among these, dysregulation of the epidermal growth factor receptor (EGFR), BRAF, and HER2 signaling pathways represents a central driver of tumor progression and therapeutic resistance. Targeted agents directed against these pathways—including the anti-EGFR monoclonal antibody panitumumab, the selective BRAF inhibitor encorafenib, and the HER2-selective tyrosine kinase inhibitor tucatinib—have substantially expanded treatment options for molecularly defined subgroups of patients with mCRC. Anti-EGFR therapy remains a cornerstone of treatment for patients with RAS/BRAF wild-type, left-sided tumors. Panitumumab combined with chemotherapy has demonstrated significant improvements in response rates and overall survival compared with anti-angiogenic-based regimens in randomized clinical trials. For tumors harboring BRAF V600E mutations, which are associated with poor prognosis, combination strategies incorporating encorafenib with EGFR blockade have shown clinically meaningful survival benefits and represent an important therapeutic advance. In HER2-amplified colorectal cancer, HER2-targeted therapies have emerged as an effective treatment strategy. Trastuzumab-based combinations and HER2-selective tyrosine kinase inhibitors such as tucatinib have demonstrated durable responses and favorable safety profiles in heavily pretreated patients. This review summarizes current evidence from pivotal phase II and III clinical trials, translational studies, and real-world data evaluating EGFR-, BRAF-, and HER2-directed therapies in colorectal cancer. Particular emphasis is placed on biomarker-guided patient selection, mechanisms of resistance, and emerging combination strategies that continue to refine precision oncology approaches in mCRC. Full article

With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation. Full article

The fabrication of NiTiZr alloys by solid-state routes remains challenging due to limited atomic diffusion and the high reactivity of Ti and Zr. Mechanical alloying offers a potential pathway for synthesising such systems; however, complete alloy formation is not always achieved under practical milling conditions. Researchers have infrequently explored the mechanical alloying of NiTiZr, and this study systematically investigates the effect of milling time on microstructural evolution rather than claiming complete alloy synthesis. A high-energy planetary ball mill was used to mechanically process elemental powders of Ni, Ti, and Zr for 5–28 h. The examination revealed that longer milling times resulted in progressive crystallite refinement and increased lattice strain, while particle morphology evolved from irregular to more globular shapes due to repeated fracture and cold welding. After 28 h of milling, limited reacted regions containing Ni, Ti, and Zr were observed (~4.6% area fraction), while most of the powder remained heterogeneous and polyphasic, with no evidence of complete Ni₅₀Ti₃₀Zr₂₀ alloy formation. X-ray diffraction showed significant peak broadening without systematic 2θ peak shifts, indicating severe plastic deformation and crystallite refinement rather than definitive solid-solution formation of the allot. Differential scanning calorimetry revealed exothermic thermal events between 300 °C and 470 °C, which are attributed to defect recovery and thermally activated structural rearrangements rather than confirmed martensitic or crystallisation transformations. These results demonstrate that high-energy ball milling alone is effective for particle size reduction and defect generation but insufficient for producing a fully homogeneous Ni₅₀Ti₃₀Zr₂₀ alloy within 28 h. Additional activation energy, such as post-milling heat treatment or extended processing, is required to promote complete alloying in this system. Full article

Employees were required to adopt new working methods within a very short time frame during the COVID-19 period through digitalization. While digitalization has been largely perceived as an enabler during the pandemic, its impact on employee health and well-being remains complex and underexplored, particularly in the public sector, where employees have less discretion to adapt digital tools. This study examines how rapid workplace digitalization during COVID-19 affected employee health and well-being in the public sector. Drawing on the job demands–resources (JD-R) framework, we focus on three specific forms of digital work—digital meetings, digital clearance, and digital training—selected because they represent distinct theoretical pathways through which digitalization affects well-being, such as digital meetings and digital training can increase job demands that can deplete employee energy and increase stress, whereas digital clearance operates as a job resource that reduces bureaucratic hurdles and enhances autonomy. To test these ideas, this study uses data from the 2020 Australian Public Service Commission Census (n = 108,085), and applies ordinal and multinomial generalized structural equation modeling (GSEM) to assess the effects of three new ways of working—digital meetings, digital clearance, and digital training—on employees’ health and well-being, as well as the mediating roles of organizational support. The results demonstrate that while digital clearance is positively associated with employee health and well-being, digital meetings and digital training are negatively associated. Organizational support mediates these relationships, underscoring its importance in mitigating adverse effects. These findings highlight the mixed consequences of digitalization for public employees’ health and well-being and point to the need for supportive organizational strategies in times of crisis. As a practical implication, this study suggests that public sector organizations should prioritize employee mental health in teleworking policies, adopt employee-centered digital transformation strategies that provide adequate resources and training support, and implement digital clearance processes that enhance employee well-being, particularly during a crisis. Full article

