Search Results (2,563)

The combination of scaffold materials and bioactive factors is a promising strategy for promoting bone defect repair in tissue engineering. Previous studies have shown that osteoglycin (OGN) is highly expressed in the bone repair process using deer antler as an animal model of bone defects. It suggests that OGN may be a key active component involved in the bone repair process. The aim of this study was to investigate whether deer OGN (dOGN) could effectively promote bone regeneration. We successfully expressed dOGN using the E. coli pET30a system and evaluated its biological activity through cell proliferation and migration assays. At a concentration of 5 μg/mL, dOGN significantly promoted cell proliferation and migration. We then incorporated dOGN onto decellularized antler cancellous bone (DACB) scaffolds and assessed their osteogenic potential both in vitro and in vivo. The results indicated that dOGN loading enhanced cell proliferation, adhesion, and osteogenic activity. In vivo experiments confirmed that the dOGN-DACB scaffold significantly improved bone regeneration compared to DACB alone. This study demonstrates that dOGN-loaded DACB scaffolds hold great potential for clinical applications in treating critical-sized bone defects by mimicking the rapid regenerative properties of deer antlers. Full article

Automated quality control plays a critical role in modern industries, particularly in environments that handle large volumes of packaged products requiring fast, accurate, and consistent inspections. This work presents an anomaly detection system for candle jars commonly used in industrial and commercial applications, where obtaining labeled defective samples is challenging. Two anomaly detection strategies are explored: (1) a baseline model using convolutional neural networks (CNNs) as an end-to-end classifier and (2) a hybrid approach where features extracted by CNNs are fed into One-Class classification (OCC) algorithms, including One-Class SVM (OCSVM), One-Class Isolation Forest (OCIF), One-Class Local Outlier Factor (OCLOF), One-Class Elliptic Envelope (OCEE), One-Class Autoencoder (OCAutoencoder), and Support Vector Data Description (SVDD). Both strategies are trained primarily on non-defective samples, with only a limited number of anomalous examples used for evaluation. Experimental results show that both the pure CNN model and the hybrid methods achieve excellent classification performance. The end-to-end CNN reached 100% accuracy, precision, recall, F1-score, and AUC. The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. Other OCC algorithms consistently delivered strong results, with all metrics above 95%, indicating solid generalization from predominantly normal data. This approach demonstrates strong potential for quality inspection tasks in scenarios with scarce defective data. Its ability to generalize effectively from mostly normal samples makes it a practical and valuable solution for real-world industrial inspection systems. Future work will focus on optimizing real-time inference and exploring advanced feature extraction techniques to further enhance detection performance. Full article

Enhancing the wear resistance of structural steels used in demanding industrial applications is critical for extending components’ lifespan and ensuring mechanical reliability. In this study, we investigated the influence of flux-cored arc welding (FCAW) hardfacing on the tensile behavior of S355MC steel. Protective Fe-Cr-C alloy layers were deposited in one and two successive passes using automated FCAW, followed by tensile testing of specimens oriented at varying angles relative to the weld bead direction. The methodology integrated 3D scanning and digital image correlation to accurately capture geometric and deformation parameters. The experimental results revealed a consistent reduction in tensile strength and ductility in all the welded configurations compared to the base material. The application of the second weld layer further intensified this effect, while specimen orientation influenced the degree of mechanical degradation. Microstructural analysis confirmed carbide refinement and good adhesion, but also identified welding-induced defects and residual stresses as factors that contributed to performance loss. The findings highlight a clear trade-off between improved surface wear resistance and compromised structural properties, underscoring the importance of process optimization. Strategic selection of welding parameters and bead orientation is essential to balance functional durability with mechanical integrity in industrial applications. Full article

Background/Objectives: Hutchinson–Gilford progeria syndrome (HGPS) is a rare and fatal genetic disease caused by a silent mutation in the LMNA gene, leading to the production of progerin, a defective prelamin A variant. Progerin accumulation disrupts nuclear integrity, alters chromatin organization, and drives systemic cellular dysfunction. While autophagy and inflammation are key dysregulated pathways in HGPS, the role of microRNAs (miRNAs) in these processes remains poorly understood. Methods: We performed an extensive literature review to identify miRNAs involved in autophagy and inflammation. Through stem-loop RT-qPCR in aging HGPS and control fibroblast strains, we identified significant miRNAs and focused on the most prominent one, miR-181a-5p, for in-depth analysis. We validated our in vitro findings with miRNA expression studies in skin biopsies from an HGPS mouse model and conducted functional assays in human fibroblasts, including immunofluorescence staining, β-Galactosidase assay, qPCR, and Western blot analysis. Transfection studies were performed using an miR-181a-5p mimic and its inhibitor. Results: We identified miR-181a-5p as a critical regulator of premature senescence in HGPS. miR-181a-5p was significantly upregulated in HGPS fibroblasts and an HGPS mouse model, correlating with Sirtuin 1 (SIRT1) suppression and induction of senescence. Additionally, we demonstrated that TGFβ1 induced miR-181a-5p expression, linking inflammation to miRNA-mediated senescence. Inhibiting miR-181a-5p restored SIRT1 levels, increased proliferation, and alleviated senescence in HGPS fibroblasts, supporting its functional relevance in disease progression. Conclusions: These findings highlight the important role of miR-181a-5p in premature aging and suggest its potential as a therapeutic target for modulating senescence in progeroid syndromes. Full article

