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Proteinuria is a risk factor for chronic kidney disease (CKD) progression and associated complications. However, there is insufficient information on individual protein components in urine and the severity of CKD. We aimed to investigate urinary proteomics and its association with proteinuria and kidney function in early-stage CKD and in healthy individuals. A 24 h urine sample of 42 individuals (21-CKD and 21-healthy individuals) was used for mass spectrometry-based proteomics analysis. An exponentially modified protein abundance index (emPAI) was calculated for each protein. Data were analyzed by Mascot software using the SwissProt database and bioinformatics tools. Overall, 298 unique proteins were identified in the cohort; of them, 250 proteins belong to the control group with median (IQR) emPAI 39.1 (19–53) and 142 proteins belong to the CKD group with median (IQR) emPAI 67.8 (49–117). The level of 24 h proteinuria positively correlated with emPAI (r = 0.390, p = 0.011). The emPAI of some urinary proteomics had close positive (ALBU, ZA2G, IGKC) and negative (OSTP, CD59, UROM, KNG1, RNAS1, CD44, AMBP) correlations (r < 0.419, p < 0.001) with 24 h proteinuria levels. Additionally, a few proteins (VTDB, AACT, A1AG2, VTNC, and CD44) significantly correlated with kidney function. In this proteomics study, several urinary proteins correlated with proteinuria and kidney function. Pathway analysis identified subpathways potentially related to early proteinuric CKD, allowing the design of prospective studies that explore their response to therapy and their relationship to long-term outcomes.

<b>Background: </b>Vaccine-preventable diseases such as pertussis, measles, and influenza remain among the most significant medical and socioeconomic issues in Kazakhstan, despite significant vaccination achievements. Thus, here we aimed to analyze the long-term dynamics and provide information on the current epidemiology of pertussis, measles, and influenza in Kazakhstan.<br /> <b>Methods: </b>A retrospective analysis of the long-term dynamics of infectious diseases was carried out using the data from the statistical collections for 2010-2020 and the Unified Payment System from 2014 to 2020.<br /> <b>Results: </b>During the 2010-2020 years, the long-term dynamics show an unequal distribution of pertussis, measles, and influenza-related morbidity. In comparison with earlier years, registration of infectious disease was the highest in 2019 and 2020. The incidence cases among registered infectious diseases in 2019 were: pertussis-147, measles-13,326, and in 2020: influenza-2,678. High incidence rates have been documented in Pavlodar, North Kazakhstan, Mangystau regions, and the cities of Shymkent and Nur-Sultan. The incidence varies depending on the seasonality: pertussis (summer-autumn), measles (winter-spring), and influenza (mostly in winter).<br /> <b>Conclusion: </b>The findings highlight the importance of focusing more on the characteristics of the epidemic process of vaccine-preventable diseases in order to assess the effectiveness of implemented measures and verify new routes in strengthening the epidemiological surveillance system.

Background HIV is a growing public health burden that threatens thousands of people in Kazakhstan. Countries around the world, including Kazakhstan, are facing significant problems in predicting HIV infection prevalence. It is crucial to understand the epidemiological trends of infectious diseases and to monitor the prevalence of HIV in a long-term perspective. Thus, in this study, we aimed to forecast the prevalence of HIV in Kazakhstan for 10 years from 2020 to 2030 by using mathematical modeling and time series analysis. Methods We use statistical Autoregressive Integrated Moving Average (ARIMA) models and a nonlinear epidemic Susceptible-Infected (SI) model to forecast the HIV infection prevalence rate in Kazakhstan. We estimated the parameters of the models using open data on the prevalence of HIV infection among women and men (aged 15–49 years) in Kazakhstan provided by the Kazakhstan Bureau of National Statistics. We also predict the effect of pre-exposure prophylaxis (PrEP) control measures on the prevalence rate. Results The ARIMA (1,2,0) model suggests that the prevalence of HIV infection in Kazakhstan will increase from 0.29 in 2021 to 0.47 by 2030. On the other hand, the SI model suggests that this parameter will increase to 0.60 by 2030 based on the same data. Both models were statistically significant by Akaike Information Criterion corrected (AICc) score and by the goodness of fit. HIV prevention under the PrEP strategy on the SI model showed a significant effect on the reduction of the HIV prevalence rate. Conclusion This study revealed that ARIMA (1,2,0) predicts a linear increasing trend, while SI forecasts a nonlinear increase with a higher prevalence of HIV. Therefore, it is recommended for healthcare providers and policymakers use this model to calculate the cost required for the regional allocation of healthcare resources. Moreover, this model can be used for planning effective healthcare treatments.