Factors affecting the early establishment of neonatal intestinal flora and its intervention measures.

In recent years, it has become evident that early-life intestinal flora plays a pivotal role in determining human health. Consequently, it is imperative to explore the establishment of neonatal intestinal flora and its influencing factors. Early neonatal intestinal flora is influenced by a multitude of factors, including maternal and infant-related factors, as well as external environment. This review summarizes the colonization mechanism of intestinal flora in the early life of newborns and discussed their influence on the establishment of neonatal intestinal flora, taking into account factors such as delivery mode, gestational age and feeding mode. Additionally, this review delves into the natural or artificial reconstruction of intestinal flora colonization defects in infants born via cesarean section and premature infants, with the goal of establishing a theoretical foundation for preventing and treating issues related to neonatal intestinal flora colonization and associated diseases.

Changes in the BTK/NF-κB signaling pathway and related cytokines in different stages of neuromyelitis optica spectrum disorders.

OBJECTIVE: Neuromyelitis optica spectrum disorders (NMOSDs) are blindness-causing neuritis; their pathogenesis is still not fully elucidated. Although it has been determined that Bruton tyrosine kinase (BTK) and NF-κB are associated with NMOSD, the changes that occur in different periods remain unknown. The study aimed to demonstrate the changes in the BTK/NF-κB pathway and related chemokines in different stages of NMOSDs. METHODS: A total of 32 patients with NMOSD were selected as the experimental group, and 32 healthy volunteers were included in the control group. In this study, the BTK/NF-κB pathway and related chemokines in the cerebrospinal fluid and peripheral blood samples of patients with NMOSD were analyzed in the acute or remission phase. RESULTS: BTK, NF-κB, PI3K, IKK, CXCL2, and CXCL12 levels in the NMOSD group in the acute or remission phase were significantly higher than those in the control group (p < 0.05). CONCLUSION: The BTK/NF-κB pathway plays a vital role in the progression of NMOSD pathology. Our results shed light on its important role as a therapeutic target for NMOSD.

Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown. RESULTS: In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein-protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93). CONCLUSION: This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis

BACKGROUND: Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown. RESULTS: In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein-protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93). CONCLUSION: This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.