ABSTRACT
Background: Premature infants, defined as those born before 37 weeks of gestation, face numerous health challenges due to their underdeveloped systems. One critical aspect of their health is the gut microbiota, which plays a vital role in their immune function and overall development. This study provides a comprehensive bibliometric analysis of research trends, influential contributors, and evolving themes in the study of gut microbiota in premature infants over the past two decades. Methods: We conducted a bibliometric analysis using the Web of Science Core Collection database, covering publications from January 1, 2004, to June 17, 2024. We employed VOSviewer, the R package "bibliometrix", and Citespace for data visualization and analysis, focusing on co-authorship, co-citation, and keyword co-occurrence networks. Results: The temporal analysis revealed a significant increase in research output on gut microbiota in premature infants, particularly in the last decade. Early research primarily focused on characterizing the gut microbiota of premature infants, identifying less diversity and a higher prevalence of pathogenic bacteria compared to full-term infants. Key research themes identified include probiotics, necrotizing enterocolitis (NEC), and breastfeeding. Probiotic studies highlighted the potential of strains like Bifidobacterium and Lactobacillus in reducing NEC and sepsis incidences. Breastfeeding research consistently showed the benefits of human milk in fostering a healthier gut microbiota profile. Co-authorship and co-citation analyses identified key contributors and influential studies, emphasizing strong international collaborations, particularly among researchers from the United States, China, and European countries. Conclusion: This bibliometric analysis underscores the growing recognition of the gut microbiota's crucial role in the health of premature infants. The field has seen significant advancements, particularly in understanding how interventions like probiotics and breastfeeding can modulate gut microbiota to improve health outcomes. Continued research and international collaboration are essential to further unravel the complexities of gut microbiota in premature infants and develop effective therapeutic strategies.
ABSTRACT
BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. To further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000).