The poultry industry is continuously seeking strategies to improve chicken health, welfare, and productivity while minimizing the use of antibiotics. Probiotics, as a natural alternative, have gained considerable attention due to their ability to modulate the gut microbiota, enhance immune function, and improve productive performance. The aim of this article is to provide updated information on the importance of probiotics in chicken. To achieve this, a systematic review was conducted to synthesize current findings on the impact of probiotics on chicken gut microbiota composition, immune responses, behavior, productive traits, and meat quality using literature databases such as PubMed, CABI Abstract, Google Scholar, and ScienceDirect from April 2010. The PRISMA method was adopted, where 85 articles met the criteria for this review article after several exclusion criteria. The review stated that due to the influence of the intestinal microbial balance, probiotics promote beneficial bacterial populations, suppress pathogens, improve gut health, and enhance nutrient absorption, improving growth performance. Additionally, the immunomodulatory effects of probiotics help strengthen the chicken’s immune system, reducing disease susceptibility. Moreover, recent studies suggest that probiotics may positively influence chicken behavior, particularly by reducing stress, enhancing overall health, and improving welfare conditions. This review also addresses gaps in knowledge, highlighting areas where further research is needed to optimize probiotic use in poultry production systems. Understanding both the short- and long-term effects of probiotics on chicken health and performance will provide critical insights for developing sustainable strategies to boost poultry industry outcomes.

Background and Aim: A piglet’s pre-weaning performance significantly influences both animal welfare and profitability in pig production. Understanding piglet pre-weaning performance influencing factors is key to enhancing animal welfare, reducing losses, and boosting profitability. The study aimed to evaluate the impact of parity, season of birth, and sex on within-litter variation and pre-weaning performance of F1 Large White × Landrace pigs. Materials and Methods: Information regarding total litter size, number of born alive, number of stillbirths, piglet weight at birth, mortality, and count of weaned F1 Large White × Landrace piglets was acquired from the farm database (April 2022–February 2023). 2602 females and 2882 males, a total of 5484 piglets were utilized, with records from 360 sows. The coefficient of variation (CV) of birth weights among piglets within a litter was calculated. The general linear model analysis in MiniTab 17 was used to evaluate the data, with Fisher’s least significant difference test (p < 0.05) used for mean separation and Pearson’s moment correlation coefficient calculated to assess relationships between survival rates, mortality rates, litter size, birth weight, and birth weight CV. Results: Parity had a statistically significant impact on litter size, birth weight, and survival rate (p < 0.05). The sow’s parity did not significantly (p > 0.05) impact the number of piglets born alive or weaned. Multiparous sows had a significantly larger litter size (p < 0.05) than primiparous sows at birth. The litter weights for parities 2, 4, and 5 did not significantly differ (p > 0.05), with averages of 20.95, 20.74, and 20.03 kg, respectively. About 91.29% was the highest survival rate recorded in parity 2 (p < 0.05). The 1st week of life recorded an 8.02% mortality rate. The mortality rate in parity 3–5 group was significantly (p < 0.05) higher (11.90%) in week 1 than in the other groups (parity 1: 6.79%, parity 2: 5.74%, parity 3–5: 8.54 and 9.21%). The litter sizes in autumn (17.34) and spring (17.72) were significantly larger (p < 0.05) than those in summer (16.47) and winter (16.83). In autumn and spring, the survival rate (83.15 and 85.84%, respectively) was significantly lower (p < 0.05) compared to summer (88.40%) and winter (89.07%). In all seasons, the litter weights did not significantly differ (p > 0.05). The birth weight CV was significantly (p < 0.05) lower during summer (20.11%) than during spring (22.43%), autumn (23.71%), and winter (21.69%). The season of birth had no significant effect (p > 0.05) on the number of live piglets. Males (1.34 kg) were heavier (p < 0.05) than females (1.30 kg) at birth. Notably, the birth weight CV was similar between males (22.43%) and females (22.52%). Litter size was positively correlated with average litter weight (rp = 0.576, p < 0.001), birth weight CV (rp = 0.244, p < 0.001), and mortality rate (rp = 0.378, p < 0.001). An insignificant relationship was observed between average litter weight and birth weight CV (rp = –0.028, p > 0.05) and survival rate (rp = –0.032, p > 0.05). Conclusion: In F1 Large White × Landrace pigs, birth uniformity among piglets declines as litter size grows larger. In parity 3–5, multiparous sows yield litters with reduced uniformity. With an increase in litter size, uniformity among piglets at birth worsens. A larger litter size and greater piglet birth weight variation are linked to a higher pre-weaning mortality rate. Producers need a balanced selection approach to boost litter size and must cull aging sows carefully to introduce younger, more productive females. Keywords: birth weight coefficient of variation, born alive, pig production, pre-weaning mortality, survival rate.

