Ellas Spyratou

Background Oxidative stress (OS) plays a key role in many pathologies, yet the non-invasive, label-free, and cost-effective detection remains a challenge. This study evaluates Hyperspectral Imaging (HSI) combined with AI to detect OS by identifying changes in red blood cell (RBC) membranes. Methods An OS model for the HSI procedure is established by treating EDTA-anticoagulated whole blood with 1.5% hydrogen peroxide (H₂O₂) to induce stress without cell lysis. Membrane fatty acid composition (lipidome) is analysed via gas chromatography, while HSI in dark-field microscopy captures spectral signatures and their distributions in healthy and insulted RBC. The HSI methodology is then applied to RBC samples from 31 neurotypical (NT) children and 27 children with Autism Spectrum Disorder (ASD), a condition linked to OS. A deep learning algorithm is used to classify the clinical samples based on the identified OS signatures. Results Here, we show that significant spectral distribution differences are present in OS-exposed RBCs, which correlate with membrane lipidome remodelling. Notably, the OS-induced spectral differences in the H₂O₂ model mirror those observed between the ASD and NT groups. The AI-assisted analysis successfully classifies the pediatric cohort, achieving 93.2% accuracy in identifying ASD subjects. Conclusions HSI, guided by OS-specific modeling and integrated with AI, provides a robust, scalable method for membrane diagnostics. This approach offers a promising pathway for personalized medicine and the non-invasive monitoring of oxidative stress-related conditions.

Orthodontic treatment with fixed appliances increases the risk of enamel demineralization and biofilm accumulation around brackets and other devices. Conventional orthodontic bonding materials provide adequate mechanical retention but limited bioactive protection. This narrative review summarizes current in vitro, in vivo, and clinical evidence on nanoparticles (NPs) incorporated into orthodontic adhesives and cements, focusing on antimicrobial and remineralizing effects, mechanical performance, potential clinical relevance, and safety. Electronic searches of PubMed, Science Direct and Google Scholar identified laboratory, animal, and human studies evaluating NP-modified orthodontic bonding systems. Most available data derive from in vitro experiments, which consistently show that silver, zinc oxide, titanium dioxide, calcium phosphate-based particles, and related nanoparticles can inhibit cariogenic biofilms, reduce enamel demineralization surrogates, and, in many formulations, maintain clinically acceptable shear bond strength while enabling fluoride or calcium/phosphate ion release. A smaller number of in vivo and short-term clinical studies suggest reduced plaque accumulation and fewer or less severe white-spot lesions when nanoparticle-containing materials are used, although study designs and outcome measures are heterogeneous. Overall, NP-enhanced orthodontic bonding materials appear promising for combining mechanical durability with biological protection. However, the current level of evidence is limited by the predominance of in vitro data, small sample sizes, and short follow-up in clinical studies. Well-designed, long-term clinical trials with standardized outcomes are required before routine clinical adoption can be recommended.

Nanotechnology has significantly advanced cancer therapy, particularly through the development of multifunctional nanoparticles (NPs) capable of acting as both therapeutic and diagnostic agents. This review focuses on the synergistic integration of radiotherapy (RT) and photothermal therapy (PTT) mediated by engineered NPs—a rapidly evolving strategy that enhances tumor specificity, minimizes healthy tissue damage, and enables real-time imaging. By analyzing the recent literature, we highlight the dual role of NPs in amplifying radiation-induced DNA damage and converting near-infrared (NIR) light into localized thermal energy. The review classifies various metal-based and composite nanomaterials (e.g., Au, Pt, Bi, Cu, and Fe) and evaluates their performance in preclinical RT–PTT settings. We also discuss the physicochemical properties, targeting strategies, and theragnostic applications that contribute to treatment efficiency. Unlike conventional combinatorial therapies, NP-mediated RT–PTT enables high spatial–temporal control, immunogenic potential, and integration with multimodal imaging. We conclude with the current challenges, translational barriers, and outlooks for clinical implementation. This work provides a comprehensive, up-to-date synthesis of NP-assisted RT–PTT as a powerful approach within the emerging field of nano-oncology.

This study explores the application of Explainable Artificial Intelligence (XAI) techniques to the spectral analysis of electromagnetic field (EMF) measurements conducted across various frequency bands in Cyprus. Using an extensive data set obtained from mobile telephony, radio broadcasting, and terrestrial digital television sources, this work aims to capture real-world EMF exposure levels spanning frequencies from 30 MHz to 6 GHz. The frequency bands analyzed include FM radio (87.5-108 MHz), VHF, UHF television, and various mobile telephony zones (700 MHz, 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, 2600 MHz, and 3600 MHz), representing both traditional and emerging wireless technologies. A comparative evaluation of six machine learning algorithms was conducted: XGBoost, LightGBM, Random Forests, k-Nearest Neighbors, Neural Networks and Decision Trees to assess prediction performance across each frequency band. Furthermore, SHAP (SHapley Additive exPlanations) was employed to elucidate the contribution of spatial and demographic characteristics to the intensity of the EMF. The results show that ensemble tree-based methods, particularly Random Forests and LightGBM, consistently outperformed simpler models in accuracy and interpretability. The integration of SHAP enabled transparent feature attribution, revealing distinctive exposure patterns linked to urban topology, population density, and built environment metrics. This approach enables data-driven EMF exposure mapping, aiding urban infrastructure planning, compliance monitoring, and public health risk evaluation.

