This study examined how perceptions of artificial intelligence (AI) relate to technostress in healthcare professionals, testing whether AI acceptance mediates this relationship and whether self-efficacy moderates the formation of acceptance. Seventy-one participants completed measures of Perceptions of AI (Shinners), AI Acceptance (UTAUT), Self-Efficacy, and four technostress outcomes: Technostress Overall, Techno-Overload, Techno-Complexity/Insecurity, and Techno-Uncertainty. Conditional process analyses (PROCESS Model 7; 5000 bootstrap samples) were performed controlling for sex, age (years), and professional role (radiology residents, attending radiologists, PhD researchers). Perceptions of AI were directly and positively associated with Technostress Overall (b = 0.57, p = 0.003), Techno-Overload (b = 0.58, p = 0.008), and Techno-Complexity/Insecurity (b = 0.83, p < 0.001), but not with Techno-Uncertainty (b = −0.02, p = 0.930). AI Acceptance negatively predicted the same three outcomes (e.g., Technostress Overall b = −0.55, p = 0.004), and conditional indirect effects indicated significant negative mediation at low, mean, and high self-efficacy for these three outcomes. Self-efficacy moderated the Perceptions → Acceptance path (interaction b = −0.165, p = 0.028), with a stronger X→M effect at lower self-efficacy, but indices of moderated mediation were not significant for any outcome. The results suggest that perceptions of AI exert both demand-like direct effects and buffering indirect effects via acceptance; implementation should therefore foster acceptance, build competence, and address workload and organizational clarity.

Background/Objectives: At the international level, harmonized networks of dementia clinical studies are available, but Italian participation remains limited. This systematic review aims to define harmonization rules to facilitate the inclusion of Italian clinical studies in existing networks and to propose standardized data collection methods to enable comparison of the study results. Methods: A systematic review was conducted (January 2019–December 2024) to identify Italian clinical studies evaluating Alzheimer’s disease and other dementias as outcomes. Eight modifiable risk factors were extracted: BMI, arterial hypertension, diabetes, dietary patterns, alcohol consumption, smoking habits, depressive symptomatology, and physical activity. WHO definitions and internationally accepted criteria were used as reference standards. Variable harmonization potential was assessed using the DataSHaPER methodology and classified as complete, partial, or impossible, considering information loss across studies. Results: Of 365 records identified, 18 studies met the inclusion criteria. Obesity assessed via BMI showed the highest harmonization potential (44% complete, 33% partial), along with dietary habits measured by food frequency questionnaires (44% complete). Diabetes and physical inactivity followed (33% complete), assessed through fasting glucose or pharmacological treatment and the IPAQ, respectively. Smoking habits classified as current, former, or never smokers were reported in 28% of studies. Depression (assessed by GDS or CES-D) and hypertension (blood pressure measurement or antihypertensive treatment) showed complete harmonization in only 22% of studies. Conclusions: Italian studies show substantial limitations in the harmonization of modifiable risk factor data for Alzheimer’s disease, mainly due to heterogeneous and non-standardized data collection methods, highlighting the need for uniform research protocols.

Background/Objectives: Alzheimer’s disease (AD) is the most common cause of dementia. Among the various factors associated with the risk of AD, growing attention in recent years has focused on environmental influences, particularly air pollution. The association between air pollutants and AD remains inconclusive due to heterogeneity in available studies. Given these gaps, we performed a systematic review on the topic. Methods: We systematically searched Pubmed, Embase and Scopus. Retrieved records underwent screening by title and abstract and then in full text. We included studies quantitatively exploring the association between exposure to air pollutants and risk of AD. We performed a meta-analysis to identify a pooled estimate of the impact of each pollutant on the probability of developing AD. Results: We retrieved 1081 records and included 27 studies. We found a significant association between PM_2.5 levels and AD risk (HR 1.74, 95%CI 1.36–2.23). Our data did not support a relevant role for the other pollutants we analyzed (for PM₁₀ HR = 1.35, 95%CI: 0.86–2.11; for NO₂ HR = 1.34, 95%CI 0.96–1.86; and for O₃ HR = 1.03, 95%CI 0.68–1.57). Conclusions: PM_2.5 emerged as the pollutant most strongly and consistently associated with an increased risk of AD. This robust and statistically significant association underscores the potential neurotoxic effects of fine particulate matter. For other pollutants, a clear role was not found. These results should be interpreted with caution, due to high heterogeneity in the definition of AD in the included studies (in most cases, a clinical definition was used). More research will be needed in the future.

