Search Results (4,326)

Silk fibroin (SF)–polyphenol systems have emerged as a versatile class of gels and hydrogels in which supramolecular interactions and dynamic crosslinking regulate network formation, responsiveness, and multifunctional performance. Polyphenols interact with SF through hydrogen bonding, hydrophobic interactions, π–π stacking, metal coordination, and covalent crosslinking, thereby modulating conformational transitions, gelation behavior, structural stability, and interfacial functionality. These interaction mechanisms enable the development of SF–polyphenol gel systems with tunable mechanical properties, wet adhesion, antioxidant activity, self-healing capability, and stimuli responsiveness. This review summarizes recent advances in SF–polyphenol gels and hydrogels, with particular emphasis on molecular interaction mechanisms, gelation and fabrication strategies, responsive behaviors, and structure–property relationships. Representative preparation approaches, including solution blending, electrospinning, impregnation–adsorption, enzymatic crosslinking, metal–phenolic coordination, and photo-initiated processing, are systematically discussed in relation to their effects on network architecture and functional output. The responsive behaviors of these systems under pH, redox, electrical, thermal, and optical stimuli are also analyzed from the perspective of dynamic gel networks and adaptive material design. Emerging applications of SF–polyphenol gels in bioadhesives, delivery platforms, flexible bioelectronics, wound-related materials, and sustainable functional systems are highlighted. Current limitations associated with polyphenol instability, formulation sensitivity, reproducibility, and scale-up are critically discussed, together with future opportunities for predictive design of gel-based natural polymer systems. This review provides a comprehensive framework for understanding SF–polyphenol gelation and for guiding the development of next-generation multifunctional gels and hydrogels. Full article

Loneliness and social isolation among older adults pose significant challenges for social sustainability. Intergenerational interaction is a key to promoting social well-being and fostering inclusive communities. Using binary logistic regression and structural equation modeling, this study investigates how neighborhood environments, transportation and recreational walking, and intergenerational interactions, defined as social engagement with children, differ among 871 older adults in intergenerational (n = 436) vs. age-targeted (n = 435) communities in central Texas. Results highlight that accessible “third places”, including streets and sidewalks, churches, and restaurants, were important for supporting intergenerational interactions, with substantially higher levels of such interactions in these places among older adults from intergenerational communities. Employment status moderated the relationship between community types and intergenerational interactions. Across both community types, recreational walking emerged as a significant, positive predictor for intergenerational interactions. Modifiable neighborhood features, particularly the presence of benches along sidewalks, were positively associated with recreational walking, which in turn predicted intergenerational interactions. While age-targeted communities may offer high neighborhood satisfaction and livability, they provide fewer opportunities for routine contact with younger generations. Findings underscore the importance of walkable, inclusive communities and intentional intergenerational programming in promoting intergenerational interaction among older adults, contributing to social sustainability and healthy aging in place. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

(1) Background: Psoriasis is increasingly recognized as a systemic inflammatory disease associated with metabolic comorbidities. However, the hierarchical relationship between inflammatory activation and insulin resistance in driving metabolic syndrome (MetS) remains incompletely defined. This study aimed to characterize the integrated inflammatory–metabolic architecture of psoriasis using multivariate and latent domain modeling. (2) Methods: In this cross-sectional hospital-based study (2020–2022), 235 adult patients with psoriasis were evaluated. Systemic inflammatory markers (NLR, SII, CRP, ESR) and composite metabolic indices (TyG, AIP, METS-IR) were assessed. Correlation analysis, multivariable linear and logistic regression, interaction modeling, and principal component analysis (PCA) were performed to examine independent associations and underlying domain structure. (3) Results: Inflammatory and metabolic markers showed modest but significant correlations. In multivariable logistic regression, the TyG index was the strongest independent predictor of MetS (OR = 5.15, p < 0.001), whereas inflammatory markers did not retain independent significance. An interaction between adiposity and insulin resistance further improved model discrimination (AUC = 0.830). PCA identified two distinct latent domains explaining 69.9% of total variance: an immune–inflammatory domain (NLR, SII, ESR, CRP) and a metabolic–insulin resistance domain (TyG, AIP, METS-IR). Only the metabolic domain independently discriminated MetS. (4) Conclusions: Psoriasis exhibits a multidimensional systemic architecture characterized by partially independent inflammatory and metabolic domains. Although systemic inflammation and metabolic dysfunction coexist, insulin-resistance-related indices were more strongly associated with metabolic syndrome in this cohort. Full article

