Congenital myopathies (CMs) are a heterogeneous group of inherited muscle disorders characterized by muscle weakness at birth, while limb‐girdle muscular dystrophies (LGMD) have a later onset and slower disease progression. Thus, detailed clinical phenotyping of genetically defined disease entities are required for the full understanding of genotype–phenotype correlations. A recently defined myopathic genetic disease entity is caused by bi‐allelic variants in a gene coding for pyridine nucleotide‐disulfide oxidoreductase domain 1 (PYROXD1) with unknown substrates. Here, we present three patients from two consanguineous Turkish families with mild LGMD, facial weakness, normal CK levels, and slow progress. Genomic analyses revealed a homozygous known pathogenic missense variant (c.464A>G, p.Asn155Ser) in family 1 with two affected females. In the affected male of family 2, we found this variant in a compound heterozygous state together with a novel frameshift variant (c.329_332delTCTG, p.Leu112Valfs*8), which is the second frameshift variant known so far in PYROXD1. We have been able to define a large homozygous region in family 1 sharing a common haplotype with family 2 in the critical region. Our data suggest that c.464A>G is a Turkish founder mutation. To gain deeper insights, we performed a systematic review of all published PYROXD1‐related myopathy cases. Our analysis showed that the c.464A > G variant was found in 87% (20/23) of the patients and that it may cause either a childhood‐ or adult‐onset phenotype, irrespective of its presence in a homozygous or compound heterozygous state. Interestingly, only four patients had elevated CK levels (up to 1000 U/L), and cardiac involvement was found in few compound heterozygous cases.

Background and Objectives: Checkpoint kinase 2 (CHEK2) is a tumor suppressor gene involved in DNA repair and cell cycle regulation. Pathogenic or likely pathogenic (P/LP) variants in CHEK2 are associated with increased cancer risk. Conversely, recent large cohort studies have identified certain variants that, despite being classified as P/LP by in silico analysis, are considered low risk. Thus, the genotype–phenotype correlations of CHEK2 require a better understanding. In this study, we aimed to characterize germline CHEK2 variants from a group of individuals who applied to cancer genetic clinics in the Marmara Region of Türkiye. We also aimed to assess the phenotypic impacts of these variants by using a new score of statistically significant in silico predictors (SSIPs). Methods: We analyzed 1707 individuals with high risk cancer predisposition, focusing on germline CHEK2 variants, using SSIP scores and population-specific data. Results:CHEK2 variants appeared in approximately 8% of cases. The SSIP scores indicated that the missense mutation, p.Arg117Gly, significantly impairs DNA repair. Almost half of the variants had higher allele frequencies than the variants listed in the Genome Aggregation Database (gnomAD), and three variants had significantly higher frequencies compared to the variants listed on the Turkish Variome database (p.Thr476Met, p.Arg137Gln, c.592+3A>T), emphasizing the importance of population-specific data. Conclusions: This comprehensive analysis of CHEK2 variants in the Turkish population provides crucial insights for cancer geneticists and oncologists. Our findings will help to enhance the evaluation and management of cancer predisposition associated with CHEK2 in Türkiye and other regions that have significant Turkish populations.

Genetic generalized epilepsies (GGE) are genetically determined, as their name implies and they are clinically characterized by generalized seizures involving both sides of the brain in the absence of detectable brain lesions or other known causes. GGEs are yet complex and are influenced by many different genetic and environmental factors. Methylation specific epigenetic marks are one of the players of the complex epileptogenesis scenario leading to GGE. In this study, we have set out to perform genome-wide methylation profiling to analyze GGE trios each consisting of an affected parent-offspring couple along with an unaffected parent. We have developed a novel scoring scheme within trios to categorize each locus analyzed as hypo or hypermethylated. This stringent approach classified differentially methylated genes in each trio and helped us to produce trio specific and pooled gene lists with inherited and aberrant methylation levels. In order to analyze the methylation differences from a boarder perspective, we performed enrichment analysis with these lists using the PANOGA software. This collective effort has led us to detect pathways associated with the GGE phenotype, including the neurotrophin signaling pathway. We have demonstrated a trio based approach to genome-wide DNA methylation analysis that identified individual and possibly minor changes in methylation marks that could be involved in epileptogenesis leading to GGE.

The modulatory effect of C-Vx, a novel therapeutic agent, on the immune system of COVID-19 patients was investigated. The functions of T and NK cells of COVID-19 patients with different disease severity were evaluated by flow cytometry in response to C-Vx stimulation. The levels of pro- and anti-inflammatory cytokines were detected by multiplex assay in supernatants after cell culture with C-Vx. Bradykinin, IRF3 and IFN-α levels were also measured by ELISA in the presence or absence of C-Vx stimulation. As a result, increased CD107a expression was observed on NK cells in response to C-Vx addition. The proliferation of T cell subsets was increased by C-Vx, decreasing by disease severity. IL-4 and IL-10 levels were elevated while IFN-γ and IL-17 levels were reduced in T cells following C-Vx stimulation. However, the levels of pro-inflammatory IL-1β, IL-6, IL-8, IFN-γ and GM-CSF were significantly increased upon C-Vx stimulation. IFN-α levels tended to increase after incubation with C-Vx. These findings support an immunomodulatory action of C-Vx on the immune system of patients with mild and moderate phase of COVID-19.

Purpose: Vancomycin therapeutic drug monitoring is essential for optimizing efficacy and reducing the risk of vancomycin-associated acute kidney injury (VA-AKI). Although recent guidelines recommend the ratio of area under the curve over 24 h to minimum inhibitory concentration (AUC ₂₄ /MIC)–based monitoring over trough concentration-based monitoring, differences between AUC ₂₄ /MIC estimation methods (e.g., pharmacokinetic equations and Bayesian approaches) may influence therapeutic classification in clinical practice. In addition, pharmacogenetic factors contributing to interindividual variability in vancomycin exposure remain incompletely characterized. Methods: This prospective, single-center cohort study included adult patients who received intravenous vancomycin for at least 72 h between June 2024 and April 2025. Vancomycin trough and peak plasma concentrations were measured using an enzyme-linked immunosorbent assay. AUC ₂₄ /MIC values were calculated using pharmacokinetic equation and Bayesian method. Agreement in therapeutic classification between trough concentration–based and AUC ₂₄ /MIC-based monitoring was assessed. Associations between the rs2789047 genetic variant and relevant parameters were also evaluated. Results: Thirty-six patients were included, of whom 38.9% developed VA-AKI. Despite strong correlations between trough concentration and AUC ₂₄ /MIC values ( r = 0.84–0.87), substantial discordance in therapeutic classification was observed, with agreement rates of 63.9% for pharmacokinetic equation-based and 66.7% for Bayesian-based compared with trough concentration-based monitoring. In contrast, pharmacokinetic equation-based and Bayesian-based methods demonstrated strong concordance (86.1%). Higher trough concentrations and AUC ₂₄ /MIC values were significantly associated with VA-AKI ( p < 0.001). Carriers of rs2789047 A-allele exhibited higher trough concentrations and reduced elimination rates. Conclusion: Trough concentration-based monitoring frequently misclassified vancomycin exposure compared with AUC ₂₄ /MIC–based approaches. Pharmacogenetic variability may further influence vancomycin exposure.