Search Results (42)

This study explores fine-tuning methods and dataset structures for multilingual neural machine translation using the No Language Left Behind model, with a case study on Kazakh, English, and Russian. We compare single-stage and two-stage fine-tuning approaches, as well as triplet versus non-triplet dataset configurations, to improve translation quality. A high-quality, 50,000-triplet dataset in information technology domain, manually translated and expert-validated, serves as the in-domain benchmark, complemented by out-of-domain corpora like KazParC. Evaluations using BLEU, chrF, METEOR, and TER metrics reveal that single-stage fine-tuning excels for low-resource pairs (e.g., 0.48 BLEU, 0.77 chrF for Kazakh → Russian), while two-stage fine-tuning benefits high-resource pairs (Russian → English). Triplet datasets improve cross-linguistic consistency compared with non-triplet structures. Our reproducible framework offers practical guidance for adapting neural machine translation to technical domains and low-resource languages. Full article

Background: The Kidney Disease and Quality of Life Short Form (KDQOL-SF™ 1.3) is widely used to assess health-related quality of life (HRQoL) in patients with end-stage renal disease. However, no prior validation had been conducted in Kazakhstan, where both Kazakh and Russian are commonly spoken. This study aimed to validate the Kazakh and Russian versions of the KDQOL-SF™ 1.3 and to identify predictors of HRQoL among hemodialysis patients in Kazakhstan. Methods: A cross-sectional survey was conducted among 217 adult hemodialysis patients from February to April 2025 using a mixed-methods approach (in-person interviews and online data collection). Psychometric testing included Cronbach’s alpha, floor and ceiling effect analysis, and Pearson correlations with self-rated overall health. Multiple linear regression was used to identify predictors of the Kidney Disease Component Summary (KDCS), Physical Component Summary (PCS), and Mental Component Summary (MCS) scores. Results: Both language versions demonstrated acceptable to excellent internal consistency (Cronbach’s α = 0.692–0.939). Most subscales were significantly correlated with self-rated health, supporting construct validity. Regression analyses revealed that greater satisfaction with care, better economic well-being, and more positive dialysis experiences were significant predictors of higher KDCS and MCS scores. Lower PCS scores were associated with female gender, comorbidities, and financial burden. Importantly, financial hardship and access challenges emerged as strong negative influences on HRQoL, underscoring the role of socioeconomic and care-related factors in patient well-being. Conclusions: The KDQOL-SF™ 1.3 is a valid and reliable tool for assessing quality of life among Kazakh- and Russian-speaking hemodialysis patients in Kazakhstan. Integrating this instrument into routine clinical practice may facilitate more personalized, patient-centered care and help monitor outcomes beyond traditional clinical indicators. Addressing economic and access-related barriers has the potential to significantly improve both physical and mental health outcomes in this vulnerable population. Full article

Endometriosis (EM) is a common estrogen-dependent chronic inflammatory disorder affecting reproductive-aged women, yet its pathogenesis remains incompletely understood. Recent evidence suggests that the gut microbiota significantly influence immune responses, estrogen metabolism, and systemic inflammation, potentially contributing to EM progression. This narrative review explores the relationship between the gut microbiota and EM, emphasizing microbial dysbiosis, inflammation, estrogen regulation, and potential microbiome-targeted therapies. Studies published within the last 30 years were included, focusing on the microbiota composition, immune modulation, estrogen metabolism, and therapeutic interventions in EM. The selection criteria prioritized peer-reviewed articles, clinical trials, meta-analyses, and narrative reviews investigating the gut microbiota’s role in EM pathophysiology and treatment. Microbial dysbiosis in EM is characterized by a reduced abundance of beneficial bacteria (Lactobacillus, Bifidobacterium, and Ruminococcaceae) and an increased prevalence of pro-inflammatory taxa (Escherichia/Shigella, Streptococcus, and Bacteroides). The gut microbiota modulate estrogen metabolism via the estrobolome, contributing to increased systemic estrogen levels and lesion proliferation. Additionally, lipopolysaccharides (LPS) from Gram-negative bacteria activate the TLR4/NF-κB signaling pathway, exacerbating inflammation and EM symptoms. The interaction between the gut microbiota, immune dysregulation, and estrogen metabolism suggests a critical role in EM pathogenesis. While microbiota-targeted interventions offer potential therapeutic benefits, further large-scale, multi-center studies are needed to validate microbial biomarkers and optimize microbiome-based therapies for EM. Integrating microbiome research with precision medicine may enhance the diagnostic accuracy and improve the EM treatment efficacy. Full article

