Search Results (14,308)

Background: Chitosan and Lavandula angustifolia (lavender) exhibit antibacterial, antioxidant, and anti-inflammatory effects, making them potential candidates for managing infected wounds. This study investigated the therapeutic efficacy of a chitosan nanoparticle-loaded hydrogel, lavender extract, and their combination in treating Staphylococcus aureus-infected wounds in rats. Methods: Forty-eight male Sprague-Dawley rats (250–350 g, 8–10 weeks) were divided into six groups: healthy control, infected untreated, Fucidin, lavender extract, chitosan hydrogel, and chitosan–lavender combination. Wound healing was evaluated on days 3, 7, and 14 using clinical assessment, histopathology, and biochemical markers. Non-parametric statistical tests were applied, with significance set at p < 0.05. Results: The chitosan–lavender group showed the most pronounced healing response, with significantly reduced WBC counts, lower levels of TNF-α, IL-6, and MDA, and enhanced SOD activity (p < 0.05). Histological analysis confirmed superior re-epithelialization, granulation tissue development, collagen deposition, and wound contraction in chitosan-based treatments, particularly their combination, compared to lavender or Fucidin alone (p < 0.001). Inflammatory infiltrates, angiogenesis, necrosis, and hemorrhage were also notably reduced across treated groups. Conclusion: Combining chitosan hydrogel with lavender extract exerts synergistic antibacterial and wound healing effects, offering a promising alternative therapy for infected wounds. Full article

Flooding poses a major hazard to rapidly urbanising cities in Southeast Asia, and risks are projected to intensify under climate change. Accurate risk assessment, however, is hindered by scarcity of hydrological and topographic data. Focusing on the Lower Prek Thnot River Basin, a peri-urban catchment of Phnom Penh, Cambodia, the study applied the Rainfall–Runoff–Inundation model and systematically augmented inputs: hourly satellite rainfall data, field-surveyed river cross-sections and representation of hydraulic infrastructure such as weirs and pumping. Validation used Sentinel-1 SAR-derived flood-extent maps for the October 2020 event. Scenario comparison shows that rainfall input and channel geometry act synergistically: omitting either degrades performance and spatial realism. The best configuration (Sim. 5) Accuracy = 0.891, Hit Ratio = 0.546 and True Ratio = 0.701 against Sentinel-1, and reproduced inundation upstream of weirs while reducing overestimation in urban districts through pumping emulation. At the study’s 500 m grid, updating land use from 2002 to 2020 had only a minor effect relative to rainfall, geometry and infrastructure. The results demonstrate that targeted data augmentation—combining satellite products, field surveys and operational infrastructure—can deliver robust inundation maps under data scarcity, supporting hazard mapping and resilience-oriented flood management in rapidly urbanising basins. Full article

Chlorobenzoic acids (CBAs) are a group of chlorinated persistent environmental pollutants with hard biodegradability, high water solubility, and well-documented carcinogenic and endocrine-disrupting properties. Electrocatalytic hydrodechlorination (ECH) is a highly efficient method under mild conditions without harmful by-products, but the ECH process commonly requires adding precious metal catalysts such as palladium (Pd). To address the economic constraints and more effective utilization of Pd, a palladium/manganese dioxide (Pd/MnO₂) composite catalyst was developed in this study by chemical deposition. This method utilized the excellent electrochemical activity of MnO₂ as a carrier as well as the hydrogen storage and activation capacity of Pd. The test showed the optimal Pd loading was 7.5%, and the removal percent of 2,4-dichlorobenzoic acid (2,4-DCBA), a typical CBA, reached 97.3% using 0.5 g/L of Pd/MnO₂ after 120 min of electrochemical reaction. Under these conditions, the dechlorination percent can also be as high as 89.6%. A higher current density enhanced the dechlorination efficiency but showed the lower current utilization efficiency. In practical applications, current density should be minimized on the premise of compliance with the water treatment requirement. Mechanistic studies showed that MnO₂ synergistically promoted hydrolysis dissociation and hydrogen spillover and facilitated Pd-mediated adsorption of atomic hydrogen (H*) for dehydrogenation of 2,4-DCBA. The presence of MnO₂ can effectively disperse the loaded Pd and reduce the amount of Pd via the above process. The catalyst exhibited excellent stability over multiple cycles, and the 2,4-DCBA removal could still reach more than 80% after the five cycles. This work establishes electrocatalytic strategies for effectively reducing Pd usage and maintaining high removal of typical CBAs to support CBA-related water treatment. Full article

