Eryptosis is a type of regulated cell death of mature erythrocytes characterized by excessive Ca²⁺ accumulation followed by phosphatidylserine externalization. Eryptosis facilitates erythrophagocytosis resulting in eradication of damaged erythrocytes, which maintains the population of healthy erythrocytes in blood. Over recent years, a wide array of diseases has been reported to be linked to accelerated eryptosis, which leads to anemia. A growing number of studies furnish evidence that eryptosis is implicated in the pathogenesis of liver diseases. Herein, we summarize the current knowledge of eryptosis signaling, its physiological role, and the impact of eryptosis on anemia and hypercoagulation. In this article, upon systemically analyzing the PubMed-indexed publications, we also provide a comprehensive overview of the role of eryptosis in the spectrum of hepatic diseases, its contribution to the development of complications in liver pathology, metabolites (bilirubin, bile acids, etc.) that might trigger eryptosis in liver diseases, and eryptosis-inducing liver disease medications. Eryptosis in liver diseases contributes to anemia, hypercoagulation, and endothelial damage (via ferroptosis of endothelial cells). Treatment-associated anemia in liver diseases might be at least partly attributed to drug-induced eryptosis. Ultimately, we analyze the concept of inhibiting eryptosis pharmaceutically to prevent eryptosis-associated anemia and thrombosis in liver diseases.

A compelling body of evidence links pesticide exposure to human diseases. The liver plays a central role in the detoxification of pesticides, suggesting intense pesticide–liver cell interactions. A growing body of studies highlighted in this review supports the contribution of pesticides of various chemical classes to the development of non-alcoholic fatty liver disease (NAFLD), alcohol-associated liver disease (ALD), liver cirrhosis, viral hepatitis, hepatocellular carcinoma, etc., via disrupting lipid and carbohydrate metabolism and redox homeostasis, promoting endoplasmic reticulum stress and mitochondrial dysfunction, as well as stimulating apoptosis, fibrosis, and inflammation. In this review, we systematically illustrated an underappreciated mechanism of pesticide-induced overall and hepatic toxicity, i.e., the ability to induce non-apoptotic regulated cell death (RCD) pathways such as ferroptosis, necroptosis, and pyroptosis. Our analysis indicates that pesticides are implicated in driving liver diseases by inducing ferroptosis, necroptosis, and pyroptosis. Non-apoptotic RCDs mediate pesticide-induced liver steatosis and fibrosis. Furthermore, these cell death modalities fuel inflammation through the promotion of pro-inflammatory cytokine production and the generation of damage-associated molecular patterns. Understanding of deeper mechanisms of pesticide-induced effects on the non-apoptotic cell death machinery and subsequent immunogenic effects in liver pathology might help develop novel preventive strategies to reduce liver damage.

Background: Candida spp. are opportunistic fungi whose pathogenic potential arises from interactions among multiple phenotypic traits that may not be captured by conventional single-factor analyses. This study aimed to identify multidimensional phenotypic virulence profiles among clinical Candida isolates using unsupervised machine learning. Methods: A total of 107 nasopharyngeal isolates recovered from COVID-19 convalescent patients in Almaty, Kazakhstan, during 2021–2022 were evaluated for biofilm formation, phospholipase, proteinase, hemolytic activities and susceptibility to fluconazole, voriconazole, itraconazole, and amphotericin B. Phenotypic data were analyzed using principal component analysis, Uniform Manifold Approximation and Projection, hierarchical clustering, permutational multivariate analysis of variance (PERMANOVA), Random Forest, and SHAP analysis. Results: Candida albicans predominated among the isolates (69.2%), and non-albicans Candida species accounted for 30.8%. Interspecies differences were observed in biofilm formation (p = 0.048) and itraconazole minimum inhibitory concentrations (p < 0.001). Three phenotypic clusters independent of species were identified (PERMANOVA: pseudo-F = 6.72, p = 0.0002). Across 20 repeated 5-fold cross-validation runs, the Random Forest model achieved an aggregated accuracy of 0.785, a mean balanced accuracy of 0.518 (95% CI: 0.461–0.577), and a macro-F1 score of 0.504 (95% CI: 0.444–0.559). SHAP identified itraconazole susceptibility, biofilm formation, and phospholipase activity as the strongest predictors. Conclusions: These findings demonstrate that integrating microbiological phenotyping with machine learning can reveal multidimensional virulence profiles beyond conventional species-based classification.