Zamart Ramazanova

Cutaneous melanoma and basal cell carcinoma (BCC) represent the two primary malignancies driven by solar ultraviolet (UV) radiation. To map the spatial topology and distance dependence of their mutational signatures, we deployed new analytical bioinformatics workflow utilizing two convergent strategies: a data-driven read-level phasing framework to discriminate between collateral mutational events versus independent ones and a hypothesis-driven spatial permutational simulation model to capture density-dependent local deviations. A whole-genome analysis employing this framework unexpectedly reveals two fundamentally distinct lesion-processing landscapes, present in both BCC and melanoma. While independent mutations consistently reproduce canonical UV signatures (SBS7a–c) in both tumor types, collateral mutations tell a distinct and more varied narrative between BCC and melanoma. These collateral mutations are unusually abundant for non-UV characteristics, such as age-related SBS1 and SBS5, and display a notable 3′ to 5′ asymmetry near UV-induced lesions in pyrimidine dimers. We also demonstrated that BCC displays a pronounced enrichment of dinucleotide base substitutions flanking UV-signature sites on the 3′ side, particularly CA>TG and CG>TA changes. Ultimately, these topological patterns in both tumors indicate that a primary photoproduct seeds secondary mutagenesis within its local chromatin environment, dramatically altering our understanding of UV-induced lesion processing.

Objectives: Automation of quantitative analysis of breast cancer (BC) immunohistochemistry (IHC) specimens is important to optimize pathologists’ workflow and improve diagnostic reproducibility. This is especially important in low- and middle-income countries where there is a shortage of highly trained pathologists. However, existing approaches face challenges in implementing fully automated quantitative IHC due to the difficulty of both delineating tumor areas, including discrete areas, especially in IHC slides with poor quality. Moreover, accurate identification of invasive carcinoma areas and accurate quantification of positive and negative cells in the specimen are critical for quantitative analysis. Methods and results: This study presents a method to automatically identify types of carcinoma areas in whole slide IHC images of BC, focusing on quantifying IHC images on realms of Kazakhstan. The used model is a combination of morphological characteristics and boundary features, which provides high accuracy of segmentation of tumor zones of images of mild and low quality. We used several methods includes convolutional neural network based on the Keras framework, k-nearest neighbors machine learning methods, and self-developed image analysis methods. The developed model showed high accuracy, where the results corresponded to the diagnoses of pathologists. As expected, the method proved to be ineffective when applied to severely degraded slides, such as those with insufficient staining or inadequate washing. Slides of inferior quality were excluded from analysis, which negatively affected the statistical robustness. On slides of moderate quality, the reliability of nucleus segmentation dropped significantly. Conclusions: The combination of models we used showed high accuracy in differentiating BC cells between the basal-like subtype of BC and its invasiveness and recurrence in Kazakhstan. However, IHC specimens with low DPI or low-quality IHC need further optimizations and improvements in algorithm design. The main issue can be considered methodological differences between the approaches of AI and humans: AI operates in a large number of cases (more than 10,000), yet its accuracy is relatively low. In contrast, humans work with a much smaller number of cases but achieve a level of precision that AI cannot currently match. This discrepancy necessitates a revision of the methodology of IHC analysis for AI, including the development of new requirements, methods, and thresholds from scratch. This approach provides analysis of the entire area of the slide, increases the speed of interpretation of IHC results, and reduces human errors in diagnosis, especially in low- and middle-income countries.

Breast cancer continues to be the most encountered malignancy in women globally and a leading cause of cancer-related mortality. This study describes an Information Technology approach to evaluate interpretable machine-learning methods for breast cancer prediction using routine clinical data and to situate performance against prior literature. All calculations are based on the Breast Cancer Wisconsin Diagnostic dataset (569 instances; malignant/benign labels) hosted by the UCI Machine Learning Repository. Each sample corresponds to a breast mass classified as malignant or benign. Four supervised machine learning models were applied: Logistic Regression with L1 penalty, Random Forest, Decision Tree, and Naïve Bayes, and compared the area under the ROC curve (AUC), accuracy, sensitivity, and specificity using DeLong’s test with Holm correction. The reproducible pipeline consisted of preprocessing, recursive feature elimination for feature selection, and a 5-fold cross-validation for hyperparameter tuning. Among the four models, the L1-penalized Logistic Regression yielded the best results, with an AUC indicating accuracy, sensitivity, and specificity of 99.6% (97.3%, 95.2%, 98.6%) on the test sets, respectively. This study illustrates the effective integration of supervised machine learning methods into diagnostic systems to produce early, accurate, interpretable diagnoses of disease. This study reinforces the proposed information technology approach for breast cancer prognosis. Limitations of the study are a moderately sized, homogeneous cohort, and restricted focus on structured variables, which may enhance internal validity while restricting generalizability. Our findings contribute to an emerging body of literature that well-tuned, regularized logistic regression provides a reasonable baseline against which breast cancer risk and other study outcomes can be compared, and a pragmatic route toward trustworthy AI in oncology.

