Neutrosophic method for the analysis of the antibiotic resistance profile of Staphylococcus aureus isolated from cows with subclinical mastitis

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Andrea Carolina Vela Chiriboga
Kevin Marcelo Lozada Escobar
Paúl Sebastián Bedoya Lozada

Abstract

Subclinical mastitis is a disease with a major impact on dairy production, which requires a comprehensive approach for its control and prevention. One of the microorganisms responsible for this pathology in dairy cattle is Staphylococcus aureus, a pathogen that can cause serious infections and is difficult to treat therapeutically due to increasing antimicrobial resistance. In this context, the objective of this research is to develop a neutrosophic method for the analysis of the antibiotic resistance profile of Staphylococcus aureus isolated from cows with subclinical mastitis. The antibiotic resistance profile of S. aureus strains isolated from milk samples of cows with subclinical mastitis in the province of Tungurahua, Ecuador, was determined, with an emphasis on resistance to β-lactam antibiotics. The results obtained show that a significant proportion of the isolates were resistant to antibiotics commonly used in the treatment of infections in cattle, such as oxacillin and tetracycline. This analysis not only allows us to understand the current situation of antimicrobial resistance in the region, but also provides a solid basis for the development of effective management and treatment strategies, thus contributing to the sustainability of dairy production and improving the health of affected animals.

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Neutrosophic method for the analysis of the antibiotic resistance profile of Staphylococcus aureus isolated from cows with subclinical mastitis. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 254-264. http://fs.unm.edu/NCML2/index.php/112/article/view/720
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How to Cite

Neutrosophic method for the analysis of the antibiotic resistance profile of Staphylococcus aureus isolated from cows with subclinical mastitis. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 254-264. http://fs.unm.edu/NCML2/index.php/112/article/view/720