Using an artificial neural network (ANN), the values of the antiradical potential of 1315 items of food and agricultural raw materials were calculated. We used an ANN with the structure of a “multilayer perceptron” (MLP) and with the hyberbolic tangent (Tanh) as an activation function. Values reported in the United States Food and Nutrient Database for Dietary Studies (FNDDS) were taken as input to the analysis. When training the ANN, 60 parameters were used, such as the content of plastic substances, food calories, the amount of mineral components, vitamins, the composition of fatty acids and additional substances presented in this database. The analysis revealed correlations, namely, a direct relationship between the value of the antiradical potential (ARP) of food and the concentration of dietary fiber (r = 0.539) and a negative correlation between the value of ARP and the total calorie content of food (r = −0.432) at a significance level of p < 0.001 for both values. The average ARP value for 10 product groups within the 95% CI (confidence interval) was ≈23–28 equivalents (in terms of ascorbic acid) per 1 g of dry matter. The study also evaluated the range of average values of the daily recommended intake of food components (according to Food and Agriculture Organization—FAO, World Health Organization—WHO, Russia and the USA), which within the 95% CI, amounted to 23.41–28.98 equivalents per 1 g of dry weight. Based on the results of the study, it was found that the predicted ARP values depend not only on the type of raw materials and the method of their processing, but also on a number of other environmental and technological factors that make it difficult to obtain accurate values.

Background: Type 2 diabetes mellitus (T2DM) remains one of the most significant public health problems, and its incidence rate is steadily increasing worldwide despite scientific and technological progress in the field of medicine. The focus of research in this area is gradually shifting from classic risk factors—such as obesity, sedentary lifestyle and genetic predisposition—toward additional, potentially modifiable contributors such as micronutrient imbalances; among them are disturbances in zinc homeostasis that may influence glucose metabolism and oxidative stress. Objective: This systematic review with narrative synthesis aims to examine the bidirectional relationship between zinc status and T2DM and to evaluate whether zinc screening and personalized nutritional support could contribute to comprehensive metabolic management. Methods: A literature search was conducted in the PubMed database and the Cochrane library for studies published between 2010 and 2024. Studies assessing zinc status or supplementation in relation to the risk, progression, or management of T2DM were included. Data were synthesized narratively, focusing on clinical and mechanistic evidence. Results: Thirty studies met the inclusion criteria. Evidence indicates that zinc imbalance (both deficiency and excess) is associated with T2DM risk and outcomes. Zinc deficiency may impair insulin synthesis and signaling, promote oxidative stress and inflammation, while excessive zinc intake may induce metabolic disturbances. T2DM itself may lead to reduced zinc status via altered absorption and increased excretion. While some studies suggest modest improvements in glycemic or lipid parameters following zinc supplementation, findings remain inconsistent and context-dependent. The prevalence of suboptimal zinc status in certain populations supports the rationale for targeted screening rather than routine supplementation. Conclusions: Zinc is mechanistically involved in insulin synthesis, antioxidant defense, and inflammation control, but current clinical evidence does not justify its use as a therapeutic agent in T2DM. Instead, assessment of zinc status and individualized correction of deficiency may represent a component of personalized nutritional support, particularly for patients with long disease duration, poor dietary quality, or genetic predispositions affecting zinc metabolism.

Antiradical potential (ARP) is an important measure of food safety. In addition, it directly or indirectly affects the rate of occurrence of a number of human pathologies. Using a photocolorimetric analysis of DPPH (2,2-diphenyl-1-picrylhydrazyl) solutions, we estimated the antiradical potential of food raw materials, food concentrates, biologically active substances, and wild plants. We conducted approximately 1500 analyses of almost 100 food products selected from 11 food groups: vegetables, milk, meat, fish, cereals and bread, drinks (including tea and coffee), etc. With a confidence interval (CI) of 95%, the average values for animal products range from 15.87 to 18.70 ascorbic acid equivalents per gram of dry matter. For plant materials, the range is 474.54–501.50 equivalents when wild herbs are included and 385.02–408.83 equivalents without taking herbs into account. The antiradical potential of the biologically active substances we studied ranged from 706.84 to 847.77 equivalents per gram of dry matter, which makes it possible to use some of the components to repair products with low ARP values, for example, bread and baked goods, confectionery, milk and dairy products, carbonated drinks, and juice. In this study, a low ARP value is associated with a reduction in the shelf life of products and a deterioration in their organoleptic properties; therefore, we propose using ARP as an important reference for describing the quality of food products and raw food materials.