Sleep disorders represent a growing global health concern with significant socio-economic impacts. GABA, a natural bioactive compound abundant in various fermented foods, especially probiotic-fermented foods, has garnered increasing attention for its potential to improve sleep quality. This review systematically elucidates the multi-pathway mechanisms by which GABA regulates sleep, focusing on (1) indirect modulation of central sleep–wake circuits via the gut–brain axis through vagal nerve, neuroendocrine, and immune pathways; (2) potential entry into the brain by leveraging the dynamic permeability of the blood–brain barrier (BBB) and transporter-mediated active transport; and (3) metabolic conversion into active substances like γ-hydroxybutyrate (GHB), which synergistically optimizes sleep architecture via multiple receptor systems and energy metabolism. Furthermore, we summarize the sleep-promoting effects of GABA-enriched foods observed in animal and clinical studies and discuss emerging applications, including high-GABA-yielding probiotics and personalized nutrition strategies for sleep intervention. This review provides a theoretical basis and innovative directions for the development of GABA-based functional foods and sleep health management. Full article

The synergistic application of biochar and straw could improve soil properties and influence soil microbial community. However, its impacts on microbial community interactions and functions within various aggregate fractions remain unclear. We conducted a three-year field trial in black soil in northeastern China, under the restoration measures of biochar application (BR, 30 t ha⁻¹ once), straw return (SR, 5 t ha⁻¹ year⁻¹), and the combination of BR and SR (BS, BR at 30 t ha⁻¹ once and SR at 5 t ha⁻¹ year⁻¹). Utilizing high-throughput sequencing, we assessed the influence of different straw-returning methods on the structure and function of microbial communities in the mega-aggregates (ME, >2 mm), macroaggregates (MA, 0.25–2 mm), and microaggregates (MI, <0.25 mm). Relative to the control (CK), the BR, SR and BS treatments significantly decreased the bacterial Shannon index, mainly dependent on ME (p < 0.05). Conversely, compared with the CK and SR treatments, both BR and BS treatments notably reduced the fungal Shannon index, largely influenced by MI (p < 0.05). Moreover, the BS treatment significantly increased the relative abundance (RA) of Mortierellomycota (p < 0.05) compared to the CK, BR and SR treatments. Meanwhile, the SR and BS treatments substantially reduced the RA of Nitrospirae (p < 0.05) in comparison to the CK and BR treatments. Furthermore, compared with the CK, the BR and SR treatments enhanced microbial network connectivity, while the BS treatment diminished it, especially in ME and MI. Concurrently, the keystone of co-occurrence networks shifted from Phycisphaeraceae, Blastocatellaceae, and Glomeraceae in the CK treatment to uncultured_bacterium_c_JG37-AG-4 and DA111 in the BS treatment. Additionally, BR and SR exhibited synergistic effects on most microbial community functions (e.g., enhanced chitinolysis and carbon fixation but reduced nitrogen-cycling functions), but they also possessed distinct differential functions. In short, the combined application of biochar and straw adversely impacted soil microbial community diversity and stability, especially in ME and MI. Full article

Skin aging, including photoaging, is primarily triggered by chronic exposure to solar radiation, which induces free radical formation, cellular deoxyribonucleic acid (DNA) damage, and structural skin alterations. Achillea millefolium L. (Asteraceae) is rich in phenolic compounds and alkamides, substances known for their antioxidant activity. This study aimed to develop and characterize a photoprotective phytocosmetic nanoemulsion containing crude root extract of A. millefolium. The extract exhibited a total phenolic content of 3.067 ± 1.911 µg GAE/mL, potent antioxidant activity (EC₅₀ = 69.11 ± 8.899 µg/mL), moderate tyrosinase inhibition (19 ± 1.8%), and no cytotoxicity in keratinocytes. The extract was incorporated into nanoemulsions at concentrations of 0.1%, 0.5%, and 1%. The resulting droplets showed mean diameters of 217 to 230 nm, with a significant increase in the polydispersity index (PDI) after extract addition (p < 0.05). Transmission electron microscopy (TEM) confirmed the spherical morphology of the droplets. The in vitro sun protection factor (SPF) was 14 ± 0.9 in the control formulation and increased to 15 ± 2.0 (0.1%), 22 ± 5.2 (0.5%), and 17 ± 1.0 (1%), suggesting a synergistic effect between the extract and chemical filters. All formulations demonstrated UVA/UVB ratio > 0.6, a pH near to 5, occlusive properties, and good spreadability. The results indicate that A. millefolium extract holds potential for safe photoprotective formulations, offering a valuable antioxidant and depigmenting activity in addition to enhancing the SPF. This position is an innovative alternative to phytocosmetic development. Full article

