Search Results (212)

Background/Objectives: Oral leukoplakia (OL) is a prevalent oral potentially malignant disorder. Despite its clinical relevance, the molecular basis of its progression to malignancy is not yet fully elucidated. This scoping review of systematic reviews and meta-analyses aimed to synthesize current knowledge and evidence gaps regarding the implications of hallmarks of cancer expression in OL malignant transformation. Methods: A systematic search was conducted in MEDLINE, Embase, DARE, and the Cochrane Library to identify systematic reviews (with or without meta-analysis) published up to April-2025. Results: Twenty-two systematic reviews were included. The most frequently explored hallmark was activation of invasion and metastasis (n = 12; 32.40%), followed by tumor-promoting inflammation (n = 10; 27.03%), evasion of growth suppressors (n = 8; 21.60%), sustained proliferative signaling (n = 3; 8.10%), energy metabolism reprogramming (n = 2; 5.40%), replicative immortality (n = 1; 2.70%), and resistance to cell death (n = 1; 2.70%). No evidence was found for angiogenesis or immune evasion in OL. Conclusions: Available evidence indicates that OL may develop oncogenic mechanisms in early stages of oral oncogenesis, especially those related to sustained proliferation, evasion of growth suppressor signals, and cellular migration and invasion. Chronic inflammation also may facilitate the acquisition of other hallmarks throughout the multistep process of oral carcinogenesis. These findings also reveal evidence gaps in underexplored hallmarks of cancer, which highlights the need to expand future primary- and secondary-level investigations to better define the molecular mechanisms underlying OL malignant transformation. Full article

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare. Full article

This article explores the role of the European Union (EU) as a normative gender actor promoting women’s participation in STEM (Science, Technology, Engineering, and Mathematics) within the Eastern Partnership (EaP) region. In a context marked by global inequality and overlapping international efforts, this paper assesses the extent to which EU-driven Europeanisation influences national gender policies in non-EU states. Using a postfunctionalist lens, this research draws on a qualitative analysis of EU-funded programmes, strategic documents, and a detailed case study encompassing Armenia, Georgia, Moldova, Ukraine, Belarus, and Azerbaijan. This study highlights both the opportunities created by EU initiatives such as Horizon Europe, Erasmus+, and regional programmes like EU4Digital and the challenges presented by political resistance, institutional inertia, and socio-cultural norms. The findings reveal that although EU interventions have fostered significant progress, structural barriers and limited national commitment hinder the long-term sustainability of gender equality in STEM. Moreover, the withdrawal of other global actors increases pressure on the EU to maintain leadership in this area. This paper concludes that without stronger national alignment and global cooperation, EU gender policies risk becoming symbolic rather than transformative. Full article

Follicular lymphoma (FL) is one of the most common types of non-Hodgkin’s lymphomas. The tumor is characterized by a wide range of clinical manifestations, ranging from indolent forms to early transformation and progression with a poor prognosis. The search for clinically significant genetic changes is essential for personalized risk assessment and treatment selection. The CREBBP gene is frequently mutated in this type of lymphoma, with changes occurring at the level of the earliest tumor precursor cells. However, the prognostic and diagnostic significance of the CREBBP gene mutation status in FL has not been fully established. In this study, we analyzed sequencing data of exons 22–30 of the CREBBP gene in 86 samples from patients with different grades of FL (1–3B), including those in the 3A–3B subgroup without the t(14;18) translocation. We also investigated the prognostic significance of CREBBP gene mutations in relation to the treatment options, namely high-dose chemotherapy with autologous hematopoietic stem cell transplantation (HDCT/auto-HSCT) and conventional chemotherapy programs (CCT). It was found that FL patients with a single missense mutation in the KAT domain of the CREBBP gene experienced an extremely low number of early adverse events related to lymphoma and had better long-term survival rates, regardless of treatment option. In contrast, when comparing patients with FL without a missense mutation in the KAT domain or those with multiple mutations in the CREBBP gene, overall and progression free survival were worse, and early progression and histological transformation were more common. Compared to standard therapy, patients who underwent HDCT/auto-HSCT in the FL 1–3B (14;18)-positive group without a single missense mutation in the KAT domain had better survival rates and lower rates of transformation and early progression. In addition, among patients with FL 3A–3B (14;18)-negative, we found that there were no cases of a missense mutation in the KAT domain of the CREBBP gene. This suggests that a single missense mutation in the CREBBP gene may be a feature that discriminates 14;18-positive FL with a favorable prognosis from a high-risk disease. FL 3A–3B (14;18)-negative may represent a distinct variant with different biology and underlying mechanisms of development compared to classical FL. Full article

The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial–frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

