Background/Objectives: Precocious puberty (PP), defined as the onset of secondary sexual characteristics before 8 years in girls and 9 years in boys, is associated with psychosocial distress, compromised adult height, and long-term metabolic risk. Early identification remains challenging, as current diagnostic approaches are largely reactive and rely on invasive or resource-intensive testing. This narrative review examines how artificial intelligence (AI) can support earlier risk prediction and detection of PP through integration of clinical, hormonal, imaging, lifestyle, and environmental data. Methods: A narrative literature review was conducted using PubMed, Scopus, Embase, Web of Science, and Google Scholar to identify relevant studies published between 2005 and 2025. Eligible studies included original research and high-quality reviews that examined AI-based approaches, such as machine learning and deep learning, in pediatric endocrinology, particularly for the prediction or diagnosis of central or peripheral precocious puberty. Studies incorporating clinical, hormonal, radiological, lifestyle, environmental, or multi-omics data relevant to AI modeling were included. Results: AI models, including XGBoost, random forest, convolutional neural networks, and regression-based approaches, have demonstrated potential utility in predicting central precocious puberty using hormonal, imaging, and growth data. Reported applications include automated bone age assessment, lifestyle and dietary risk stratification, and exploratory use of wearable-derived behavioral data. However, progress is limited by small pediatric datasets, population bias, limited interpretability, and unresolved ethical challenges related to privacy, consent, and equity. Conclusions: Artificial intelligence represents a promising decision-support approach for earlier, non-invasive, and individualized risk assessment in precocious puberty. Future progress will depend on the integration of longitudinal, multimodal data, the development of ethical models, and interdisciplinary collaboration among pediatric endocrinologists, data scientists, and public health stakeholders.

Introduction: Fluid management in sepsis patients with pre-existing heart failure presents a complex clinical challenge, as these patients require adequate resuscitation while avoiding fluid overload that could worsen cardiac function. This article aims to explore optimal fluid resuscitation strategies for patients with pre-existing heart failure who develop sepsis, a group at high risk for fluid management complications. Methods: We conducted a narrative review of studies published between 2010 and 2024, utilizing PubMed, ScienceDirect, and Google Scholar. We employed Boolean formulas and search terms that evolved from broad to specific, refining the focus on fluid resuscitation in septic heart failure patients. Human studies focusing on fluid resuscitation, sepsis management, and outcomes in heart failure were included. Exclusion criteria included animal studies, non-English articles, and case reports. Results: Guideline-recommended fluid resuscitation (≥30 mL/kg within 3 hours) shows a neutral or positive effect on mortality in sepsis patients with pre-existing heart failure when monitored appropriately. Patients with reduced ejection fraction (HFrEF) and those with preserved ejection fraction (HFpEF) exhibit different tolerances to fluids. Advanced hemodynamic monitoring — including bedside echocardiography, lactate clearance, central venous pressure, and BNP levels — is essential for personalizing fluid therapy. Conclusion: Early guideline-compliant fluid resuscitation followed by a conservative, individualized fluid strategy guided by hemodynamic monitoring optimizes outcomes in sepsis patients with heart failure. Future prospective studies are needed to develop standardized protocols.

Acute hepatitis (AH) is a common cause of pediatric hospitalization, with infectious and non-infectious etiologies. This study aims to assess the clinical profile, etiologies, and predictors of adverse outcomes in hospitalized children with AH. Medical case records of children aged >6 months to 14 years from June 2022 to December 2024, identified from pediatric admission and discharge registers, were screened. Children satisfying the criteria for elevated transaminases were included in this study. Those with chronic liver disease/chronic hepatitis were excluded. A total of 165 children were included, with the majority belonging to the 5-14 years age group (70.3%) and being male (53.9%). The median age of the children enrolled was 8 (5–10) years. Infectious etiologies accounted for 83.03% of cases, with dengue (23%), HAV (21.2%), and enteric fever (13.3%) being the most common causes. Non-infectious causes accounted for 9.09% of cases, while 7.88% were idiopathic. Multivariate analysis identified younger age (adjusted OR: 0.79, 95% CI: 0.67–0.91; P-value .002), fever (adjusted OR: 9.13, 95% CI: 2.1–55.9; P-value .007), lethargy at presentation (adjusted OR: 3.1, 95% CI: 1.0–9.61, P-value .047), ascites (adjusted OR: 8.12, 95% CI: 2.44–30, P-value <.001), and hypoalbuminemia (adjusted OR: 0.3, 95% CI: 0.11–0.76, P-value <.014) as significant risk factors for adverse outcomes. Younger age, fever, lethargy, ascites, and hypoalbuminemia were identified as independent risk factors for adverse outcomes. Early recognition of these predictors and targeted management strategies may improve clinical outcomes in hospitalized children with AH.