Search Results (11,193)

Background/Objectives: The gold standard of treatment for both melanoma and non-melanoma skin cancers is wide surgical resection to obtain oncological radicality, which occasionally results in functional or aesthetic impairment, potentially affecting quality of life. Despite the increased complexity of the technique, extended duration of hospitalization, and prolonged surgical operative times, microsurgery can facilitate the reconstruction of locally invasive skin cancers following ablative surgery and may yield superior functional and aesthetic outcomes. Consequently, microsurgical reconstruction is more likely to be necessary if a large skin tumor requires excision. However, the impact of this extensive and complex procedure on patients with skin cancer has not yet been fully elucidated. The objective of this research was to critically analyze the utilization of free flap reconstruction subsequent to skin cancer therapy. Through a comprehensive examination of published data, this study aimed to assess the potential benefits and drawbacks associated with this reconstructive approach. Methods: A systematic review of studies that were published from January 2004 to May 2024 was conducted using the MEDLINE online database search. To present an evidence summary and provide a systematic approach and quality assessment, the GRADE^® rating was applied to the results. Results: This review summarizes the oncological and clinical data, including previous interventions, adjuvant and neoadjuvant therapies, nodal status, distant metastasis, and follow-up time. Surgical outcome parameters such as healing time, flap survival, revision rate success, and minor and major complications were documented. Along with the findings, a quality assessment of the studies was also provided. Conclusions: This systematic review underscores the extensive use and efficacy of microsurgery for reconstruction after skin cancer excision; however, the literature remains limited by inconsistent reporting of oncological outcomes and the lack of a standardized approach to evaluate the impact of free flap reconstruction on both immediate and long-term cancer-specific results. Full article

Inhaled corticosteroids (ICSs) are widely used to manage chronic respiratory conditions such as asthma, chronic obstructive pulmonary disease (COPD), and human immunodeficiency virus (HIV). However, prolonged use of ICS is associated with the development of oropharyngeal candidiasis, a fungal infection primarily caused by Candida albicans, due to local immunosuppression in the oral cavity. The incidence of oropharyngeal candidiasis varies depending on geographic region, patient age, and comorbidities, with immunocompromised individuals, those with diabetes, and the elderly being particularly vulnerable. Key risk factors include high ICS doses, poor oral hygiene, and improper use of inhalers. Prevention is the cornerstone of managing oropharyngeal candidiasis associated with the chronic use of inhaled corticosteroids. Patient education on proper inhaler technique and oral hygiene is essential to reduce the risk of fungal overgrowth in the oral cavity. Additional preventive strategies include the use of spacers, mouth rinsing after inhalation, and proper denture care. In cases where these measures fail to prevent the infection, prompt detection and early intervention are crucial to prevent progression or recurrence. This narrative review aims to analyze the most effective prophylactic measures to prevent oropharyngeal candidiasis associated with the chronic use of inhaled corticosteroids, emphasizing patient education, oral hygiene, and proper use of inhalation devices. Full article

Background: The accurate prediction of pathological complete response (pCR) following total neoadjuvant therapy (TNT) is crucial for optimising treatment protocols in locally advanced rectal cancer (LARC). Although conventional imaging techniques such as MRI show limitations in assessing treatment response, metabolic imaging utilising 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) provides distinctive information by quantifying tumour glycolytic activity. This study investigates the predictive value of sequential 18F-FDG PET-CT parameters, focusing on Total Lesion Glycolysis (TLG), in predicting pCR after TNT. Methods: We conducted a retrospective analysis of 33 LARC patients (T3–4/N0–1) treated with TNT (neoadjuvant-chemoradiation followed by consolidation FOLFOX chemotherapy). Sequential PET-CT scans were performed at baseline, interim (after 4 cycles of FOLFOX), and post-TNT. Metabolic parameters, including maximum standardised uptake value (SUVmax) and TLG, were measured. Receiver operating characteristic (ROC) analysis assessed the predictive performance of these parameters for pCR. Results: The pCR rate was 21.2% (7/33). Post-TNT TLG ≤ 10 demonstrated excellent predictive accuracy for pCR (AUC 0.887, 92.3% sensitivity, 85.7% specificity, and 96.0% PPV), outperforming SUVmax (AUC 0.843). Interim TLG ≤ 10 also showed a strong predictive value (AUC 0.824, 100% sensitivity, and 71.4% specificity). Conclusions: TLG may serve as a reliable metabolic biomarker for predicting pathologic complete response (pCR) after total neoadjuvant therapy (TNT) in locally advanced rectal cancer (LARC). Its inclusion in clinical decision-making could improve patient selection for organ preservation strategies, thereby reducing the need for unnecessary surgeries in the future. However, given that the study is based on a small retrospective design, the findings should be interpreted with caution and used alongside other decision-making tools until more comprehensive data are collected from larger studies. Full article

