Growth Performance and Fecal Microbiota of Dairy Calves Supplemented with Autochthonous Lactic Acid Bacteria as Probiotics in Mexican Western Family Dairy Farming.

Probiotic supplementation in dairy cattle has achieved several beneficial effects (improved growth rate, immune response, and adequate ruminal microbiota). This study assessed the effects on the growth parameters and gut microbiota of newborn dairy calves supplemented with two Lactobacillus-based probiotics, individually (6BZ or 6BY) or their combination (6BZ + 6BY), administrated with the same concentration (1 × 109 CFU/kg weight) at three times, between days 5 and 19 after birth. The control group consisted of probiotic-unsupplemented calves. Growth parameters were recorded weekly until eight weeks and at the calves' ages of three, four, and five months. Fecal microbiota was described by high-throughput sequencing and bioinformatics. Although no significant effects were observed regarding daily weight and height gain among probiotic-supplemented and non-supplemented calves, correlation analysis showed that growth rate was maintained until month 5 through probiotic supplementation, mainly when the two-strain probiotics were supplied. Modulation effects on microbiota were observed in probiotic-supplemented calves, improving the Bacteroidota: Firmicutes and the Proteobacteria ratios. Functional prediction by PICRUSt also showed an increment in several pathways when the two-strain probiotic was supplemented. Therefore, using the three-administration scheme, the two-strain probiotic improved the growth rate and gut microbiota profile in newborn dairy calves. However, positive effects could be reached by applying more administrations of the probiotic during the first 20 days of a calf's life.

Adverse Effect of Antibiotics Administration on Horse Health: An Overview.

Antibiotics-based therapy plays a paramount role in equine medicine because of their potential pharmacokinetics and pharmacodynamics properties. Conventional antibiotics show bacteriostatic and bactericidal properties by interfering bacterial cell wall and protein synthesis as well as inhibiting RNA polymerase, DNase 1, and DNA gyrase. Antibiotics are extensively used not only for the treatment of varied bacterial infections but also the prevention of postoperative and secondary infections. Surprisingly, antibiotics such as sulfonamides or trimethoprim/sulfonamide combinations, benzylpenicillin, cefquinome, fluphenazine, enrofloxacin, and sodium ceftriaxone cause detrimental effects on horses' health, namely, diarrhea, colitis, nephrotoxicity, ototoxicity, dysrhythmia, arthropathy, ataxia, anorexia, seizures, peripheral neuropathy, and certain neurologic abnormalities. Therefore, in equine practice, it is essential to optimize and analyze the combinations, formulations, route of administration, and dosages of certain antibiotics before administration. This review overviews the mode of actions and pharmacologic attributes of certain antibiotics, commonly used toward the treatment of disparate horse diseases. Most importantly, special emphasis was given to spotlight the potential adverse effects encountered during the administration of antibiotics as therapeutics in horses.

Antagonistic trait of Staphylococcus succinus strain AAS2 against uropathogens and assessment of its in vitro probiotic characteristics.

The desideratum aim of the present context was to isolate a promising antagonist probiotic bacterium from fermented food item as biocontrol agent against uropathogens. Among diversified isolates evaluated for antagonistic trait, Staphylococcus succinus strain AAS2 was found to be an auspicious candidate against urinary tract infection (UTI) causing bacterial pathogens, being the most active against Staphylococcus aureus with substantial activity of 352.5 ±â€¯5.4 AU/mL. Further, the in vitro probiotic attributes of strain AAS2 were assessed using systematic methodology. The isolate exhibited tolerance to acidic condition (up to pH 3.0) and simulated gastric juice (at pH 3.0) with fairly high survival logarithmic cell counts of 5.3 ±â€¯0.15 and 5.23 ±â€¯0.02 log cfu/mL, respectively. Additionally, strain AAS2 showed capability to resist 0.5% w/v bile salt too. It also revealed significant values of auto-aggregation (32.5 ±â€¯1.3-56.5 ±â€¯1.4%) and cell surface hydrophobicity (38.35 ±â€¯1.4%) properties. The isolate showed resistivity towards phenol (6.8 ±â€¯0.08 log cfu/mL) and lysozyme (58.6 ±â€¯1.6%). Further, the susceptibility trait of strain AAS2 to conventional antibiotics made this isolate a promising probiotic bacterium. Most importantly, the isolate depicted DPPH (2,2-Diphenyl-1-picrylhydrazyl) and hydroxyl radical scavenging activities in a concentration dependent manner, thereby exhibiting its propitious antioxidative properties. In a nutshell, the outcomes of this investigation divulge the plausible use of S. succinus strain AAS2 as biocontrol agent against uropathogens, and recommended further applications in pharmaceutics due to its pronounced probiotic traits.

Experimental and computational studies of fatty acid distribution networks.

Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products (e.g., meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables (Vk) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of ω-6/ω-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions (cj) like c1 = acid and/or base methylation treatments, c2 = with/without fermentation, c3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters (V(k)), the corresponding Box-Jenkins (ΔV(kj)) and PT operators (ΔΔV(kj)) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy > 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erdos-Rényi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.