This study aimed to investigate the potential application of ceragenins (CSAs) as new candidacidal agents to prevent biofilm formation on voice prostheses (VPs). The deterioration of the silicone material of VPs is caused by biofilm growth on the device which leads to frequent replacement procedures and sometimes serious complications. A significant proportion of these failures is caused by Candida species. We found that CSAs have significant candidacidal activities in vitro (MIC; MFC; MBIC), and they effectively eradicate species of yeast responsible for VP failure. Additionally, in our in vitro experimental setting, when different Candida species were subjected to CSA-13 and CSA-131 during 25 passages, no tested Candida strain showed the significant development of resistance. Using liquid chromatography–mass spectrometry (LC-MS), we found that VP immersion in an ethanol solution containing CSA-131 results in silicon impregnation with CSA-131 molecules, and in vitro testing revealed that fungal biofilm formation on such VP surfaces was inhibited by embedded ceragenins. Future in vivo studies will validate the use of ceragenin-coated VP for improvement in the life quality and safety of patients after a total laryngectomy.

The aim of this study was to compare the aqueous humor (AH) and serum concentrations of metabolites in diabetic (n = 36) and nondiabetic (n = 36) senior adults undergoing cataract surgery. Blood samples were collected before surgery and AH during surgery. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based targeted metabolomic and lipidomic analyses of samples were performed using the AbsoluteIDQ^® p180 kit. Out of 188 metabolites targeted by the kit, 41 and 133 were detected in >80% of AH and serum samples, respectively. Statistical analysis performed to indicate metabolites differentiating diabetic and nondiabetic patients showed 8 and 20 significant metabolites in AH and serum, respectively. Pathway analysis performed for significant metabolites revealed that galactose metabolism is mostly affected in the AH, while arginine biosynthesis is mostly affected in the serum. Among metabolites that differentiate diabetic and nondiabetic patients, arginine was the only metabolite common to both serum and AH samples, as well as the only one with a decreased concentration in both body fluids of diabetic patients. Concentrations of the rest were elevated in AH and lowered in serum. This may suggest different mechanisms of diabetes-related dysregulation of the local metabolism in the eye in comparison to systemic changes observed in the blood.

Risk factors for type 2 diabetes mellitus (T2DM) consist of a combination of an unhealthy, imbalanced diet and genetic factors that may interact with each other. Single nucleotide polymorphism (SNP) in the prospero homeobox 1 (PROX1) gene is a strong genetic susceptibility factor for this metabolic disorder and impaired β-cell function. As the role of this gene in T2DM development remains unclear, novel approaches are needed to advance the understanding of the mechanisms of T2DM development. Therefore, in this study, for the first time, postprandial changes in plasma metabolites were analysed by GC–MS in nondiabetic men with different PROX1 genotypes up to 5 years prior to prediabetes appearance. Eighteen contestants (12 with high risk (HR) and 6 with low risk (LR) genotype) participated in high-carbohydrate (HC) and normo-carbohydrate (NC) meal-challenge tests. Our study concluded that both meal-challenge tests provoked changes in 15 plasma metabolites (amino acids, carbohydrates, fatty acids and others) in HR, but not LR genotype carriers. Postprandial changes in the levels of some of the detected metabolites may be a source of potential specific early disturbances possibly associated with the future development of T2DM. Thus, accurate determination of these metabolites can be important for the early diagnosis of this metabolic disease.

This study aims to evaluate aqueous humor (AH) and serum metabolite concentrations in myopic and control adults undergoing cataract surgery and to correlate them with axial length (AL) to gain a better insight into the pathogenesis at both the local and systemic levels. Ninety-three patients were assigned to the myopic (n = 52) and control (n = 41) groups. Venous blood and aqueous humor samples were collected and analyzed by means of targeted metabolomics based on liquid chromatography-tandem mass spectrometry. The univariate analysis has revealed distinct metabolomic profiles between the myopic and control groups in AH but not in serum. In the AH of myopic patients, symmetric dimethylarginine (SDMA) and taurine concentrations were significantly lower. SDMA and taurine levels in the AH correlated negatively with the AL. These findings account for the insight into the local metabolic alterations in the case of myopia, potentially leading to novel therapeutic targets.

Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.