Search Results (24,706)

Metabolic syndrome (MetS) is a multifactorial disorder characterized by central obesity, insulin resistance, dyslipidemia, and hypertension, all of which increase the risk of type 2 diabetes and cardiovascular diseases. This study investigates the potential complementary effects of the standardized green and black ADM ComplexTea Extract (CTE) and the heat-treated postbiotic (BPL1^® HT) on the cardiometabolic alterations associated with MetS in a murine model. C57BL/6J mice were fed a high-fat/high-sucrose (HFHS) diet and treated with CTE, BPL1^® HT, or their combination for 20 weeks. Metabolic, inflammatory, oxidative, vascular parameters, and fecal microbiota composition were assessed. Both CTE and BPL1^® HT individually attenuated weight gain, organ hypertrophy, insulin resistance, and inflammation. However, their combined administration exerted synergistic effects, fully normalizing body weight, adipocyte size, lipid profiles, HOMA-IR index, and insulin sensitivity to levels comparable to lean controls. Co-treatment also restored PI3K/Akt signaling in liver and muscle, reduced hepatic steatosis, and normalized the expression of inflammatory and oxidative stress markers across multiple tissues. Furthermore, vascular function was significantly improved, with enhanced endothelium-dependent relaxation and reduced vasoconstrictor responses, particularly to angiotensin II. CTE, BPL1^®HT, and the blend prevented bacterial richness reduction caused by HFHS; the blend achieved higher bacterial richness than mice in Chow diet. Additionally, the blend prevented the increase in Flintibacter butyricus, which is associated with MetS clinical parameters, and showed a tendency to increase the abundance of Bifidobacterium. These findings suggest that the combination of CTE and BPL1^® HT offers a potential nutritional strategy to counteract the metabolic and cardiovascular complications of MetS through complementary mechanisms involving improved insulin signaling, reduced inflammation and oxidative stress, enhanced vascular function, and modulation of gut microbiota. Full article

This research explores the optimization of mechanical properties and predictive modeling of polylactic acid (PLA) reinforced with boron nitride nanoplatelets (BNNPs) using data-driven machine learning (ML) models. PLA-BNNP composites were fabricated through injection molding, with a focus on how key processing parameters influence their mechanical performance. A Taguchi L27 orthogonal array was applied to assess the effects of BNNP composition (0.02 wt.% and 0.04 wt.%), injection temperature (135–155 °C), injection speed (50–70 mm/s), and pressure (30–50 bar) on properties such as tensile strength, Young’s modulus, and hardness. The results indicated that a 0.04 wt.% BNNP loading improved tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively, compared to pure PLA. Taguchi analysis highlighted that higher BNNP concentrations, along with optimal injection temperatures, improved all mechanical properties, although excessive temperatures compromised tensile strength and modulus, while enhancing hardness. Analysis of variance (ANOVA) revealed that injection temperature was the dominant factor for tensile strength (68.88%) and Young’s modulus (86.39%), while BNNP composition played a more significant role in influencing hardness (78.83%). Predictive models were built using machine learning (ML) models such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost). Among the ML models, XGBoost demonstrated the highest predictive accuracy, achieving R² values above 98% for tensile strength, 92–93% for Young’s modulus, and 96% for hardness, with low error metrics i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These findings underscore the potential of using BNNP reinforcement and machine learning-driven property prediction to enhance PLA nanocomposites’ mechanical performance, making them viable for applications in lightweight packaging, biomedical implants, consumer electronics, and automotive components, offering sustainable alternatives to petroleum-based plastics. Full article