Tyrosine kinase inhibitors (TKIs), such as sunitinib and sorafenib, are standard treatments for renal cell carcinoma (RCC). However, most patients treated with these drugs eventually develop drug resistance and relapse; therefore, new treatment options for RCC are urgently required. Recent studies have focused on combination therapies targeting distinct molecular pathways that may produce synergistic effects and help overcome drug resistance in RCC. Niclosamide, an anthelmintic agent, is effective against various cancers; however, its potential in combination with sunitinib for treating RCC has not been evaluated. In this study, we assessed the therapeutic efficacy of niclosamide in combination with sunitinib against RCC and explored the underlying molecular mechanisms. Niclosamide alone inhibited RCC cell proliferation, whereas its combination with sunitinib produced a synergistic anticancer effect, both in vitro and in vivo. RNA sequencing (RNA-seq) and bioinformatic analyses showed that niclosamide modulated critical pathways, including BRIP1- and FANCA-mediated DNA repair and E2F2-regulated cell cycle progression. These findings provide proof-of-concept that niclosamide enhances TKI efficacy through modulation of DNA repair and cell cycle pathways, supporting the rationale for DNA damage response (DDR)-targeted combination strategies in RCC. Full article

Long-term traffic flow prediction (LTFP) is crucial for intelligent transportation systems but remains challenging due to complex spatiotemporal dependencies and multi-scale temporal patterns. While recent models like Autoformer have introduced decomposition techniques, they often lack tailored mechanisms for traffic data′s unique characteristics, such as strong periodicity and long-range spatial correlations. To address this gap, we propose STLLformer, a novel spatiotemporal Transformer that establishes a seasonal-dominated, multi-component collaborative forecasting paradigm. Unlike existing approaches that merely combine decomposition with graph networks, STLLformer features: (1) a dedicated encoder–decoder architecture for separate yet synergistic modeling of trend, seasonal, and residual components; (2) a seasonal-driven autocorrelation mechanism that purely captures cyclical patterns by filtering out trend and noise interference; and (3) a low-rank graph convolutional module specifically designed to capture dynamic, long-range spatial dependencies in road networks. Experiments on two real-world traffic datasets (PEMSD8 and HHY) demonstrate that STLLformer outperforms strong baseline methods (including LSTGCN, LSTM, and ARIMA), achieving an average improvement of over 10% in MAE and RMSE (e.g., on PEMSD8 for 6-h prediction, MAE drops from 36.87 to 30.34), with statistical significance (p < 0.01). This work provides a more refined and effective decomposition-fusion solution for traffic forecasting, which holds practical promise for enhancing urban traffic management and alleviating congestion. Full article

The You Only Look Once (YOLO) series of models, particularly the recently introduced YOLOv12 model, have demonstrated significant potential in achieving accurate and rapid recognition of electric power operation violations, due to their comprehensive advantages in detection accuracy and real-time inference. However, the current YOLO models still have three limitations: (1) the absence of a dedicated feature extraction for multi-scale objects, resulting in suboptimal detection capabilities for objects with varying sizes; (2) naive integration of spatial and channel attentions, which restricts the enhancement of feature discriminability and consequently impairs the detection performance for challenging objects in complex backgrounds; and (3) weak representation capability in low-level features, leading to insufficient accuracy for small-sized objects. To address these limitations, a novel YOLO model named DFA-YOLO is proposed, a real-time object detection model with YOLOv12n as its baseline, which makes three key contributions. Firstly, a dynamic weighted multi-scale convolution (DWMConv) module is proposed to address the first limitation, which employs lightweight multi-scale convolution followed by learnable weighted fusion to enhance feature representation for multi-scale objects. Secondly, a full-dimensional attention (FDA) module is proposed to address the second limitation, which gives a unified attention computation scheme that effectively integrates attention across height, width, and channel dimensions, thereby improving feature discriminability. Thirdly, a set of auxiliary detection heads (Aux-Heads) are introduced to address the third limitation and inserted into the backbone network to strengthen the training effect of labels on the low-level feature extraction module. The ablation studies on the EPOVR-v1.0 dataset demonstrate the validity of the proposed DWMConv module, FDA module, Aux-Heads, and their synergistic integration. Relative to the baseline model, DFA-YOLO achieves significant improvements in mAP@0.5 and mAP@0.5–0.95, by 3.15% and 4.13%, respectively, meanwhile reducing parameters and GFLOPS by 0.06M and 0.06, respectively, and increasing FPS by 3.52. Comprehensive quantitative comparisons with nine official YOLO models, including YOLOv13n, confirm that DFA-YOLO achieves superior performance in both detection precision and real-time inference, further validating the effectiveness of the DFA-YOLO model. Full article