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.

Background: The composition of polyphenolic compounds in various parts of the Rosebay willowherb (Chamerion angustifolium) has been studied in detail and published. A number of these compounds have sufficient antiradical activity (ARA) to create functional foods based on extracts from this plant that are preventive against free-radical pathologies in humans. Part of substances with antiradical activity (e.g., vitamins, polyphenol components) are destroyed during the technological processing of food raw materials, and the values of ARA drop in the finished food product concerning the initial values (before and after technical processing) are not fully evaluated. Objective: The object of the study is to study the thermal stability of polyphenol components possessing ARA (dried at 60°C and 150°C) and evaluate their residual ARA in the finished food products, N-acetylcysteine (NAC), as one of the known antiradical, was used as a reference substance. Methods: Spectrophotometric method was used to evaluate the change of ARA in prepared foods. Gas chromatography-mass spectrometry (GC-MS) was used as an additional method to evaluate the difference in the chemical composition of polyphenol components. Results: The residual ARA in the finished products (for caramel and bakery products) was ≈ 23.9% for rapidly extractable substances, for polyphenol compounds ≈ 46.1%, and for amino acids and easily hydrolyzable substances ≈ 34.5%. Conclusion: Rosebay willowherb extract and N-acetylcysteine are recommended for increasing the ARA (between 1.6 – 4.9 times) of foods with low ARA (confectionery, bread, or other heavily processed foods) and for diets and diets preventive of free-radical pathologies by adding polyphenolic plant components and cysteine derivatives.

The sample distribution of observations is studied in order to study the variational feature, while the measured value is considered as random. For practical purposes, the empirical distribution of a random variable is estimated by proximity to the theoretical law. The statistical hypothesis of the sample data belonging to the general population is tested using the consent criteria. The object of this study was the observation of the falling number of corn flour, which is a prescription component of bakery products. In world practice, the falling number method for raw materials from corn is used as a reference for establishing reference interval values, detecting a variation of the studied trait. The purpose of this work was to study the sample distribution of observations of the falling number of corn flour to determine the representativeness of the sample of experimental data. Relying on Glivenko's theorem on the expediency of grouping sample data into a variation series in order to replace the distribution function of the general population with a sample distribution function, at the beginning of the research, the sample data were converted into a statistical series. The research material was an industrial sample of fine-ground corn flour that meets the requirements of GOST 14176-69 "Corn flour. Technical conditions". The falling number was studied when implementing the standard method on the PPP-99 device according to GOST ISO 2093-2016 "Grain and its processed products. Determination of the number of falls by the Hagberg-Perten method". The interval variation series was studied according to a wide list of characteristics: distribution center indicators (sample mean, mode, median, quartiles, deciles), variation indicators (span, average linear deviation, variance, unbiased variance estimate, mean square deviation, coefficient of variation, linear coefficient of variation, oscillation coefficient), distribution form indicators (relative quartile variation index, asymmetry coefficient, Pearson structural asymmetry coefficient, kurtosis index), indicators of interval estimation of the center of the general population (confidence intervals for the general average, interval estimation of the general share, i.e. the probability of an event). For the studied random variable of the falling number of corn flour, the hypothesis of a normal distribution was proved using the indicators of asymmetry and kurtosis, as well as using the 3σ sigma rule. As a result of the calculations, the representativeness of the sample data with respect to the variation of the studied trait in the general population was established