Motivation: Schizophrenia is a complex psychiatric disorder characterized by the deterioration of intellectual processes and emotional responses, affecting ∼0.32% of the global population. The study of single nucleotide polymorphisms (SNPs) associated with schizophrenia is crucial to identifying pathogenic genetic variants and understanding the genetic architecture of this complex disorder. This study aims to demonstrate the feasibility of predicting schizophrenia using an individual’s SNP profile. Results: We used genome-wide association (GWA) schizophrenia data from a case-control study across European-American (EA) and African-American (AA) populations, consisting of 4693 participants (46.1% diagnosed with schizophrenia). Machine learning techniques were employed to construct SNP-based predictive models specific to ethnicity-gender groups (EA-F, EA-M, AA-F, and AA-M) where “F” and “M” identify female and male populations, respectively. Feature selection and association analysis were utilized to rank and detect significantly associated SNPs. Model selection was based on stratified five-fold cross-validation. Our EA-F-, EA-M-, AA-F-, and AA-M-specific models achieved classification accuracies (AUC, sensitivity, specificity) of 75.1% (69.2%, 95.4%, 34.2%), 65.4% (74.3%, 73.6%, 58.6%), 68.6% (69.5%, 85.6%, 41.1%), and 73.9% (74.0%, 39.7%, 93.3%), respectively, in independent test sets from the same ethnicity-gender population. The high sensitivity (>70%) of AA-F, EA-F, and EA-M models can make them auxiliary clinical tools to assess the risk of developing schizophrenia disorder in AA-F, EA-F, and EA-M populations. Availability and implementation: The R and Python codes, along with the dataset used in this study are available at https://github.com/Zam-gif/Prediction-Models-of-Schizophrenia/tree/main.

Cellular organisms store heritable information in two forms, genetic and epigenetic, the latter being largely dependent on cytosine methylation (5mC). Chargaff’s Second Parity Rule (CSPR) describes the nucleotide composition of cellular genomes in terms of intra-strand DNA symmetry. However, it remains unknown whether DNA methylation patterns display intra-strand DNA symmetry. Computational analysis was conducted of the DNA methylation patterns observed in human cell lines and in tissue samples from healthy donors. Analysis of 5mC marks in mutually reverse-complementary pairs of short oligomers, containing CpG dinucleotide in the middle, revealed deviations from CSPR and methylation asymmetry that can be observed for two non-overlapping mirror groups defined by CpG methylation values. Deviations from CSPR, together with combinatorial probabilities of pattern distributions and computer simulations, highlight the non-random nature of methylation processes and enabled us to identify specific cell types as outliers. Further analysis revealed a compensatory methylation asymmetry that reduces deviations from intra-strand symmetry and implies the existence of strand-specific methylation during cell differentiation. Among six pairs of reverse-complementary tetranucleotides, four pairs with specific sequence motifs display pronounced methylation asymmetry. This mirror asymmetry may be associated with chromosome folding and the formation of a complex three-dimensional landscape.

Over the past 10 years, developments in precision oncology have led to a rise in the demand for predictive tools that assist the screening and stratification of patients for treatment. The possibility digitizing tissue slide images has led to the creation of artificial intelligence (AI) and machine learning algorithms in digital pathology, which could ultimately improve patient care. In this context, we assessed a variety of AI-based computational models for digital pathology. In this review, we described the latest AI applications in oncology, current challenges and future prospects.

The water droplet motion processes actuated by applying surface acoustic waves at various RF powers and frequencies were investigated by numerically modelling and compared with experiment. A three-dimensional computational model of a free water droplet streaming on the surface of the substrate have been developed using Finite Element Method (FEM) with Laminar Two-Phase Flow Moving Mesh approach for Navier–Stokes equations which were coupled with Convection Wave equation (CWE) module of the COMSOL Multiphysics. Water droplet motion speeds were experimentally measured and confirmed for water droplets with the volumes of 2 and 5 μl, at SAW frequencies 34 and 58 MHz, and power range 0.1–1.23 W. The effect of frequency on microfluidic performance such as streaming flows and droplet motion has been studied both numerically and experimentally toward developing MEMS devices for future energy sources, e.g., for direct methanol fuel cells, hydrogen energy, as well as for use in a wide variety of chemical, water desalination and purification of other fluids from salts, germs, bacteria, and viruses based on perspective multiphysical effects.

Aluminum-oxygen reaction is important in highly energetic and high pressure generating systems. Nanoenergetic thermites include mixtures of nanostructured Al and oxidizer particles. The main distinguishing features of these reactive systems are their significant enthalpy release and tunable rate of energy discharge, which gives rise to a wide range of combustion rates, energy release, and ignition sensitivity. In this paper, we consider rapid oxidation of a spherically symmetric aluminum particle. We use the Cabrera Mott oxidation model to describe the kinetics of oxide growth of an aluminum nanoparticle and to predict reaction temperature and oxidation time. We assume that aluminum particle of diameter 10 to 50 nm is covered by a thin oxide layer (1-4 nm) and is surrounded by abundant amount of oxygen stored by oxidizers. The particle is rapidly heated up to ignition temperature to initiate self-sustaining oxidation reaction as a result of highly exothermic reaction. We numerically investigated the oxidation model by using COMSOL multiphysics software. The software creates a mesh fitted to geometric configuration of a nanoparticle, and performs finite element method computations.