Polycystic ovary syndrome (PCOS) is a hormonal disorder with complex, multifactorial and still not fully explained etiopathogenesis. It is believed that the cause is a combination of genetic and environmental factors. Background: With the aim to better understand PCOS etiology, the study examines body composition and compares the occurrence of lipid disorders and visceral adipose tissue depending on the adopted Rotterdam phenotypes. Methods: The study included 242 patients classified into four classic Rotterdam phenotypes. Clinical data from patients were collected and carefully analyzed to determine the relationship between the occurrence of lipid disorders and the visceral adiposity index (VAI). Results: The results obtained after assessing the differences between the Rotterdam phenotypes were not statistically significant. Differences in the levels of coefficients included in the VAI equation in the given phenotypes were also analyzed, as follows: waist circumference (p-value = 0.3415), BMI (p-value = 0.7112), TG [mmol/L] (p-value = 0.5341) and HDL [mmol/L] (p-value = 0.2302). None of the differences were statistically significant. Conclusions: Although the results did not show a clear association between VAI and the individual Rotterdam PCOS phenotypes, this coefficient can be used in the assessment of cardiometabolic risk in women with PCOS regardless of the adopted classification.

To date, hundreds of millions tons of plastics has been produced worldwide. Their production and disposal are associated with high pollution and carbon release into the atmosphere. A more environmentally friendly alternative is bioplastics, and the most popular is polyhydroxybutyrate (PHB) polymer. Large amounts of PHB can be obtained from activated sludge where used cooking oil or other industrial waste can be used as potential substrates. In this work, efficient bioplastic production strategies are studied, and the considered substrate is a mixture of oil and peptone. Pseudomonas fluorescens bacteria are used to accumulate PHB, and the cultivation of microorganisms is carried out in batch and continuous-flow bioreactors. Microscopic observations and laboratory essays are performed to confirm presence of PHB and other key parameters. The obtained results allow us to determine the optimal feeding strategy.

Infertility affects approximately 15% of couples globally, with male factors contributing to 20–70% of cases. These issues often stem from low sperm concentration, abnormal morphology, or poor motility, highlighting the critical importance of sperm selection in both natural and assisted reproduction. The quality of selected spermatozoa impacts not only fertilization success but also the health of future offspring. Despite the advances in in vitro fertilization, no universally accepted method exists for selecting and assessing spermatozoa based on morphology, motility, and DNA quality. Over the past decade, significant progress has been made in sperm selection technologies, aimed at enhancing fertilization efficiency. Traditional methods are now complemented by innovative technologies like microfluidics and artificial intelligence, enabling more precise sperm data analysis. These tools improve the identification of the most fertile spermatozoa while reducing the risk of reproductive failures. Additionally, advancements in mathematical and computational modeling have enhanced our ability to simulate and predict sperm selection processes. These models incorporate crucial factors, offering deeper insights into sperm quality and behavior. This review discusses the current methods and devices used for sperm selection and assessment, recent trends in modeling these processes, and potential future directions. By analyzing the latest scientific literature, we explore how emerging technologies impact the effectiveness of sperm selection. These advancements hold promise for improving reproductive outcomes and developing innovative clinical and biotechnological tools.

Circulating tumour cells (CTCs) are rare cells frequently profiled on a single cell level. A key indicator of single-cell transcriptome quality is the percentage of reads encoded by genes in the mitochondrial genome (MT-reads). No systematic data exists on MT-reads content in CTCs, making it unclear whether this parameter can reliably indicate CTCs’ transcriptome quality. We collected RNA-Seq profiles of 349 single breast cancer (BC) CTCs and 129 white blood cells (WBCs) enriched with four methods to assess the level of MT-reads and the expression of markers of the most abundant blood components—red blood cells (RBCs) and platelets (PLTs). The median percentage of PLTs and RBCs transcripts in CTCs was 2% (0–38%) and 0.8% (0.0006-84%), respectively, with significant differences between enrichment methods (p < 2.2⁻¹⁶). As a result, 44% of CTCs showed MT-reads level exceeding 10%, commonly considered a threshold indicating low-quality cells. Level of MT-reads in CTCs was frequently positively correlated with PLTs and RBCs markers; though in selected datasets increased MT-reads correlated with higher expression of oxidative phosphorylation and mitochondria biogenesis genes. Isolated CTCs themselves showed varied degree of cancer cells characteristics depending on the applied criterion (BC and WBC marker levels, aneuploidy score), underlining the challenges in CTCs identification. To conclude, the proportion of MT-reads in single CTCs is not a reliable standalone indicator of CTCs’ transcriptome. Elevated MT-read fraction in CTCs may arise from multiple factors, including technical aspects of blood sample processing, potential contributions of platelet- and erythroid-derived transcripts or intrinsic biological processes occurring in CTCs. Graphical abstract