In recent years, remarkable progress in nanomedicine has been achieved, leading to the development of several nanocarriers which aim to enhance the therapeutic efficacy in cancer treatment. Owing to their high versatility and highly tunable physicochemical properties, alumina (Al₂O₃) and silica (SiO₂) substrates represent promising and innovative nanoplatforms that are widely used in biomedical applications, such as drug-delivery, diagnosis, and biosensing in cancer. In particular, such platforms possess multiple advantageous properties, including mechanical stability, high loading capacity, tunable porosity, excellent biocompatibility, and in vitro and in vivo low toxicity. In this review article, we discuss their emerging role as biosensing platforms and drug delivery systems in oncology. As such, we describe how these substrates enable the incorporation of antibodies against various cancer biomarkers [e.g., cancer antigen 15-3 (CA15-3), serum amyloid A1 (SAA1), epithelial cell adhesion molecule (EpCAM), or human epidermal growth factor receptor 2 (HER2)] for the detection of multiple malignancies. Furthermore, we highlight the development of highly promising alumina- and silica-based platforms for drug delivery (e.g., chemotherapeutics, photosensitizers, or gene delivery agents) in cancer. Ultimately, by providing a comprehensive overview alongside a critical analysis, we demonstrate that such nanostructures represent promising platforms for potential clinical translation in cancer medicine, helping to mitigate the limitations of conventional cancer therapies.

Respiratory syncytial virus (RSV) remains a major cause of severe lower respiratory tract infection in early infancy, a period characterized by immunological immaturity and limited capacity for effective antiviral responses. Maternal RSV vaccination has emerged as a successful strategy to protect newborns by inducing high concentrations of IgG1-dominant, prefusion F-specific antibodies, which are selectively and actively transported across the placenta. This review synthesizes current mechanistic insights into how maternally derived antibodies confer neonatal protection, focusing on (i) FcRn-mediated transplacental transport, (ii) IgG subclass-specific differences in transfer and half-life, and (iii) the role of Fc glycosylation in modulating Fcγ receptor engagement and effector functions. Beyond neutralization, vaccine-induced antibodies mediate Fc-dependent mechanisms such as antibody-dependent cellular cytotoxicity and phagocytosis, which may be preferentially enriched in the neonatal circulation. Although emerging systems serology data suggest qualitative selectivity in placental transfer, evidence remains heterogeneous and highlights the need for further clarification of glycosylation-dependent and glycosylation-independent pathways. The timing of vaccination, maternal antibody characteristics, and placental integrity critically influence neonatal antibody levels and the duration of passive immunity. Understanding these molecular determinants is essential for optimizing maternal RSV immunization strategies and improving early-life protection.

Antimicrobial resistance is a growing threat in neonatal intensive care units (NICUs) worldwide, challenging the management of neonatal sepsis for decades. The aim of this narrative review is to compare the epidemiology and resistance patterns of neonatal sepsis in NICUs between two periods, 2000-2005 and 2020-2025, and to identify key insights that may inform future practices to limit the emergence and dissemination of antimicrobial resistance in NICUs. During the early 2000s, resistant pathogens, including extended-spectrum beta-lactamases (ESBL)-producing Enterobacterales, vancomycin-resistant Enterococcus (VRE), methicillin-resistant Staphylococcus aureus (MRSA), and coagulase-negative Staphylococcus (CoNS), were increasingly reported in NICUs. The increasing prevalence of antibiotic-resistant strains was associated with the widespread use of broad-spectrum antibiotics, exerting selective pressure that contributed to the emergence of multidrug-resistant pathogens in the 2020s, including carbapenem-resistant Enterobacterales (CRE) and multidrug-resistant Acinetobacter baumanni, and to the further dissemination of resistant strains in NICUs. The evolution of antimicrobial resistance over the past twenty years highlights that preserving the effectiveness of antibiotics, through rational antibiotic use, is a key strategy to limit the emergence of resistant pathogens. This is of particular importance for the neonatal population due to the limited therapeutic options. Although antimicrobial stewardship programs have been implemented in numerous NICUs with encouraging results, optimization of antibiotic use requires the identification of biomarkers that can promptly and accurately diagnose sepsis and the development of new effective antimicrobial agents against multidrug-resistant pathogens. Future studies are expected to further enhance diagnostic precision, therapeutic options, and stewardship strategies to limit the spread of antibiotic resistance.

