Introduction: Streptococci are the major etiology in mastitis in dairy cattle, a cause of huge economic losses in the dairy industries. This study was aimed to determine the diversity of spp. isolated from clinical mastitis of cattle reared in Bangladesh.
View Article and Find Full Text PDFObjectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics.
Materials And Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g.
This study was designed to identify from clinical mastitis of cattle and determine their antimicrobial resistance and virulence determinants to evaluate their potential public health significance. A total of 105 composite milk samples (80 from cattle with clinical mastitis and 25 from apparently healthy cattle) were analyzed. were isolated by culturing on enterococcal selective media and identified by PCR and sequencing.
View Article and Find Full Text PDFis one of the major significant pathogens causing mastitis, the most complex and costly diseases in the dairy industry worldwide. Present study was undertaken to isolate, detect the virulence factors, phylogroup, antimicrobial susceptibility and antimicrobial resistance genes in from cows with clinical mastitis. A total of 68 milk samples comprising 53 from clinical mastitis and 15 from apparently healthy cattle were collected from four different established dairy farms in Bangladesh.
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