Mining and metallurgical activities are among the main sources of heavy metal (HM) contamination of terrestrial ecosystems and the creation of persistent technogenic pollution hotspots. This study aimed to provide a comprehensive assessment of the accumulation of zinc (Zn), cooper (Cu), cadmium (Cd) and lead (Pb) in soils and vegetation under conditions of long-term industrial impact in Ridder, East Kazakhstan Region. A total of 52 soil samples were collected from 0–5 cm and 5–20 cm depths at 26 sites, and 44 species of natural vegetation, as well as three dominant agricultural crops, were examined. Soil concentrations of Zn (4415 mg·kg⁻¹), Cu (1177 mg·kg⁻¹), Cd (179 mg·kg⁻¹), and Pb (1996 mg·kg⁻¹) were classified as extremely high. Cadmium contributed most to the potential ecological risk (Cd > Pb > Zn > Cu). The industrial zone’s vegetation cover was predominantly formed by stress-tolerant and ruderal species, including Artemisia vulgaris, Calamagrostis epigeios, Bunias orientalis, Dactylis glomerata, Convolvulus arvensis, and Urtica dioica. The agricultural crops (Helianthus annuus, Avena sativa, and Triticum aestivum) mainly accumulated HMs in their root systems, with limited translocation to their aboveground organs (TF < 1). This indicates the predominance of phytostabilisation mechanisms, and highlights the potential of locally adapted plants for managing contaminated areas.

Heavy metal pollution poses a serious threat to soil ecosystems worldwide, as long-term exposure can alter microbial community functioning and reduce overall ecosystem resilience. This study investigated the impact of heavy metal contamination in technogenic industrial areas of the East Kazakhstan Region on soil microbial communities. Soil samples were collected for chemical and metagenomic analyses. Concentrations of Zn, Pb, Cu, and Cd were quantified by flame atomic absorption spectrometry (FAAS). Using long-read whole-metagenome nanopore sequencing, we conducted strain-level profiling of soils with different levels of metal contamination. This approach provided high-resolution taxonomic data, enabling detailed characterization of microbial community structure. Heavy metal exposure did not significantly reduce microbial diversity or richness but influences the quality of community composition. Metal-resistant taxa dominated contaminated soils. Overall, the results highlight the value of long-read sequencing for resolving strain-level responses to environmental contamination.

In this study, we developed and evaluated prognostic models for mortality in very-low-birth-weight (VLBW) neonates in the ICU. Our goal was to thoroughly compare the performance of the DeepSurv model with that of traditional machine learning models, such as Cox proportional hazards (CoxPH) and random survival forest (RSF), including temporal metrics.A retrospective analysis of 958 VLBW neonates was performed using data from an integrated health information system from January 2021 to May 2025. The Boruta algorithm, a feature selection method that identifies statistically significant predictors by comparing their importance scores with those of random, shadow variables, identified 11 such predictors in our study.The results showed that DeepSurv demonstrated superior discriminatory ability with an overall AUC of 0.95, significantly outperforming RSF (AUC of 0.89) and CoxPH (AUC of 0.86). The temporal AUC analysis also favored DeepSurv. Moreover, DeepSurv recorded the lowest Brier scores (IBS 0.071) throughout the entire follow-up period compared with CoxPH (IBS 0.129) and RSF (IBS 0.126). Calibration curves confirmed the high predictive accuracy of all the models, and Kaplan–Meier analysis along with log-rank tests demonstrated their effectiveness in risk stratification. Decision curve analysis revealed that the DeepSurv model had the highest clinical utility at both day 7 and day 28, underscoring its potential impact on neonatal care. This study confirms the superiority of DeepSurv in predicting neonatal mortality, providing a valuable tool for risk stratification in clinical practice.

