Background and Aim: Ensuring the genetic potential of ruminants through nutrition studies using medicinal plants and trace element metals is an urgent task. This study aimed to study the effect of Artemisia absinthium L. (Asteraceae) herb plant separately and in combination with cobalt (II) chloride (CoCl2) chelate compounds on the course of metabolic processes in the rumen, methane concentration, and biochemical blood parameters in bulls. Materials and Methods: Control group (BD: Basal diet); experimental Group I – BD + A. absinthium herb at a dose of 2.0 g/kg dry matter (DM), experimental Group II – BD + A. absinthium herb at a dose of 2.0 g/kg DM + CoCl2 (1.5 mg/kg), and experimental Group III – BD + CoCl2 were set (1.5 mg/kg). The study was conducted on 16 beef bulls (Kazakh white-headed breed) aged 13–14 months, with an average live weight of 330–335 kg. Enzymatic processes in the rumen were studied, including the level of volatile fatty acids (using the gas chromatography method), nitrogen and its fractions (using the Kjeldahl method), methane concentration, and morphological and biochemical blood composition. Results: There was a decrease in the concentration of acetic acid in experimental group I (15.9%) (p < 0.05) and in the III group (60.3%) and propionic acid in all experimental groups by 5.6%–47.3% (p < 0.05). Feeding A. absinthium herb as part of the diet of experimental Group I contributed to a decrease in methane concentration by 17.8% (p = 0.05) and the lowest methane concentration was noted for experimental Group III. It was less than in control by 59.1% (p < 0.05). An increase in the concentration of glucose, total protein, and creatinine was found in the experimental groups (p < 0.05). The digestibility of organic matter (3.5%), crude fiber (3.6%), and hemicellulose (11.0%) increased with the feeding of A. absinthium herb. Conclusion: Thus, using biocomplexes based on A. absinthium herb and CoCl2 do not harm the rumen fermentation of cattle. Still, further microbiome studies are required to evaluate the effects of A. absinthium on cattle properly.

ADAPTIVE RESPONSES OF CATTLE DIGESTIVE SYSTEM AS INFLUENCED BY DIETARY ULTRAFINE IRON PARTICLES COMBINED WITH FAT DIETS E.V. Sheida, S.V. Lebedev, S.A. Miroshnikov, V.V. Grechkina, O.V. Shoshina

Relevance.On the processes of fermentation in the rumen of ruminants depends, how much final metabolites and their derivatives will be formed, which are necessary for the full development of the body, thehigh level of productivity, those processes also affect the level of formation of endogenous substances, in particular greenhouse gas emissions. These facts lead us to the search for new feed products that improve the metabolic processes in the rumen and the digestive system as a wholePhytochemicals can serve as an alternativeto traditional feed additives. The purpose of the article was to study the effect ofSalviae folia,Scutellaria baicalensis,Origanum vulgareon the formation of methane, the synthesis of volatile fatty acids and nitrogen, as the main indicators of the enzymatic activity of the rumen.Methods.The object of the study was the rumen fluid obtained from bulls of the Kazakh white-headed breed, aged 9–10 months, with an average weight of 225–230 kg (n = 4) with a chronic fistula of the rumen. For the study, a microration was prepared, including 70% of roughage and 30% of concentrated feed as a substrate, and nine prototypes were formed:Salviae folia— 0.8; 1.6; 2.4 g/kg of dry matter (DM),Scutellaria baicalensis— 0.15; 0.2; 0.3 g/kg ofDM,Origanum vulgare— 0.2; 0.3; 0.6 g/kg of DMof ration. The studies were carried out in vitro using the “ANKOM DaisyII” incubator and a specialized technique.Results.It was found that with the use of phytosubstancesSalviae foliaandScutellaria baicalensis, more acetic and propionic acids were formed, and with the use ofOriganum vulgarein various dosages there was a shift towards formation ofpropionic and valeric acids. The formation of a larger amount of microbial protein has been reliably established with the use ofSalviae folia,Scutellaria baicalensis,Origanum vulgarein various dosages. Methanogenesis decreased to a greater extent with the use of higher dosages of phytochemicals, regardless of the source.

The intestines of animals are a complex ecosystem consisting of a microbiome, host cells, and nutrients. There are about 100 trillion bacteria in the intestinal tract that form the intestinal microbiota. They are taxonomically classified by genus, family, order, and type. The intestinal microflora mainly consists of six types: Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, Fusobacteria and Verrucomicrobia. Bacterioidetes and firmicutes occupy a dominant position in the intestinal tract of animals and play a key role in the nutrient absorption system and help strengthen the intestinal barrier. The composition of the microbial community ecosystem is dynamic, and its composition depends on many factors including genes, medications, and feeding.The purpose of this review is to analyze the microbiome of the gastrointestinal tract of animals and factors affecting their biodiversity. The composition of the microbial community ecosystem is dynamic and depends on many factors, including genes, medications, and feeding. Changes in the feeding diet can cause time shifts (within 24 hours) in a large number of microorganisms. It follows that feeding is also a vital regulator of the intestinal microbiota. The prospects for applying the results of the study of the microbiota of the body of farm animals are obvious, since they can become the basis for creating technologies that allow correcting undesirable changes in the microbiome of animals that arise as negative consequences of the intensification of highly productive agricultural production.

Background and Aim: The gut microbiota of broilers plays a pivotal role in nutrient absorption, immune modulation, and mineral metabolism. Feed additives can influence these microbial and physiological processes, yet their integrated effects remain insufficiently understood. This study aimed to intelligently evaluate the impact of various feed additives on the intestinal microbiota and mineral composition of broiler chickens and to develop machine learning (ML) models for clustering and classification of diet-associated mineral and microbial profiles. Materials and Methods: A total of 385 Arbor Acres broilers (7 days old) were allocated into 11 groups, including one control semi-synthetic diet (SSD), one group with a semi-synthetic deficient diet (SSDD), and nine experimental groups receiving SSDD with different additives: Probiotics (Soya-bifidum and Sporobacterin), dietary fibers (cellulose, lactulose, and chitosan), enterosorbents (enterosgel and activated carbon), and ultrafine particles (UFPs) (Cu and Fe). Microbiota composition was assessed by 16S ribosomal RNA sequencing, and body mineral composition was determined through inductively coupled plasma mass spectrometer. To overcome data scarcity, synthetic records were generated using conditional tabular generative adversarial networks. K-means and hierarchical agglomerative clustering were used for mineral profile grouping, while logistic regression, SVM, and decision tree models classified diet types. Results: Hierarchical clustering revealed six distinct mineral profile groups (Silhouette = 0.524), with SSD and SSDD forming separate clusters. Feed additives such as UFPs, chitosan, and activated carbon induced similar mineral patterns. Key differentiating biomarkers were cobalt, zinc, strontium, arsenic, and lithium (p < 0.05). The decision tree classifier achieved 74% accuracy in predicting diet types based on microbiota data. Alpha diversity analysis showed enhanced microbial richness in groups fed lactulose, enterosgel, cellulose, or activated carbon. Conclusion: ML effectively elucidated complex relationships between diet, microbiota composition, and mineral metabolism in broilers. The integration of clustering and predictive models demonstrates the feasibility of intelligent feeding systems tailored to optimize gut health and nutrient utilization. Future studies integrating multi-omics data and broader farm-level validation will strengthen precision nutrition frameworks for sustainable poultry production. Keywords: broilers, clustering, conditional tabular generative adversarial networks, decision tree, feed additives, gut microbiota, machine learning, mineral metabolism.