Magna Graecia University

Adverse Childhood Experiences (ACEs) are associated with negative psychological outcomes in adulthood. This study examined the mechanisms through which ACEs influence resilience, considering positive humor and tolerance for ambiguity as mediators, and social media use as a potential moderator. A sample of 400 Italian young adults (aged 18-35) completed measures of ACEs, resilience, positive humor styles, tolerance for ambiguity, and social media use. Serial and moderated mediation analyses (PROCESS, 5000 bootstrap samples) indicated that ACEs negatively predict resilience. Positive humor and tolerance for ambiguity mediated this association, both individually and sequentially, showing that positive humor facilitates tolerance for ambiguity, which in turn enhances resilience. Social media use emerged as a moderator. These findings suggest that specific psychological resources play a protective role against the effects of ACEs, with implications for interventions aimed at enhancing resilience and well-being.

PD-1 inhibitors have reshaped the treatment landscape of classical Hodgkin lymphoma, yet a substantial proportion of patients exhibit primary or acquired resistance driven by tumor-intrinsic alterations, immunosuppressive microenvironmental signals, metabolic constraints, and EBV-mediated modulation. This review summarizes key mechanisms underlying PD-1 resistance and highlights emerging biomarkers-including early 18F-FDG PET response, circulating tumor DNA kinetics, molecular subtyping, and spatial immune profiling-that enable early identification of nonresponders and support precision immunotherapy. Novel therapeutic strategies such as macrophage-targeted agents, metabolic modulators, bispecific antibodies, low-dose PD-1 regimens, and CD30-directed CAR-T cells show promise in overcoming resistance, particularly when integrated into adaptive, biomarker-guided treatment algorithms. Early incorporation of PET and ctDNA monitoring may inform timely treatment adaptation, while combination approaches addressing macrophage-driven suppression or redundant immune checkpoints should be considered in biologically high-risk patients. Overall, a deeper mechanistic understanding coupled with biomarker-driven stratification is essential to optimize PD-1-based therapy and improve long-term outcomes in cHL.

Hepatocellular carcinoma (HCC) remains a significant global health issue, linked to chronic liver diseases such as viral hepatitis, cirrhosis, and metabolic dysfunction-related steatohepatitis. Early, accurate diagnosis is vital for treatment, but many cases are diagnosed at advanced stage. Contrast-enhanced ultrasound (CEUS) is a valuable, radiation-free tool for real-time liver lesion assessment with high accuracy. This review examines the growing role of ultrasound and CEUS in diagnosing, monitoring, and post-treatment care of HCC. CEUS has sensitivity and specificity similar to computed tomography and magnetic resonance imaging, especially for nodules ≥ 1 cm, and helps clarify uncertain Liver Imaging Reporting and Data System findings. Dynamic CEUS improves diagnosis by allowing microvascular perfusion measurement. Artificial intelligence (AI) and machine learning integration promises automated lesion classification and better consistency. CEUS is especially useful in outpatient and resource-limited settings, enabling quick decision-making and reducing delays. Meta-analyses and studies support CEUS for initial detection and post-treatment follow-up. As advanced ultrasound becomes more accessible, CEUS can be more widely used in hepatology. Future steps include standard protocols, clinician training, and AI integration. Overall, CEUS complements other imaging methods and aids precision medicine in liver cancer management.