Background and Objectives: The study aimed at identification and analysis of adverse COVID-19 outcomes (admission to intensive care units due to COVID-19, acute respiratory distress syndrome, mechanical ventilation, and death) among hospitalized pregnant and nonpregnant women, which are critical for informed decision-making in obstetric diagnostics and healthcare. Materials and Methods: This was a retrospective observational study conducted on a series of inpatient pregnant women comparatively followed up with nonpregnant women hospitalized between 15 July 2020 to 20 January 2022 across multidisciplinary hospitals in three cities of Kazakhstan. Following group matching with propensity score for COVID-19 disease severity, residence status, and age, the study ultimately included 156 participants, of whom 50% were pregnant, from an initial sample of 314 female inpatients diagnosed with COVID-19. All findings were considered statistically significant at a p-value < 0.05. Results: Laboratory investigations revealed significantly elevated levels of erythrocyte sedimentation rate, creatinine, neutrophils, platelet count, alanine aminotransferase, aspartate aminotransferase, lymphocyte count, and C-reactive protein in pregnant inpatients compared to nonpregnant inpatients. Furthermore, pregnant women exhibited significantly higher levels of D-dimer (2402.97 ng/mL vs. 793.91 ng/mL) and procalcitonin (0.398 ng/mL vs. 0.134 ng/mL) compared to their nonpregnant counterparts. Overall, 16.88% of the pregnant women were admitted to the intensive care unit, whereas among the nonpregnant women, only 2.6% were hospitalized. The most lethal outcomes (8.3%) occurred among pregnant women, while for nonpregnant women, there were two cases (1.3%). Conclusions: Pregnant women diagnosed with COVID-19 may exhibit more severe clinical symptoms and encounter more adverse outcomes compared to their nonpregnant counterparts. Future research should incorporate larger matched samples to comprehensively explore the association between additional factors and clinical conditions. Full article

This article explores the features of speech practices of young people in Kazakhstan in the conditions of interaction between Kazakh, Russian, and English, taking into account the influence of the digital environment and modern socio-cultural factors. The relevance of this study is determined by the rapid transformation of the language situation in Kazakhstan, where traditional bilingualism is evolving under the influence of globalisation and digital factors, contributing to the formation of new models of language interaction in the youth environment. The aim of this research is to study the mechanisms of language functioning in different communicative contexts, including digital communication. As a methodological basis, the methods of sociolinguistic and discourse analysis were applied, including the collection and interpretation of young people’s written texts, as well as interviewing respondents to identify their language preferences and communication strategies. The empirical analysis allowed us to identify new models of young people’s linguistic behaviour in various communicative environments, including online space. The leading factors influencing the choice of language code were identified, and the characteristic mechanisms of integrating elements of Kazakh, Russian, and English into a single speech act were recorded. Special attention is paid to the specifics of language interaction in digital environments, where hybrid forms of communication are observed due to technological and globalisation processes. This study considers the speech of young people not only as a linguistic phenomenon, but also as an instrument of intercultural communication, reflecting trends in the development of polylingualism. The findings of this study can be used to improve language policy and to develop educational programmes that take into account modern trends in youth communication. Full article

Background: The human leukocyte antigen (HLA) system represents the most polymorphic segment within human DNA sequences and constitutes a core component of immune defense responses and in understanding population genetics. This research investigates the distribution of HLA class I and II polymorphisms across different ethnic groups in Kazakhstan, offering valuable insights into the genetic diversity and demographic evolution within this region. Methods: We performed an in-depth examination of HLA class I and II polymorphisms across diverse ethnic communities living in Kazakhstan, including Kazakhs, Russians, Uzbeks, Ukrainians, Germans, Tatars, and Koreans. Utilizing data from high-resolution HLA typing studies allowed us to assess allele frequencies alongside haplotype distributions while analyzing genetic interrelations between these populations. Additionally, we performed comparative assessments with global HLA databases to determine the genetic affiliations between these groups and their relationships with neighboring and more distant populations. Results: Our study revealed over 200 HLA alleles within the analyzed populations, and significant variations were observed in their allele and haplotype frequencies. Notably, the Kazakh group exhibited strong genetic ties to Asian and Siberian demographics; conversely, other ethnicities showed associations reflective of their historical roots. Notable alleles included HLA-A*02:01, B*07:02, C*07:02, DRB1*07:01, and DQB1*03:01, commonly observed across various groups. Linkage disequilibrium analysis revealed the presence of population-specific haplotypes, highlighting distinct genetic structures within these communities. Conclusions: The findings highlight the significant genetic diversity in Kazakhstan, influenced by its geographical location at the crossroads of Europe and Asia. These results are pertinent to immunogenetics, transplantation medicine, and personalized healthcare within Kazakhstan and adjacent regions. Future research should expand the sample size and explore disease associations to enhance our comprehension of HLA genetics across Central Asia. Full article

