Search Results (194,231)

Hyperuricemia, a common metabolic disorder characterized by elevated serum uric acid (UA) levels, can lead to severe complications such as gout and renal impairment. Conventional therapies, while effective, are frequently accompanied by significant adverse effects, underscoring the urgent need for safer therapeutic alternatives. Recent evidence identifies the intestine as a novel, pivotal regulator of UA homeostasis, presenting a promising therapeutic axis. This review delineates the intestinal mechanisms governing UA regulation and evaluates the therapeutic potential of natural active substances that target these pathways. We conducted a comprehensive review of recent preclinical studies focusing on intestinal mechanisms involved in UA metabolism, including the roles of gut microbiota, urate transport proteins, intestinal barrier function, and inflammation. Studies evaluating natural active substances—such as polyphenols, polysaccharides, peptides, and plant extracts—were systematically analyzed for their effects on gut-mediated UA regulation. Natural active substances have been shown to effectively alleviate hyperuricemia by modulating gut microbiota, enhancing UA intestinal excretion, reinforcing intestinal barrier function, and suppressing inflammatory pathways. Collectively, these findings demonstrate the multi-target efficacy of natural active substances within the intestines, offering a promising therapeutic strategy that warrants further investigation into nutrition-based intestinal interventions and novel pharmacological treatments for hyperuricemia. Full article

Objective: To evaluate the biomechanical effects of varying posterior implant inclinations and loading protocols on peri-implant stress distribution in full-arch maxillary rehabilitations using the All-on-Four concept. Methodology: A three-dimensional finite element model of an edentulous atrophic maxilla was developed from a digital point cloud. Four implants were placed according to the All-on-Four protocol: two anterior vertical implants and two posterior implants with inclinations of 0°, 15°, 30°, or 45°. Mini-abutments and a titanium bar prosthesis were included. Material properties were assumed as homogeneous, isotropic, and linearly elastic. Immediate loading was simulated using frictional contacts (µ = 0.3), whereas delayed loading assumed complete osseointegration (bonded contacts). The models were meshed using 10-node quadratic tetrahedral elements (SOLID187) in ANSYS^®. Maximum von Mises stress in cortical bone, cancellous bone, implants, abutments, and the prosthetic bar was assessed. Results: Posterior implant tilt significantly reduced peri-implant stress. Under immediate loading, the highest stress occurred at 0° inclination in the posterior left implant (82.36 MPa) and decreased progressively with increasing tilt, reaching 33.63 MPa at 45° (≈59% reduction). Delayed loading generally produces lower stress magnitudes, particularly at extreme tilts. Anterior implants experienced lower stress levels across all configurations. Comparative analysis demonstrated that immediate loading increased stress at lower angulations, while differences between loading protocols were minimal at higher inclinations. Conclusions: Posterior implant angulation and loading protocol critically influence peri-implant stress distribution. Increased posterior tilt combined with appropriate loading reduces peak cortical bone stresses, supporting biomechanical optimization in All-on-Four maxillary rehabilitations. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

With the widespread integration of smart buildings equipped with distributed photovoltaics (PV) and electric vehicles (EVs), distribution networks face significant challenges arising from source-load fluctuations. Conventional centralized dispatch approaches are constrained by communication bottlenecks and data privacy requirements. These limitations make it difficult to achieve global coordination while preserving the autonomy of individual entities. This paper proposes a hierarchical coordination framework for the coordinated operation of distribution networks and smart buildings. The distribution management system (DMS) and building energy management systems (BEMSs) perform independent optimization within their respective domains. Only aggregated boundary power information is exchanged to protect data privacy, enabling cross-entity coordination under information boundary constraints. Building-side models incorporating thermal dynamics, EV charging and discharging, and PV generation are developed, along with a distribution network power flow model. To solve the coordinated optimization problem, an Anderson-accelerated alternating direction method of multipliers (AA-ADMM) is introduced. A safeguarding mechanism based on combined residuals is incorporated to enhance convergence efficiency and stability. Case studies on the IEEE 33-bus test system demonstrate that compared with the uncoordinated baseline, the proposed method reduces network loss by 12.1% and lowers PV curtailment from 9.20% to 0.52%, while improving voltage profiles without significantly compromising occupant comfort or EV travel requirements. In addition, AA-ADMM achieves convergence with up to 66% fewer iterations than standard ADMM. Full article