Mentalising skills may be conceptualised as composed by both personal and interpersonal competencies, in turn shaped by early adverse experiences and coping strategies. Although cross-sectional observations described a role for mentalising skills in burnout development, large-scale longitudinal studies on the topic remain limited, especially in relation to psychiatry training. The primary aim was to investigate protective and risk factors for higher burnout scores across time. Secondary aims included testing whether psychiatry exhibited different burnout scores across time in comparison to other medical residents. A cohort of 1803 medical residents (1131 psychiatry residents) was assessed for mentalising skills, conceptualised as composed of emotional regulation (Difficulties in Emotion Regulation Scale), interpersonal competencies (Interpersonal Competence Questionnaire), coping strategies (Coping Orientation to Problems Experienced) and burnout dimensions (Maslach Burnout Inventory). One-year follow-up was available for 520 psychiatry residents (73.03% response rate) and 234 other medical residents (35.94% response rate). Longitudinal mixed models and bivariate latent change models were employed. Across all residents, greater levels of burnout were associated with higher scores in insecure attachment, emotional dysregulation and maladaptive coping, as well as lower scores in interpersonal skills. Attending at least one supervision per month was associated with lower burnout scores across time. According to a bivariate latent change model, emotional regulation improvements through years were associated with lower burnout scores across time. In psychiatry residents, lower burnout scores across time were observed as compared to other medical residents. Psychiatry residents benefited from a higher protective effect of interpersonal competencies (group by moderator by time interaction) and coping strategies against burnout. Mentalising skills may mitigate burnout development. Training in psychiatry emerged as a potential mitigating factor against burnout increases. Structured supervisions may foster professional development and emotional resilience.

Interest in artificial intelligence (AI) is rapidly growing. In healthcare, especially through machine learning and deep learning, AI is emerging as a promising tool to support the diagnosis, management, and prevention of lung diseases and to advance personalized care, although it requires large, well-structured datasets. Clinicians must learn how to integrate AI into routine practice for conditions such as asthma and chronic obstructive pulmonary disease (COPD), while ensuring patient safety and building trust in these tools. Chronic respiratory diseases are major global causes of morbidity and mortality and place a substantial burden on healthcare systems; among them, asthma and COPD are chronic disorders characterized by airway obstruction and inflammation. This review highlights the rapid advancement of AI, and it aims to explore the literature's evidence of its applicability in controlling chronic respiratory disorders, particularly in asthma and COPD. We conducted a narrative literature review by searching ScienceDirect, PubMed, and Google Scholar for English-language studies on artificial intelligence applications in asthma and COPD and by screening the references of relevant articles. The reviewed literature suggests that AI-based approaches are being applied across the asthma-COPD spectrum to support diagnosis and phenotyping, improve risk stratification and prediction of clinically relevant outcomes, and enable more continuous monitoring using heterogeneous data sources (e.g., clinical records, imaging, and digital health data). AI-based tools are poised to support clinicians in asthma and COPD across diagnosis, phenotyping, and monitoring; however, their safe implementation in routine care will require robust validation, transparency, and governance to ensure reliability and patient safety.

Heart failure (HF) due to left ventricular systolic dysfunction (LVSD) remains a major clinical challenge, particularly among patients with chronic total occlusions (CTO). CTO are present in up to 30% of patients with LVSD undergoing coronary angiography and are independently associated with worse outcomes. Although advances in interventional techniques have increased success rates of CTO percutaneous coronary intervention (CTO-PCI), high-quality evidence supporting this procedure in patients with LVSD remains limited. Observational studies report potential benefits, including improved survival, alleviation of HF symptoms, and recovery of left ventricular ejection fraction (LVEF). However, randomized controlled trials (RCTs) have largely excluded patients with LVEF <35% and those with advanced, complex coronary artery disease (CAD), including CTO, thereby restricting generalizability. Assessment of myocardial viability remains central to patient select for CTO-PCI, its prognostic value for hard clinical endpoints has not been definitively established. The use of mechanical circulatory support (MCS) during high-risk CTO-PCI is increasing, particularly in patients with reduced LVEF and complex coronary anatomy; available data provides inconsistent evidence regarding its impact on clinical outcomes and appears to be largely influenced by individual patient characteristics. Finally, in the setting of acute coronary syndromes (ACS), the effect of CTO revascularization on clinical endpoints and arrhythmic risk is unclear, with conflicting observational data. Future research should prioritize this underrepresented high-risk cohort and be conducted in experienced centers within an integrated multidisciplinary care framework.