This study investigates the strengthening mechanisms of char in silicon dioxide thermal reduction through systematic high-temperature experiments using three char types (YQ1, CW1, HY1) characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and scanning electron microscopy. HY1 char demonstrated superior reactivity due to its highly ordered microcrystalline structure, characterized by the largest aromatic cluster size (L_a) and lowest defect ratio (I_D/I_G = 0.37), which directly correlated with enhanced reaction completeness. The carbon–silicon reaction reactivity increased progressively with temperature, achieving optimal performance at 1550 °C. Addition of Fe and Fe₂O₃ significantly accelerated the reduction process, with Fe₂O₃ exhibiting superior catalytic performance by reducing activation energy and optimizing reaction kinetics. The ferrosilicon formation mechanism proceeds through a two-stage pathway: initial char-SiO₂ reaction producing SiC and CO, followed by SiC–iron interaction generating FeSi, which catalytically promotes further reduction. These findings establish critical structure–performance relationships for char selection in industrial silicon production, where microcrystalline ordering emerges as the primary performance determinant. The identification of optimal temperature and additive conditions provides practical pathways to enhance energy efficiency and product quality in silicon metallurgy, enabling informed raw material selection and process optimization to reduce energy consumption and improve operational stability. Full article

Accurate weld defect detection is critical for ensuring structural safety and evaluating welding quality in industrial applications. Manual inspection methods have inherent limitations, including inefficiency and inadequate sensitivity to subtle defects. Existing detection models, primarily designed for natural images, struggle to adapt to the characteristic challenges of weld X-ray images, such as high noise, low contrast, and inter-defect similarity, particularly leading to missed detections and false positives for small defects. To address these challenges, a multi-dimensional feature fusion model (MADet), which is a multi-branch deep fusion network for weld defect detection, was proposed. The framework incorporates two key innovations: (1) A multi-scale feature fusion network integrated with lightweight attention residual modules to enhance the perception of fine-grained defect features by leveraging low-level texture information. (2) An anchor-based feature-selective detection head was used to improve the discrimination and localization accuracy for five typical defect categories. Extensive experiments on both public and proprietary weld defect datasets demonstrated that MADet achieved significant improvements over the state-of-the-art YOLO variants. Specifically, it surpassed the suboptimal model by 7.41% in mAP@0.5, indicating strong industrial applicability. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

Introduction: Biodegradable Temporizing Matrix (BTM) is a new synthetic dermal substitute suitable for wound closure and tissue regeneration. The data in paediatric population remain limited. The study purpose is to review the indications for BTM application in paediatric patients; evaluate the short-term and long-term results, including complications and functional outcomes, as well as to share some unique observations regarding the use of BTM in paediatric population. Patients and Methods: Patients undergoing reconstructive surgery and BTM application during the last three years were included. Data collected included patient demographics, primary diagnosis, previous surgical management, post-operative complications and final outcomes. BTM was used in 32 patients. The indications varied including epidermolysis bullosa (n = 6), burns (n = 4), trauma (n = 7), infection (n = 4), ischemia or necrosis (n = 11). Results: The results were satisfying with acceptable aesthetic and functional outcomes. Complications included haematomaunderneath the BTM leading to BTM removal and re-application (n = 1), BTM infection (n = 1) and split-thickness skin graft failure on top of BTM requiring re-grafting (n = 2). Conclusions: BTM can be a good alternative to large skin grafts, locoregional flaps or even free flaps. The big advantages over other dermal substitutes or skin grafts are that BTM is less prone to infection and offers excellent scarring by preserving the normal skin architecture. Specifically in children, BTM might not require grafting, resulting in spontaneous healing with good scarring. In critically ill patients, BTM reduces the operation time and there is no donor site morbidity. BTM should be considered in the reconstructive ladder when discussing defect coverage options in children and young people. Full article