This study examines the role of multilingualism in intercultural business communication among professionals in Kazakhstan, where business discourse reflects a unique combination of language planning, individual competencies, and integration of traditions with modern economic demands. Shaped by globalization, historical influences, and geopolitical factors, Kazakhstan’s business communication is characterized by the use of Kazakh, Russian, and English, along with other foreign languages. Using semi-structured interviews with 19 business professionals from 13 companies, the study examines multilingual practices, language learning processes, and the impact of cultural factors on workplace interactions. Findings reveal that Kazakh, Russian, and English serve distinct functions in professional settings: Kazakh, as the state language, is gaining prominence in the business sphere, particularly in official documentation and government-mandated communication; Russian remains dominant in private and regional business interactions; and English is indispensable for international business. While proficiency in multiple languages facilitates overcoming intercultural barriers, differences in negotiation styles, non-verbal communication, and decision-making processes highlight the need for cultural flexibility in business settings. The study underscores the necessity of implementing well-structured and context-sensitive language policies while advocating for the integration of professional multilingual training into educational curricula to bridge the gap between academic preparation and workplace demands. This research contributes to a broader understanding of how multilingualism shapes professional identity, workforce mobility, and intercultural competence in Kazakhstan’s increasingly globalized business landscape. Full article

Understanding the factors that influence teacher well-being is crucial as it significantly affects students, teachers, schools, and the sustainability of the education system, especially during prolonged emergencies. This study contributes to the field by empirically testing a conceptual model of teacher well-being in emergency contexts, specifically addressing the COVID-19 school closures with a sample of over 19,600 teachers from Kazakhstan through an online survey design. Utilising a multidimensional socio-ecological framework that considers individual, school and home, community, and national factors, this study identifies key predictors of teacher self-reported well-being. Individual-level predictors explained 9.3% of the variation in physical well-being (F = 118, p < 0.001, R² = 0.093) and 4.5% in psychological well-being (F = 72.2, p < 0.001, R² = 0.045). In contrast, school- and home-level predictors demonstrated significantly greater explanatory power, accounting for 21.9% (F = 128, p < 0.001, R² = 0.219) and 15.5% (F = 89.5, p < 0.001, R² = 0.155) of the variation in physical and psychological well-being, respectively. Community-level predictors explained 12.8% of the variation in physical well-being (F = 191, p < 0.001, R² = 0.128) and 10.2% in psychological well-being (F = 324, p < 0.001, R² = 0.102), while national-level predictors accounted for much smaller proportions: 0.67% for physical well-being (F = 21.8, p < 0.001, R² = 0.0067) and 1.4% for psychological well-being (F = 83.589, p < 0.001, R² = 0.014). These findings highlight the significant influence of home and school, as well as community-level predictors, on teacher well-being during emergency contexts, suggesting that interventions targeting these areas may be particularly effective in supporting teacher well-being. The findings reveal that while Kazakhstani teachers reported poor physical well-being, they generally had a more positive assessment of their psychological well-being. Vulnerable groups included women, older teachers, non-Kazakh teachers, and those with higher education levels, as well as teachers in Russian medium and mixed-medium schools, all of whom reported lower physical and psychological well-being. Additional risk factors identified were a lack of student engagement, difficult relationships with parents, a directive leadership style, family conflicts, and inadequate resources at home and school. Conversely, protective factors such as teacher autonomy, collegiality, networking opportunities, and self-efficacy emerged as significant contributors to well-being. These findings reveal a complex interplay between cultural factors and subjective perceptions of well-being. This study emphasises the critical role of these predictors in both emergency and non-emergency contexts, underscoring the urgent need for targeted policies and programmes that sustainably support and enhance teacher well-being holistically. This approach will promote Sustainable Development Goal (SDG) 3 (well-being) and ensure access to equitable quality education (SDG 4) for all learners, ultimately contributing to the overall resilience of educational systems. Full article