Objective: To identify and characterize a unique set of biomarker genes in peripheral blood on day 5 of life in preterm infants with subsequent development of bronchopulmonary dysplasia. Methods: The open dataset GSE32472 was used to analyze gene expression profiles. The sample included preterm infants with a gestational age of less than 32 weeks and a body weight of ≤1500g. Peripheral blood samples collected within a 5-day window were analyzed to identify early transcriptional changes. Differential gene expression analysis was performed using the limma package with thresholds of |log_2FC|>0.5 and p.adj<0.05. Coexpression network analysis and functional enrichment were used to narrow the pool of potential target genes. At the final stage, three independent machine learning algorithms, such as SVM-RFE, LASSO, and Random Forest, were used to select the most prognostically significant genes. The diagnostic model based on the selected genes was visualized as a nomogram, and its predictive ability was assessed using ROC analysis and decision curve analysis. Results: Comparative analysis identified 451 differentially expressed genes in the BPD group on day 5 of life. WGCNA analysis identified the MEpink module as the one most significantly correlated with the BPD phenotype. The intersection of DEGs and genes within the MEpink module identified 106 common genes, functional analysis of which revealed significant enrichment for processes associated with T-cell activation and T-cell receptor signaling pathways. A combined machine learning approach reliably identified four key hub genes with high prognostic significance: BTN3A3, AK5, NOG, and GCSAM. A diagnostic model based on these four genes demonstrated high predictive ability (AUC = 0.888) and clinical utility. Regulatory analysis revealed that the AK5, BTN3A3, and GCSAM genes are central nodes in the miRNA–mRNA regulatory network. Conclusion: The identified day 5 gene window reflects key pathophysiological processes: developmental impairment, cellular stress, and inflammatory immune dysregulation. These results shift the focus from BPD diagnosis to early risk prediction. Day 5 molecular signatures possess high prognostic value and can be used to develop targeted therapeutic interventions.

This study aimed to evaluate platform-specific immune memory trajectories in patients with breakthrough COVID-19 over a 12-month period. This prospective longitudinal study enrolled adults hospitalized with PCR-confirmed COVID-19. Participants were stratified according to prior COVID-19 vaccination status into three groups: unvaccinated, adenoviral-vector-vaccinated, and inactivated-virus-vaccinated. Comprehensive immune profiling included quantification of humoral responses and flow cytometric analysis of T- and B-cell memory subsets. Group differences were assessed using Kruskal–Wallis tests; longitudinal naïve CD8⁺ T-cell dynamics were modeled using linear mixed-effects models; multivariate immune signatures were identified using random forest classification with SHAP analysis. The most distinct cellular finding was a sustained reduction in naïve CD8⁺ T cells in the adenoviral-vector-vaccine group, which persisted for 12 months in patients without pneumonia. Machine learning models demonstrated high discriminative performance for patients without pneumonia (AUC = 0.89–0.92), identifying a specific triad of markers in the adenoviral-vector-vaccine group: decreased naïve CD8⁺ T cells, decreased double-negative effector memory T cells, and increased naïve CD19⁺ B cells. Linear mixed-effects modeling confirmed that contraction of the naïve CD8⁺ T-cell compartment over time was significant exclusively in AdV recipients, whereas pneumonia was the dominant predictor in unvaccinated individuals. No significant temporal or disease-related dynamics were observed in the inactivated vaccine group, whose immune profiles largely overlapped with those of unvaccinated individuals. COVID-19 vaccine platforms induce distinct, durable immune imprints that modulate responses to subsequent breakthrough infections. The adenoviral-vector platform was associated with unique phenotypic remodeling of the T-cell compartment, specifically persistent contraction of the naïve CD8⁺ pool. These findings suggest that the vaccine platform itself, beyond the target antigen, shapes the trajectory of immune competence and long-term memory.

Heavy-metal contamination poses a serious threat to plant growth, productivity, and resilience, as it causes complex changes in cellular homeostasis, redox balance, element transport, and metabolism. This review summarizes current understanding of the molecular mechanisms by which plants adapt to heavy-metal stress, with a particular focus on transcriptional and omics approaches. The main detoxification and defense mechanisms are discussed, including chelation and vacuolar sequestration, antioxidant regulation, hormonal signaling, and ion transport. Particular attention is paid to the role of MAPK cascades and transcription factors in coordinating the expression of genes involved in metal transport, maintenance of redox homeostasis, and detoxification. Analysis of transcriptomic studies shows that the plant response is characterized by pronounced temporal, tissue-specific, and genotypic specificity and involves the reprogramming of genes involved in transport, antioxidant defense, secondary metabolism, and signaling pathways. At the same time, transcript levels do not always reflect the functional state of proteins and metabolic processes. Therefore, the integration of transcriptomics with genomics, epigenomics, proteomics, metabolomics, and single-cell and spatial approaches combined with functional validation is proposed as a key direction that may facilitate the identification of robust molecular markers and functionally validated genes for improving crop tolerance to heavy-metal contamination.