Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as “Genome map association”. Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming.

Non-alcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease worldwide, affecting nearly 25% of the global adult population. Increasing evidence suggests that functional and compositional changes in the gut microbiota may contribute to the development and promote the progression of NAFLD. 16S rRNA gene next-generation sequencing is widely used to determine specific features of the NAFLD microbiome, but a complex system such as the gut microbiota requires a comprehensive approach. We used three different approaches: MALDI-TOF-MS of bacterial cultures, qPCR, and 16S NGS sequencing, as well as a wide variety of statistical methods to assess the differences in gut microbiota composition between NAFLD patients without significant fibrosis and the control group. The listed methods showed enrichment in Collinsella sp. and Oscillospiraceae for the control samples and enrichment in Lachnospiraceae (and in particular Dorea sp.) and Veillonellaceae in NAFLD. The families, Bifidobacteriaceae, Lactobacillaceae, and Enterococcaceae (particularly Enterococcus faecium and Enterococcus faecalis), were also found to be important taxa for NAFLD microbiome evaluation. Considering individual method observations, an increase in Candida krusei and a decrease in Bacteroides uniformis for NAFLD patients were detected using MALDI-TOF-MS. An increase in Gracilibacteraceae, Chitinophagaceae, Pirellulaceae, Erysipelatoclostridiaceae, Muribaculaceae, and Comamonadaceae, and a decrease in Acidaminococcaceae in NAFLD were observed with 16S NGS, and enrichment in Fusobacterium nucleatum was shown using qPCR analysis. These findings confirm that NAFLD is associated with changes in gut microbiota composition. Further investigations are required to determine the cause-and-effect relationships and the impact of microbiota-derived compounds on the development and progression of NAFLD.

The gut microbiota plays an important role in maintaining human health, as well as in the development of various pathologies, as indicated by a large amount of research. One of the manifestations of an imbalance in the gut microbiome composition is the appearance of various diseases or immune reactions, in particular, atopic dermatitis (AD) and/or food allergies (FA). In this research, using 16S NGS sequencing, it was found that the gut microbiome of children with food allergies and children with atopic dermatitis can be characterized as having higher inflammatory potential. Both groups exhibited an abundance of representatives from the Pasteurellaceae and Erysipelotrichaceae families, as well as a decrease in the relative number of representatives from the Barnesiellaceae family compared to healthy participants. In the group of participants with food allergies, there was a decrease in the relative number of Desulfovibrionaceae representatives and Bifidobacteriaceae family enrichment in relatively healthy participants. In addition, when comparing this group with patients with atopic dermatitis, it was revealed that a number of representatives of such families as Erysipelotrichaceae, Ruminococcaceae and Sutterellaceae prevailed. This information confirms that AD and FA correlate with changes in the composition of the gut microbiota. Further research is needed to determine the cause–effect connections and the effect of compounds derived from the microbiota on the AD and FA development and progression, as well as to create new probiotic drugs to prevent and modulate immune responses, including at an early age.

The aim of this study was to determine the associations between genotypes of the prolactin receptor gene (PRLR) and reproductive, growth, and meat traits in pigs. Genetic groups of sows were used: Large White (LW), Danish Landrace (LD), and Landrace × Yorkshire × Duroc hybrids (L × Y × D). Reproductive traits studied were the total number of born (TNB) and the number born alive (NBA). The growth traits were the number of days to 100 kg and the average daily gains (ADG). The meat traits were the average backfat thickness (BFT), half carcass weight (HCW), area of M. longissimus dorsi (MLT), and lean meat content (LM). The polymorphism was detected using the polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP) method. It was found that the genotype BB is associated with the best NBA. In sows LD, the AB genotype is associated with best number of days to 100 kg; in hybrids L × Y × D, the AA genotype is associated with better meat traits as compared to the genotype BB.