The accurate characterization of signal propagation is critical for optimizing wireless network performance and supporting applications such as electromagnetic field (EMF) exposure assessment and the development of Radio Environmental Maps (REMs). This study proposes a novel, explainable machine learning system to predict electric field strength across diverse urban, semi-urban, and rural environments in Cyprus. The system is trained on a rich dataset comprising 6,543 EMF measurements collected in 2023 at mobile phone and digital TV stations, following CEPT/ECC/REC/(02)04 recommendations. The dataset includes geospatial and environmental features such as antenna distance, population density, urbanization level, and detailed built environment characteristics (e.g., volume, surface, and height). We evaluate multiple machine learning models—kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM—using a two-semester split for training and assessment. Best performance was achieved with the Random Forest model, which yielded the lowest RMSE among all models. Gradient boosting models (XGBoost and LightGBM) also performed well, with RMSE values slightly higher than RF while offering flexible and scalable configurations. In contrast, k-NN and neural networks showed higher RMSE values, indicating they were less effective for this specific task. Across all models, confidence intervals were narrow, demonstrating stable and reliable predictions. Explainable AI techniques revealed that antenna distance, building volume, and population density are the most influential predictors of EMF intensity. Our approach outperforms traditional signal models by incorporating urban morphology and demographic context. As part of this system, we also create a Geographic Information System (GIS) that displays electromagnetic field strength maps derived from our explainable machine learning models. This contributes a scalable, interpretable framework for EMF exposure mapping to support regulatory monitoring, urban planning, and smart city initiatives.

Nanomedicine is a novel and emerging field that has noted significant progress in both the fields of ophthalmology and cancer treatment. Expanding into ocular oncology, it holds the potential to overcome the limitations of conventional therapies, such as poor drug penetration due to anatomical and physiological ocular barriers and insufficient targeting, which can lead to collateral damage to healthy tissues. By reviewing a series of clinical and preclinical studies, we aim to outline the recent advancements, current trends and future perspectives in nanomedicine for ocular cancer treatment. Beyond improving the existing therapies, like chemotherapy, phototherapies and brachytherapy, nanomedicine enables multimodal applications by combining multiple treatments or integrating imaging for theranostic approaches. Additionally, it paves the way for experimental therapies, such as gene therapy, offering new possibilities for more effective and less invasive treatment strategies in ocular oncology.

The ubiquity of wireless communications in our daily environments has raised public concern about exposure to radio frequency (RF) electromagnetic fields (EMF). Manual collection of RF-EMF measurements is a time-consuming and labor-intensive task, while IoT-based monitoring systems still have quite limited coverage. Electromagnetic field strength maps have been employed to estimate EMF values at locations where a sensing/measuring infrastructure does not exist, inferring their values from measurements made at close distances, thus providing wider coverage in a cost-effective manner. Such maps, however, have only been created for indoor settings and relatively small geographical areas. The objective of this study is to identify the most optimal geospatial interpolation method to construct an electromagnetic field strength maps map of large-scale urban areas. We employed five different models to create an electromagnetic exposure map, with four of them being Gaussian process regression methods (Kriging), and the fifth being the classical weighted average method of nearest neighbors. The map was constructed for the city of Paris with a dataset consisting of 3632 measurements. Our results showed that Kriging, especially with the exponential model, outperformed the nearest-neighbor method in handling spatial autocorrelation in EMF data. In particular, removing outliers from the data set significantly improved all interpolation methods, reducing the mean mean square error (MSE) and variability.

The management of ocular conditions, including cancer, is hindered by anatomical and physiological ocular barriers. Chemotherapy is limited by sub-optimal drug delivery, and similarly to other conventional treatments such photothermal therapies and radiotherapy, can often cause collateral damage to healthy tissues. Nanomedicine-based approaches have transformed the field of ophthalmology, and their application can be potentially extended to ocular oncology. A comprehensive literature review was conducted to analyze advancements in nanoparticle-based therapies for ocular cancers, mainly retinoblastoma and uveal melanoma. A series of preclinical and clinical studies are presented, where organic and inorganic nanoparticles have been used to enhance traditional therapies through improved drug bioavailability, targeted delivery, and multimodal applications. Nanomedicine-based approaches have emerged as a transformative solution for ocular cancer management.

Nanomedicine is emerging as a groundbreaking strategy for the management of the neuro-visual symptoms of neuroinflammatory and neurodegenerative diseases. This innovative field of study leverages nanoscale materials and technologies to improve drug delivery, enabling targeted treatments to reach the affected ocular tissues. By facilitating the transport of therapeutic agents across the blood–retinal barrier and boosting their bioavailability, nanomedicine holds the potential to significantly mitigate the symptoms of conditions such as Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), etc. This review summarizes the latest developments in nanomedicine applications for the management of these ocular conditions, highlighting their capacity to foster more effective disease diagnosis and treatment.