Canagliflozin (CFZ), a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is extensively utilized in the management of type 2 diabetes. Among its various polymorphic forms, the hemi-hydrate (Hemi-CFZ) has been selected as the active pharmaceutical ingredient (API) for CFZ tablets due to its superior solubility. However, during the production, storage, and transportation of CFZ tablets, Hemi-CFZ can undergo transformations into anhydrous (An-CFZ) and monohydrate (Mono-CFZ) forms under the influence of environmental factors such as temperature, humidity, and pressure, which may adversely impact the bioavailability and clinical efficacy of CFZ tablets. Therefore, it is imperative to develop rapid, accurate, non-destructive, and non-contact methods for quantifying An-CFZ and Mono-CFZ content in CFZ tablets to control polymorphic impurity levels and ensure product quality. This research evaluated the feasibility and reliability of using near-infrared spectroscopy (NIR) combined with partial least squares regression (PLSR) for simultaneous quantitative analysis of An-CFZ and Mono-CFZ in CFZ tablets, elucidating the quantifying mechanisms of the quantitative analysis model. Orthogonal experiments were designed to investigate the effects of different pretreatment methods and ant colony optimization (ACO) algorithms on the performance of quantitative models. An optimal PLSR model for simultaneous quantification of An-CFZ and Mono-CFZ in CFZ tablets was established and validated over a concentration range of 0.0000 to 10.0000 w/w%. The resulting model, Y_{An-CFZ/Mono-CFZ} = 0.0207 + 0.9919 X, achieved an R² value of 0.9919. By analyzing the relationship between the NIR spectral signals selected by the ACO algorithm and the molecular structure information of An-CFZ and Mono-CFZ, we demonstrated the feasibility and reliability of the NIR-PLSR approach for quantifying these polymorphic forms. Additionally, the mechanism of PLSR quantitative analysis was further explained through the variance contribution rates of latent variables (LVs), the correlations between LVs loadings and tablets composition, and the relationships between LV scores and An-CFZ/Mono-CFZ content. This study not only provides a robust method and theoretical foundation for monitoring An-CFZ and Mono-CFZ content in CFZ tablets throughout production, processing, storage, and transportation, but also offers a reliable methodological reference for the simultaneous quantitative analysis and quality control of multiple polymorphic impurities in other similar drugs. Full article

High-resolution remote sensing images are gradually playing an important role in land cover mapping, urban planning, and environmental monitoring tasks. However, current segmentation approaches frequently encounter challenges such as loss of detail and blurred boundaries when processing high-resolution remote sensing imagery, owing to their complex backgrounds and dense semantic content. In response to the aforementioned limitations, this study introduces HMA-UNet, a novel segmentation network built upon the UNet framework and enhanced through a hybrid attention strategy. The architecture’s innovation centers on a composite attention block, where a lightweight split fusion attention (LSFA) mechanism and a lightweight channel-spatial attention (LCSA) mechanism are synergistically integrated within a residual learning structure to replace the stacked convolutional structure in UNet, which can improve the utilization of important shallow features and eliminate redundant information interference. Comprehensive experiments on the WHDLD dataset and the DeepGlobe road extraction dataset show that our proposed method achieves effective segmentation in remote sensing images by fully utilizing shallow features and eliminating redundant information interference. The quantitative evaluation results demonstrate the performance of the proposed method across two benchmark datasets. On the WHDLD dataset, the model attains a mean accuracy, IoU, precision, and recall of 72.40%, 60.71%, 75.46%, and 72.41%, respectively. Correspondingly, on the DeepGlobe road extraction dataset, it achieves a mean accuracy of 57.87%, an mIoU of 49.82%, a mean precision of 78.18%, and a mean recall of 57.87%. Full article

Accurate prediction of erosion rates in polymeric and composite materials is essential for their effective design and maintenance in diverse industrial environments. This study presents a predictive modelling framework developed using the JMP Pro machine learning integrated system to estimate erosion rates of polymers and polymer composites. For better model generalisation under various conditions, a curated dataset was compiled from peer-reviewed literature, standardised, and subjected to outliers and multivariate exploratory data analysis to identify dominant variables. The model utilises key input parameters, including impact angle, impact velocity, sand content, particle size, material type, and fluid medium, to predict the erosion rate as the target output variable. Six machine learning algorithms were evaluated through a systematic model comparison process, and two were selected. Model performance was assessed using robust error metrics, and the interpretability of erosion behaviour was validated through prediction profilers and variable importance analyses. Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) demonstrated the best training and validation performance based on the evaluation metrics. While both models yielded high training performance, the ANN model demonstrated superior predictive accuracy and generalisation capability across a broad range of conditions. Beyond prediction, the model outputs also showed a meaningful representation of the influence of input variables on erosion rates. Full article

This study investigated the fabrication and characterization of polylactic acid (PLA)-based ceramic composite filaments for fused deposition modeling (FDM) 3D printing. Boron carbide (B₄C) and silicon carbide (SiC) were incorporated into PLA at various weight fractions (1–40 wt. % for B₄C and 1–20 wt. % for SiC) to produce composite filaments using a commercial extruder. The rheological properties, thermal stability, and printability of the filaments were evaluated. Filaments with low ceramic content exhibited satisfactory quality, whereas those with higher loadings required reprocessing to improve their dimensional stability and surface morphology. Successful printing was achieved with SiC contents of up to 8 wt. % using single-extruded filaments and up to 20 wt. % using double-extruded filaments. Rheological tests revealed that filaments with low ceramic content exhibited shear-thinning behavior, whereas those with higher loadings displayed nearly Newtonian-like behavior. Thermal analysis using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) determined the optimal processing temperature range for the composite filaments to be between 200 °C and 270 °C. High-temperature microscopy was used to study the temperature behavior of the B₄C-containing filaments and set the optimum printing temperature. The results demonstrate the feasibility of producing PLA-based ceramic composite filaments for FDM 3D printing with the potential to tailor the thermal and functional properties of the printed parts for specific applications. Full article

