Importance:Metabolic dysfunction-associated steatotic liver disease (MASLD) is caused by dysregulated lipid metabolism, inflammation, and mitochondrial dysfunction. Given the rising burden of MASLD in children and adolescents, identifying experimentally tractable mechanisms relevant to pediatric diseases is of considerable interest. Tryptophan hydroxylase-1 (TPH1), the rate-limiting enzyme for peripheral serotonin synthesis, has been implicated in metabolic disorders, but its hepatic role in MASLD remains unclear. Objective:To investigate whether Tph1 suppression alleviates lipid accumulation, mitochondrial dysfunction, and hepatocyte injury through changes associated with Parkin-mediated mitophagy. Methods:MASLD was induced in young adult male C57BL/6J mice by a 12-week high-fat diet (HFD). At HFD onset, mice received a single tail-vein injection of liver-tropic AAV8-shTph1 or the control AAV8. The HFD feeding was continued for 12 weeks until the end of the experiment. Hepatic histology, serum biochemistry, oxidative stress, inflammatory responses, apoptosis, and mitochondrial function were also evaluated. Mechanistic studies were conducted in palmitic acid/oleic acid-treated AML12 hepatocytes using siRNA targeting Tph1 and Parkin. Results:Hepatic TPH1 expression was elevated in MASLD mice. Tph1 knockdown alleviated diet-induced steatosis, reduced serum triglycerides, total cholesterol, low-density lipoprotein cholesterol, and transaminase levels, suppressed oxidative stress, and attenuated inflammatory and fibrotic markers. Tph1 silencing decreased hepatocyte apoptosis and restored mitochondrial function. Transmission electron microscopy and fractionated Western blotting analyses revealed increased Parkin-associated mitophagy. In AML12 cells, siTph1 reduced lipid accumulation and apoptosis, whereas co-silencing Parkin partially reversed these effects. Interpretation:Hepatic TPH1 is associated with MASLD progression and impaired Parkin-mediated mitophagy. Tph1 suppression may represent a potential therapeutic strategy for experimental MASLD, with possible translational relevance to pediatric MASLD, though direct clinical validation is still needed.
Atopic dermatitis (AD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur in children and adolescents, increasing the disease burden and clinical complexity. Accumulating evidence suggests that children with AD have a higher risk of incident ADHD, a risk that is particularly pronounced in those with early-onset, severe AD, or significant sleep disturbances. The underlying mechanisms are hypothesized to involve the dysregulation of the "inflammation-itching-neurobehavioral" axis. This narrative review synthesizes current evidence regarding the epidemiology, potential mechanisms, and clinical management of this comorbidity. We propose that proactive screening and aggressive control of skin inflammation and pruritus are key to disrupting this vicious cycle.
Early autism identification in children aged 0-5 years is often discussed in terms of screening accuracy, yet consequential delay frequently occurs across the pathway from first concern to referral, diagnostic assessment, and support. This critical narrative review examines early autism identification as a pathway problem rather than as a single testing event. It synthesizes evidence on developmental surveillance, autism-specific screening, parental and clinician concern, referral conversion, diagnostic waiting, service capacity, inequity, family burden, and pre-diagnostic support. Screening tools can identify an elevated likelihood of autism and may accelerate diagnosis for some screen-positive children, but they cannot confirm diagnosis or safely exclude autism when concern persists, and they do not compensate for failures in follow-up, referral, assessment capacity, or support initiation. Recent evidence supports a cautious interpretation of universal autism screening because diagnostic stability, screening accuracy, and intervention benefit in screen-detected children remain uncertain. Comparative evidence on the Modified Checklist for Autism in Toddlers, Revised with Follow-Up suggests context-dependent performance, including variable sensitivity, low or inconsistent positive predictive value, and age-dependent accuracy. Multicultural surveillance and implementation studies indicate that adding tools without aligning workflow, language support, follow-up systems, and service capacity may not improve pathway performance. The review argues that early autism identification should be evaluated through linked quality measures: response to concern, repeated surveillance following negative or ambiguous screening, referral completion, time to diagnostic assessment, support initiation before diagnostic closure, and equity of access. The clinical priority is not a perfect screening instrument in isolation, but faster, more coherent, and more equitable local pathways that translate concern into timely action.