Semen quality plays a crucial role in the successful implementation of breeding programs, especially where artificial insemination (AI) is practiced. Bulls with good semen traits have good fertility and can produce a volume of high semen per ejaculation. The aim of this review is to use an information approach to highlight candidate genes and their relation to bull semen production traits. The use of genome-wide association studies (GWAS) has been demonstrated to be successful in identifying genomic regions and individual variations associated with production traits. Studies have reported over 40 genes associated with semen traits using Illumina BeadChip single-nucleotide polymorphism (SNPs).

Sows are selected for their prolificacy. Therefore, assessing difficulties that can be associated with large litters is crucial. This review aims to highlight the factors that can affect reproductive performance such as breed, parity, litter s...

Goat milk has gained recognition for its medicinal, cosmetic, and health benefits, particularly its potential to improve human skin conditions. Its therapeutic properties are attributed to bioactive compounds influenced by genes such as lactoferrin (LTF), lysozyme (LYZ), and β-casein (CSN2), known for their antimicrobial, immunomodulatory, and anti-inflammatory effects. Genetic factors are hypothesized to shape goat milk’s composition and its effectiveness in managing dermatological conditions like eczema and psoriasis. Understanding these genetic determinants is critical to optimizing the use of goat milk in skin health applications. This review aims to explore the application of genomic tools to elucidate the medicinal properties of goat milk and its implications for skin care. By identifying the specific genes and molecular mechanisms underpinning its therapeutic effects, genomic studies have provided insights into the bioactive constituents of goat milk, such as peptides, proteins, and lipids, which contribute to its dermatological efficacy. Candidate genes, including growth hormone receptor (GHR), butyrophilin (BTN1A1), and lactoglobulin (LGB), have been identified as critical for enhancing milk quality and functionality. Future research should integrate genomic data with functional studies to further investigate goat milk’s immunomodulatory, antimicrobial, and antioxidant activities. Such insights could advance targeted breeding strategies and innovative formulations for managing inflammatory skin conditions and promoting skin health.

In this study, runs of homozygosity (ROH) and quantitative trait locus/association (QTL) for semen parameters in selected Chinese and South African beef cattle breed were estimated. The computed results showed 7516 ROH were observed between classes 0–5 Mb with no ROH observed in classes >40 Mb. Distribution of ROH showed high level of genomic coverage for ANG, NGU, CSI, and BEL breeds. Approximately 13 genomic regions with QTL were controlling sperm motility, sperm concentration, semen volume, sperm count, sperm head abnormalities, sperm tail abnormalities, sperm integrity, and percentage of abnormal sperm traits. Nine candidate genes, CDF9, MARCH1, WDR19, SLOICI, ST7, DOP1B, CFAF9, INHBA, and ADAMTS1, were suggested to be associated with above mentioned QTL traits. The results for inbreeding coefficient showed moderate correlation between FROH vs FHOM at 0.603 and high correlation between FROH 0–5 Mb 0.929, and lowest correlation for 0–>40 Mb 0.400. This study suggested recent inbreeding in CSI, BEL, ANG, BON, SIM, and NGU breeds. Furthermore, it highlighted varied inbreeding levels and identified QTL for semen traits and genes of association. These results can assist in implementation of genetic improvement strategies for bulls and provide awareness and proper guidelines in developing breeding programs.