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.
Chronic granulomatous disease (CGD) is a rare inborn error of immunity (IEI) characterized by a defective respiratory burst by phagocytes and defective clearance of phagocytosed microorganisms; these phenomena, caused by a defect in NADPH oxidase, result in severe and life-threatening infections in affected children. The genetically heterogeneous X-linked recessive (XL-CGD) form of GCD is caused by mutations in the CYBB gene, whereas the autosomal recessive (AR-CGD) form is caused by mutations in the CYBA, NCF1, NCF2, NCF4, or CYBC1 genes. Mutations in the CYBA gene account for a small number of CGD cases; the vast majority of these patients become symptomatic in childhood, but rarely within the first weeks of life. Here, we report a 19-day-old neonate who developed pustular rashes and invasive pulmonary aspergillosis, which was identified by a galactomannan (GM) assay of both bronchoalveolar lavage fluid (BALF) and peripheral blood samples, and by metagenomic next-generation sequencing (mNGS) of BALF. A diagnosis of CGD was based on the respiratory burst test. Detailed assessment of neutrophil activity revealed that production of reactive oxygen species (ROS) was entirely absent. Whole-exome sequencing (WES) detected a nonsense mutation (c.7G>T). In addition, copy number variation (CNV) analysis detected a novel de novomicrodeletion of 200 kb at 16q24.2-q24.3. Thus, we have identified novel compound heterozygous CYBA mutations that cause neonatal AR-CGD, thereby expanding the clinical spectrum of CYBA deficiency.
At present, the study on the pathogenesis of Alzheimer’s disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.
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