Vessel detection technology based on marine remote sensing imagery is of great importance. However, it often faces challenges, such as small vessel targets, cloud occlusion, insufficient data volume, and severely imbalanced class distribution in datasets. These issues result in conventional models failing to meet the accuracy requirements for practical applications. In this paper, we first construct a novel remote sensing vessel image dataset that includes various complex scenarios and enhance the data volume and diversity through data augmentation techniques. Secondly, we address the class imbalance between foreground (small vessels) and background in remote sensing imagery from two perspectives: the sensitivity of IoU metrics to small object localization errors and the innovative design of a cost-sensitive loss function. Specifically, at the dataset level, we select vessel targets appearing in the original dataset as templates and randomly copy–paste several instances onto arbitrary positions. This enriches the diversity of target samples per image and mitigates the impact of data imbalance on the detection task. At the algorithm level, we introduce the Normalized Wasserstein Distance (NWD) to compute the similarity between bounding boxes. This enhances the importance of small target information during training and strengthens the model’s cost-sensitive learning capabilities. Ablation studies reveal that detection performance is optimal when the weight assigned to the NWD metric in the model’s loss function matches the overall proportion of small objects in the dataset. Comparative experiments show that the proposed NWD-YOLO achieves Precision, Recall, and ${\mathrm{AP}}_{50}$ scores of 0.967, 0.958, and 0.971, respectively, meeting the accuracy requirements of real-world applications. Full article

Ensuring the safety and extending the shelf life of chilled poultry meat is vital in modern poultry meat production, particularly given the recent increase in demand in this area. Chilled meat has a short shelf life, so producers have limited time to sell their products and must rely on various methods of extending shelf life. Compared with other non-thermal methods, electron beam irradiation is a new non-thermal meat preservation technique with low cost, avoidance of contamination, and antibacterial effects. In this study, we investigate the effect of electron beam irradiation on the microbiological and physicochemical quality of chilled poultry meat produced in Kazakhstan to assess its suitability for use in local food processing systems. The samples were electron-beam-treated at doses of 2, 4, 6, and 8 kGy and stored in a refrigerator. Microbiological and physicochemical property evaluations were carried out for a period of 14 days. Our results demonstrated a significant decrease in total aerobic and facultative anaerobic microorganisms, and no detectable levels of Salmonella spp. and Listeria monocytogenes in the irradiated samples. The pH measurements remained stable at low doses; in comparison, higher doses resulted in a slight decrease. Moisture, protein, fat, and ash content were also evaluated and showed minimal changes as functions of irradiation dose. Our results indicate that electron beam irradiation, particularly at a dose of 2–4 kGy, effectively improves microbiological safety and extends the shelf life of chilled poultry meat up to 5–6 days, making it a promising solution for the modern poultry meat industry. Full article

Generalising deep-learning models to perform well on unseen data domains with minimal retraining remains a significant challenge in computer vision. Even when the target task—such as quantifying the number of elements in an image—stays the same, data quality, shape, or form variations can deviate from the training conditions, often necessitating manual intervention. As a real-world industry problem, we aim to automate stock level estimation in retail cabinets. As technology advances, new cabinet models with varying shapes emerge alongside new camera types. This evolving scenario poses a substantial obstacle to deploying long-term, scalable solutions. To surmount the challenge of generalising to new cabinet models and cameras with minimal amounts of sample images, this research introduces a new solution. This paper proposes a novel ensemble model that combines DenseNet-201 and Vision Transformer (ViT-B/8) architectures to achieve generalisation in stock-level classification. The novelty aspect of our solution comes from the fact that we combine a transformer with a DenseNet model in order to capture both the local, hierarchical details and the long-range dependencies within the images, improving generalisation accuracy with less data. Key contributions include (i) a novel DenseNet-201 + ViT-B/8 feature-level fusion, (ii) an adaptation workflow that needs only two images per class, (iii) a balanced layer-unfreezing schedule, (iv) a publicly described domain-shift benchmark, and (v) a 47 pp accuracy gain over four standard few-shot baselines. Our approach leverages fine-tuning techniques to adapt two pre-trained models to the new retail cabinets (i.e., standing or horizontal) and camera types using only two images per class. Experimental results demonstrate that our method achieves high accuracy rates of 91% on new cabinets with the same camera and 89% on new cabinets with different cameras, significantly outperforming standard few-shot learning methods. Full article