Constipation is a global health issue, with a prevalence of approximately 16%, and insufficient dietary fiber intake is a major contributing factor. Citrus peel residue contains a high proportion of dietary fiber, accounting for about 20–44% of its composition. In this study, the constipation-relieving effects of three functional components derived from citrus peel residue—cellulose (CEL), pectin (PEC), and citrus peel powder (CPP)—were systematically compared using a loperamide-induced mouse model. All groups were administered an equivalent dose of 200 mg/kg daily. The results showed that supplementation with CEL, PEC, and CPP improved defecation parameters. Among these, PEC effectively modulated the SCF/C-kit and Nrf2/HO-1 pathways. Compared with the model group, PEC increased Akkermansia abundance by approximately 34% and reduced Desulfovibrio abundance by about 26% Additionally, the smaller particle size and improved solubility of PEC promote the production of beneficial metabolites, thereby alleviating constipation. In contrast, CEL primarily alleviates constipation through its physical properties. At equivalent doses, CPP provides less constipation relief due to its lower component concentrations and a primary composition of insoluble dietary fiber. These findings provide preliminary mechanistic insights and support further exploration of citrus by-products as functional food candidates for the management of constipation. Full article

Background: Carotid plaque composition is central to stroke risk, but some aspects of plaque characterization are derived from ex vivo imaging, while clinical decision-making relies on in vivo ultrasound (US) and computed tomography (CT). High correlation of clinical in vivo and ex vivo imaging is necessary when including ex vivo plaque features in artificial intelligence (AI) models, but the extent of this correlation between CT and US remains poorly understood. Methods: Patients undergoing carotid endarterectomy (n = 188) were enrolled. Preoperative carotid US (n = 182) and CT (n = 156) were performed. Plaque specimens from 187 patients were imaged on ex vivo CT and US. Quantitative metrics included plaque volumes, relative calcified/non-calcified volumes, HU and grayscale distributions, Agatston and calcification scores, and heterogeneity indices (coefficient of variation). Qualitative US parameters (echogenicity, juxtaluminal echolucency, discrete white areas) were visually graded. Correlation between in vivo and ex vivo imaging was assessed, and agreement was quantified for parameters with the highest correlation with Bland–Altman analysis. Results: CT of patients and ex vivo CT showed moderate to strong correlation for total, calcified, and non-calcified plaque volumes and whole-plaque mean HU (r = 0.55–0.79; CCC = 0.43–0.74). Agatston and calcification scores correlated strongly (r = 0.78–0.80; CCC = 0.63–0.76). In contrast, most non-calcified and heterogeneity metrics showed negligible-to-weak correlation. Correlations between in vivo and ex vivo US were substantially weaker (maximum correlation: 75th grayscale percentile r = 0.35). In vivo CT overestimated calcified volume (bias: 8.7%) and in vivo US underestimated the 75th grayscale quantile (bias: −25.5 grayscale). Conclusions: Quantitative CT metrics—particularly relative calcified plaque volume and calcium scores—translate reasonably well from ex vivo to in vivo imaging and represent robust candidates for radiomics and AI-based stroke risk models, even ex vivo. Ultrasound parameters show limited translational validity, underscoring the need for volumetric clinical US and discouraging the inclusion of ex vivo ultrasound features for machine learning applications. Full article

