Background and Aim: Artificial intelligence (AI) is among the most promising innovations for transforming nursing education, making it more interactive, personalized, and competency-based. However, its integration also raises significant ethical and practical concerns. This scoping review aims to analyze and summarize key studies on the application of AI in university-level nursing education, focusing on its benefits, challenges, and future prospects. Methods: A scoping review was conducted using the Population, Concept, and Context (PCC) framework, targeting nursing students and educators in academic settings. A comprehensive search was carried out across the PubMed, Scopus, and Web of Science databases. Only peer-reviewed original studies published in English were included. Two researchers independently screened the studies, resolving any disagreements through team discussion. Data were synthesized narratively. Results: Of the 569 articles initially identified, 11 original studies met the inclusion criteria. The findings indicate that AI-based tools—such as virtual simulators and ChatGPT—can enhance students’ learning experiences, communication skills, and clinical preparedness. Nonetheless, several challenges were identified, including increased simulation-related anxiety, potential misuse, and ethical concerns related to data quality, privacy, and academic integrity. Conclusions: AI offers significant opportunities to enhance nursing education; however, its implementation must be approached with critical awareness and responsibility. It is essential that students develop both digital competencies and ethical sensitivity to fully leverage AI’s potential while ensuring high-quality education and responsible nursing practice.

Background: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, particularly in drug and biomarker discovery, where it can enhance precision, streamline discovery processes, and optimize treatment strategies. Despite its potential, the application of AI in glioblastoma (GB) research, especially in identifying novel biomarkers and therapeutic targets, remains underexplored. The aim of this review is to map the existing literature on AI-driven approaches for biomarker and drug discovery in GB, highlighting key trends and gaps in current research. Design: Following a PRISMA methodology, this scoping review examined studies published between 2012 and 2024. Searches were conducted across multiple databases, including MEDLINE (PubMed), Scopus, the Cochrane Library, and Web of Science (WOS). Eligible studies were screened, and relevant data were extracted and synthesized to provide a comprehensive overview of AI applications in GB research. Results: A total of 224 records were identified, including 210 from PubMed, 104 from Scopus, 4 from WOS, and 6 from the Cochrane Library. After screening and applying eligibility criteria, 33 studies were included in the final review. These studies showcased diverse AI methodologies applied to both drug discovery and biomarker identification, focusing on various aspects of GB biology and treatment. Conclusions: This scoping review reveals an increasing interest in AI-driven strategies for biomarker and drug discovery in GB, with promising initial results. However, further large-scale, rigorous studies are needed to validate real-world applications of AI and the development of standardized protocols to enhance reproducibility and clinical translation.

Background: Vaccine-induced immunity is at present the main strategy to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent evidences suggested a protective effect of influenza vaccination against coronavirus disease 2019 (COVID-19) severity, while impact on the immune response to BNT162b2 messenger RNA (mRNA) vaccine is under investigation. Methods: We aimed to evaluate this aspect in a cohort of 297 healthcare workers (108 males, 189 females) after seasonal influenza vaccination compared to no-flu-vaccination. VAX+ (165 individuals; 63 males and 102 females) had tetravalent influenza vaccine, and VAX- (132 individuals; 45 males and 87 females) had no flu vaccination. Anti-spike-receptor binding domain (RBD) level was tested 15 - 70 days after BNT162b2 second inoculum. Results: Increased antibody response was observed in total VAX+ compared to VAX- (2,047.4 vs. 1,494.2 binding antibody unit (BAU)/mL, P = 0.0039), independently from gender and body mass index (BMI). Younger total individuals (< 35 years) showed significant increase of the level of binding antibodies (2,184.8 vs. 1,590.9 BAU/mL, P = 0.0038) compared to ? 35 years; young/old difference was lost restricting to VAX+ subgroup. Flu vaccinations appear associated to better antibody response in older individuals (P = 0.027, ? 35 years VAX+ vs. VAX-). A decreasing trend during time was observed for both VAX+ and VAX-, except for < 35 years VAX- individuals. Early response was higher in VAX+ compared to VAX-; however a more rapid waning was observed in VAX+ subjects. Conclusions: Our data showed better antibody response to SARS-CoV-2 vaccine in subjects already vaccinated against seasonal influenza; this may represent one of the mechanisms underlying the cross-protective effects of influenza vaccination against heterologous infections reported in recent epidemiological studies. J Clin Med Res. 2021;13(12):549-555 doi: https://doi.org/10.14740/jocmr4590

Breast cancer (BC) is one of the most common malignancies among women worldwide. Despite advances in early detection and treatment, prognosis remains highly variable. Molecular biomarkers, such as microRNAs (miRNAs), have emerged as promising tools to refine prognostic assessment. Among them, miR-21 is consistently overexpressed in solid tumors and implicated in key oncogenic pathways. This systematic review and meta-analysis aimed to clarify the prognostic significance of miR-21 in BC and explore its molecular mechanisms through bioinformatic analyses. A systematic search of PubMed, Scopus, and Web of Science up to April 2025 identified 18 eligible observational studies. Pooled analyses showed that high miR-21 expression was significantly associated with poorer overall survival (OS) (HR = 2.37, 95% CI: 1.42–3.98) and recurrence-related outcomes (DFS/RFS) (HR = 2.10, 95% CI: 1.32–3.34). Subgroup analyses confirmed robust associations across different cut-off definitions and revealed particularly strong effects in triple-negative BC (HR = 5.69) and mixed subtypes (HR = 2.55), but no significant association in HER2-positive BC. Bioinformatic analysis identified target genes such as PTEN, BCL2, STAT3, and MYC, involved in apoptosis regulation, proliferation, NF-κB signaling, and immune modulation. These findings provide consistent evidence that miR-21 is a promising minimally invasive prognostic biomarker in BC, particularly in aggressive subtypes, and support its integration into future multimodal prognostic models.