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Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifactorial condition requiring multi-target therapeutic strategies beyond traditional single-marker approaches. In this work, we present a fully in silico nutraceutical screening pipeline that integrates molecular prediction, systemic aggregation, and technological design. A curated panel of ten MASLD-relevant targets, spanning nuclear receptors (FXR, PPAR-α/γ, THR-β), lipogenic and cholesterogenic enzymes (ACC1, FASN, DGAT2, HMGCR), and transport/regulatory proteins (LIPG, FABP4), was assembled from proteomic evidence. Bioactivity records were extracted from ChEMBL, structurally standardized, and converted into RDKit descriptors. Predictive modeling employed a stacked ensemble of Random Forest, XGBoost, and CatBoost with isotonic calibration, yielding robust performance (mean cross-validated ROC-AUC 0.834; independent test ROC-AUC 0.840). Calibrated probabilities were aggregated into total activity (TA) and weighted TA metrics, combined with structural clustering (six structural clusters, twelve MOA clusters) to ensure chemical diversity. We used physiologically based pharmacokinetic (PBPK) modeling to translate probabilistic profiles into minimum simulated doses (MSDs) and chrono-specific exposure (%T>IC50) for three prototype concepts: HepatoBlend (morning powder), LiverGuard Tea (evening aqueous form), and HDL-Chews (postprandial chew). Integration of physicochemical descriptors (MW, logP, TPSA) guided carrier and encapsulation choices, addressing stability and sensory constraints. The results demonstrate that a computationally integrated pipeline can rationally generate multi-target nutraceutical formulations, linking molecular predictions with systemic coverage and practical formulation specifications, and thus provides a transferable framework for MASLD and related metabolic conditions.

Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those and to the drug incubation. The model uses features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice. A machine learning model allows the identification of new small-molecule kinase inhibitors in days.

Background The aim of the study was to obtain feedback from medical students in Russia regarding their e-learning experience during COVID-19 Pandemic. Methods Thirteen thousand forty students from 46 Medical Schools in Russia completed an original evaluation form validated by 6 experts. Criterion and construct validity were determined in a pilot study ( n  = 46). The study design was based on the use of Google Forms. Participants used the Visual Analog Scale from 1 to 10 to assess the level of knowledge acquired. Results 95.31% of medical schools in Russia switched to e-learning during the Pandemic. 39.8% of the students stated that the time to prepare for the class has doubled. For 19.9% of them, it increased by one third, while 26.6% did not report any changes. 38,4% of the participants are satisfied with particular elements of e-learning, 27.5% like such a format, 22.9% do not like it, and 11.2% could not answer the question. The average scores for the knowledge assessment were 5.9 for the humanities, 6.1 for fundamental science, and 6.0 for clinical training. Conclusions The most important findings are increased self-instruction time, insufficient knowledge gained and territorial and socio-economic inequalities within the country. Meanwhile, most students favor distance learning or its particular elements. Consequently, medical education leaders in Russia should consider the implementation of blended training in medicine taking into account specific regional factors, ensuring its effectiveness at all stages.

In this study, the operation features of a transformerless voltage divider, with transistor–diode commutation of switchable capacitors, designed to operate as a part of low-power autonomous electronic systems with reduced output voltage are studied both theoretically and experimentally. The analysis is carried out for a divider operation with a constantly or periodically connected voltage source V0 with unlimited power. It is found that the divider’s efficiency during operation with a constantly connected primary voltage source V0 with unlimited power is very low. However, the efficiency can reach 60% during the divider’s operation using a periodically connected voltage source V0 with unlimited power. It has been shown that the efficiency can only reach 40% in the case of using a voltage source with limited power connected to the divider periodically. It has been established that for circuits with transistor–diode commutation of the capacitors, the stabilization effect is much stronger than for circuits with diode commutation of the capacitors. Therefore, an excess of the maximum load voltage relative to the expected value V0/N is significantly lower for transistor–diode commutation in comparison with diode commutation (N is the number of divider stages). Based on the ideas developed regarding the divider operation, analytical expressions are obtained, enabling us to calculate the parameters of the studied divider circuits in a wide range. The good agreement between the analytical estimations and experimental data suggests that these calculations adequately describe the operation of the dividers, and that the derived analytical expressions can be successfully used during the preliminary design stage. In general, the analysis carried out herein and the developed approach make it possible to significantly narrow the range of search for the necessary system parameters when designing voltage dividers.