Antiretroviral drugs (ARVDs) remain the cornerstone of HIV/AIDS management, but their therapeutic efficacy and safety are highly influenced by bioconversion processes such as hepatic metabolism and enzymatic transformation. Variability in metabolic pathways, mediated by cytochrome P450 enzymes and other liver-based systems, contributes to interindividual differences in drug response, toxicity, and resistance. Recent advances in artificial intelligence, particularly artificial neural networks (ANNs), offer promising tools for modeling and optimizing these complex bioconversion processes. ANNs are capable of learning nonlinear relationships from high-dimensional datasets, making them ideal for predicting the pharmacokinetic parameters, enzyme–substrate interactions, and metabolic stability of ARVDs. This review explores the emerging role of ANNs in understanding and optimizing the metabolic transformation of antiretroviral agents. Key applications are discussed, including prediction of drug–enzyme interactions, in silico modeling of hepatic clearance, and simulation of enzyme kinetics. The integration of molecular descriptors, omics data, and clinical parameters into ANN models allows for improved prediction accuracy and personalized therapy. Furthermore, ANN-based tools can aid in early-stage drug development by identifying metabolic liabilities and guiding structural modifications to enhance metabolic stability. Despite their potential, challenges such as data scarcity, model interpretability, and standardization remain. Future research should focus on hybrid models combining ANN with mechanistic pharmacokinetics, the incorporation of real-world patient data, and validation against experimental outcomes. Overall, ANNs represent a powerful approach to optimizing ARVDs bioconversion, with the potential to improve efficacy, reduce toxicity, and support the development of next-generation antiretroviral therapies Full article

The tectonic setting of Taiwan and its surrounding regions is characterized by the complex interaction between the northwest-oriented Ryukyu subduction zone and the east-oriented Manila subduction zone. Within this subduction framework, the elastic thickness of the lithosphere (Te) serves as a critical parameter for elucidating the mechanical behavior of the area. In this study, we employed the admittance–correlation method to estimate Te values across Taiwan and adjacent territories. The findings indicate that sedimentary loading results in an overestimation of the maximum Te by approximately 50 km; after adjustment, the Te values range from 0 to 60 km throughout the study area. On Taiwan, Te values predominantly lie between 20 and 30 km, decreasing to 10–20 km near the margins adjacent to the Ryukyu and Manila subduction fronts. The Philippine Sea Plate exhibits comparatively higher Te values, ranging from 40 to 65 km. The spatial distribution of Te broadly corresponds with major tectonic subdivisions. Statistical analyses reveal a weak negative correlation between Te and surface heat flow (r = −0.44) and a weak positive correlation with shear-wave velocity anomalies at a depth of 100 km (r = 0.22), suggesting that the thermal structure exerts only a moderate influence on lithospheric strength in this region. Nonetheless, within oceanic crustal domains, the relationship between Te and oceanic crustal age largely adheres to models of crustal cooling and lithospheric thickening, consistent with isotherm depths of approximately 200–400 °C. Additionally, dynamic topography associated with slab subduction may locally diminish Te by up to 25 km. Cross-sectional profiles through northern Taiwan and the Philippine Sea block reveal pronounced coupling between subduction geometry and Te distribution. The observed spatial patterns of Te reflect the mechanical imprint of prolonged tectonic evolution, with the orientation of Te gradients generally aligned with the direction of maximum principal compressive stress. Collectively, these results suggest that subduction geometry and tectonic processes are important factors influencing the spatial variability and evolutionary trajectory of lithospheric strength in Taiwan and its environs. Full article

This study investigates the determinants of economic growth in the Next-11 economies over the period 1996–2024, with particular emphasis on the roles of structural, institutional, and human capital factors. Using a comprehensive panel dataset for eleven emerging economies, the analysis employs three robust estimation techniques—Driscoll–Kraay Standard Errors (DKSEs), Feasible Generalized Least Squares (FGLSs), and Panel-Corrected Standard Errors (PCSEs)- to address common econometric issues such as heteroskedasticity, serial correlation, and cross-sectional dependence. The empirical results reveal that industrial output, energy consumption, human capital, institutional quality, and foreign direct investment significantly contribute to economic growth. Among these factors, industrial output and energy consumption exhibit particularly strong and consistent positive effects across all estimation methods, highlighting the importance of structural transformation and energy availability in supporting economic expansion. In contrast, trade openness shows a negative and statistically significant relationship with economic growth in most model specifications, suggesting that structural constraints, import dependence, and limited domestic productive capacity may restrict the growth benefits of external integration in these economies. The study also explores the conditional effects of foreign direct investment through interaction terms with human capital and institutional quality. The findings indicate that the growth-enhancing impact of foreign investment depends significantly on domestic absorptive capacity, particularly the availability of skilled labor and effective governance structures. These results emphasize the importance of complementary policies aimed at strengthening education systems, improving institutional quality, and enhancing regulatory effectiveness. From a policy perspective, the findings suggest that the Next-11 economies should prioritize industrial development, energy infrastructure expansion, human capital investment, and institutional reforms to maximize the benefits of globalization and foreign investment. Overall, the study contributes to the literature by providing robust empirical evidence on the interconnected roles of structural, institutional, and human capital factors in shaping economic growth in emerging economies. Full article