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early prediction and diagnosis of neonatal sepsis through the analysis of large and complex clinical datasets. This structured narrative review summarizes current evidence regarding AI-based approaches for neonatal sepsis prediction and diagnosis. A literature search of PubMed, Scopus, and Google Scholar identified studies evaluating machine learning, deep learning, and advanced predictive analytics using clinical, laboratory, physiological, electronic health record, and multi-omics data. Current evidence suggests that AI models, particularly ensemble learning, gradient boosting, and deep learning approaches, can achieve promising predictive performance and identify infants at increased risk of sepsis hours before conventional clinical recognition. Continuous physiological monitoring and multimodal data integration appear particularly promising for real-time prediction. However, important challenges remain, including limited external validation, small and heterogeneous datasets, concerns regarding interpretability, and unresolved ethical and regulatory issues. Future progress will depend on multicenter collaboration, explainable AI frameworks, federated learning, and multimodal predictive models. Although current evidence supports the predictive potential of AI-based models, prospective multicenter validation and clinical impact studies are required before improvements in neonatal clinical outcomes can be established. Artificial intelligence has the potential to become a valuable clinical decision support tool to support early sepsis recognition and precision neonatal care.

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early prediction and diagnosis of neonatal sepsis through the analysis of large and complex clinical datasets. This structured narrative review summarizes current evidence regarding AI-based approaches for neonatal sepsis prediction and diagnosis. A literature search of PubMed, Scopus, and Google Scholar identified studies evaluating machine learning, deep learning, and advanced predictive analytics using clinical, laboratory, physiological, electronic health record, and multi-omics data. Current evidence demonstrates that AI models, particularly ensemble learning, gradient boosting, and deep learning approaches, can achieve strong predictive performance and identify infants at increased risk of sepsis hours before conventional clinical recognition. Continuous physiological monitoring and multimodal data integration appear particularly promising for real-time prediction. However, important challenges remain, including limited external validation, small and heterogeneous datasets, concerns regarding interpretability, and unresolved ethical and regulatory issues. Future progress will depend on multicenter collaboration, explainable AI frameworks, federated learning, and multimodal predictive models. Artificial intelligence has the potential to become a valuable decision-support tool that enhances early sepsis recognition and supports precision neonatal care.

Antimicrobial resistance is a growing threat in neonatal intensive care units (NICUs) worldwide, challenging the management of neonatal sepsis for decades. The aim of this narrative review is to compare the epidemiology and resistance patterns of neonatal sepsis in NICUs between two periods, 2000–2005 and 2020–2025, and to identify key insights that may inform future practices to limit the emergence and dissemination of antimicrobial resistance in NICUs. During the early 2000s, resistant pathogens, including extended-spectrum beta-lactamases (ESBL)-producing Enterobacterales, vancomycin-resistant Enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA), and coagulase-negative Staphylococci (CoNS), were increasingly reported in NICUs. The increasing prevalence of antimicrobial resistant strains was associated with the widespread use of broad-spectrum antibiotics, exerting selective pressure that contributed to the emergence of multidrug-resistant pathogens in the 2020s, including carbapenem-resistant Enterobacterales (CRE) and multidrug-resistant Acinetobacter baumannii, and to the further dissemination of resistant strains in NICUs. The evolution of antimicrobial resistance over the past twenty years highlights that preserving the effectiveness of antibiotics, through rational antibiotic use, is a key strategy to limit the emergence of resistant pathogens. This is of particular importance for the neonatal population due to the limited therapeutic options. Although antimicrobial stewardship programs have been implemented in numerous NICUs with encouraging results, optimization of antibiotic use requires the identification of biomarkers that can promptly and accurately diagnose sepsis and the development of new effective antimicrobial agents against multidrug-resistant pathogens. Future research is expected to improve diagnostic precision, therapeutic options, and stewardship strategies to limit the spread of antimicrobial resistance.