Background: The study examined sex differences in countermovement jump (CMJ) force plate metrics and neuromuscular responses to a standardized dynamic warm-up in physically active college students. Methods: Forty-one participants (21 males, 20 females) completed pre- and post-warm-up assessments of CMJ performance using a dual force plate system. Body composition was measured via bioelectrical impedance analysis, and performance metrics included force, velocity, power, and other jump metrics. Percent change scores were calculated for all metrics. Results: Males demonstrated significantly greater improvements in braking force metrics compared to females, including force at minimum displacement (11.4% Δ male vs. 5.7% Δ female, p = 0.043), average braking force (10.6% Δ male vs. 5.0% Δ female, p = 0.043), and peak braking force (11.5% Δ male vs. 5.7% Δ female, p = 0.043). No significant sex differences were found in velocity, power, propulsive force, or other general CMJ performance variables. Hierarchical regression analyses revealed that sex was a significant (p ≤ 0.043 for all) predictor of changes in braking force metrics, while lean body mass did not enhance model fit or independently predict force changes. The addition of lean body mass slightly attenuated the sex effect but did not contribute meaningfully to the models. Conclusions: Findings suggest males may experience greater braking force adaptation to a dynamic warm-up, while other performance outcomes appear similar between sexes. These results may inform sex-specific warm-up strategies targeting neuromuscular readiness and braking force development. Full article

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

Although carbon filters (CF) can exhibit limited adsorption/selectivity for certain emerging pollutants and operating conditions, incorporating carbon–metal-oxide composites provides a platform to study how surface chemistry, charge distribution and oxide dispersion influence adsorption behavior. This study investigates the incorporation of metal oxides (Fe₃O₄, TiO₂ and CaO) into a commercial carbon filter via mechanical milling, focusing on fundamental changes in surface properties and methylene blue (MB) adsorption mechanisms. The synthesized oxides were characterized by X-ray diffraction and scanning electron microscopy, confirming crystalline structures with crystalline sizes between 11 and 23 nm. Composite filters with varying oxide contents (10–30 wt%) were evaluated for point of zero charge (PZC), surface charge distribution and methylene blue (MB) adsorption. The kinetic experiments were adjusted to pseudo-second order (PSO). Although the maximum adsorption capacity (2.75 mg·g⁻¹ for CaO-modified filters) is lower than commercially activated carbons, this work clarifies how oxide type and dispersion control adsorption performance and interaction mechanisms. Langmuir and Freundlich models revealed monolayer adsorption with favorable dye-surface interactions. These models provide key insights into the role of oxide type and pH in the dye removal process. Full article

The present study investigates the potential of poplar (Populus spp.) biomass from phytoremediation plantations as a feedstock for downdraft fixed bed gasification. The biomass was characterized in terms of moisture, ash content, elemental composition (C, H, N, O), and calorific values (HHV and LHV), confirming its suitability for thermochemical conversion. Gasification tests yielded a volumetric syngas production of 1.79 Nm³ kg⁻¹ biomass with an average composition of H₂ 14.58 vol%, CO 16.68 vol%, and CH₄ 4.74 vol%, demonstrating energy content appropriate for both thermal and chemical applications. Alkali and alkaline earth metals (AAEM), particularly Ca (273 mg kg⁻¹) and Mg (731 mg kg⁻¹), naturally present enhanced tar reforming and promoted reactive gas formation, whereas heavy metals such as Cd (0.27 mg kg⁻¹), Pb (0.02 mg kg⁻¹), and Bi (0.01 mg kg⁻¹) were detected only in trace amounts, posing minimal environmental risk. The results indicate that poplar pruning residues from phytoremediation sites can be a renewable and sustainable energy resource, transforming a waste stream into a process input. In this perspective, the integration of soil remediation with syngas production constitutes a tangible model of circular economy, based on the efficient use of resources through the synergy between environmental remediation and the valorization and sustainable management of marginal biomass—i.e., pruning residues—generating environmental, energetic, and economic benefits along the entire value chain. Full article