Background: The global rise in antimicrobial resistance (AMR) has created an urgent need for innovative antibacterial strategies and localized delivery systems. This study aimed to develop and characterize electrospun poly (vinyl alcohol) (PVA) nanofibers co-loaded with atorvastatin (ATR) and zinc oxide (ZnO) nanoparticles for use as a multifunctional topical platform for wound healing and infection control. Methods: ZnO nanoparticles were prepared via ball milling and characterized for size and zeta potential. Four PVA-based nanofiber formulations were fabricated using electrospinning: blank (F1), ZnO-loaded (F2), ATR-loaded (F3), and ATR/ZnO co-loaded (F4). The nanofibers were evaluated for morphology, thermal properties, crystallinity, and drug release. Antibacterial efficacy was tested against S. aureus, S. epidermidis, MRSA, and P. aeruginosa using broth microdilution and checkerboard assays. Biocompatibility and wound healing potential were assessed via MTT and fibroblast scratch assays on human foreskin fibroblasts (hFFs). Results: SEM imaging confirmed the production of uniform, bead-free nanofibers. ATR and ZnO nanoparticles were successfully incorporated in the nanofiber. The co-loaded formulation (F4) demonstrated a sustained release profile, releasing approximately 78.7% of ATR over 24 h. While all treatments showed limited activity against P. aeruginosa, the ATR/ZnO co-loaded nanofibers exhibited significantly enhanced antibacterial activity against Gram-positive strains, achieving the lowest MIC values (1.5–2.0 mg/mL). Synergy analysis confirmed an enhanced effect with ATR and ZnO against MRSA. Furthermore, F4 achieved the highest wound closure rate of 92.41% in 24 h while maintaining acceptable cytocompatibility. Conclusions: The integration of ATR and ZnO into PVA nanofibers provides an enhanced antibacterial effect consistent with the synergistic potential observed between free agents targeting Gram-positive wound pathogens. The platform’s ability to simultaneously inhibit bacterial growth and promote rapid fibroblast migration positions it as a promising localized therapeutic for managing infected wounds. Full article

Molecular point-of-care testing (POCT) for respiratory infections has undergone remarkable advancement over the past two decades, driven by technological innovation and urgent clinical needs highlighted by the COVID-19 pandemic. This comprehensive systematic review was conducted following PRISMA 2020 guidelines, synthesizing evidence from 254 peer-reviewed studies published between 2006 and 2026, with detailed analysis of the 30 most relevant papers selected through a rigorous four-stage screening process. The review examines the evolution of molecular POCT technologies, including reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and CRISPR-based detection systems. Key findings demonstrate that modern molecular POCT platforms achieve diagnostic performance comparable to laboratory-based testing, with sensitivities ranging from 88% to 100% and specificities from 98% to 100%, while delivering results in 15 to 80 min. These technologies enable rapid, accurate detection of major respiratory pathogens, including SARS-CoV-2, influenza A/B, respiratory syncytial virus (RSV), and atypical bacteria. The integration of microfluidic systems, portable devices, and smartphone-based analysis has expanded access to testing in resource-limited settings, emergency departments, and wearable platforms. This review provides critical insights for clinicians, researchers, and policymakers regarding the current state, clinical applications, and future directions of molecular POCT for respiratory infections. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

The mixed lymphocyte reaction (MLR) is a classic functional assay that models in vitro interactions between responder T cells and allogeneic antigen-presenting cells (APCs). It quantifies the magnitude and quality of alloreactivity, integrating signals from allorecognition, co-stimulation, inflammatory context, and minor histocompatibility antigens that may not be captured by molecular matching alone. This review is narrative in nature and is intended as a practical, non-systematic synthesis of the field. To provide a modern, practice-oriented synthesis of MLR designs, readouts, and translational uses, highlighting how new technologies have expanded MLR from bulk proliferation into multidimensional immune profiling.We summarize why MLR remains valuable as a functional compatibility probe beyond HLA typing, including the high baseline frequency of alloreactive T cells that produces robust signals without priming. We then review key design options (one-way vs. two-way formats; stimulator inactivation; responder definition; APC source and maturation) and how these choices affect interpretation for rejection and graft-versus-host disease risk modeling, tolerance-focused studies, and immunomodulatory screening. Next, we outline major readouts—radiometric and flow cytometric proliferation (dye dilution, Ki-67), cytokine/chemokine profiling, cytotoxicity adaptations, and next-generation add-ons (e.g., scRNA-seq, TCR sequencing)—emphasizing complementary strengths and common pitfalls. Finally, we consolidate practical quality and reproducibility controls (donor variability, dynamic range, timing, batch effects, and acceptance criteria) to improve cross-study comparability and translational readiness. Modern MLR platforms combine controllable allogeneic stimulation with scalable, high-resolution readouts for mechanistic discovery, immune monitoring and translational immune profiling. Standardized modular design and rigorous quality control can improve reproducibility and support broader adoption across transplantation, immunotherapy, and immune-modulation research. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

