Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier disruption, immune dysregulation, and increased susceptibility to inflammatory skin diseases, including psoriasis, atopic dermatitis, and acne. For instance, hyperglycemia in T2DM leads to the formation of advanced glycation end products (AGEs), which bind to the receptor for AGEs (RAGE) on keratinocytes and immune cells, promoting oxidative stress and inflammation while facilitating Staphylococcus aureus colonization in atopic dermatitis. Similarly, obesity-induced dysregulation of sebaceous lipid composition increases saturated fatty acids, favoring pathogenic strains of Cutibacterium acnes, which produce inflammatory metabolites that exacerbate acne. Advances in metabolomics and microbiome sequencing have unveiled critical biomarkers, such as short-chain fatty acids and microbial signatures, predictive of therapeutic outcomes. For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. Emerging therapies, such as probiotics and postbiotics, aim to restore microbial diversity, while phage therapy selectively targets pathogenic bacteria like Staphylococcus aureus without disrupting beneficial flora. Clinical trials have demonstrated significant reductions in inflammatory lesions and improved quality-of-life metrics in patients receiving these microbiota-targeted treatments. This review synthesizes current evidence on the bidirectional interplay between metabolic disorders and skin microbiota, highlighting therapeutic implications and future directions. By addressing systemic metabolic dysfunction and microbiota-mediated pathways, precision strategies are paving the way for improved patient outcomes in dermatologic care.

Large Language Models (LLMs) have revolutionized natural language processing but present significant technical and legal challenges when confronted with the General Data Protection Regulation (GDPR). This paper examines the complexities involved in reconciling the design and operation of LLMs with GDPR requirements. In particular, we analyze how key GDPR provisions—including the Right to Erasure, Right of Access, Right to Rectification, and restrictions on Automated Decision-Making—are challenged by the opaque and distributed nature of LLMs. We discuss issues such as the transformation of personal data into non-interpretable model parameters, difficulties in ensuring transparency and accountability, and the risks of bias and data over-collection. Moreover, the paper explores potential technical solutions such as machine unlearning, explainable AI (XAI), differential privacy, and federated learning, alongside strategies for embedding privacy-by-design principles and automated compliance tools into LLM development. The analysis is further enriched by considering the implications of emerging regulations like the EU’s Artificial Intelligence Act. In addition, we propose a four-layer governance framework that addresses data governance, technical privacy enhancements, continuous compliance monitoring, and explainability and oversight, thereby offering a practical roadmap for GDPR alignment in LLM systems. Through this comprehensive examination, we aim to bridge the gap between the technical capabilities of LLMs and the stringent data protection standards mandated by GDPR, ultimately contributing to more responsible and ethical AI practices.

Artificial intelligence (AI) has significantly driven advancement in the healthcare field by enabling the integration of highly advanced algorithms to improve diagnostics, patient surveillance, and treatment planning. Nonetheless, dependence on sensitive health data and automated decision-making exposes such systems to escalating risks of privacy breaches and is under rigorous regulatory oversight. In particular, the EU AI Act classifies AI uses pertaining to healthcare as “high-risk”, thus requiring the application of strict provisions related to transparency, safety, and privacy. This paper presents a comprehensive overview of the diverse privacy attacks that can target machine learning (ML)-based healthcare systems, including data-centric and model-centric attacks. We then propose a novel privacy-preserving architecture that integrates federated learning with secure computation protocols to minimally expose data while ensuring strong model performance. We outline an ongoing monitoring mechanism compliant with EU AI Act specifications and GDPR standards to further improve trust and compliance. We further elaborate on an independent adaptive algorithm that automatically tunes the level of cryptographic protection based on contextual factors like risk severity, computational capacity, and regulatory environment. This research aims to serve as a blueprint for designing trustworthy, high-risk AI systems in healthcare under emerging regulations by providing an in-depth review of ML-specific privacy threats and proposing a holistic technical solution.

The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastructures. However, the unique characteristics of these networks—extensive connectivity, device heterogeneity, and architectural flexibility—impose significant security challenges. This paper introduces a comprehensive framework for enhancing the security of current and emerging wireless networks by integrating state-of-the-art machine learning (ML) techniques into intrusion detection and prevention systems. It also thoroughly explores the key aspects of wireless network security, including architectural vulnerabilities in both 5G and future 6G networks, novel ML algorithms tailored to address evolving threats, privacy-preserving mechanisms, and regulatory compliance with the EU AI Act. Finally, a Wireless Intrusion Detection Algorithm (WIDA) is proposed, demonstrating promising results in improving wireless network security.

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize patient care, diagnostics, and treatment planning. However, this integration also introduces significant challenges related to data governance, privacy, and compliance with emerging regulations. The European Union’s (EU) AI Act proposes a comprehensive regulatory framework aimed at ensuring that AI systems are trustworthy and respect fundamental rights. This paper provides an in-depth analysis of the data governance requirements stipulated by the EU AI Act specifically within the context of healthcare AI. Furthermore, it explores strategies for compliance, examines the interplay with existing regulations such as the General Data Protection Regulation (GDPR), and addresses the ethical considerations inherent in deploying AI in healthcare settings.

Integrating artificial intelligence into border control systems may help to strengthen security and make operations more efficient. For example, the emerging application of artificial intelligence for lie detection when inspecting passengers presents significant opportunities for future implementation. However, as it makes use of technology that is associated with artificial intelligence, the system is classified as high risk, in accordance with the EU AI Act and, therefore, must adhere to rigorous regulatory requirements to mitigate potential risks. This manuscript distinctly amalgamates the technical, ethical, and legal aspects, thereby offering an extensive examination of the AI-based lie detection systems utilized in border security. This academic paper is uniquely set apart from others because it undertakes a thorough investigation into the categorization of these emerging technologies in terms of the regulatory framework established by the EU AI Act, which classifies them as high risk. It further makes an assessment of practical case studies, including notable examples such as iBorderCtrl and AVATAR. This in-depth analysis seeks to emphasize not only the enormous challenges ahead for practitioners but also the progress made in this emerging field of study. Furthermore, it seeks to investigate threats, vulnerabilities, and privacy concerns associated with AI, while providing security controls to address difficulties related to lie detection. Finally, we propose a framework that encompasses the EU AI Act’s principles and serves as a foundation for future approaches and research projects. By analyzing current methodologies and considering future directions, the paper aims to provide a comprehensive understanding of the viability and consequences of deploying AI lie detection capabilities in border control.