Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (p < 0.01), and would be used as potential tools in antimicrobial resistance surveillance and clinical diagnosis in veterinary medicine.

The accumulation of reactive oxygen species (ROS) triggers oxidative stress in cells by oxidizing and modifying various cellular components, preventing them from performing their inherent functions, ultimately leading to apoptosis and autophagy. Glutathione (GSH) is a ubiquitous intracellular peptide with multiple functions. In this study, a hydrogen peroxide (H${}_2$O${}_2$)-induced oxidative damage model in IPEC-J2 cells was used to investigate the cellular protection mechanism of exogenous GSH against oxidative stress. The results showed that GSH supplement improved the cell viability reduced by H${}_2$O${}_2$-induced oxidative damage model in IPEC-J2 cells in a dose-dependent manner. Moreover, supplement with GSH also attenuated the H${}_2$O${}_2$-induced MMP loss, and effectively decreased the H${}_2$O${}_2$-induced mitochondrial dysfunction by increasing the content of mtDNA and upregulating the expression TFAM. Exogenous GSH treatment significantly decreased the ROS and MDA levels, improved SOD activity in H${}_2$O${}_2$-treated cells and reduced H${}_2$O${}_2$-induced early apoptosis in IPEC-J2 cells. This study showed that exogenous GSH can protect IPEC-J2 cells against apoptosis induced by oxidative stress through mitochondrial mechanisms.

Aflatoxin B₁ (AFB₁) is one of the most toxic, naturally occurring carcinogen compounds and is produced by specific strains of fungi. Crop contamination with AFB₁ can cause huge economic losses and serious health problems. Many studies have examined the microbiological degradation of AFB₁, especially the use of efficient AFB₁-degrading microorganisms, to control AFB₁ contamination. Here, we reported the identification of a new Rhodococcus pyridinivorans strain (4-4) that can efficiently degrade AFB₁ (degradation rate 84.9%). The extracellular component of this strain showed the strongest capacity to degrade AFB₁ (degradation rate 83.7%). The effects of proteinase K, SDS, temperature, pH, incubation time, and AFB₁ concentration on the AFB₁ degradation ability of the extracellular component were investigated. We sequenced the complete genome of this strain, encoding 5246 protein-coding genes and 169 RNA genes on a circular chromosome and two plasmids. Comparative genomic analysis revealed high homology with other Rhodococcus strains with high AFB₁-degradation ability. Further proteomic analyses of this strain identified a total of 723 proteins in the extracellular component, including multiple potential AFB₁-degrading enzymes, along with enzymes that are reported to response to AFB₁ treatment. Overall, the results demonstrate that R. pyridinivorans 4-4 would be an excellent candidate for the biodegradation and detoxification of AFB₁ contamination.

Diets containing higher-amylose-content starches were proved to have some beneficial effects on monogastric animals, such as promoting the proliferation of intestinal probiotics. However, current research on the effects of diets with different starch sources on animals at the extraintestinal level is still very limited. We hypothesized that diets with different starch sources may affect lipid-related gene expression and metabolism in the liver of pigs. This study aimed to use adult pig models to evaluate the effects of diets with different starch sources (tapioca starch, TS; pea starch, PS) on the liver gene expressions and metabolism. In total, 48 growing pigs were randomly assigned to the TS and PS diets with 8 replicate pens/group and 3 pigs per pen. On day 44 of the experiment, liver samples were collected for metabolome and transcriptome analysis. Metabolome data suggested that different starch sources affected (p < 0.05) the metabolic patterns of liver. Compared with the TS diet, the PS diet increased (p < 0.05) some unsaturated fatty acids and several amino acids or peptide levels in the liver of pigs. Moreover, transcriptome data indicated the PS diets elevated (p < 0.05) fatty acid β-oxidation-related gene expression in the liver of pigs, and reduced (p < 0.05) unsaturated fatty acid metabolism-related gene expression. The results of quantitative real-time PCR confirmed that the PS diet upregulated (p < 0.05) the expression of acyl-CoA dehydrogenase very long chain (ACADVL), carnitine palmitoyl transferase (CPT) 1A, and malonyl-CoA decarboxylase (MLYCD), and downregulated (p < 0.05) the expression level of cytochrome P450 2U1 (CYP2U1) and aldehyde dehydrogenase 1B1 (ALDH1B1) in the liver. In addition, the results of a Mantel test indicated the muscle fatty acids were significantly closely correlated (p < 0.05) with liver gene expressions and metabolites. In summary, these findings suggest that diets containing higher amylose starches improved the lipid degradation and the unsaturated fatty acid levels in pig livers, and thus can generate some potential beneficial effects (such as anti-inflammatory and antioxidant) on pig health.

During weaning, piglets are exposed to multiple nutritional, environmental, pathogenic, and psychological stressors and are therefore susceptible to structural and functional intestinal injury. The resulting damage can lead to diarrhea, impaired growth, and increased susceptibility to disease, seriously compromising piglet health and the efficiency of pig production. Establishing stable and reliable experimental models that match specific research objectives is essential for elucidating the mechanisms of intestinal injury in piglets and for evaluating nutritional interventions and prevention and control strategies. Current research on porcine intestinal injury uses a multilevel framework centered on porcine intestinal epithelial cells, porcine intestinal organoids, and in vivo piglet models. These models offer distinct advantages in experimental controllability, tissue-level physiological relevance, and whole-organism biological fidelity, respectively, while each has a defined scope of application. Focusing on intestinal injury in piglets, this review comprehensively summarizes the construction methods, injury-induction approaches, and evaluation systems used across these model levels and analyzes the key factors affecting model stability and the comparability of research findings. This review is intended to inform mechanistic studies of intestinal injury in piglets, model standardization, and the evaluation of nutritional intervention strategies. On this basis, the review concludes that porcine intestinal epithelial cell lines are most appropriate for high-throughput mechanistic screening, organoids for questions that require epithelial diversity and donor- or region-specific context, and in vivo piglet models for confirming physiological and production relevance. Because model reproducibility depends strongly on challenge dose, animal or donor age, culture conditions, and assessment endpoints, a coordinated sequence from cell lines to organoids and finally to in vivo validation should be combined with standardized reporting of model parameters.