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.

Verification of the hypothesis of the normality of small samples is required to establish whether the empirical distribution obtained belongs to the theoretical distribution. The condition for testing the hypothesis of normality for a set of small independent samples is the presence of a sufficient number of them with the same volume. In this case, it is possible to test the hypothesis of the normality of the general aggregates from which the studied samples were taken, assuming that the parameters of these aggregates have different values. When testing the hypothesis of normality for a large number of small samples, only one value of the first, second, etc. measurements is randomly selected from each sample, thereby allowing simplification and random selection of data. The object of this study is small samples of the falling number of oat flour used in bakery production in the development of bakery products. The purpose of this work was to test the hypothesis of normality for small samples of the experiment using the nonparametric criterion of agreement ω2 of the smallest of each of the four definitions of the incidence number, since rounding the values of direct measurements excludes the random nature of the quantity or its normal distribution in favor of a uniform one. It was found that at a significance level of p = 0?05, the table value (nω2)1-p is greater than the calculated value of nω2 for all four definitions, hence the hypothesis of the normal distribution of small samples for all four definitions (as random variables) of the falling number of oatmeal does not deviate. The results obtained in this work are consistent with the generally accepted classical concepts of testing the statistical hypothesis of the normal distribution of samples. The statistical method provides sufficient accuracy of the studied indicator in technical systems and does not require the synthesis of a statistical criterion to test the hypothesis of the normality of small sample.

The paper presents the result of assessing the antiradical status of consumers (in the context of Russia) in connection with their well-being. This approach is based on a multistage study, in which the results of sociological surveys were applied, as well as estimates of the antiradical potential (ARP) of diets obtained using neural networks, bootstrapping the chemical composition of diets, and calculating reference values using mathematical models. The paper presents data collected from residents living in the territories of at least 21 regions and cities of Russia: Magadan, Saint Petersburg, Moscow, Krasnodar, Lipetsk, Vladivostok, Novosibirsk, Omsk, Voronezh, etc. A total of 1001 people were interviewed, which, according to our calculations, gives a margin of error in value of approximately 3.1%. To calculate the lack of vitamins in the diets of residents of the Russian Federation, data on the chemical composition of food products from the FNDDS database were used. The assessment of dietary habits showed a lack of vitamins below the recommended level in 73% of Russians for vitamin D, 59% for retinol, 38% for β-carotenes, 13% for vitamin E, and 6% for ascorbic acid. The study showed that at least 36% of the Russian population has a low antiradical status, while it was found that “poor” consumers are more likely to consume economically more expensive foods (in terms of their nutritional value). The “poor” segments of the population consume 180–305% more canned food and 38–68% more sweet carbonated drinks than other social groups, but their consumption of vegetables is 23–48% lower. On the contrary, “wealthy” consumers consume 17–25% more complex (varied) dishes, 10–68% more fresh vegetables, and 8–39% more fish. From the obtained values it follows that consumers with low levels of ARP in their diets are in a group with an increased probability of a number of “excess” diseases (diseases of the cardiovascular system, obesity, etc.). In general, the ARP values of food consumed for low-income segments of the population were 2.3 times lower (the ratio was calculated as the percentage of consumers below the level of 11,067 equivalents necessary for the disposal of free radicals generated in the human body per day) than for those who can afford expensive food (consumers with high income). A simple increase in consumption of unbalanced foods, in our opinion, will only contribute to the entry of these consumers into the “average diet trap”. All this makes it imperative to develop comprehensive measures to create a new concept of public catering; otherwise, we can expect a reduction in both the health of the population and the performance of the economy of the whole country.