This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.

In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top–down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain–computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.

The aim of this work is to find a good mathematical model for the classification of brain states during visual perception with a focus on the interpretability of the results. To achieve it, we use the deep learning models with different activation functions and optimization methods for their comparison and find the best model for the considered dataset of 31 EEG channels trials. To estimate the influence of different features on the classification process and make the method more interpretable, we use the SHAP library technique. We find that the best optimization method is Adagrad and the worst one is FTRL. In addition, we find that only Adagrad works well for both linear and tangent models. The results could be useful for EEG-based brain–computer interfaces (BCIs) in part for choosing the appropriate machine learning methods and features for the correct training of the BCI intelligent system.

The Dps protein of Escherichia coli, which combines ferroxidase activity and the ability to bind DNA, is effectively used by bacteria to protect their genomes from damage. Both activities depend on the integrity of this multi-subunit protein, which has an inner cavity for iron oxides; however, the diversity of its oligomeric forms has only been studied fragmentarily. Here, we show that iron ions stabilize the dodecameric form of Dps. This was found by electrophoretic fractionation and size exclusion chromatography, which revealed several oligomers in highly purified protein samples and demonstrated their conversion to dodecamers in the presence of 1 mM Mohr’s salt. The transmission electron microscopy data contradicted the assumption that the stabilizing effect is given by the optimal core size formed in the inner cavity of Dps. The charge state of iron ions was evaluated using Mössbauer spectroscopy, which showed the presence of Fe₃O₄, rather than the expected Fe₂O₃, in the sample. Assuming that Fe²⁺ can form additional inter-subunit contacts, we modeled the interaction of FeO and Fe₂O₃ with Dps, but the binding sites with putative functionality were predicted only for Fe₂O₃. The question of how the dodecameric form can be stabilized by ferric oxides is discussed.

We analyze the synthesis of carbon nanostructures from the gas phase (mixture of acetylene or ethylene with hydrogen) on the surface of copper particles without using other catalysts. The synthesized structures (multilayer graphene and carbon nanofibers) are analyzed by transmission electron microscopy and Raman scattering. It is shown that the fiber structure is determined by the C: H ratio in the gas phase. The kinetics of synthesis is analyzed in terms of the formal kinetics of conversion in accordance with the Johnson—Mehl—Avrami equation.