This review aims to provide a brief historical overview of genetic counseling and its evolution, addressing the qualified professionals and clinical requirements necessary to obtain a diagnosis and post-test guidance across different stages of patients' lives. Recent advances in the understanding of genetic diseases and contemporary diagnostic tools offer a novel approach to evaluating conditions both prenatally and in pediatric patients, facilitating precise diagnoses and tailored clinical interventions, including modern treatment regimens. Genetic counseling is a dynamic, multidisciplinary medical process that translates the complexities of hereditary and genomic conditions into actionable clinical care. Historically rooted in pediatric dysmorphology and conventional cytogenetics, the field has evolved into an essential specialty spanning multiple organ systems and life stages. A definitive diagnostic pathway requires detailed family history, gestational assessment, and meticulous physical examination—with dysmorphology analysis serving a pivotal role from the neonatal period onward. While conventional tools such as karyotype, chromosomal microarray analysis, and Sanger sequencing remain clinically relevant, next-generation sequencing technologies—including whole-exome sequencing, whole-genome sequencing, and optical genome mapping—have dramatically improved diagnostic yield, particularly in complex or atypical phenotypes. Following molecular confirmation, genetic counseling guides the interpretation of findings, recurrence risk assessment, family screening, and reproductive planning. Importantly, therapeutic paradigms have advanced beyond supportive and symptomatic care to encompass precision medicine, such as gene editing, cell therapies, and targeted pharmacological agents. Comprehensive care demands close integration among geneticists, pediatricians, subspecialists, and allied health professionals to provide long-term clinical management, surveillance, and psychosocial support. For genetic counseling, it is essential to have an evaluation by a geneticist, who is part of the multidisciplinary team. Physical examination is central to genetic evaluation, especially for the identification of phenotypic alterations. The face concentrates a large part of syndromic anomalies, and facial evaluation is a fundamental diagnostic tool. The assessment should be systematic and segmental, including anthropometric measurements and analysis of body proportions. The integration between clinical findings, family history, and development is essential for the formulation of diagnostic hypotheses, complementation with specific tests, and therapeutic institution.
In this review, we describe current genetic testing modalities available for pediatricians, provide guidance on test selection and result interpretation, and inform providers on proper genetic testing consent practices. For each type of genetic test, details regarding types of mutations detected, limitations, and common genetic diagnoses detected are included. The most recent advances in pediatric genetic testing include adoption of next-generation sequencing (NGS), such as whole exome and whole genome sequencing (WES, WGS), at younger ages and earlier in clinical presentations. Using NGS decreases time to diagnoses, reduces medical expenses, and decreases patient morbidity/mortality. Pediatricians are positioned to become the first to encounter patients with genetic testing and it is essential for them to have a working knowledge of genetic testing. As genetic testing technologies rapidly improve, the amount of genetic information clinicians can obtain has increased, requiring competency in consenting for genetic tests, result interpretation, and genetic counseling. While NGS approaches greatly enhance genetic testing options, there are still situations that benefit from using older genetic testing modalities, such as karyotype and single-gene testing, and it is crucial for pediatricians to be aware of these nuances.
Access to medically recommended foods has important implications in the health outcomes of patients with pediatric chronic kidney disease (CKD). This review aims to highlight the role of food access (food and nutrition insecurity) in pediatric CKD, summarize strengths and limitations of existing research conducted in this area, and describe opportunities to strengthen future research and eventually develop interventions to address food and nutrition insecurity in pediatric CKD. The potential consequences of food and nutrition insecurity in pediatric CKD across growth, development, disease management, and long-term health outcomes are described. Methods of screening for food and nutrition insecurity in pediatric CKD are explored, along with a discussion of approaches to utilize screening data to guide intervention referral, inform quality improvement initiatives, and generate data for prospective studies of long-term outcomes related to food access in pediatric CKD. Food and nutrition insecurity are highly correlated with other social determinants of health, and these require consideration in future research when determining causality and guiding interventions. Finally, strategies to improve food access for vulnerable patients at the healthcare, community, and policy level are explored. Food and nutrition insecurity has the potential to impact several health outcomes in pediatric CKD, however further longitudinal research is needed. Screening for food and nutrition insecurity in pediatric nephrology settings may help to identify at-risk individuals for poorer health outcomes, and contribute to quality improvement and research activities that can help inform interventions to address this modifiable social determinant of health.
Artificial intelligence (AI) is increasingly used in echocardiography and point-of-care ultrasound (POCUS) to support image acquisition, view recognition, image-quality assessment, segmentation, automated quantification, disease classification, reporting, and bedside decision support. This narrative review summarizes clinically relevant applications, with emphasis on clinical integration, pediatric and congenital heart disease considerations, and safe implementation. The strongest clinical evidence supports automated left ventricular segmentation and ejection fraction estimation, AI-guided acquisition, and workflow efficiency. Video-based deep learning has enabled beat-to-beat assessment of ventricular function, and a randomized workflow trial showed that AI-generated initial ejection fraction assessment was noninferior to sonographer assessment and required fewer cardiologist corrections. Regulatory-authorized acquisition and analysis tools demonstrate growing clinical adoption for specified adult indications. Recent multiview and disease-phenotyping models extend AI toward more comprehensive interpretation, while AI-enabled POCUS may improve focused image acquisition by non-expert users. However, external validation, pediatric and congenital heart disease data, cross-device generalizability, clinical outcome evidence, uncertainty communication, automation bias, and medicolegal responsibility remain important limitations. AI should currently be viewed as an augmentative rather than autonomous technology. The safest near-term model is human-AI collaboration, in which validated tools improve acquisition, reproducibility, and workflow while clinicians retain responsibility for interpretation and patient-centered decisions. Pediatric and congenital heart disease applications require age- and anatomy-specific datasets, multicenter validation, local performance monitoring, and clinician-supervised deployment. AI can assist across the echocardiography workflow, from acquisition guidance and view recognition to segmentation, quantification, disease screening, and structured reporting. The most mature clinical evidence supports left ventricular segmentation, ejection fraction estimation, AI-guided acquisition, and workflow efficiency rather than autonomous diagnosis. AI-enabled POCUS may improve acquisition by non-expert users, but image adequacy, interpretation, and clinical integration must remain clinician-supervised. Pediatric and congenital heart disease applications are promising but remain less mature than adult applications and require anatomy-specific datasets and multicenter validation. Safe implementation requires external validation, local monitoring, bias assessment, uncertainty display, audit trails, and institutional governance.
Pediatric nephrotic syndrome (PNS) is a clinically defined disorder characterized by proteinuria and edema but exhibits substantial biological heterogeneity that is not captured by conventional classifications. This review synthesizes current transcriptomic evidence to better understand disease mechanisms and explore its relevance for precision medicine. Advances in transcriptomic technologies, including bulk RNA sequencing, single-cell and single-nucleus approaches, and spatial transcriptomics, have revealed distinct molecular signatures in PNS. Key pathways involve podocyte cytoskeletal disruption, immune and inflammatory activation, extracellular matrix remodeling, and mitochondrial dysfunction. These findings differentiate molecular profiles across phenotypes such as minimal change disease, focal segmental glomerulosclerosis, and steroid-sensitive versus steroid-resistant disease. Emerging high-resolution approaches further enable cell-type-specific and spatial insights into disease progression. Transcriptomics has redefined PNS as a spectrum of molecularly distinct endotypes and offers promising avenues for biomarker discovery and targeted therapy. However, clinical translation remains limited by small sample sizes, technical variability, and lack of longitudinal validation. Integration with multi-omics and functional studies is essential to develop reproducible, clinically actionable strategies for improving pediatric outcomes.