Corrosion in steel pipelines can cause critical failures in industrial systems, while conventional inspection methods such as radiography and ultrasonic testing are costly and require specialized personnel. This study presents a mobile computer vision system for automated corrosion detection inside steel pipes using deep learning-based visual analysis. The proposed system consists of a Raspberry Pi 4-based mobile robot equipped with a high-resolution camera for internal inspection. Acquired images were processed using color-space transformations (RGB–HSV), filtering, and segmentation. Convolutional neural networks and semantic segmentation models, including YOLOv8-seg (Instance segmentation) and DeepLabV3 (Semantic segmentation), were trained on a custom corrosion image dataset to identify corroded regions. Real-time visualization was implemented via Flask-based video streaming. Experimental results demonstrated high detection accuracy for uniform corrosion, achieving a mean Intersection over Union (mIoU) above 0.98 and a precision of 0.99 with the YOLOv8-seg model. These results indicate that the proposed system enables reliable and automated corrosion inspection, with the potential to reduce inspection costs and improve operational efficiency. Future work will focus on enhancing real-time performance through hardware optimization. Full article

The immunosuppressive nature of porcine reproductive and respiratory syndrome virus (PRRSV) remains the central obstacle to its effective control. Conventional microRNA (miRNA)-based antiviral approaches are limited by their modest potency and the high risk of viral escape. Here, we rationally designed an engineered miRNA carrying a 5′-triphosphate (5′-PPP) terminus that integrates RIG-I-driven innate immune activation and sequence-specific gene silencing within a single molecule. In vitro-transcribed 5′-PPP miRNAs are efficiently recognized by the pattern-recognition receptor RIG-I, triggering a robust type I interferon response that counteracts PRRSV-induced immunosuppression. In MARC-145 cells, one such construct, 5′-PPP BZL-sRNA-20, potently inhibited PRRSV replication through the synergistic action of immune activation and gene silencing. However, in porcine alveolar macrophages (PAMs)—the natural host cells for PRRSV—the antiviral effect depended primarily on 5′-PPP-induced interferon responses, with the targeting sequence providing limited or context-dependent benefits. Dual-luciferase assays confirmed that the gene-silencing activity depends on 5′-PPP modification, which enhances the stability of BZL-sRNA-20. This bifunctional strategy establishes an “immune activation plus targeting” paradigm by simultaneously acting as a RIG-I ligand that triggers broad antiviral responses and specifically cleaves viral RNA via direct base-pairing to conserved regions of the PRRSV genome. These findings reveal the potential of engineered 5′-PPP miRNAs as immunomodulatory antiviral agents, while highlighting that the contribution of RNAi targeting varies depending on the cellular context. Full article

The sustainable renewal of old residential communities faces increasing challenges in addressing the diverse environmental needs of older residents while respecting spatial constraints. Conventional approaches often treat older adults as a homogeneous group, overlooking how functional and social heterogeneity shape spatial perception. To address this gap, this study examines perceptual priorities in micro-public spaces of old residential communities in Qingdao, China, by classifying 60 community-dwelling older adults into four profiles using the Successful Aging framework. Participants performed free-viewing tasks using eye-tracking to observe 18 areas of interest (AOIs). Results reveal a clear perceptual hierarchy structured by individual profiles. Older adults with lower functional ability (Q3, Q4) allocate significant visual resources to safety-critical elements as a form of compensatory monitoring. Conversely, a systematic perceptual shift from survival-oriented assessment to quality-oriented evaluation was observed as functional and participatory reserves increased. High-participation groups (Q1, Q3) prioritized comfort facilities, while esthetic features attracted sustained attention primarily among the high-function/high-participation group (Q1). These findings provide empirical evidence for differentiated micro-renewal strategies that prioritize perceptual stress reduction and affordance enrichment in old residential communities. Full article

The enzymatic production of low-molecular-weight chitosan and chitooligosaccharides (COS), with broad application potential in agriculture, food, medicine, and cosmetics, has emerged as an attractive alternative to chemical chitosan depolymerization owing to its substrate specificity and environmentally benign catalytic action. However, the functional properties of available chitosanases need to be enhanced to meet the demands of industrial COS manufacturing under high temperature and substrate concentrations. In this work, we performed directed evolution on a recombinant Bacillus subtilis chitosanase to increase chitosan hydrolysis performance and thermal resistance. Three rounds of directed evolution screening (~9000 clones) yielded variants MT1, MT2, and MT3 with higher specific activity, achieved through Vmax improvement and increased T_1/2 at 60 °C. HPLC, DLS, and MALDI-TOF results indicate differences in the hydrolysis kinetics and size distribution of COS products over reaction time, suggesting a narrower distribution and a lower average molecular weight. Molecular dynamics simulations and docking studies revealed potential modulation of chitosanase activity via changes in the opening and closing dynamics of the active-site cleft. These results suggest that future efforts targeting the cleft interface could significantly advance both the catalytic performance and the mechanistic understanding of GH46 family chitosanases. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article