Coal gangue, the primary solid by-product of coal mining, presents severe environmental challenges due to massive accumulation. At the same time, it represents potential as a secondary resource if properly utilized. This review systematically summarizes the mineralogical characteristics, modification strategies, and utilization pathways of coal gangue. Current treatment methods, including thermal, chemical, and microbial activation, are discussed, highlighting their respective efficiencies, economic feasibility, and environmental impacts. Furthermore, this review emphasizes the transition of coal gangue from low-value disposal to high-value utilization. Representative applications are summarized, including its use as a precursor for advanced construction materials, as a functional material for environmental remediation, and as a feedstock for energy recovery. Finally, the major technological challenges and research gaps are identified. Future development should focus on intelligent sorting technologies, low-carbon activation processes, and synergistic multi-waste integration. These directions are expected to promote the transformation of coal gangue from an environmental liability into a valuable resource for the circular economy. Full article

Corporate risk-taking, crucial for sustainable development, is shaped by the interplay of multiple institutional logics. However, existing research lacks a systematic understanding of how government, market, and technology logics collectively drive corporate risk-taking. This study addresses this gap by employing fuzzy-set Qualitative Comparative Analysis on data from Chinese intelligent manufacturing firms to explore the configurational pathways leading to high risk-taking. Our analysis reveals three distinct pathways: (1) An innovation-driven transformation pathway, characterized by a strong synergy between government and technology logics, with market logic playing a supplementary role. (2) A green transformation pathway, where government logic dominates, supported by market and technology logics in a hierarchical structure. (3) A resource synergy pathway, marked by the high-level integration of all three logics for strategic breakthroughs. Theoretically, this study advances institutional theory by developing an integrative framework that moves beyond a single-logic perspective, revealing the synergistic and substitutive relationships among multiple logics. Practically, our findings provide managers with a configurational roadmap for strategically aligning with institutional forces to enhance risk-taking capacity. Full article

The commercialization of V-based catalysts for the selective catalytic reduction of NOx by NH₃ (NH₃-SCR) is hindered by their narrow operating temperature window, insufficient low-temperature (LT) activity, and severe SO₂-to-SO₃ oxidation. To bridge this gap, we herein introduced Nb and hexagonal BN into a VW/TiO₂ system to simultaneously enhance its LT SCR activity, suppress undesired side reactions, and improve durability. Nb incorporation promoted V⁵⁺/V⁴⁺ redox cycling and enhanced lattice oxygen mobility, thus reducing the apparent activation energy and suppressing SO₂ oxidation at elevated temperatures. However, excessive Nb loading induced NH₃ oxidation and N₂O formation. This drawback was mitigated by introducing BN as a dispersion promoter, which helped secure high catalytic performance at a reduced Nb content. The VWNb/Ti-BN catalyst achieved superior NOx conversion and N₂ selectivity over a wide temperature range and benefited from notably suppressed NH₃ oxidation and SO₂-to-SO₃ oxidation. Kinetic analysis revealed that Nb primarily lowered the reaction energy barrier via redox property enhancement, whereas BN accelerated surface reaction turnover by stabilizing and dispersing active acidic sites, markedly increasing the turnover frequency without reducing the activation energy. In situ spectroscopic analysis confirmed the accelerated consumption of adsorbed NH₃ species and enhanced formation of reactive NOx intermediates, indicating SCR pathway enhancement. After aging in the presence of SO₂ and H₂O, the best-performing honeycomb-type monolithic catalyst retained and NOx conversion of >80%, demonstrating excellent long-term durability under practical conditions. A composition-aware machine learning model based on log-ratio-transformed variables quantitatively identified the synergistic balance among V, Nb, W, BN, and TiO₂ as the dominant factor governing LT SCR performance. Thus, this work provides valuable mechanistic insights and a strategy for designing wide-temperature-window SCR catalysts with improved activity, selectivity, and resistance to sulfur poisoning. Full article