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Identification and Molecular Detection of Klebsiella spp. from the Buccal Cavity of Humans and Dogs
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Klebsiella infections in the oral cavities of both humans and dogs have been increasingly reported and are associated with various buccal infections, as well as systemic infections. These infections appear to be rising particularly among pets and their owners, suggesting a possible bidirectional transmission between humans and dogs. Therefore, this study aimed to investigate the potential link of mixed infections involving Klebsiella pneumoniae and Enterococcus spp. Buccal cavity samples were collected from humans (n = 25) and dogs (n = 25). Samples were initially enriched in tryptic soy broth and subsequently cultured on tryptic soy agar, MacConkey agar, and blood agar. All isolates were identified using the VITEK 2 system, and eight selected isolates were further analyzed by 16S rRNA gene PCR. In humans, Klebsiella spp. were detected in 24% of samples by primary isolation, 44% by VITEK 2 analysis, and 12% were confirmed by 16S rRNA PCR. In dogs, primary isolation and VITEK 2 identification both showed a prevalence of 28%, while 20% were confirmed by 16S rRNA PCR. Among human samples, isolates were detected in 10% of males and 13.3% of females, whereas in dogs, 14.3% of males and 27.3% of females were positive. The results revealed that K. pneumoniae accounted for 66.7% and Enterococcus faecalis for 33.3% of isolates from humans, while in dogs, K. pneumoniae represented 80% and E. faecalis 20% of the isolates. These findings highlight the potential significance of transmission of these bacterial species between humans and dogs.

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Publication Date
Sat Jan 01 2022
Journal Name
Turkish Journal Of Physiotherapy And Rehabilitation
classification coco dataset using machine learning algorithms
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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

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