Introduction: The significant impact of attention deficit/hyperactivity disorder (ADHD) on health in adult life has been widely recognized. Among the comorbidities of this disorder in later life, dementia is one of the most relevant ones. We performed a systematic review and meta-analysis to explore the impact of previous ADHD diagnosis on dementia risk. Materials and methods: We systematically searched Pubmed, Embase and Scopus for the relevant literature. Cohort and case–control studies were included in our review. Retrieved records were selected by title and abstract and then by full text reading. For quality appraisal, the Newcastle–Ottawa scale was used. A meta-analysis of hazard ratios (HRs) was performed for each type of dementia. Results: Four cohort studies and one case–control study were included, for a total of 3,703,877 and 400 participants, respectively. For all-cause dementia, the pooled HR was 2.52 (95%CI 1.51–4.22, p < 0.001), pointing out a significantly higher hazard in subjects with ADHD. For Alzheimer’s disease, vascular dementia and Lewy body dementia, no meta-analysis was performed due to the low number of available studies. Discussion and conclusions: Our results support a significant association between ADHD and risk of dementia. The results regarding specific types of dementia are more challenging to interpret and could have been influenced by sample size issues. These findings show that ADHD deserves attention in future research on cognitive disorders of the elderly; in particular, more studies are needed to reveal if a true causal relationship links ADHD and dementia.

Introduction Acinetobacter baumannii is recognized by the World Health Organization (WHO) as one of the ESKAPE priority pathogens because of its environmental persistence and ability to acquire antimicrobial resistance. Carbapenem-resistant A. baumannii (CRAb) has been included in the WHO Global Priority List of Antibiotic-Resistant Bacteria since 2017 and remains a critical threat. In Europe, CRAb is responsible for a substantial burden of healthcare-associated infections and mortality, particularly among critically ill patients. Objectives To evaluate temporal trends in antimicrobial resistance among clinical isolates of Acinetobacter baumannii collected at the Microbiology Laboratory of the University Hospital “Paolo Giaccone” of Palermo over a 13-year period (2013–2025). Methods A retrospective analysis was conducted on all clinical isolates of A. baumannii collected between 2013 and 2025 (N = 3,010). One isolate per patient was included. Data collected comprised year of isolation, patient demographics (age and sex), hospital ward (Intensive Care Unit/Emergency, Medical Area, Surgical Area), and biological sample type. Antimicrobial susceptibility testing was performed against polymyxins, fluoroquinolones, aminoglycosides, and carbapenems. Isolates were classified as multidrug-resistant (MDR) when resistant to at least three antimicrobial classes. Comparisons between MDR and non-MDR isolates were performed using chi-square or Fisher’s exact tests for categorical variables and the Kruskal–Wallis test for age. To identify independent predictors of MDR isolation and evaluate temporal variability, a mixed-effects logistic regression model was applied, with isolates nested within years. Age, sex, clinical area, and sample type were included as fixed effects, while year of isolation was modelled as a random effect. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (95% CIs). Results Among the 3,010 isolates analyzed, 2,727 (90.6%) were classified as MDR. Patients were predominantly male (60.3%), with a mean age of 71.0 ± 15.7 years; 71.4% were aged 65 years or older. Most isolates originated from Intensive Care/Emergency Units (43.8%), followed by Medical (33.9%) and Surgical Wards (21.0%). Resistance rates remained consistently high throughout the study period, reaching 95.8% for carbapenems, 95.1% for fluoroquinolones, and 91.0% for aminoglycosides. Polymyxin resistance remained low (1.6%). Univariable analyses did not identify significant associations between patient or clinical characteristics and MDR status. In the mixed-effects model, age emerged as the only independent predictor of MDR isolation (OR 1.01; 95% CI 1.00–1.02; p = 0.019). Significant between-year variability was observed (random intercept SD = 0.404; 95% CI 0.046–0.526). Year-specific analyses showed a significantly increased probability of MDR isolation in 2020, whereas significantly lower-than-expected odds were observed in 2022 and 2023. Conclusions The observed temporal variability suggests that healthcare-system factors, beyond individual patient characteristics, influenced MDR A. baumannii circulation. The increase observed in 2020 coincided with the COVID-19 pandemic, characterized by increased intensive care admissions, broad-spectrum antibiotic use, prolonged mechanical ventilation, and critical care overcrowding. Conversely, the reduction observed in 2022–2023 may reflect strengthened infection prevention and control measures and reduced antibiotic selective pressure associated with declining COVID-19-related critical care admissions.