Equine trypanosomosis remains an important veterinary concern in regions where horses play a significant economic and cultural role. In Kazakhstan, comprehensive nationwide data on the seroepidemiological status of equine trypanosomes are limited. The aim of this study was to assess the serological distribution of equine trypanosomosis across all administrative regions of Kazakhstan using complement fixation testing (CFT). A total of 6065 equine serum samples were collected from seventeen regions between 2023 and 2025. Antibodies against members of the Trypanozoon subgenus were detected using a WOAH-recommended CFT protocol. Overall seropositivity was 4.73%, with substantial regional variation ranging from 0% to 16.52%. Statistically significant differences in seroprevalence were observed between regions (p < 0.001), and mixed-effects modelling indicated considerable regional clustering. PCR testing of seropositive samples did not confirm the presence of Trypanosoma equiperdum, while one sample tested positive for Trypanosoma evansi. These findings suggest that CFT seropositivity reflects exposure to equine trypanosomes rather than confirmed dourine infection. Given the inability of CFT to reliably distinguish between T. equiperdum and T. evansi, species-level attribution remains uncertain. This study provides the first nationwide overview of serological reactivity to equine trypanosomes in Kazakhstan. The results highlight regional heterogeneity in antibody detection and underscore the need for expanded molecular surveillance and improved species-specific diagnostic tools to clarify the epidemiological status of equine trypanosomosis in the country.

Bovine Viral Diarrhea Virus (BVDV) is a globally important cattle pathogen causing substantial economic losses. In Kazakhstan, BVDV’s epidemiological status remains poorly characterized due to the absence of systematic surveillance. We carried out a cross-sectional study of cattle herds across Kazakhstan, using ELISA to detect anti-BVDV antibodies and RT-PCR to identify active infections. Positive samples underwent sequencing for phylogenetic analysis of circulating strains. Additionally, a standard reference serum panel was developed to measure virus neutralization titers (ND₅₀) and to evaluate cross-neutralization with Border Disease virus (BDV). Antibodies against BVDV were prevalent, with seropositivity ranging from 28.89% to 96.13% across surveyed regions. Active BVDV infection was confirmed by RT-PCR in 17 animals. Phylogenetic analysis with 2 samples from Mangystau region classified the virus as BVDV2 genotype. The reference serum panel exhibited high neutralizing titers ND₅₀ up to 1:286 against the local BVDV-1 isolate. Notably, these sera also neutralized BDV, albeit at lower titers ND₅₀ 1:45. These findings provide crucial baseline epidemiological data and enhanced diagnostic tools for BVDV in Kazakhstan. They highlight the need for improved surveillance and will inform strategic control measures against this economically significant cattle disease.

The issue of class imbalance in medical data poses a significant challenge for developing robust machine learning models aimed at medical diagnosis. A characteristic feature of such data is the substantial dominance of instances belonging to majority classes (e.g., healthy patients or those with common diseases) over instances representing rare conditions. This disproportion leads to machine learning models trained on such data being prone to systematic classification errors, predominantly predicting the most frequent class. Consequently, the ability of models to accurately identify rare cases is severely diminished. This paper proposes a hybrid class-balancing algorithm that combines Inverse Quadratic Radial Undersampling (IQRBU) and genetic oversampling to address this issue. The integration of these two methods within a single algorithm achieves an optimal balance between preserving information and enhancing the representation of rare classes. Experimental results conducted on several medical datasets demonstrated the effectiveness of the proposed approach. The obtained results showed that the hybrid algorithm significantly improves classification metrics, such as the F1-score and accuracy. These findings underscore the potential of our approach to enhance the reliability and precision of medical diagnostic systems.

Background: Cervical cancer, largely attributable to persistent infection with high-risk human papillomavirus (HPV), remains a major public health burden worldwide, including in Kazakhstan, where limited screening coverage and low public awareness contribute to substantial incidence and mortality. This study evaluated the cost-effectiveness and epidemiological impact of a nationwide HPV vaccination programme for 10-year-old girls in Kazakhstan using the quadrivalent Gardasil-4 vaccine. Methods: A 10-year modelling analysis (2025–2035) was conducted using the World Health Organization (WHO)-endorsed Universal Vaccination Impact and Cost-Effectiveness Assessment (UNIVAC) tool adapted to Kazakhstan-specific epidemiological and economic parameters. Vaccination coverage was projected at 98.0% for the first dose and 96.5% for the second dose. Incremental cost-effectiveness ratios (ICERs) and disability-adjusted life years (DALYs) averted were estimated from governmental and societal perspectives. Sensitivity analyses assessed uncertainty in vaccine coverage, vaccine costs, and epidemiological inputs. Results: Over the 10-year period, the vaccination programme was projected to reduce HPV-related disease cases by 68.2% (from 112,198 to 35,628) and deaths by 68.3% (from 15,921 to 5056), while averting 67,445 DALYs. The ICER was estimated at US$ 533 per DALY averted from the governmental perspective and US$ 1169 from the societal perspective. Projected healthcare cost savings reached US$ 42.8 million, driven largely by reductions in 21,748 hospitalisations and 13,706 outpatient visits. These findings remained robust in probabilistic sensitivity analysis, with the probability of cost-effectiveness increasing as the willingness-to-pay threshold rose. Conclusions: UNIVAC-based modelling suggests that introduction of a national HPV vaccination programme for 10-year-old girls in Kazakhstan using Gardasil-4 could substantially reduce cervical cancer burden and related mortality while generating considerable healthcare savings. These findings support the cost-effectiveness of nationwide HPV vaccination in Kazakhstan.

Currently, the popularization of the integration of machine learning into the field of medicine for data processing and analysis is being traced, but at the same time difficulties such as class imbalance and noisy datasets arise. Due to the prevalence of the problem, there are already existing solutions, but in all of them there is an abstraction from the field of medicine, namely, gender, racial and other differences are not taken into account. It is this side of the problem that is solved in our resampling algorithm. A feature of our algorithm is the use of splitting the dataset by an important feature through the p-value of Spearman correlation, which helps to consider subgroups of observations without losing their unique characteristics and removing noise data using LOF and Z-score separately for minority and majority classes, respectively. Synthetic data is generated in a flexible way, adapting to the data set using algorithm parameters. Work is provided with both quantitative and nominative features. The algorithm was tested on datasets for heart attack, chronic kidney disease, and liver disease, and the Random Forest ensemble method was used to train the model. After applying this class balancing method, improvements were recorded on average in Accuracy by 36%, in AUC by 15-25%, in Precision by 39-42%, and in Recall by 21-37% compared with SMOTE, ADASYN algorithms and the data set before balancing. Applying the algorithm on medical data can improve the accuracy of the algorithm and reduce the loss of reliability compared to other resampling methods.