Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Women population screening using mammography has dramatically reduced breast cancer rates worldwide. Nowadays, in many countries, the prevention of breast cancer policy is based on frequent and repeated mammographies, followed by breast ultrasound and if necessary, by histological examination in the biological material of the biopsy. However, evaluating mammography findings is considered as a difficult process which can properly performed only by a highly experienced and well-trained medical staff. Subsequently, the interpretation of those findings could be easily influenced by subjective factors and therefore can be prone to diagnostic errors as evidenced in the present study. Breast MRI, is a diagnostic practice indicated in cases of high breast density and at the same time, a high-cost examination for healthcare systems. Furthermore, genetic testing that is used to diagnose hereditary breast cancer, represents a small proportion of breast cancers and at the same time it is a high-cost specialized test. Even the most widely used biopsy, Fine Needle Aspirate (FNA), in some cases involves risks and provides false negative results. This study presents a novel intelligent Clinical Decision Support System (CDSS), which uses data from common practice, non-invasive and low-cost diagnostic tests, together with medical health record, in order to provide clinicians with a viable, cost-effective and accurate diagnostic solution. After implementing several algorithms, Random Forest classifier showed the highest values of sensitivity 96,2 %, specificity 94,6%, PPV 96,2% and NPV 94,6%, being thus an effective algorithm in the development of our innovative CDSS model aiming to constitute a very useful tool in clinical practice for breast cancer early diagnosis.

Colorectal cancer (CRC) represents a significant global health challenge, ranking among the leading causes of cancer-related morbidity and mortality worldwide. However, despite its high incidence and mortality, CRC is considered a most preventable cancer through a healthy lifestyle adoption that may impede the development of colorectal adenomas and their transition into malignant states. In this regard, although the notions of healthy living and behavioral pathways are well-known, population adherence and awareness in relation to cancer prevention are lacking. Moreover, the evaluation of their impact on biological CRC-related processes and risk via quantifiable biomarkers remains under research. In this regard, this paper presents the design and implementation strategy of a multi-center clinical study investigating the potential of a risk assessment and behavioral change framework for CRC prevention, delivered via a digital solution. Particularly, the DIOPTRA mobile app will integrate accessible risk assessment and personalized behavioral recommendations to empower users in shifting towards healthier lifestyles. Key features of the app will include behavioral questionnaires, risk assessment, personalized suggestions, health literacy, and a diary function to track behaviors and mild symptoms. Upon study enrollment, participants will receive personalized assessment and recommendations, before a follow-up that will investigate adherence, effects, and quality of life. Moreover, a biomarker-based evaluation will be integrated, based on an under-development minimally-invasive screening blood test. Overall, this study aims to highlight the importance of mHealth solutions for CRC prevention on a population level, as well as to establish clinical relevance by seeking to link behavioral change with novel CRC biomarkers.

Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments. Conventional intrusion detection and SIEM systems provide limited protection against zero-day and lateral-movement attacks. This paper presents the ASKOS architecture, a federated and deception-enhanced security framework that integrates Conpot honeypots with federated machine learning and software-defined networking (SDN) to enable privacy-preserving, adaptive cyber defense. By combining deception telemetry, distributed anomaly detection, and risk-aware orchestration, ASKOS delivers an intelligent and compliant security layer that can be implemented across healthcare infrastructures.