Phytoene synthase (PSY) is a multimeric enzyme that serves as the first enzyme in carotenoid synthesis within plant tissues and plays a crucial role in the production of carotenoids in plants. To understand the function of the PSY gene in Panax japonicus C. A. Meyer. fruit, the gene’s transcript was obtained by analyzing the transcriptome sequencing data of Panax japonicus fruit. The CDS sequence of the gene was cloned from Panax japonicus fruit using the RT-PCR cloning technique and named PjPSY1, which was then subjected to biosynthetic analysis and functional verification. The results showed that the open reading frame of the gene was 1269 bp, encoding 423 amino acids, with a protein molecular mass of 47,654.67 KDa and an isoelectric point (pI) of 8.63; the protein encoded by these amino acids was hydrophilic and localized in chloroplasts, and its three-dimensional structure was predicted by combining the pymol software to annotate the N site of action and active centre of the protein. Phylogenetic analysis demonstrated that PjPSY1 had the closest affinity to DcPSY from Daucus carota. Overexpression of PjPSY1 led to a significant increase in the content of carotenoid-related monomers in Arabidopsis thaliana, with Violaxanthin being synthesized in transgenic Arabidopsis thaliana but not in wild-type Arabidopsis thaliana. The PjPSY1 clone obtained in this study was able to promote carotenoid synthesis in the fruits of Panax japonicus, revealing that the mode of action of PjPSY1 in the carotenoid biosynthesis pathway of Panax japonicus fruits has a positive regulatory effect. Full article

Objective: Radical therapies are associated with significant morbidity in patients with localized prostate cancer (PCa). While advances in nuclear magnetic resonance techniques have enabled the development of focal ablation procedures that can selectively destroy tumors, preserve the gland and surrounding structures, and minimize side effects, existing vascular-targeted photodynamic therapy (VTP) and nanodrug therapies often face limitations, such as recurrence and insufficient drug concentration at the tumor site. This study investigated a novel approach that combines VTP with systemic treatment using drug-loaded nanoparticles in a murine model, demonstrating substantial advancements beyond current monotherapies. Methods: SCID (severe combined immunodeficiency) mice were engrafted with androgen-sensitive prostate tumor cells (LNCaP-AR) and treated with a combination of VTP and two different drugs linked to fucoidan nanoparticles (Enzalutamide and Paclitaxel). Experiments were performed using different cohorts: the evaluation of oncological effect, the administration time and concentration of systemic therapy, a comparison of efficacy between VTP and radiotherapy, and the induction of the abscopal effect in untreated synchronous tumors. Results: The groups that received combination therapy showed better tumor control. After eight weeks, the recurrence-free survival rates were 87.5%, 62.5%, and 50% in the VTP + N-PAC, VTP + N-ENZ, and VTP monotherapy groups, respectively (p < 0.05). There was a significant difference in the intra-tumoral concentration of nanodrugs between the groups with combined treatment and monotherapy. After two weeks, the monotherapy groups showed almost total elimination of the drugs, whereas in the combined therapy groups, this concentration remained high, starting to decrease after three weeks (p < 0.05). Treatment with nanodrugs associated with VTP showed superior oncological benefits compared to radiotherapy alone or in combination with other therapies. The abscopal effect on synchronous tumors was not demonstrated with VTP alone or in combination with nanodrugs. Conclusions: Combining vascular photodynamic therapy with nanodrugs was highly effective in treating a prostate tumor model, leading to increased survival and a reduced risk of tumor recurrence. This approach significantly advances beyond existing VTP and nanodrug therapies by improving tumor control, ensuring sustained intra-tumoral drug concentration, and yielding superior oncological outcomes. Our results suggest that this therapy is a potential treatment option for prostate tumors treated with VTP in future clinical trials. Full article

