The burden of pneumococcal disease remains a global challenge. The purpose of this work was to assess the serotype composition of S. pneumoniae circulating among children under 5 years of age in Eastern European (the Russian Federation and the Republic of Belarus) and Central Asian (the Republic of Kazakhstan) countries which are characterized by close cultural and economic ties. The most pronounced changes were noted in the Russian Federation. In the pre-vaccination period (before 2015) PCV13 covered 66 % - 90 % of prevalent serotypes. In 2016-2018 coverage decreased to 59.2 %. During this study period (2020-2023) PCV 13 related serotypes in healthy children and those with respiratory infections dropped further to 23.2 % and 35.7 %, respectively. In Kazakhstan, vaccine-related serotypes among healthy children and children with RTI were more prevalent than in Russia (51.5-58.6 % vs 23.2-35.7 %) despite both countries having introduced pneumococcal vaccination at the same time, and both countries reporting vaccination rates above 90 % in children under 5 years of age. In the Republic of Belarus, in the absence of a universal vaccination program, PCV13 covers 86.4 % of serotypes. Serotypes 19F (31 %), 3 (16.2 %), 19 A (10.3 %), and 14 (10 %) predominate among S. pneumoniae causing acute otitis media in children. Expanding the serotype/serogroup composition of new vaccines may provide protection against potentially invasive non-vaccine strains in the future.

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.