Background: The differential diagnosis of tuberculosis (TB) and lung cancer (LC) is often challenging due to similar clinicopathological presentations when bacterial shedding is negative, which can lead to delays in treatment. In this study, we tested the potential of plasma-circulating microRNAs (miRNAs) for the early and differential diagnosis of TB and LC. Methods: We conducted a two-phase study: profiling 188 miRNAs in pooled plasma samples and validating 14 selected miRNAs in individual plasma samples from 68 LC patients, 38 pulmonary TB patients, and 41 healthy controls. Results: Twelve miRNAs were significantly elevated in LC patients compared to controls and TB patients, while two miRNAs were significantly elevated in TB patients compared to controls. ROC analysis demonstrated that miR-130b-3p, miR-1-3p, miR-423-5p, and miR-200a-3p had good discriminatory ability to distinguish LC patients (including those with stage I tumours) from healthy individuals and miR-130b-3p, miR-423-5p, miR-15b-5p, and miR-18b-5p effectively distinguished LC patients (including those with stage I tumours) from TB patients. Additionally, miR-18b-5p showed good discriminatory ability between SCLC and NSCLC patients. Conclusions: Circulating miRNAs hold strong potential for the early detection of LC and for distinguishing LC from TB. Full article

Exploring phytochemicals from ethnomedicinal plants for pharmacological applications is a promising research area. By studying ethnomedicine, researchers can identify plants used for centuries to treat ailments and investigate their phytochemicals. Consequently, phytochemicals can be isolated, characterized, and tested for pharmacological activities, leading to new drug development. This research also helps preserve traditional knowledge and biodiversity. Lithospermum officinale L., found in Eurasia, Argentina (South), Colombia, and the United States, is valued for its medicinal properties, including anti-inflammatory, antioxidant, and antimicrobial effects. The current review emphasizes L. officinale L. as a significant reservoir of bioactive phytochemicals, with alkaloids, quinones, glucosides, phenolics, flavonoids, and lipids identified as the principal metabolites. It also unveils the unexplored potential of this plant for future research endeavors. Continued research on L. officinale L. can unlock its full potential, providing insights into its medicinal uses and contributing to biodiversity preservation. Full article

Atraphaxis is a genus of flowering plants in the family Polygonaceae, with approximately 60 species. Species of Atraphaxis are much-branched woody plants, forming shrubs or shrubby tufts, primarily inhabiting arid zones across the temperate steppe and desert regions of Central Asia, America, and Australia. Atraphaxis species have been used by diverse groups of people all over the world for the treatment of various diseases. However, their biologically active compounds with therapeutic properties have not been investigated well. Studying the biologically active components of Atraphaxis laetevirens, Atraphaxis frutescens, Atraphaxis spinosa L., and Atraphaxis pyrifolia is crucial for several reasons. Firstly, it can unveil the therapeutic potential of these plants, aiding in the development of novel medicines or natural remedies for various health conditions. Understanding their bioactive compounds enables scientists to explore their pharmacological properties, potentially leading to the discovery of new drugs or treatments. Additionally, investigating these components contributes to preserving traditional knowledge and validating the historical uses of these plants in ethnomedicine, thus supporting their conservation and sustainable utilization. These herbs have been used as an anti-inflammatory and hypertension remedies since the dawn of time. Moreover, they have been used to treat a variety of gastrointestinal disorders and problems related to skin in traditional Kazakh medicine. Hence, the genus Atraphaxis can be considered as a potential medicinal plant source that is very rich in biologically active compounds that may exhibit great pharmacological properties, such as antioxidant, antibacterial, antiulcer, hypoglycemic, wound healing, neuroprotective, antidiabetic, and so on. This study aims to provide a collection of publications on the species of Atraphaxis, along with a critical review of the literature data. This review will constitute support for further investigations on the pharmacological activity of these medicinal plant species. Full article