With the rapid advancement of deep learning techniques, side-scan sonar image segmentation has become a crucial task in underwater scene understanding. However, the complex and variable underwater environment poses significant challenges for salient object detection, with traditional deep learning approaches often suffering from inadequate feature representation and the loss of global context during downsampling, thus compromising the segmentation accuracy of fine structures. To address these issues, we propose SonarNet, a Global Feature-Based Hybrid Attention Network specifically designed for side-scan sonar image segmentation. SonarNet features a dual-encoder architecture that leverages residual blocks and a self-attention mechanism to simultaneously capture both global structural and local contextual information. In addition, an adaptive hybrid attention module is introduced to effectively integrate channel and spatial features, while a global enhancement block fuses multi-scale global and spatial representations from the dual encoders, mitigating information loss throughout the network. Comprehensive experiments on a dedicated underwater sonar dataset demonstrate that SonarNet outperforms ten state-of-the-art saliency detection methods, achieving a mean absolute error as low as 2.35%. These results highlight the superior performance of SonarNet in challenging sonar image segmentation tasks. Full article

This paper details the development and implementation of an intelligent energy efficiency system for an electrical grid that incorporates renewable energy sources, specifically photovoltaic systems. The system is applied in a small locality of approximately 8000 inhabitants and aims to optimize energy consumption in public institutions by scheduling electrical appliances during periods of surplus PV energy production. The proposed solution employs a hybrid neuro-fuzzy approach combined with scheduling techniques to intelligently shift loads and maximize the use of locally generated green energy. This enables appliances, particularly schedulable and schedulable non-interruptible ones, to operate during peak PV production hours, thereby minimizing reliance on the national grid and improving overall energy efficiency. This directly reduces the cost of electricity consumption from the national grid. Furthermore, a comprehensive power quality analysis covering variables including harmonic distortion and voltage stability is proposed. The results indicate that while photovoltaic systems, being switching devices, can introduce some harmonic distortion, particularly during peak inverter operation or transient operating regimes, and flicker can exceed standard limits during certain periods, the overall voltage quality is maintained if proper inverter controls and grid parameters are adhered to. The system also demonstrates potential for scalability and integration with energy storage systems for enhanced future performance. Full article

Chemically strengthened glass is widely used for its remarkable fracture strength, mechanical performance, and scratch resistance. Assessing its hardness is crucial to evaluating improvements from chemical tempering. However, conventional methods like Vickers hardness tests are destructive, altering the sample surface. This study presents a novel, rapid, and nondestructive testing (NDT) approach by correlating the nonlinear refractive index (n₂) with surface hardness. Using ultrafast laser pulses, we measured the n₂ cross-section via the nonlinear ellipse rotation (NER) signal in Gorilla^®-type glass subjected to ion exchange (Na⁺ by K⁺). A microscope objective lens provided a penetration resolution of ≈5.5 μm, enabling a localized NER signal analysis. We demonstrate a correlation between the NER signal and hardness, offering a promising pathway for advanced, noninvasive characterization. This approach provides a reliable alternative to traditional destructive techniques, with potential applications in industrial quality control and material science research. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Provable data possession (PDP) is a technique that enables the verification of data integrity in cloud storage without the need to download the data. PDP schemes are generally categorized into public and private verification. Public verification allows third parties to assess the integrity of outsourced data, offering good openness and flexibility, but it may lead to privacy leakage and security risks. In contrast, private verification restricts the auditing capability to the data owner, providing better privacy protection but often resulting in higher verification costs and operational complexity due to limited local resources. Moreover, most existing PDP schemes are based on classical number-theoretic assumptions, making them vulnerable to quantum attacks. To address these challenges, this paper proposes an identity-based PDP with a designated verifier over lattices, utilizing a specially leveled identity-based fully homomorphic signature (IB-FHS) scheme. We provide a formal security proof of the proposed scheme under the small-integer solution (SIS) and learning with errors (LWE) within the random oracle model. Theoretical analysis confirms that the scheme achieves security guarantees while maintaining practical feasibility. Furthermore, simulation-based experiments show that for a 1 MB file and lattice dimension of n = 128, the computation times for core algorithms such as TagGen, GenProof, and CheckProof are approximately 20.76 s, 13.75 s, and 3.33 s, respectively. Compared to existing lattice-based PDP schemes, the proposed scheme introduces additional overhead due to the designated verifier mechanism; however, it achieves a well-balanced optimization among functionality, security, and efficiency. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article