Guillain-Barre Syndrome After Two COVID-19 Vaccinations: Two Case Reports With Follow-up Electrodiagnostic Study.

Guillain-Barre syndrome (GBS) is an immune-mediated acute polyradiculoneuropathy and commonly occurs after a preceding infection or immunization sequalae. Following the severe acute respiratory syndrome-coronavirus-2 virus pandemic with co-introduction of massive vaccinations, several GBS cases associated with coronavirus disease 2019 (COVID-19) infection per se or after vaccination for COVID-19 were reported internationally. Herein, we report two cases of Korean GBS presenting with tetraplegia after two different COVID-19 vaccinations (42-year old man by AstraZeneca and 48-year woman by Pfizer vaccines) within four weeks after vaccination. The patients were diagnosed with clinical examination, serial electromyography, and compatible laboratory results and improved after comprehensive rehabilitative treatment and intravenous immunoglobulin therapy. Furthermore, we performed an electrodiagnostic follow-up study of each case to examine their unique characteristics.

Helicobacter pylori antibody and pepsinogen testing for predicting gastric microbiome abundance.

BACKGROUND: Although the high-throughput sequencing technique is useful for evaluating gastric microbiome, it is difficult to use clinically. We aimed to develop a predictive model for gastric microbiome based on serologic testing. METHODS: This study was designed to analyze sequencing data obtained from the Hanyang University Gastric Microbiome Cohort, which was established initially to investigate gastric microbial composition according to the intragastric environment. We evaluated the relationship between the relative abundance of potential gastric cancer-associated bacteria (nitrosating/nitrate-reducing bacteria or type IV secretion system [T4SS] protein gene-contributing bacteria) and serologic markers (IgG anti-Helicobacter pylori [HP] antibody or pepsinogen [PG] levels). RESULTS: We included 57 and 26 participants without and with HP infection, respectively. The relative abundance of nitrosating/nitrate-reducing bacteria was 4.9% and 3.6% in the HP-negative and HP-positive groups, respectively, while that of T4SS protein gene-contributing bacteria was 20.5% and 6.5% in the HP-negative and HP-positive groups, respectively. The relative abundance of both nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria increased exponentially as PG levels decreased. Advanced age (only for nitrosating/nitrate-reducing bacteria), a negative result of IgG anti-HP antibody, low PG levels, and high Charlson comorbidity index were associated with a high relative abundance of nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria. The adjusted coefficient of determination (R2) was 53.7% and 70.0% in the model for nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria, respectively. CONCLUSION: Not only the negative results of IgG anti-HP antibody but also low PG levels were associated with a high abundance of nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria.

BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways.

BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. RESULTS: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. CONCLUSIONS: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer.