Necrotizing enterocolitis (NEC) is the most devastating gastrointestinal emergency in preterm neonates. Research on early predictive biomarkers is fundamental. This is a systematic review of studies applying untargeted metabolomics and gut microbiota analysis to evaluate the differences between neonates affected by NEC (Bell’s stage II or III), and/or by spontaneous intestinal perforation (SIP) versus healthy controls. Five studies applying metabolomics (43 cases, 95 preterm controls) and 20 applying gut microbiota analysis (254 cases, 651 preterm controls, 22 term controls) were selected. Metabolomic studies utilized NMR spectroscopy or mass spectrometry. An early urinary alanine/histidine ratio >4 showed good sensitivity and predictive value for NEC in one study. Samples collected in proximity to NEC diagnosis demonstrated variable pathways potentially related to NEC. In studies applying untargeted gut microbiota analysis, the sequencing of the V3–V4 or V3 to V5 regions of the 16S rRNA was the most used technique. At phylum level, NEC specimens were characterized by increased relative abundance of Proteobacteria compared to controls. At genus level, pre-NEC samples were characterized by a lack or decreased abundance of Bifidobacterium. Finally, at the species level Bacteroides dorei, Clostridium perfringens and perfringens-like strains dominated early NEC specimens, whereas Clostridium butyricum, neonatale and Propionibacterium acnei those at disease diagnosis. Six studies found a lower Shannon diversity index in cases than controls. A clear separation of cases from controls emerged based on UniFrac metrics in five out of seven studies. Importantly, no studies compared NEC versus SIP. Untargeted metabolomics and gut microbiota analysis are interrelated strategies to investigate NEC pathophysiology and identify potential biomarkers. Expression of quantitative measurements, data sharing via biorepositories and validation studies are fundamental to guarantee consistent comparison of results.

Necrotizing enterocolitis (NEC), the first cause of short bowel syndrome (SBS) in the neonate, is a serious neonatal gastrointestinal disease with an incidence of up to 11% in preterm newborns less than 1500 g of birth weight. The rate of severe NEC requiring surgery remains high, and it is estimated between 20–50%. Newborns who develop SBS need prolonged parenteral nutrition (PN), experience nutrient deficiency, failure to thrive and are at risk of neurodevelopmental impairment. Prevention of NEC is therefore mandatory to avoid SBS and its associated morbidities. In this regard, nutritional practices seem to play a key role in early life. Individualized medical and surgical therapies, as well as intestinal rehabilitation programs, are fundamental in the achievement of enteral autonomy in infants with acquired SBS. In this descriptive review, we describe the most recent evidence on nutritional practices to prevent NEC, the available tools to early detect it, the surgical management to limit bowel resection and the best nutrition to sustain growth and intestinal function.

Background/Objectives: Gastric cancer (GC) incidence remains high worldwide, and the survival rate is poor. GC develops from atrophic gastritis (AG), associated with Helicobacter pylori (Hp) infection, passing through intestinal metaplasia and dysplasia steps. Since Hp eradication does not exclude GC development, further investigations are needed. New data suggest the possible role of unexplored gastric microbiota beyond Hp in the progression from AG to GC. Aimed to develop a score that could be used in clinical practice to stratify GC progression risk, here was investigate gastric microbiota in AG Hp-negative patients with or without high-grade dysplasia (HGD) or GC. Methods: Consecutive patients undergoing upper endoscopy within an endoscopic follow-up for AG were considered. The antrum and corpus biopsies were used to assess the microbiota composition along the disease progression by sequencing the 16S ribosomal RNA gene. Statistical differences between HGD/GC and AG patients were included in a multivariate analysis. Results: HGD/GC patients had a higher percentage of Bacillus in the antrum and a low abundance of Rhizobiales, Weeksellaceae and Veillonella in the corpus. These data were used to calculate a multiparametric score (Resident Gastric Microbiota Dysbiosis Test, RGM-DT) to predict the risk of progression toward HGD/GC. The performance of RGM-DT in discriminating patients with HGD/GC showed a specificity of 88.9%. Conclusions: The microbiome-based risk prediction model for GC could clarify the role of gastric microbiota as a cancer risk biomarker to be used in clinical practice. The proposed test might be used to personalize follow-up program thanks to a better cancer risk stratification.

Necrotizing enterocolitis remains one of the most severe gastrointestinal diseases in neonates, particularly affecting preterm infants. It is characterized by intestinal inflammation and necrosis, with significant morbidity and mortality despite advancements in neonatal care. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown potential in improving NEC prediction, early diagnosis, and management. A systematic search was conducted across multiple databases to explore the application of AI and ML in predicting NEC risk, diagnosing the condition at early stages, and optimizing treatment strategies.AI-based models demonstrated enhanced accuracy in NEC risk stratification compared to traditional clinical approaches. Machine learning algorithms identified novel biomarkers associated with disease onset and severity. Additionally, deep learning applied to medical imaging improved NEC diagnosis by detecting abnormalities earlier than conventional methods. The integration of AI and ML in NEC research provides promising insights into patient-specific risk assessment. However, challenges such as data heterogeneity, model interpretability, and the need for large-scale validation studies remain. Future research should focus on translating AI-driven findings into clinical practice, ensuring ethical considerations and regulatory compliance.

Necrotizing enterocolitis (NEC) is the main gastrointestinal emergency of preterm infants for whom bowel rest and parenteral nutrition (PN) is essential. Despite the improvements in neonatal care, the incidence of NEC remains high (11% in preterm newborns with a birth weight <1500 g) and up to 20–50% of cases still require surgery. In this narrative review, we report how to optimize PN in severe NEC requiring surgery. PN should begin as soon as possible in the acute phase: close fluid monitoring is advocated to maintain volemia, however fluid overload and electrolytes abnormalities should be prevented. Macronutrients intake (protein, glucose, and lipids) should be adequately guaranteed and is essential in each phase of the disease. Composite lipid emulsion should be the first choice to reduce the risk of parenteral nutrition associated liver disease (PNALD). Vitamin and trace elements deficiency or overload are frequent in long-term PN, therefore careful monitoring should be planned starting from the recovery phase to adjust their parenteral intake. Neonatologists must be aware of the role of nutrition especially in patients requiring long-term PN to sustain growth, limiting possible adverse effects and long-term deficiencies.

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare, with growing interest in their application to rare pediatric surgical conditions. In these settings, limited data availability often brakes traditional research. Although pediatric surgery has historically been slower than other specialties in adopting ML, recent years have seen an increase in AI-driven tools designed for surgical care. This review presents an overview of AI applications in pediatric surgery, highlighting current uses, benefits, challenges, and their potential clinical impact. A comprehensive literature search was conducted to identify studies on AI and ML models relevant to pediatric surgery. The findings indicate that ML is mainly applied in predictive decision support, particularly for preoperative risk stratification, intraoperative navigation, and postoperative outcome prediction. AI is especially valuable in endoscopic and minimally invasive procedures, where it enhances the visualization of anatomical landmarks. In pediatric oncologic surgery, AI aids in the accurate localization and delineation of tumors. Additionally, AI improves pathology workflows through automated image analysis and annotation, supporting both diagnosis and education. Despite these advances, ethical and regulatory challenges remain. Ensuring data privacy and obtaining informed consent are essential. When responsibly implemented, AI can significantly improve pediatric surgical care.