The implementation of a carbon tax in the transportation sector aims to reduce carbon emissions and encourage the transition to sustainable mobility amid increasing urbanization. The transportation sector is one of the largest contributors of carbon emissions in Indonesia, requiring effective policies to reduce its environmental impacts. Therefore, this study aims to find a more optimal carbon tax formula that is in accordance with Indonesia’s socio-economic conditions. The approach used includes analysis of transportation emission data, the economic impact of different carbon tax schemes, and tax revenue allocation strategies to support green infrastructure and sustainable transportation. The results of the study indicate that an adaptive carbon tax formula in the transportation sector is able to balance the economic burden, emission reduction targets, social justice, behavioral changes, and revenue allocation for green infrastructure, thus ensuring a just and sustainable transition. A progressive carbon tax, based on vehicle emission levels and fuel types, can encourage the transition to low-emission vehicles without excessively burdening low-income communities. With this approach, carbon tax policy functions not only as a fiscal instrument but also as a transformative strategy in creating an environmentally friendly and equitable transportation system. Full article

In recent years, the continuous advancement of deep learning technology and its integration into the domain of low-light image enhancement have led to a steady improvement in enhancement effects. However, this progress has been accompanied by an increase in model complexity, imposing significant constraints on applications that demand high real-time performance. To address this challenge, inspired by the state-of-the-art Zero-DCE approach, we introduce a novel method that transforms the low-light image enhancement task into a curve estimation task tailored to each individual image, utilizing a lightweight shallow neural network. Specifically, we first design a novel curve formula based on Gamma correction, which we call the Gamma-based light-enhancement (GLE) curve. This curve enables outstanding performance in the enhancement task by directly mapping the input low-light image to the enhanced output at the pixel level, thereby eliminating the need for multiple iterative mappings as required in the Zero-DCE algorithm. As a result, our approach significantly improves inference speed. Additionally, we employ a lightweight network architecture to minimize computational complexity and introduce a novel global channel attention (GCA) module to enhance the nonlinear mapping capability of the neural network. The GCA module assigns distinct weights to each channel, allowing the network to focus more on critical features. Consequently, it enhances the effectiveness of low-light image enhancement while incurring a minimal computational cost. Finally, our method is trained using a set of zero-reference loss functions, akin to the Zero-DCE approach, without relying on paired or unpaired data. This ensures the practicality and applicability of our proposed method. The experimental results of both quantitative and qualitative comparisons demonstrate that, despite its lightweight design, the images enhanced using our method not only exhibit perceptual quality, authenticity, and contrast comparable to those of mainstream state-of-the-art (SOTA) methods but in some cases even surpass them. Furthermore, our model demonstrates very fast inference speed, making it suitable for real-time inference in resource-constrained or mobile environments, with broad application prospects. Full article

Background: Advanced sensor insoles and motion capture technology can significantly enhance the monitoring of rehabilitation progress for patients with distal tibial fractures. This study leverages the potential of these innovative tools to provide a more comprehensive assessment of a patient’s gait and weight-bearing capacity following surgical intervention, thereby offering the possibility of improved patient outcomes. Methods: A patient who underwent distal medial tibial plating surgery in August 2023 and subsequently required revision surgery due to implant failure, involving plate removal and the insertion of an intramedullary nail in December 2023, was meticulously monitored over a 12-week period. Initial assessments in November 2023 revealed pain upon full weight-bearing without crutches. Following the revision, precise weekly measurements were taken, starting two days after surgery, which instilled confidence in accurately tracking the patient’s progress from initial crutch-assisted walking to full recovery. The monitoring tools included insoles, hand pads for force absorption of the crutches, and a motion capture system. The patient was accompanied throughout all steps of his daily life. Objectives: The study aimed to evaluate the hypothesis that the approximation and formation of a healthy gait curve are decisive tools for monitoring healing. Specifically, it investigated whether cadence, imbalance factors, and ground reaction forces could be significant indicators of healing status and potential disorders. Results: The gait parameters, cadence, factor of imbalance ground reaction forces, and the temporal progression of kinematic parameters significantly correlate with the patient’s recovery trajectory. These metrics enable the early identification of deviations from expected healing patterns, facilitating timely interventions and underscoring the transformative potential of these technologies in patient care. Conclusions: Integrating sensor insoles and motion capture technology offers a promising approach for monitoring the recovery process in patients with distal tibial fractures. This method provides valuable insights into the patient’s healing status, potentially predicting and addressing healing disorders more effectively. Future studies are recommended to validate these findings in a larger cohort and explore the potential integration of these technologies into clinical practice. Full article