The interaction of estrogen receptor alpha (ERα) with various metabolites—both endogenous and exogenous, such as those present in food products, as well as gut microbiota-derived metabolites—plays a critical role in modulating the hormonal balance in the human body. In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. A meticulously curated dataset comprising 75 molecular descriptors derived from compounds with known ERα activity was assembled, enabling the model to achieve an accuracy of 89.4% and an F1 score of 0.93, thereby demonstrating high predictive efficacy. Feature importance analysis revealed that both topological and physicochemical descriptors—most notably FractionCSP3 and AromaticProportion—play pivotal roles in the potential binding to ERα. Subsequently, the model was applied to chemicals commonly encountered in food products, such as indole and various phenolic compounds, indicating that approximately 70% of these substances exhibit activity toward ERα. Moreover, our findings suggest that food processing conditions, including fermentation, thermal treatment, and storage parameters, can significantly influence the formation of these active metabolites. These results underscore the promising potential of integrating predictive modeling into food technology and highlight the need for further experimental validation and model refinement to support innovative strategies for developing healthier and more sustainable food products.

The article is devoted to issues of technical and economic development and the strategy of the Russian Federation entering the position of process breakthrough. The role of Russian corporations in the national strategic development is investigated. The decisive role of corporations under conditions of high-tech functioning and the resulting complexity of breakdown solutions is substantiated. The attention is drawn to the inconsistency of Russian corporations with the breakthrough strategy requirements put forward by the government as the national idea, due to low innovation potential. A proposal is made on the use of a systemic - synergetic approach to solving issues of process development. The process development is placed at the center of issues being solved and is regarded as a critical condition for the organization and implementation of a process breakthrough.

The simulation results of the voltage divider operation based on switchable capacitors with diode switching for electrostatic microelectromechanical (MEM) generators are presented. It has been established that for the divider with diode-switched capacitors, the voltage dividing coefficient is not remain the same when the load resistance changes, and for higher load resistances, the load voltage asymptotically approaches the value of the power source. Analytical expressions have been obtained for estimating the parameters of the divider with diode switching, which allow evaluating its characteristics at the preliminary stage of the MEM generator design. It has been shown that the simulation of the diode-capacitor voltage divider operation has to be carried out taking into account the dynamic volt-ampere characteristics of diodes.

This article describes the stabilization method of structurally unstable and subsiding soils with spatial structural elements of the cement soil. There are shown the examples of creating spatial structures of various shapes. There is described the program for selecting the most optimal combinations of all gain parameters. On a real example, there is shown the proposed method of designing the strengthening of subsiding soils at the base of the plate foundation of a two-storey dormitory building.

Pseudo-spherical cubic-shaped nanoparticles of spinel ferrite CuFe2O4 have been prepared using a two-stage process. At first, an evaporation of levitating copper–iron drop into mix of helium–air gas flow took place, which resulting in copper ferrite-based powders. Then, such powders were additionally oxidized trough the heterogeneous auto-wave combustion in open air. We studied the effect of performing the synthesis in either an air or a helium environment on the phase composition of nanoparticles, and how the use of a post-synthesis annealing step modifies this, using room temperature 57Fe Mössbauer spectroscopy. By applying this technique, we are able to distinguish between the normal (non-magnetic) and inverse cubic (magnetic) phases of CuFe2O4, which is usually inaccessible using X-ray diffraction, as well as quantifying their relative amounts within each sample. Furthermore, we have been able to quantify trace amounts of the tetragonal CuFe2O4, phases that are typically obscured by line-broadening effects within our X-ray diffraction data, which indicates that annealing using a propane flame can also cause a cubic to tetragonal distortion in the crystal structure of the spinel lattice. Finally, by combining our Mössbauer parameters, which are sensitive to the Fe-containing phases only, with X-ray diffraction data, which are sensitive to all phases, we report on the full phase composition for the first time for nanoparticles produced via this synthesis route.