Animals

The success rate of embryo transfer (ET) is influenced by various factors, including embryo quality, environmental conditions, and recipient cows. This study examines the impact of ET on embryo success rates using embryos produced using the ovum pickup method, with fresh and frozen embryos generated on day 7 after in vitro fertilization and subsequently implanted into recipient cows. The factors that contributed to the success rate of ET were investigated, with a focus on embryo type, parity, and farm management. Furthermore, metabolic profile tests were conducted to determine the factors underlying the observed differences in pregnancy rates across the farms. The analysis revealed that pregnancy rates did not significantly differ according to embryo type or parity. However, significant differences (p < 0.05) were observed in the glucose, cholesterol, non-esterified fatty acid, total protein, globulin, albumin/globulin, and aspartate aminotransferase levels of the recipients. Furthermore, a comparison of farms with high and low pregnancy rates revealed significant differences in the glucose, cholesterol, total protein, albumin, globulin, and albumin/globulin levels (p < 0.05). Differences between farms were associated with the feed management strategy, which highlights the significance of optimal recipient cow management.

Early-life development of immune functions is crucial for calf health, growth, and future productivity. Galacto-oligosaccharides (GOSs) have been reported to facilitate ruminal microbial establishment and improve growth in Holstein dairy calves, but their prolonged influence on immunoglobulin levels, hindgut microbiota, and metabolic regulation remains insufficiently understood. This study evaluated the effects of early-life GOS supplementation on immune-related indicators, intestinal microbial ecology, and metabolic profiles in Holstein calves. Twenty-four newborn Holstein female dairy calves were randomly assigned to a control group (CON, n = 12) or a GOS group (GOS, n = 12; 10 g/day from birth to day 28). After supplementation ceased on day 28, calves previously receiving GOS were referred to as the GOSS group (n = 6). Immunoglobulin levels, gut microbiota, and fecal and serum metabolomes were evaluated during supplementation and six weeks after withdrawal. GOS supplementation significantly increased serum IgA and IgG levels during the treatment, with IgG levels remaining elevated for six weeks after discontinued supplementation. Although overall microbial diversity was not markedly altered, GOS selectively enriched bacterial taxa and function pathways linked to amino acid synthesis, unsaturated fatty acid production, and coenzyme-related metabolism. On day 70, GOSS group displayed distinct fecal and serum metabolomic profiles, with altered metabolites primarily associated with vitamin B6, folate, cobalamin metabolism, branched-chain amino acid biosynthesis, and purine and arginine pathways. These results demonstrate that early-life GOS supplementation promotes sustained immune and metabolic alterations following supplementation cessation, potentially mediated by modulation of gut microbial functions. These findings suggest that early dietary GOS supplementation may support physiological maturation in calves and could be useful as a nutritional strategy in calf-rearing systems.

In animal experiments, causal relationships in physiological or disease processes are investigated by using interventions. Applying second-generation statistical methods could be used to identify important links in life processes. This article as a first step describes how second-generation statistical methods that are often used in social sciences are currently applied in veterinary medicine, including a single-animal experimental study, or an ecotoxicity study in fish. It explains how second-generation statistical methods allow flexible modeling to simultaneously calculate causal relationships between constructs in several layers. It continues with a discussion on how theoretical concepts from this statistical approach could be transferred to experimental or medical data. As an applied example, an investigation on a data set analyzed with a second-generation method is presented, showing how this allows us to calculate relationships between variables within a complex theoretical model. Limitations of the use of second-generation statistical methods as strict requirements on the data sets are overcome by technical developments; however, causality cannot be established by statistically testing hypothesized causal structures. Using second-generation statistical methods in the future might promote obtaining more data from one animal and thereby potentially even reducing animals in line with the 3R principle.