This article examines the ‘King Jesus Gospel’ concept proposed by Matthew Bates and Scott McKnight, which frames the biblical gospel as a proclamation of Jesus’ kingship. It addresses the ‘Failure Objection’ that Jesus was merely a failed apocalyptic prophet who died without fulfilling his predictions. Drawing on N.T. Wright’s work, this article constructs the ‘King Jesus Hypothesis’ and evaluates it using evidence from religious transformation, cultural values, and human progress. Employing the Criterion of Predictive Power, it argues that historical religious innovations (drawing on the work of Larry Hurtado), Western moral values (drawing on the work of Tom Holland), and measurable human flourishing (drawing on the work of Steven Pinker) are best explained by Jesus successfully inaugurating God’s Kingdom through cultural transformation rather than apocalyptic intervention. Through this analysis, the article demonstrates that compelling evidence supports Jesus’ kingship despite the Failure Objection. Full article

Hepatocellular carcinoma (HCC) is a frequent complication of various liver diseases, occurring with or without underlying cirrhosis. While cirrhosis and chronic liver inflammation are well-established major drivers of hepatocarcinogenesis, HCC can also develop in patients with vascular liver diseases (VLDs), highlighting an alternative pathway of disease progression. Alterations in liver perfusion appear to underlie the process of carcinogenesis. However, the specific molecular mechanisms involved in this process as well as the clinical presentation and imaging features of HCC in the most common VLDs are still a matter of debate. This review aims to evaluate the available literature on the topic to provide a deeper comprehension and analysis of current knowledge about the relation between VLDs and HCC. Specifically, we investigate how HCC affects VLDs such as Budd–Chiari syndrome, Fontan-associated liver disease, congenital portosystemic shunts, cavernous transformation of the portal vein, and porto-sinusoidal vascular disorder. Exploring the pathogenetic mechanisms and diagnostic challenges in HCC related to VLDs may have important therapeutic implications, helping to define targeted treatments for this poorly understood medical entity. Full article

The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance AD staging using structural MRI. The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. The proposed technique, referred to as Red-KPLS-CNN, refines MRI features into discriminative biomarkers while minimizing redundancy. As a result, the framework achieves 96.9% accuracy and an F1-score of 97.8% in the multiclass classification of AD cases using the Kaggle dataset. The dataset was strategically partitioned into 60% training, 20% validation, and 20% testing sets, preserving class balance throughout all splits. The integration of Red–KPLS enhances feature selection, reducing dimensionality without compromising diagnostic sensitivity. Compared to conventional models, our approach improves classification robustness and generalization, reinforcing its potential for scalable and interpretable AD diagnostics. These findings emphasize the importance of hybrid wavelet–kernel–deep learning architectures, offering a promising direction for advancing computer-aided diagnosis (CAD) in clinical applications. Full article

Topology optimization has been present in modern engineering for several decades, becoming an important tool for solving design problems. Today, it is difficult to imagine progress in engineering design without the search for new approaches to the generation of optimal structural topologies and the development of efficient topological optimization algorithms. The generation of topologies for structures under random loads is one of many research problems where topology optimization is present. It is important to predict the topologies of structures in the case of load uncertainty, since random load changes can significantly affect resulting topologies. This paper proposes an easy-to-implement numerical approach that allows the prediction of the resulting topologies of structures. The basic idea is to transform a random loads case into the deterministic problem of multiple loads. The concept of equivalent load scheme (ELS) is introduced. Instead of generating hundreds of loads applied at random, the selection of a few representative load cases allows the reduction of the numerical effort of the computations. The numerical implementation of proposed concepts is based on the cellular automaton mimicking colliding bodies, which has been recently introduced as an efficient structural topology generator. The examples of topology optimization under randomly applied loads, performed for both plane and spatial structures, have been selected to illustrate the proposed concepts. Confirmed by results of numerical simulations, the efficiency, versatility and ease of implementation of the proposed concept can make an original contribution to research in topological optimization under loads applied in a random manner. Full article

Yogurt fortification with purslane (Portulaca oleracea L.) can improve its health benefits, but it may alter the fermentation step and its final properties. Thus, the current study investigated the suitability of Fourier Transform-Near Infrared (FT-NIR) spectroscopy for in-line monitoring of lactic acid fermentation of purslane-fortified yogurt compared with fundamental rheology. Changes in the yogurt properties during storage were also assessed. Set-type yogurts without and with lyophilized purslane leaves (0.55%) were produced and stored at 4 °C for up to 18 days. Lactic acid bacteria concentrations before and after fermentation at 43 °C for 2.5 h showed that the presence of purslane did not interfere with bacterial growth. The purslane addition increased the milk viscosity, resulting in a yogurt with complex modulus values higher than those of the reference sample (360 vs. 172 Pa). The elaboration of spectral data with Principal Component Analysis and the Gompertz equation enabled calculation of the kinetic critical points. Applying the Gompertz equation to the rheological data, it was evident that FT-NIR spectroscopy detected earlier the fermentation progression (the critical times were about 18% earlier on average), thus enabling better control of yogurt production. No significant changes in microbial or textural properties were noted during yogurt storage, demonstrating that purslane addition did not affect the product stability. Full article