Importance:Malnutrition is highly prevalent in children with cerebral palsy (CP) and adversely affects rehabilitation outcomes. Preterm birth is a major risk factor for CP, yet the relationship between functional status and malnutrition in preterm children with CP remains unclear. Objective:To investigate the effect of gestational age on the association between nutritional status and function levels in children with CP. Methods:This study included 575 children with CP enrolled between July 2020 and December 2021. Participants were stratified into four groups by gestational age at birth: <28, 28-32, 33-36, and ≥37 weeks. Nutritional status was assessed using weight-for-age, weight-for-height, height-for-age, and body mass index-for-age z-scores. Gross motor function, eating/drinking ability, and activities of daily living (ADL) were evaluated using Gross Motor Function Classification System (GMFCS), Eating And Drinking Ability Classification System (EDACS), and ADL scales, respectively. Results:Of the 575 children, 49.0% were born preterm, and 40.4% were categorized as malnutrition. Nutritional status was significantly correlated with gestational age (Kendall's Tau-b = 0.13, P < 0.01) and all functional measures (P < 0.01). Gestational age was correlated with ADL level (Kendall's Tau-b = 0.11, P < 0.01). Stratified analyses revealed significant correlations between nutritional status and GMFCS, EDACS, and ADL levels in both the 28-32-week and ≥37-week groups (all P < 0.01). Multinomial logistic regression analysis further demonstrated that gestational age <32 weeks was independently associated with moderate undernutrition (<28 weeks: OR: 4.61, 95% CI: 1.83-11.63, P = 0.001; 28-32 weeks: OR: 2.71, 95% CI: 1.56-4.72, P < 0.001). Interpretation:Nutritional status in preterm children with CP, particularly those born before 32 weeks of gestation, correlates significantly with motor function, feeding ability, and daily living skills. These findings may inform the development of individualized rehabilitation strategies for this high-risk population.
ABSTRACT Artificial intelligence (AI) possesses the transformative potential to reshape pediatric medicine, offering powerful tools for diagnosis, prognosis, and personalized therapy. This review focuses on three domains selected for their relative maturity in AI development and proximity to clinical translation—pediatric critical care, perinatal and neonatal medicine, and precision oncology—evaluating current evidence for clinical utility and outlining challenges to implementation. AI is demonstrating significant potential across these domains: in critical care, deep learning models outperform traditional scoring systems for dynamic prediction of adverse events; in perinatal and neonatal medicine, AI enhances prenatal ultrasonography and integrates multiomics data to guide complex therapies; and in oncology, radiomics, and genomic analysis enable non‐invasive tumor characterization and personalized treatment strategies. However, significant hurdles remain. Foundational data challenges—including scarcity, heterogeneity, and limited sharing of pediatric data—are being addressed through transfer learning, federated learning, and synthetic data generation. Clinical translation is further impeded by algorithmic bias, the ‘black box’ problem, and the unique developmental physiology of children, which demands age‐specific model validation. Future progress depends on multi‐institutional collaboration, a research focus that extends beyond prediction to encompass causal inference and explainability, and the establishment of robust ethical, regulatory, and economic frameworks. Ultimately, responsible implementation of AI in pediatrics requires building systems that are not merely accurate but transparent, equitable, and trustworthy.
Pediatric nasal skull base tumors are rare and difficult to diagnose early due to their deep anatomical location and children's limited ability to describe symptoms. When the tumors progress to advanced stages, nonspecific symptoms such as nasal congestion, nosebleeds, and facial swelling are easily confused with sinusitis or trauma, complicating diagnosis and treatment. Moreover, the special tumor anatomical location and the narrow nasal cavity in children increase the risk of tumor involvement with the surrounding structures. Given children's longer life expectancy and higher postoperative quality of life demands, personalized treatment and comprehensive medical management are essential. Advances in artificial intelligence (AI) and digital medicine have played an important role in early diagnosis, multidisciplinary treatment, prognosis assessment, and follow-up. However, despite the widespread use of emerging AI-related technologies, their application in pediatric nasal skull base tumors remains limited. This review discusses the potential applications of AI and digital medicine in the whole-process medical management of these tumors, including diagnosis, treatment, prognosis, and follow-up, along with current challenges.
