Intraventricular hemorrhage (IVH) is a common complication of prematurity and continues to represent a considerable threat due to its association with significant short- and long-term morbidity and mortality. Despite the advances in neonatal care, the prevalence of IVH, particularly in the extremely preterm neonates, remains high. Therefore, it is imperative to recognize and implement in clinical practice preventive strategies, non-pharmacological or pharmacological, to reduce IVH effectively. The aim of this narrative review is to provide an overview of novel and debatable preventive measures for IVH that are promising for clinical use and could potentially improve outcomes for very preterm neonates. IVH prevention bundles (IVHPBs) consist of strategies that aim to minimize hemodynamic and cerebral perfusion fluctuations, which are a crucial component of IVH pathogenesis. Early postnatal prophylactic indomethacin, erythropoietin, and insulin-growth factor-1 administration have shown encouraging results on IVH prevention; however, the literature is still inconclusive. Stem-cell-based interventions represent novel and promising techniques with the potential to contribute to the prevention of IVH. The prevention of IVH remains a field of investigation, and there is a requirement for conclusive evidence and recommendations. The necessity for further large-scale prospective studies is therefore evident.

Methicillin-susceptible Staphylococcus aureus (MSSA) remains a major pathogen in neonatal intensive care units (NICUs), with colonization and infection posing significant risks. MSSA colonization occurs in up to 42.8% of neonates, while 12–41% of healthcare personnel also carry MSSA, contributing to nosocomial transmission. MSSA accounts for approximately 12% of neonatal S. aureus bloodstream infections, with mortality rates up to 20.5%, particularly among very-low-birth-weight infants. This review analyzes the molecular attributes, epidemiology, risk factors, clinical presentations, decontamination methods, and treatment alternatives for MSSA infections in newborns. MSSA strains show considerable genetic heterogeneity, being distinguished by a wide variety of sequence types (STs) and Staphylococcal Protein A types (SpA). They harbor several pathogenicity genes—including hemolysins, superantigens, adhesins, and Panton–Valentine leukocidin (PVL)—which are implicated in severe infections, while biofilm-associated genes enhance environmental persistence. Prematurity, low birth weight, prolonged hospitalization, and exposure to invasive devices are key risk factors. Active surveillance and decolonization programs have achieved reductions of up to 73% in MSSA infections. β-lactam antibiotics remain first-line therapy, with adjunctive aminoglycosides reserved for severe cases. Ongoing genomic surveillance and targeted preventive strategies are essential to reduce MSSA-associated morbidity and mortality in this vulnerable population.

Respiratory disorders remain a major cause of morbidity and mortality in neonatal intensive care units, particularly among preterm infants. Advances in physiological monitoring, medical imaging, and electronic health records have enabled the growing application of artificial intelligence in neonatal respiratory care. This narrative review summarizes current applications and emerging directions of artificial intelligence in the diagnosis, monitoring, and management of neonatal respiratory disorders. Machine learning and deep learning approaches have demonstrated promising performance in respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, ventilatory management, and severe respiratory complications. By integrating multimodal clinical, physiological, and imaging data, these methods support earlier detection of respiratory deterioration and improved clinical decision-making. However, challenges related to data quality, generalizability, interpretability, and limited prospective validation continue to constrain widespread clinical implementation, highlighting the need for careful integration into neonatal care workflows.

Maternal vaccination against respiratory syncytial virus (RSV) represents a major advance in early-life infection prevention. Although clinical efficacy and early real-world effectiveness are well established, sustained population-level impact depends on equitable uptake. This review synthesizes determinants influencing maternal RSV vaccination within the evolving dual-strategy landscape that includes both maternal vaccination and infant monoclonal antibody prophylaxis. A structured narrative review was conducted following PRISMA principles. PubMed/MEDLINE and Google Scholar were searched for studies published between January 2022 and February 2026. Eligible studies examined behavioral, interpersonal, structural, economic, and policy determinants of maternal RSV vaccination uptake, as well as early implementation and modelling evidence. Findings were integrated within a multilevel analytical framework. Maternal uptake is shaped by interacting determinants across individual, healthcare provider, and health system domains. Key drivers include perceived infant disease severity, vaccine safety confidence, perceived effectiveness, and prior antenatal vaccination behavior. Healthcare provider recommendation consistently emerges as the strongest facilitator. Coverage variability reflects differences in reimbursement, antenatal care integration, and national policy endorsement. The coexistence of maternal vaccination and infant monoclonal antibody strategies introduces additional comparative decision-making complexity. Early implementation data indicate heterogeneous uptake and socioeconomic gradients, while modelling demonstrates sensitivity to coverage, timing, epidemiology, and cost. Translating biological efficacy into sustained public health benefit requires coordinated behavioral, structural, and policy strategies, strong provider engagement, and context-sensitive implementation frameworks to ensure equitable coverage.

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.

Digital Twins (DTs) are virtual, patient-specific representations that integrate re-al-time data to model, predict, and optimize biological and clinical processes. In neo-natology, DTs are gaining attention as powerful tools for managing the profound physiological complexity and variability of newborns, particularly preterm infants re-quiring intensive care. Emerging applications include cardiopulmonary modeling, prediction of sepsis and necrotizing enterocolitis (NEC), optimization of mechanical ventilation, individualized nutrition, and longitudinal monitoring of neuromotor de-velopment. This review synthesizes current research on neonatal digital twins, high-lighting clinical use cases and ethical considerations. We discuss persistent challenges, including limited data availability, rapid developmental change, model validation, and regulatory oversight. Finally, we outline a roadmap for integrating DTs into neo-natal intensive care units (NICUs) and identify future research priorities, including multi-organ integration, predictive closed-loop systems, and personalized life-course care trajectories.