In recent years, machine translation has made significant advancements; however, its effectiveness can vary widely depending on the language pair. Languages with limited resources, such as Kazakh, Uzbek, Kalmyk, Tatar, and others, often encounter challenges in achieving high-quality machine translations. Kazakh is an agglutinative language with complex morphology, making it a low-resource language. This article addresses the task of post-editing machine translation for the Kazakh language. The research begins by discussing the history and evolution of machine translation and how it has developed to meet the unique needs of languages with limited resources. The research resulted in the development of a machine translation post-editing system. The system utilizes modern machine learning methods, starting with neural machine translation using the BRNN model in the initial post-editing stage. Subsequently, the transformer model is applied to further edit the text. Complex structural and grammatical forms are processed, and abbreviations are replaced. Practical experiments were conducted on various texts: news publications, legislative documents, IT sphere, etc. This article serves as a valuable resource for researchers and practitioners in the field of machine translation, shedding light on effective post-editing strategies to enhance translation quality, particularly in scenarios involving languages with limited resources such as Kazakh and Uzbek. The obtained results were tested and evaluated using specialized metrics—BLEU, TER, and WER. Full article

The agro-industrial complex plays a vital role in driving economic growth and sustainable development. This study investigates the direct and indirect impact of technological innovations, knowledge transfer, and value chain governance on intersectoral integration through the mediatory role of innovation diffusion and the moderating role of the regulatory environment. This study adopts a time-lagged quantitative survey research design, utilizing a multistage random sampling technique to collect data from employees within the agro-industrial complex in Kazakhstan. The findings reveal that technological innovations, knowledge transfer, and value chain governance positively influence intersectoral integration within the agro-industrial complex. Furthermore, innovation diffusion mediates the relationship between these variables, indicating that the diffusion of innovative practices, technologies, and ideas plays a crucial role in facilitating intersectoral integration. This study also demonstrates that the regulatory environment moderates the relationship between innovation diffusion and intersectoral integration, highlighting the importance of supportive regulatory frameworks in facilitating collaboration and innovation diffusion. The results contribute to the theoretical understanding of intersectoral integration and provide practical implications for policymakers, industry stakeholders, and managers. Full article

Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large collection of Kazakh speech recordings along with their corresponding Russian translations. The automatic speech recognition (ASR) module of the system utilizes deep learning techniques to convert spoken Kazakh input into text. The machine translation (MT) module employs state-of-the-art neural machine translation methods, leveraging the parallel Kazakh-Russian translations available in the dataset to generate accurate translations. By conducting extensive experiments and evaluations, we have thoroughly assessed the performance of the cascade speech translation system on the ST-kk-ru dataset. The outcomes of our evaluation highlight the effectiveness of incorporating additional datasets for both the ASR and MT modules. This augmentation leads to a significant improvement in the performance of the cascade speech translation system, increasing the BLEU score by approximately 2 points when translating from Kazakh to Russian. These findings underscore the importance of leveraging supplementary data to enhance the capabilities of speech translation systems. Full article

Natural rubber (NR) remains an indispensable raw material with unique properties that is used in the manufacture of a large number of products and the global demand for it is growing every year. The only industrially important source of NR is the tropical tree Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg., thus alternative sources of rubber are required. For the temperate zone, the most suitable source of high quality rubber is the Russian (Kazakh) dandelion Taraxacum kok-saghyz L.E. Rodin (TKS). An obstacle to the widespread industrial cultivation of TKS is its high heterozygosity, poor growth energy, and low competitiveness in the field, as well as inbreeding depression. Rapid cultivation of TKS requires the use of modern technologies of marker-assisted and genomic selection, as well as approaches of genetic engineering and genome editing. This review is devoted to describing the progress in the field of molecular genetics, genomics, and genetic engineering of TKS. Sequencing and annotation of the entire TKS genome made it possible to identify a large number of SNPs, which were subsequently used in genotyping. To date, a total of 90 functional genes have been identified that control the rubber synthesis pathway in TKS. The most important of these proteins are part of the rubber transferase complex and are encoded by eight genes for cis-prenyltransferases (TkCPT), two genes for cis-prenyltransferase-like proteins (TkCPTL), one gene for rubber elongation factor (TkREF), and nine genes for small rubber particle proteins (TkSRPP). In TKS, genes for enzymes of inulin metabolism have also been identified and genome-wide studies of other gene families are also underway. Comparative transcriptomic and proteomic studies of TKS lines with different accumulations of NR are also being carried out, which help to identify genes and proteins involved in the synthesis, regulation, and accumulation of this natural polymer. A number of authors already use the knowledge gained in the genetic engineering of TKS and the main goal of these works is the rapid transformation of the TKS into an economically viable rubber crop. There are no great successes in this area so far, therefore work on genetic transformation and genome editing of TKS should be continued, considering the recent results of genome-wide studies. Full article