Obesity is increasingly being recognized as a heterogeneous condition with strong genetic underpinnings. Monogenic obesity, caused by single-gene mutations, primarily affects the leptin-melanocortin pathway, which regulates hunger and satiety. Mutations in genes, such as LEP, LEPR, POMC, PCSK1, and MC4R, lead to hyperphagia, early onset severe obesity, and metabolic dysregulation. LEP and LEPR mutations impair leptin signaling, resulting in defective appetite suppression, whereas POMC and PCSK1 deficiencies disrupt prohormone processing. MC4R mutations, the most common cause of monogenic obesity, impair satiety signaling and are linked to rapid weight gain. Syndromic obesity, including the Bardet-Biedl and Alström syndromes, involves ciliary dysfunction, leading to developmental abnormalities alongside obesity. Other genes such as SH2B1, SIM1, and BDNF play crucial roles in hypothalamic development and energy regulation. Advances in genomic sequencing have improved the recognition of genetic etiologies; however, many patients remain undiagnosed due to limited testing availability and a lack of clinician awareness. Precision therapies, including set-melanotide for specific melanocortin pathway defects, demonstrate the promise of targeted treatments. However, management must extend beyond pharmacology; lifestyle interventions, psychosocial support, and family-centered care remain essential, especially when intellectual disability or behavioral challenges complicate adherence. Ethical considerations surrounding access and equity are critical, as high-cost therapies and the limited availability of genetic testing risk widening disparities between health systems. Polygenic risk scores and multi-omics approaches may expand precision medicine beyond rare genetic syndromes to common obesity, highlighting the importance of integrating genetics, psychosocial care, and policy advocacy in the management of pediatric obesity.
Importance: Oscillometry - a tidal-breathing test with potential advantages over spirometry - is not well studied prior to hematopoietic stem cell transplantation (HSCT). Objective To determine whether patients awaiting HSCT have abnormal baseline oscillometry compared with controls. Methods The TRANSPIRE study (NCT04098445) is a National Institutes of Health-sponsored, multicenter, prospective observational cohort of pediatric lung injury after HSCT. We performed tidal-breathing oscillometry at baseline and during follow-up in 63 children prior to HSCT across five pediatric centers and compared results to 80 control subjects. Measurements were made at various frequencies following the European Respiratory Society 2020 guidelines and included resistance (R5, R19, and R5-R19), reactance (X5 and X11), resonant frequency (Fres), and area under the reactance curve (AX). Linear regression compared TRANSPIRE baseline values to controls; mixed-effects models assessed differences between TRANSPIRE subjects, controls, and published predicted values up to two years post-HSCT. Results All mean oscillometric parameters were normal at baseline, according to normal reference values of Ducharme. However, mean lung function in TRANSPIRE subjects was significantly different from controls, with higher respiratory system resistance (height-adjusted R5-R19, P < 0.001), and greater respiratory system stiffness and heterogeneity, with more negative height-adjusted X5 (P < 0.001) and height-adjusted X11 (P < 0.001), greater height-adjusted AX (P < 0.001), and greater height-adjusted Fres (P < 0.001). No significant changes were noted over time. Interpretation In a multicenter pediatric HSCT cohort, baseline oscillometry differed significantly from controls, showing heterogeneous increases in small-airway resistance and respiratory-system stiffness that may elevate risk for pulmonary complications.
Artificial intelligence (AI) is increasingly being used in healthcare and has the potential to improve the diagnosis, treatment, monitoring, and management of neurodevelopmental disorders (NDDs) in children. Early identification and personalized care are often constrained by subjective assessment and inequitable access. AI tools may enhance diagnostic accuracy and care delivery; however, the maturity, clinical readiness, and equity implications of this evidence base remain unclear. This scoping review mapped peer-reviewed studies published in English since 2015 that described empirical or conceptual AI applications for diagnosis, monitoring, decision support, or treatment in pediatric NDD care, with particular attention to equity considerations. From 1027 records, 13 studies met the inclusion criteria. Most focused on attention deficit/hyperactivity disorder (ADHD, n = 7), autism spectrum disorder (ASD, n = 4), with predominating diagnostic tools. Reported accuracies ranged from 76% to 100% for ADHD and from 88% to 95% for ASD. Most studies were preliminary or in early implementations, with only one externally validated. Equity considerations were limited, with overrepresentation of White males and little attention paid to socioeconomic or cultural factors. AI shows promise for pediatric NDD care, but equitable clinical adoption will require inclusive research, external validation, and equity-focused implementation.
Kaposiform hemangioendothelioma (KHE) is a rare borderline vascular tumor that occurs primarily during infancy and childhood. The tumor typically originates in the skin and exhibits invasive growth into deeper tissues, often manifesting as a firm, poorly defined purplish-red mass. Cases of KHE involving visceral organs, including the bone, retroperitoneum, or mediastinum, have also been reported; however, their clinical features are often nonspecific, which may lead to delayed diagnosis. Importantly, these tumors are frequently associated with Kasabach-Merritt phenomenon, a consumptive coagulopathy that represents a major cause of mortality in KHE. Furthermore, tumor location, size, and clinical response to pharmacotherapy are closely associated with patient prognosis. Therefore, early recognition and timely treatment of KHE are essential. This article aims to review the epidemiology, etiology, clinical manifestations, diagnosis, and management of KHE.
