ObjectiveTo better understand the alterations in gut microbiota and metabolic pathways in children with focal epilepsy, and to further investigate the changes in the related gut microbiota and metabolic pathways in these children before and after treatment.MethodsTen patients with newly diagnosed focal epilepsy in Hunan Children’s Hospital from April, 2020 to October, 2020 were recruited into the case group. The case group was further divided into a pre-treatment subgroup and a post-treatment subgroup. Additionally, 14 healthy children of the same age were recruited into a control group. The microbial communities were analyzed using 16s rDNA sequencing data. Metastas and LEfSe were used to identify different bacteria between and within groups. The Kyoto Encyclopedia of Genes and Genomes database was used to KEGG enrichment analysis.ResultsThere were significant differences in α diversity among the pre-treatment, post-treatment, and control groups. Besides, the differences in gut microbiota composition in 3 groups were identified by principal co-ordinates analysis (PCoA), which showed a similar composition of the pre-treatment and post-treatment subgroups. At the phyla level, the relative abundance of Actinobacteria in the pre-treatment subgroup was significantly higher than that in the control group, which decreased significantly after 3 months of treatment and showed no significant difference between the control group. In terms of the genus level, Escherichia/Shigella, Streptococcus, Collinsella, and Megamonas were enriched in the pre-treatment subgroup, while Faecalibacterium and Anaerostipes were enriched in the control group. The relative abundance of Escherichia/Shigella, Streptococcus, Collinsella, and Megamonas was reduced significantly after a three-month treatment. Despite some genera remaining significantly different between the post-treatment subgroup and control group, the number of significantly different genera decreased from 9 to 4 through treatment. Notably, we found that the carbohydrate metabolism, especially succinate, was related to focal epilepsy.ConclusionChildren with focal epilepsy compared with healthy controls were associated with the statistically significant differences in the gut microbiota and carbohydrate metabolism. The differences were reduced and the carbohydrate metabolism improved after effective treatment. Our research may provide new directions for understanding the role of gut microbiota in the pathogenesis of focal epilepsy and better alternative treatments.

Objective This study aimed to compare the diagnostic value of the single or combined applications of transient elastography (TE) and multivariate indicators with biopsy for the detection of liver fibrosis in children caused by chronic hepatitis B (CHB). Methods This study included 148 CHB children treated at Hunan Children’s Hospital from January 1st 2015 to December 31st 2018, aged from 0.83 to 14.58 years old. All patients underwent liver biopsy (LB), of which 43 patients underwent TE. Multiple clinical data, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), Platelet (PLT), and HBV-deoxyribonucleic acid (HBV DNA) of all patients were collected. The diagnostic values for CHB of TE and its combinations with these indicators were measured. The patients were classified in two ways: no hepatic fibrosis group (F0) versus fibrosis group (F ≥ 1), and no significant hepatic fibrosis group (F < 2) versus significant hepatic fibrosis group (F ≥ 2). The statistical assessment was performed between groups within each classification to compare the diagnostic value of different parameters. Results The operating characteristic area under curve (AUC) of liver fibrosis diagnosed by liver stiffness measurement (LSM) which obtained by TE, AST-to-PLT ratio index (APRI), and fibrosis-4 index (FIB-4) were 0.740, 0.701, and 0.651, while the corresponding cut-off values were 5.9 kPa, 0.50, and 0.10, respectively. The AUC of significant liver fibrosis diagnosed by LSM, APRI and FIB-4 were 0.849, 0.701, and 0.509, while the corresponding cut-off values were 8.4 kPa, 0.76, and 0.08, respectively. While with the combinations of LSM and APRI, LSM and FIB-4, LSM and APRI and FIB-4, APRI and FIB-4, the AUC of significant liver fibrosis were 0.866, 0.855, 0.869, and 0.684, respectively. The AUC of significant liver fibrosis diagnosed by the LSM was significantly higher than APRI and FIB-4. Conclusions The diagnostic value of transient elastography was better than that of APRI and FIB-4 for CHB children with significant liver fibrosis. In addition, TE also has relatively high application values on the diagnosis of patients with different degrees of liver fibrosis caused by CHB.

Recent research suggests that gut microbiota plays an important role in the occurrence and development of excessive weight and obesity, and the early-life gut microbiota may be correlated with weight gain and later growth. However, the association between neonatal gut microbiota, particularly in preterm infants, and excessive weight and obesity remains unclear. To evaluate the relationship between gut microbiota and body mass index (BMI) growth trajectories in preterm infants, we examined microbial composition by performing 16S rDNA gene sequencing on the fecal samples from 75 preterm infants within 3 months after birth who were hospitalized in the neonatal intensive care unit of Hunan Children’s Hospital from August 1, 2018 to October 31, 2019. Then, we collected their physical growth information during 0–10 months. Latent growth mixture models were used to estimate growth trajectories of infantile BMI, and the relationship between the gut microbiota and the BMI growth trajectories was analyzed. The results demonstrated that there were 63,305 and 61 operational taxonomic units in the higher BMI group (n = 18), the lower BMI group (n = 51), and the BMI catch-up group (n = 6), respectively. There were significant differences in the abundance of the gut microbiota, but no significant differences in the diversity of it between the lower and the higher BMI group. The BMI growth trajectories could not be clearly distinguished because principal component analysis showed that gut microbiota composition among these three groups was similar. The three groups were dominated by Firmicutes and Proteobacteria in gut microbiota composition, and the abundance of Lactobacillus in the higher BMI group was significantly different from the lower BMI group. Further intervention experiments and dynamic monitoring are needed to determine the causal relationship between gut microbiota differences and the BMI change.

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