Tendon injuries, whether resulting from trauma, repetitive strain, or degenerative conditions, present a considerable clinical challenge. The natural healing process, which involves inflammatory, proliferative, and remodeling phases, is often inefficient and leads to excessive scar tissue formation, ultimately compromising the mechanical properties of the tendon compared to its native state. This highlights the critical need for innovative approaches to enhance tendon repair and regeneration. Leveraging the regenerative properties of human amniotic membrane (HAM) and electrospun PCL/gelatin nanofibers, this study aims to develop and assess a novel composite scaffold in a rodent model to facilitate improved tendon healing. This prospective experimental study involved 12 male Sprague Dawley rats (250–300 g), randomly assigned to three groups: Group A (No Treatment/No HAM), Group B (HAM-treated), and Group C (HAM with electrospun nanofibers, HAM-NF). A surgically induced tendon injury was created in the left hind limb, while the right limb served as a control. Following surgery, HAM and HAM-NF (0.5 cm²) were applied to the respective treatment groups, and tendon healing was assessed after six weeks. Gait analysis, including stride length and toe-out angle, was conducted both pre-operatively and six weeks post-operatively. Macroscopic and microscopic evaluations were performed on harvested tendons to assess regeneration, comparing treated groups to the controls. Gait analysis demonstrated that the HAM-NF group showed a significant increase in stride length from 11.70 ± 1.50 cm to 12.79 ± 1.71 cm (p < 0.05), with only a modest change in toe-out angle (14.58 ± 2.96° to 16.27 ± 2.20°). In contrast, the No Treatment group exhibited reduced stride length (10.27 ± 2.17 cm to 8.40 ± 1.67 cm) and a marked increase in toe-out angle (16.33 ± 4.51° to 26.47 ± 5.81°, p < 0.05), while the HAM-only group showed mild changes in both parameters. Macroscopic evaluation showed a significant difference in tendon healing. HAM-NF group had the highest score that indicates more rapid tissue regeneration. Histological analysis after 6 weeks showed that tendons treated with HAM-NF achieved a mean histological score of 5.54 ± 4.14, closely resembling the uninjured tendon (6.67 ± 1.63), indicating substantial regenerative potential. The combination of human amniotic membrane (HAM) and electrospun nanofibers presents significant potential as an effective strategy for tendon regeneration. The HAM/NF group exhibited consistent improvements in gait parameters and histological outcomes, closely mirroring those of uninjured tendons. These preliminary results indicate that this biomaterial-based approach can enhance both functional recovery and structural integrity, providing a promising pathway for advanced tendon repair therapies. Full article

Osteoarthritis (OA) is the most common multifactorial joint disease characterized by progressive cartilage degradation and impaired tissue repair. Osteochondral defects represent a major clinical challenge within OA, as damage to cartilage and underlying bone can initiate degenerative changes and contribute to joint deterioration. The Wnt/β-catenin signaling pathway plays an important role in OA pathogenesis, and its dysregulation contributes to chondrocyte catabolism and cartilage loss. NOTUM, an extracellular Wnt inhibitor, has emerged as a potential therapeutic modulator capable of restoring signaling balance and promoting cartilage homeostasis. This study aimed to evaluate the efficacy of NOTUM compared with hyaluronic acid (HA), human adipose-derived mesenchymal stromal cells (hAd-MSCs), and Colchicine in a rabbit osteochondral defect model relevant to osteoarthritis. Twenty-seven New Zealand White rabbits underwent standardized femoral condyle injury and received single-dose treatments. Serum levels of cartilage biomarkers—Procollagen Type IIA N-terminal Propeptide (PIIANP) and Cartilage Oligomeric Matrix Protein (COMP)—were measured by ELISA at 4, 6, and 8 weeks post-surgery, and histological repair at week 12 was assessed using the modified O’Driscoll scoring system. NOTUM treatment significantly increased PIIANP and decreased COMP levels compared with HA, indicating enhanced cartilage synthesis and reduced degradation. Histological scores confirmed superior surface morphology and tissue composition in NOTUM-treated joints. These findings suggest that NOTUM performs a protective and regenerative effect through Wnt/β-catenin modulation, supporting the conclusion that it enhances osteochondral defect repair and motivating further studies of NOTUM as an OA therapy. Full article

Composite materials are increasingly required to operate in cryogenic environments, including liquid hydrogen and oxygen storage, deep-space structures, and polar infrastructures, where long-term strength, toughness, and reliability are essential. This review provides a unique contribution by systematically integrating recent advances in understanding cryogenic behaviour into a unified multi-scale framework. This framework synthesises four critical and interconnected aspects: constituent response, composite performance, enhancement mechanisms, and modelling strategies. At the constituent level, fibres retain stiffness, polymer matrices stiffen but embrittle, and nanoparticles offer tunable thermal and mechanical functions, which collectively define the system-level performance where thermal expansion mismatch, matrix embrittlement, and interfacial degradation dominate failure. The review further details toughening strategies achieved through nano-addition, hybrid fibre architectures, and thin-ply laminates. Modelling strategies, from molecular dynamics to multiscale finite element analysis, are discussed as predictive tools that link these scales, supported by the critical need for in situ experimental validation. The primary objective of this synthesis is to establish a coherent perspective that bridges fundamental material behaviour to structural reliability. Despite these advances, remaining challenges include consistent property characterisation at low temperature, physics-informed interface and damage models, and standardised testing protocols. Future progress will depend on integrated frameworks linking high-fidelity data, cross-scale modelling, and validation to enable safe deployment of next-generation cryogenic composites. Full article