The literature collected from various search engines and high-quality scientific databases reveals that amino acid (AA)-functionalized nanoparticles have emerged as a promising field for selective detection and remediation of heavy metals (HMs). Among the various nanoparticles (NPs), gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) have drawn considerable attention, attributed to their unique optical, catalytic, and surface plasmon resonance properties. Functionalization with amino acids significantly enhances nanoparticle stability, biocompatibility, and metal-binding affinity through diverse functional groups. AA-functionalized AuNPs, including glycine, cystine, leucine, methionine, tyrosine, aspartic acid, histidine, and lysine-capped systems, exhibit tunable selectivity toward heavy metal ions. Bifunctionalization strategies further enhance sensitivity by inducing nanoparticle aggregation or signal amplification. Beyond single amino acids, polypeptides and protein-functionalized AuNPs offer enhanced molecular recognition and multivalent binding, expanding their applicability in complex matrices. Similarly, amino acid-functionalized AgNPs, such as those capped with similar amino acids stated above, exhibit strong interactions with heavy metals, AA bifunctionalization, and bimetallic nanoparticles (BNPs), particularly amino acid-functionalized Au–Ag systems, which combine the advantages of both metals, leading to improved sensitivity, selectivity, and signal strength. Although these advances have been made, a major gap remains in the systematic comparison of different amino acids, peptides, and bimetallic systems under real-world conditions. This gap can be addressed by standardized testing methods, clearer structure–function relationships and combined experimentation to guide the rational design of more efficient AA-functionalized nanoparticles. Full article

Long-term balanced mineral fertilization is essential for sustainable sweet cherry production under variable climatic conditions. This seven-year field study (2016–2022) evaluated the effects of NP, NPK, and NPKMg fertilization including the control on six parameters: trunk cross-sectional area (TCSA), fruit yield (FY), crop load (CL), fruit diameter (FD), water-soluble dry matter content (BRIX), and cherry leaf spot incidence (CLS) in two sweet cherry cultivars (‘Vera’ and ‘Carmen’). TCSA increased continuously in both cultivars, while fertilization effects on growth, FY, CL, and FD varied among years and were significantly higher under NPK and NPKMg treatments compared with the control, particularly in specific years. Leaf spot incidence was reduced in the NPKMg treatment in epidemic years, although strong interannual and cultivar-dependent variability was observed, with ‘Carmen’ being more susceptible than ‘Vera’. Correlation and regression analyses revealed significant relationships among key traits, particularly for CL vs. FY, FD vs. CLS, TCSA vs. CLS, and BRIX vs. CL, indicating strong vegetative–generative interactions. Principal component analyses further showed that tree and fruit traits as well as disease incidence were structured along a limited number of integrated multivariate components explaining most of the variance. In conclusion, balanced fertilization improved productivity and partly reduced disease incidence, but treatment effects were strongly influenced by complex multivariate interactions and interannual climatic variability. These findings highlight the importance of integrative analytical approaches to optimize nutrient management under Central European conditions. Full article

Traditional pharmacotherapy is often constrained by suboptimal bioavailability and systemic toxicity. Biomolecularly inspired nano-drug delivery systems (nano-DDS) have emerged as precise platforms to overcome these barriers by orchestrating molecular interactions at the bio-nano interface. This review systematically evaluates the molecular recognition mechanisms and biochemical principles governing nano-DDS performance. We systematically evaluate how passive targeting relies on the EPR effect—dictated by the nanocarrier’s physicochemical properties—and how active targeting exploits ligand-receptor affinity to enhance cellular uptake. Special emphasis is placed on bioresponsive strategies that utilize pathological cues—such as pH gradients, redox potential, and enzymatic activity—for intelligent, on-demand drug release. Furthermore, we discuss structure-function relationships in lipid, polymeric, and biologically derived systems, highlighting their roles in modulating therapeutic signaling in oncology and inflammatory diseases. Finally, translational hurdles and emerging AI-driven molecular design strategies are critically examined. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusion: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads. Full article

The selectin family constitutes a well-known class of immune-regulatory molecules, among which P-selectin has emerged as a therapeutic target for inflammatory thrombotic diseases due to its capacity to mediate the adhesion between multiple immune cell subsets and endothelial cells. Currently, small-molecule or glycomimetic inhibitors targeting P-selectin have stalled in Phase III clinical trials, with a common limitation being their weak binding affinity to P-selectin. In this study, in vitro competitive binding assays were employed to evaluate the inhibitory effects of structurally distinct fucosylated glycosaminoglycan (FG) oligosaccharides, derived from sea cucumbers, on the interaction between P-selectin and its ligands. A potent inhibitor, the nonasaccharide Ta-9-2 (featuring a novel disaccharide side chain), was identified. Biolayer interferometry (BLI) analysis further confirmed its high binding affinity to P-selectin, with a K_D of 83.92 nM. Structure–activity relationship (SAR) analysis reveals that the appropriate glycan chain length, the novel disaccharide side chain (Gal_4S6S-α1,2-L-Fuc_3S-α1,3), and the favorable sulfation pattern (Fuc_2S4S) serve as the molecular basis for potent P-selectin inhibition. This study provides a robust theoretical foundation for the structural optimization of glycomimetic targeting P-selectin, while also offering a new opportunity for the development of high-efficacy drug candidates. Full article

