Aikaterini I. Nikolaou

Maternal messenger RNA (mRNA) vaccination can generate maternal immunity and antibodies for passive infant protection. However, clinical evidence derives predominantly from the SARS-CoV-2 vaccines BNT162b2 and mRNA-1273, limiting generalization across the platform. This narrative review examines three questions: what determines maternal immune responses, what governs placental antibody transfer, and how immunological findings relate to infant clinical protection. Studies generally support preserved immune responsiveness during pregnancy, but associations with gestational timing are difficult to separate from dose number, previous infection, and time since antigen exposure. Transfer requires interpretation of maternal and cord-blood concentrations, because high transfer ratios do not necessarily indicate high infant antibody levels. Postnatal antibody decline and variant-dependent neutralization further limit inference from measurements at delivery. Observational studies associate maternal vaccination with reduced COVID-19-associated infant hospitalization, although residual confounding and heterogeneous exposure histories complicate comparisons between schedules. No validated infant antibody threshold predicts protection against SARS-CoV-2 infection or hospitalization. Evidence for other pathogens and self-amplifying RNA remains predominantly preclinical. Maternal immunogenicity, placental transfer, and antibody persistence therefore provide complementary evidence but cannot substitute for clinical outcomes. Future products require pregnancy-specific safety assessment and prospective studies linking maternal responses, functional antibody transfer, and postnatal persistence to clinically meaningful protection throughout infancy.

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 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.

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 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.

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.

Familial hypercholesterolaemia remains substantially underdiagnosed worldwide despite effective preventive therapies. Evidence increasingly supports paediatric-centred detection combined with cascade and reverse cascade strategies to improve population-level identification and reduce premature atherosclerotic cardiovascular disease. We summarise contemporary familial hypercholesterolaemia screening models with emphasis on paediatric approaches, implementation barriers, and health system requirements, and we present the EPIRUS Familial Hypercholesterolaemia Reverse Cascade Project as a pragmatic regional model in Greece. A literature search (December 2025 to January 2026) was performed in PubMed, Scopus, and Google Scholar using terms related to “familial hypercholesterolemia,” “familial hypercholesterolaemia,” “screening,” “cascade,” “reverse cascade,” and “population programme.” Eligible publications included human studies and evidence syntheses evaluating universal, opportunistic, cascade, or paediatric screening strategies, and implementation or economic analyses. Evidence consistently indicates that opportunistic detection and conventional cascade screening alone cannot close the diagnostic gap because of low index-case ascertainment and fragmented pathways. Universal or structured paediatric programmes demonstrate feasibility, high diagnostic yield after infancy, and capacity to trigger family-based case-finding. Recent data show low-density lipoprotein cholesterol discrimination is limited in newborns but becomes reliable from age ≥1 year, supporting childhood screening. We describe EPIRUS Familial Hypercholesterolaemia, a targeted paediatric programme (primarily ages 4–16 years) using lipid thresholds and family history triggers, systematic phenotyping, and next-generation sequencing to confirm diagnosis and enable cascade and reverse cascade screening. EPIRUS Familial Hypercholesterolaemia illustrates how paediatric entry points can operationalise family-based detection within routine clinical care in a decentralised setting and can generate real-world implementation evidence to inform scalable national strategies.

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.