This article provides a comprehensive overview of recent studies concerning laser heat treatment (LHT) of structural and tool steels, with particular attention to the 21NiCrMo2 steel used for carburized gear wheels. Analysis includes the influence of critical laser processing conditions—including power output, motion speed, spot size, and focusing distance—on surface microhardness, hardening depth, and microstructure development. The findings indicate that the energy density is the dominant factor that affects the outcomes of LHT. Optimal results, in the form of a high surface microhardness and a sufficient depth of hardening, were achieved within the energy density range of 80–130 J/mm², allowing for martensitic transformation while avoiding defects such as melting or cracking. At densities below 50 J/mm², incomplete hardening occurred with minimal microhardness improvement. On the contrary, densities exceeding 150–180 J/mm² caused surface overheating and degradation. For carburized 21NiCrMo2 steel, the most effective parameters included 450–1050 W laser power, 1.7–2.5 mm/s scanning speed, and 2.0–2.3 mm beam diameter. The review confirms that process control through energy-based parameters allows for reliable prediction and optimization of LHT for industrial applications, particularly in components exposed to cyclic loads. Full article

Surface and sub-surface atomic configurations are critical for catalysis as they host the active sites governing electrochemical processes. This study employs density functional theory (DFT) calculations and Monte Carlo simulations combined with the cluster-expansion approach to investigate atom distribution and Pt segregation in Pt-Fe alloys across varying Pt/Fe ratios. Our simulations reveal a strong tendency for Pt atoms to segregate to the surface layer while Fe atoms enrich the sub-surface region. Crucially, the calculations predict the stability of a periodic Pt₂Fe alloy surface model, characterized by specific defect structures, at low platinum content and low annealing temperatures. Electronic structure analysis indicates that forming this Pt₂Fe surface alloy lowers the d-band center of Pt atoms, weakening CO adsorption and thereby enhancing resistance to CO poisoning. Although defect-induced strains can modulate the d-band center, crystal orbital Hamilton population (COHP) analysis confirms that such strains generally strengthen Pt-CO interactions. Therefore, the theoretical design of Pt₂Fe alloy surfaces and controlling defect density are predicted to be effective strategies for enhancing catalyst resistance to CO poisoning. This work highlights the advantages of periodic Pt₂Fe surface models for anti-CO poisoning and provides computational guidance for designing efficient Pt-based electrocatalysts. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

Subsea pipelines, operating in high-pressure and high-salinity conditions, face ongoing risks of leakage. Pipeline leaks can pollute the marine environment and, in severe cases, cause safety incidents, endangering human lives and property. Regular integrity inspections of subsea pipelines are critical to prevent corrosion-related leaks. This study develops a magnetic flux leakage (MFL)-based pig for detecting corrosion in subsea pipelines. Using a three-dimensional finite element model, this study analyzes the effects of defect geometry, lift-off distance, and operating speed on MFL signals. It proposes a defect estimation method based on axial peak-to-valley values and radial peak spacing, with inversion accuracy validated against simulation results. This study establishes a theoretical and practical framework for subsea pipeline integrity management, providing an effective solution for corrosion monitoring. Full article

Background: Mesenchymal stem cells (MSCs), multipotent and immune-regulatory cells derived from tissues such as bone marrow, dental pulp, and periodontal ligament, emerged as promising agents in regenerative dentistry. Their clinical applications include endodontic tissue regeneration, periodontal healing, and alveolar bone repair, addressing critical challenges in dental tissue restoration. Methods: A systematic review was conducted following PRISMA guidelines and registered in PROSPERO. We searched PubMed, Scopus, and Web of Science databases for open-access, English-language clinical trials and observational studies published from 2015 to 2025. Studies focusing on the application of MSCs in dental tissue regeneration were included based on predefined eligibility criteria. Results: Out of 2400 initial records, 13 studies met the inclusion criteria after screening and eligibility assessment. Most studies investigated MSCs derived from dental pulp and periodontal ligament for regenerating periodontal tissues and alveolar bone defects. The majority reported improved clinical outcomes; however, variations in MSC sources, delivery methods, sample sizes, and follow-up periods introduced methodological heterogeneity. Conclusions: MSCs show significant potential in enhancing bone and periodontal regeneration in dental practice. Nonetheless, the current evidence is limited by small sample sizes, short follow-up, and inconsistent methodologies. Future large-scale, standardized clinical trials are required to validate MSC-based regenerative therapies and optimize treatment protocols. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article