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.

Neonatal echocardiography remains an essential imaging modality for the assessment of congenital and hemodynamic cardiovascular abnormalities in critically ill neonates. Recent advances in quantitative ultrasound biomarkers, deformation imaging, volumetric reconstruction, and artificial intelligence (AI)-assisted analysis have substantially expanded the diagnostic capabilities of neonatal cardiovascular ultrasound imaging. This narrative review critically examines current developments in quantitative echocardiographic imaging, advanced volumetric methodologies, and AI-assisted cardiovascular ultrasound analysis, with emphasis on neonatal intensive care applications. A literature search was conducted using PubMed, Google Scholar, Scopus and Embase focusing on neonatal echocardiography, spatiotemporal image correlation (STIC), speckle-tracking echocardiography, artificial intelligence, machine learning, and quantitative cardiovascular imaging. Recent studies suggest that advanced methodologies, including speckle-tracking echocardiography, STIC-based reconstruction, automated segmentation algorithms, and deep learning frameworks, may improve image standardization, automated quantification, and congenital heart disease detection. However, important challenges persist, including operator dependency, dataset heterogeneity, limited external validation, cross-platform variability, and incomplete integration into routine neonatal intensive care workflows. Most AI-assisted echocardiographic systems remain investigational and require further prospective multicenter validation before widespread clinical implementation can be achieved.