In vitro characteristics of X- and Y-bearing ram spermatozoa sorted by bovine serum albumin (BSA) column and TLR7/8 ligand R848.

The quality of the separated fractions in sex-sorted semen is very important for the success of the artificial insemination. This study aimed to evaluate some in vitro characteristics (DNA quantity, kinematic parameters and enzymes activity) of X- and Y-bearing ram spermatozoa sorted by bovine serum albumin (BSA) column and toll-like receptors (TLR)7/8 ligand R848. The ejaculates from six rams were collected by artificial vagina and subjected to a computer-assisted semen analysis (CASA). Total motility and percentage of the sperms with rapid and medium progressivity or non-progressivity in whole ejaculates and in X and Y fractions were analyzed. Activity of the enzymes ALP, GGT, CK, LDH and accumulation of lactate in the seminal plasma of ejaculates and in the environmental fluid of sexed spermatozoa were measured by biochemical analyzer. DNA was isolated from precipitated spermatozoa, and its quantity was measured. For both protocols the DNA mass from X-bearing fractions was higher, than from Y-bearing fractions. The high total motility of X- and Y-bearing spermatozoa as well as greater percent sperms with progressive motility were observed after use of BSA protocol. The application of TLR7/8 ligand R848 protocol led to reducing of Y-sperm motility and enhancement of non-progressivity in both fractions, which corresponded to the determined high amount of the extracellular lactate. For both methods, the significantly reduced activity of enzymes in the X and Y spermatozoa environmental fluids was established. Both protocols produce X- and Y-sperm fractions with satisfactory quality (over 80% total motility and over 50% rapid and medium progressive spermatozoa in each fraction).

Machine Learning Approach for Muscovy Duck (Cairina moschata) Semen Quality Assessment.

This study aimed to develop a comprehensive approach for assessing fresh ejaculate from Muscovy duck (Cairina moschata) drakes to fulfil the requirements of artificial insemination in farm practices. The approach combines sperm kinetics (CASA) with non-kinetic parameters, such as vitality, enzyme activities (alkaline phosphatase (AP), creatine kinase (CK), lactate dehydrogenase (LDH), and γ-glutamyl-transferase (GGT)), and total DNA methylation as training features for a set of machine learning (ML) models designed to enhance the predictive capacity of sperm parameters. Samples were classified based on their progressive motility and DNA methylation features, exhibiting significant differences in total and progressive motility, curvilinear velocity (VCL), velocity of the average path (VAP), linear velocity (VSL), amplitude of lateral head displacement (ALH), beat-cross frequency (BCF), and live normal sperm cells in favour of fast motility ones. Additionally, there were significant differences in enzyme activities for AP and CK, with correlations to LDH and GGT levels. Although motility showed no correlation with total DNA methylation, ALH, wobble of the curvilinear trajectory (WOB), and VCL were significantly different in the newly introduced classification for "suggested good quality", where both motility and methylation were high. The performance differences observed while training various ML classifiers using different feature subsets highlight the importance of DNA methylation for achieving more accurate sample quality classification, even though there is no correlation between motility and DNA methylation. The parameters ALH, VCL, triton extracted LDH, and VAP were top-ranking for "suggested good quality" predictions by the neural network and gradient boosting models. In conclusion, integrating non-kinetic parameters into machine-learning-based sample classification offers a promising approach for selecting kinetically and morphologically superior duck sperm samples that might otherwise be hindered by a predominance of lowly methylated cells.