The synthesis of high-performance adhesives imposes stringent requirements on the design of stirred reactors: simultaneous achievement of efficient mixing and minimal energy dissipation in highly viscous media remains the principal challenge. In this study, computational fluid dynamics (CFD) was employed to solve the Navier–Stokes equations for the high-viscosity epoxy system and numerically simulating the flow fields of four representative reactor configurations across a prescribed range of rotational speeds. Specifically, the four representative reactor configurations were (i) single-serrated shaft, (ii) eccentric single-serrated shaft, (iii) uniaxial single-blade paddle combined with a single-serrated dual-axis assembly, and (iv) biaxial single-blade paddle coupled with a single-serrated triaxial assembly. The mixing performance was quantitatively assessed by systematically comparing the evolution of mixing speed, vorticity fields, restricted power consumption, and mixing time across a range of rotational speeds. The results demonstrated that the synergistic deployment of an eccentric impeller and a differential-speed single-propeller shaft effectively disrupted the axisymmetric flow pattern, compressed the chaotic isolation zones, and intensified both axial exchange and global chaotic mixing. Among the configurations examined, the uniaxial single-propeller–single-serrated biaxial arrangement reduced the mixing time by 13.43% and cut the specific energy consumption by approximately 58.32%, thereby attaining markedly higher energy efficiency. This research will provide guidance for the study of efficient mixing of adhesives. Full article

Graphene-based materials have been the subject of extensive scientific investigations owing to their distinctive properties and versatile functionalities. However, their applications are hindered due to limited material performance, difficulties in recycling, and high costs during manufacturing. Considering this, studies have developed graphene or its derivatives by combining it with other components, while some of these composites revealed significantly improved performance with lesser consumption of raw materials; the underlying mechanisms remain inadequately elucidated. Therefore, the aspiration for novel applications of graphene-based materials could be significantly improved with the full utilization of the synergistic effects of these materials. In this review, we intend to discuss the synergistic activities and their inherent mechanisms between graphene or its derivatives and metals, metal oxides, polymers, and bioactive compounds, among others. The effectiveness of synergisms in enhancing the performance of graphene-based composites is corroborated by studies in a variety of application areas such as antimicrobial materials, cancer therapy, sensors, electronic devices, catalysts, and more. The content presented will be useful to guide the future development of graphene-based materials that are highly efficient and environmentally friendly. Full article

Sperm cryopreservation is a key technique in assisted reproductive technologies (ART), livestock breeding, fertility preservation, and wildlife conservation. However, the freeze–thaw process induces significant oxidative stress through the production of reactive oxygen species (ROS) by mitochondria, which can lead to impaired sperm motility, membrane damage, DNA fragmentation, and reduced fertilization potential. MitoQ is a mitochondria-targeted antioxidant consisting of a ubiquinone moiety conjugated to triphenylphosphonium (TPP⁺). MitoQ selectively accumulates in the mitochondrial matrix, where it efficiently scavenges reactive oxygen species (ROS) at their point of origin. This targeted action helps preserve mitochondrial function, sustain ATP production, and inhibit apoptotic signaling. Extensive experimental evidence across diverse species, including bulls, rams, boars, humans, dogs, and goats, shows that MitoQ supplementation during cryopreservation enhances post-thaw sperm viability, motility, membrane integrity, and DNA stability. Optimal dosing between 50 and 150 nM achieves these benefits without cytotoxicity, although higher doses may paradoxically increase oxidative damage. Compared to conventional antioxidants, MitoQ offers superior mitochondrial protection and enhanced preservation of sperm bioenergetics. Future directions involve exploring synergistic combinations with other cryoprotectants, advanced delivery systems such as nanoparticles and hydrogels, and detailed mechanistic studies on long-term effects. Overall, MitoQ represents a promising adjunct for improving sperm cryopreservation outcomes across clinical, agricultural, and conservation settings. Full article

