Eighty steers with a mean body weight of 319 kg were used in a study to evaluate the effect of a growth-promoting implant (trenbolone acetate plus estradiol benzoate), monensin, and oxytetracycline on the steer performance and shedding of some foodborne pathogens. The steers were allotted to one of eight treatment combinations according to a randomized complete block design with 16 pens of five animals. Rectal fecal samples were collected before treatment commenced and over a period of more than 24 weeks to study the influence of treatments on the intestinal microbiology of the animals. Results supported the beneficial effect of the hormonal implant on the performance of feedlot steers (average daily gain, feed efficiency, and fat thickness), on carcass characteristics (hot carcass weight, lean yield), and economic value of the carcasses (P < 0.01). The levels of Escherichia coli in feces were not affected by treatments but remained high throughout the study period. Antibiotic-resistant isolates of E. coli were more frequently found as the study progressed but were not associated with any specific treatment. Also independently of treatment, we observed a reduction over time in the shedding of Campylobacter and Yersinia during the feeding period, whereas the shedding of Enterococcus was increased. The results of this study confirmed the beneficial economic effect of growth-promoting agents in beef production and showed that the agents tested did not specifically affect the overall microbial evolution of the animal gut. However, the study also showed, independently of the growth promoter used, the shedding of Campylobacter, Yersinia, and antibiotic-resistant E. coli in the feedlot environment. These bacteria also may be found in the colonic tissue of steers at slaughter and might be a source of carcasses contamination.
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http://dx.doi.org/10.4315/0362-028x-69.6.1256 | DOI Listing |
J Food Drug Anal
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School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan.
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Sapienza University of Rome, Rome, Rome, Italy.
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In the contemporary era, grappling with the vast expanse of big data presents a formidable obstacle, particularly when it comes to extracting vital information from extensive textual sources. The constant influx of news articles from various agencies necessitates an enormous amount of time to digest comprehensively. A viable solution to address this challenge lies in the realm of automatic text summarization, which is a pivotal and intricate endeavor within the field of natural language processing.
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