Jung IY, et al. Yonsei Med J. 2019 Jun;60(6):592-596. https://doi.org/10.3349/ymj.2019.60.6.592

Ahn K, et al. Yonsei Med J. 2021 Oct;62(10):903-910. https://doi.org/10.3349/ymj.2021.62.10.903

Carbapenemase-producing Enterobacteriaceae (CPE) is an important and increasing threat to global health. From July to September 2017, 20 inpatients at a tertiary care hospital in Korea were either colonized or infected with carbapenem-resistant Escherichia coli strains. All of E. coli isolates co-produced blaNDM-5 and blaOXA-181 carbapenemase genes and shared ≥88% clonal relatedness on the basis of a cladistic calculation of the distribution of pulsed-field gel electrophoresis patterns. Rapid detection of CPE is one of the most important factors to prevent CPE dissemination because it takes long time for CPE to become negative.

Background/Objectives: The Korean Global Antimicrobial Resistance Surveillance System monitors bloodstream Staphylococcus aureus infections by combining antimicrobial susceptibility testing (AST) with conventional polymerase chain reaction (PCR). Considering the clinical significance of methicillin-resistant S. aureus (MRSA), we performed an in-depth analysis of isolates showing genotypic–phenotypic discrepancies. Methods: Isolates were collected from designated collection centers in the Republic of Korea between 2017 and 2024. The 30 μg cefoxitin disk diffusion method was used to define the phenotypes. PCR targeting mecA and the staphylococcal cassette chromosome mec (SCCmec) was used to identify genotypes through gel electrophoresis. Long-read whole-genome sequencing (WGS) was performed using the Revio system (Pacific Biosciences) for isolates exhibiting discrepancies between phenotypes and genotypes. Results: In total, 5808 isolates were screened, and seven cases of genotypic–phenotypic discrepancies were identified, including one infant and six elderly patients with chromosomal SCCmec type IV. Although WGS confirmed intact PCR primer-binding sites, structural alterations were observed: three isolates had normal-length mecA and mecR1, two had partial deletions in mecA, and two featured either mecA or mecR1 split into two proteins. Notably, although the six isolates with intact mecR1 genes matched the nucleotide length of SCCmec type IV, their sequences exhibited high homology with SCCmec type II. Conclusions: Despite the presence of mecA, the non-standard configuration of regulatory genes within the SCCmec elements suppressed actual resistance expression. Because conventional PCR focusing on partial gene segments could overlook such phenotypic traits, the meticulous observation and implementation of WGS are crucial for the accurate characterization of genotypic–phenotypic discrepancies.

Pre-eclampsia (PE) is a pregnancy-related disease, causing significant threats to both mothers and babies. Numerous studies have identified the association between PE and renal dysfunction. However, in clinical practice, kidney problems in pregnant women are often overlooked due to physiologic adaptations during pregnancy, including renal hyperfiltration. Recent studies have reported serum creatinine (SCr) level distribution based on gestational age (GA) and demonstrated that deviations from the expected patterns can predict adverse pregnancy outcomes, including PE. This study aimed to establish a PE prediction model using expert knowledge and by considering renal physiologic adaptation during pregnancy. This retrospective study included pregnant women who delivered at the Wonju Severance Christian Hospital. Input variables, such as age, gestational weeks, chronic diseases, and SCr levels, were used to establish the PE prediction model. By integrating SCr, GA, GA-specific SCr distribution, and quartile groups of GA-specific SCr (GAQ) were made. To provide generalized performance, a random sampling method was used. As a result, GAQ improved the predictive performance for any cases of PE and triple cases, including PE, preterm birth, and fetal growth restriction. We propose a prediction model for PE consolidating readily available clinical blood test information and pregnancy-related renal physiologic adaptations.

Endometriosis is a complex disease with diverse etiologies, including hormonal, immunological, and environmental factors; however, its exact pathogenesis remains unknown. While surgical approaches are the diagnostic and therapeutic gold standard, identifying endometriosis-associated genes is a crucial first step. Five endometriosis-related gene expression studies were selected from the available datasets. Approximately, 14,167 genes common to these 5 datasets were analyzed for differential expression. Meta-analyses utilized fold-change values and standard errors obtained from each analysis, with the binomial and continuous datasets contributing to endometriosis presence and endometriosis severity meta-analysis, respectively. Approximately 160 genes showed significant results in both meta-analyses. For Bayesian analysis, endometriosis-related single nucleotide polymorphisms (SNPs), the human transcription factor catalog, uterine SNP-related gene expression, disease–gene databases, and interactome databases were utilized. Twenty-four genes, present in at least three or more databases, were identified. Network analysis based on Pearson’s correlation coefficients revealed the HLA-DQB1 gene with both a high score in the Bayesian analysis and a central position in the network. Although ZNF24 had a lower score, it occupied a central position in the network, followed by other ZNF family members. Bayesian analysis identified genes with high confidence that could support discovering key diagnostic biomarkers and therapeutic targets for endometriosis.