This review systematically examined the transformative role of machine learning in predicting polymer aging lifetime, addressing critical limitations of conventional methods such as the Arrhenius model, time–temperature superposition principle, and numerical fitting approaches. The primary objective was to establish a comprehensive framework that integrates multi-mechanism coupling with dynamic data-driven modeling to enhance prediction accuracy across complex aging scenarios. Four key machine learning categories demonstrate distinct advantages: support vector machines effectively capture nonlinear interactions in multi-stress environments; neural networks enable cross-scale modeling from molecular dynamics to macroscopic failure; decision tree models provide interpretable feature importance quantification; and hybrid approaches synergistically combine complementary strengths. These methodologies have shown significant success in critical industrial applications, including building trades, photovoltaic systems, and aerospace composites, creating an integrated predictive system that bridges molecular-level dynamics with service-life performance. By transforming life prediction from empirical extrapolation to mechanism-based simulation, this machine-learning-driven paradigm offers robust methodological support for engineering safety design in diverse polymer applications through its capacity to model complex environmental interactions, adapt to real-time monitoring data, and elucidate underlying degradation mechanisms. Full article

Backgrounds: Non-alcoholic fatty liver disease (NAFLD) is a multifaceted metabolic disorder that has become a prominent public health problem worldwide. As a traditional Chinese medicine formula, the ErChen decoction (ECD) possesses significant effects on metabolic syndrome. Methods: To determine whether ECD can relieve lipid accumulation and insulin resistance (IR) in liver cells, NAFLD and IR cell models were established by treating HepG2 cells with free fatty acids and an overdose of insulin, respectively. Bioinformatics and experimental evidence demonstrated that ECD could ameliorate NAFLD by modulating multiple pathways. The optimal combination of the key compounds in ECD was identified by the orthogonal experiment. Results: For lipid homeostasis, ECD suppressed de novo lipogenesis and reduced the cholesterol level by activating the AMPK signaling pathway. Concurrently, ECD enhanced hepatic β-oxidation by inducing PPARα-mediated upregulation of ACOX-1 and CPT-1α. ECD also resolved hepatic insulin resistance by activating the IRS1-Akt-FoxO1 pathway. The combined treatment with 100 μM liquiritin (LQ), 200 μM glycyrrhizic acid (GA) and 200 μM hesperidin (HEN) exhibited the best effect in reducing TG content in NAFLD model cells. Conclusions: ECD exhibited superior activities in activating the AMPK signaling pathway compared to the optimal compound combination. The comparison between the ECD and its key compounds demonstrated the superior synergistic effects of the herbs in ECD. Full article

Surface coatings have proven highly effective in addressing the critical challenges of friction, wear, and corrosion on steel substrates, which are responsible for over 80% of mechanical failures in industrial applications. Recent research highlights that advanced coatings—such as ceramic carbides/nitrides, high-entropy alloys, and metal-matrix composites—significantly enhance hardness, wear resistance, and environmental durability through mechanisms including protective oxide film formation, solid lubrication, and microstructural refinement. Moreover, these coatings exhibit robust performance under combined tribological-corrosive (tribocorrosion) conditions, where synergistic interactions often accelerate material degradation. Key developments include multilayer and composite architectures that balance hardness with toughness, self-lubricating coatings capable of in situ lubricant release, and active or self-healing systems for sustained corrosion inhibition. Despite these advances, challenges remain in predicting coating lifetime under multifield service conditions and optimizing interfacial adhesion to prevent delamination. Future efforts should prioritize multifunctional coating designs, improved tribocorrosion models, and the integration of sustainable materials and AI-driven process optimization. This review consolidates these insights to support the development of next-generation coatings for extending the service life of steel components across demanding sectors such as marine, aerospace, and energy systems. Full article