Chitosan-based copper composites have attracted considerable interest for biomedical and antimicrobial uses due to their biocompatibility, adjustable dielectric characteristics, and ion-mediated antimicrobial effectiveness. In this study, chitosan films doped with Cu(NO₃)₂, containing 3, 6, and 9 wt% of copper nitrate were produced using a solution-casting method at room temperature. This was done to explore the relationship between structural interactions, dielectric relaxation, optical properties, and antimicrobial efficacy. The resulting composite has been investigated physically using FTIR, XRD, optical analysis, and dielectric spectroscopy, and biologically for its antimicrobial activity. FTIR revealed the molecular structure of Cs-Cu(NO₃)₂ and changes resulting from new bond(s) formation and/or decomposition. XRD indicated that there are no peaks assigned for CuO, which weakens the composite antimicrobial activity. Optical analysis showed an increase in the band gap with copper (II) nitrate concentration over 3%. Additionally, the electrical impedance of the resulting composite increased by approximately one decade. A detailed electrical analysis of the charge-carrier types is provided. Moreover, the antimicrobial activity of chitosan is slightly enhanced by the additive copper (II) nitrate in a dose-dependent manner. The current research offers a mechanistic understanding of the structure–property relationships that govern the behavior of Cu(NO₃)₂–chitosan composites, emphasizing the significant influence of processing conditions on adapting of their dielectric and biological properties. Full article

Medication recommendation, a crucial application of artificial intelligence in healthcare, has garnered widespread attention due to its research and practical value. However, existing methods often struggle to address three key challenges: misleading co-occurrence correlations, insufficient medication representation, and the balance between recommendation accuracy and drug–drug interaction (DDI). To overcome these challenges, we propose CDMed, a medication recommendation framework based on causal inference and dual-granularity information enhancement. First, the framework applies causal inference to identify and quantify the real therapeutic pathways among diseases, procedures, and medications in electronic health record (EHR), effectively filtering out spurious correlations commonly found in co-occurrence statistics. Second, by integrating coarse-grained medical entity relationships with fine-grained molecular structural information, it achieves effective multi-scale information fusion and enhances medication representation. Additionally, CDMed jointly models the 2D and 3D molecular structures of medications, serving as the foundation for subsequent molecular feature extraction. Finally, to achieve a balance between recommendation accuracy and safety, we applied a DDI-Constrained Bias Correction at the output stage, which enhances recommendation accuracy while controlling clinical risks. Extensive experiments on two public datasets demonstrate that CDMed improves recommendation accuracy by 2.2%, while maintaining a low DDI rate of 0.0661 alongside high inference efficiency. This result proves that CDMed achieves an optimal balance among recommendation accuracy, safety, and computational efficiency. Full article

Hybrid reinforced concrete (HRC) sections combining steel and fiber-reinforced polymer (FRP) bars provide a structural solution that balances durability, load-bearing capacity and energy dissipation. However, the absence of unified design provisions and the coexistence of distinct safety formats in European and American codes complicate the consistent assessment of ultimate limit state behavior under combined axial force and bending moment. In this study, a strain-based sectional model founded on compatibility and internal force equilibrium is implemented through a layer-by-layer numerical integration procedure to generate axial force–bending moment (N–M) interaction domains and moment–curvature (M–$\chi$) relationships. The formulation is extended to a dimensionless framework in terms of normalized axial load, bending moment, total hybrid mechanical reinforcement ratio ${\omega }_h$ and hybridization parameter R. The analysis is conducted within two regulatory formats: the European framework based on Eurocode 2 and CNR-DT 203 R1/2026 and the American framework based on ACI 318-25 and ACI 440.11-22. The results show that increasing ${\omega }_h$ leads to a progressive expansion of the interaction domain and modifies the transition between FRP rupture-controlled and steel-yielding-controlled limit states. Increasing R shifts balanced conditions towards higher axial compression and bending levels. Differences between the two regulatory approaches are observed in terms of predicted curvature capacity and design resistance within the N–M domain, reflecting the distinct safety formats adopted. The proposed dimensionless parametric formulation enables consistent comparison of hybrid configurations and provides basis for interpreting failure-mode transitions and deformation capacity of HRC sections under combined axial and flexural actions. Full article