Identification and Antibiogram Assay of Escherichia Coli Isolated From Chicken Eggs

Hossain, Md. Sazzad; Rahman, Wan Aizan Wan Abdul; Ali, Sajid; Sultana, Tania; Hossain, Khandaker Monower

doi:10.3329/jbs.v29i0.54828

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The group pairs comparisons that involved Staphylococcus aureus achieved high Accuracy and F1-score rates, above 91%. One of the explanations for these results is that Staphylococcus aureus produces yellow colonies [15] on a plate, which are very typical and differentiated from the Escherichia coli and Pseudomonas aeruginosa that produce beige colonies on a plate [16,17]. As Escherichia coli and Pseudomonas aeruginosa colonies are both beige on a plate, the problem of differentiating each becomes more difficult for the classifiers.…”

Section: Resultsmentioning

confidence: 99%

Distinction of Different Colony Types by a Smart-Data-Driven Tool

2022

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Background: Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. Methods: This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies discrimination, developed in Petri-plates. In order to test and validate the system, images of three bacterial species (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) cultured in Petri plates were used. Results: The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for Pseudomonas aeruginosa vs. Staphylococcus aureus, 91% for Escherichia coli vs. Staphylococcus aureus and 84% Escherichia coli vs. Pseudomonas aeruginosa. Conclusions: These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.

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Section: Resultsmentioning

confidence: 99%

Distinction of Different Colony Types by a Smart-Data-Driven Tool

2022

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“…Compared to the forearm bacteria colonies, the lettuce bacterial colonies were much larger and appeared moist and smooth. These are all characteristics of Gram-negative E. coli grown on nutrient agar cultures (20).…”

Section: Lettuce Bacteriamentioning

confidence: 94%

Effect of Gram-positive bacteria on antibiotic resistance in Gram-negative bacteria

Willem,

Kim,

Moody

2024

J Emerg Invest

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Antibiotics are one of the most common treatments for bacterial infections, but the emergence of antibiotic resistance is a major threat to the control of infectious diseases. Many factors contribute to the development of antibiotic resistance. One is bacterial conjugation from Gram-positive to Gram-negative bacteria where there is a transfer of resistance genes from Gram-positive to Gram-negative bacteria that could increase antibiotic resistance in the latter. In light of these observations, we decided to test whether Gram-negative bacteria that came into contact with Gram-positive bacteria had a higher resistance to the antimicrobial properties of spices than Gram-negative bacteria that did not come into contact with Gram-positive bacteria. We hypothesized that Gram-negative bacteria that had been exposed to Gram-positive bacteria would be more resistant to antibiotics than unexposed Gram-negative bacteria. To test our hypothesis, we cultured and subcultured lettuce (Gram-negative) bacteria and forearm (Gram-positive) bacteria, as well as lettuce bacteria that encountered forearm bacteria, on agar plates using powdered ginger, which has previously been shown to have antimicrobial properties to assess antibiotic resistance. We found that lettuce bacteria that encountered forearm bacteria were indeed more resistant to ginger since they showed more growth than lettuce bacteria. These results indicate that Gram-positive bacteria may transfer their resistance genes to Gram-negative bacteria, resulting in increased antibiotic resistance. If thousands of Gram-positive bacteria become antibiotic-resistant and pass those genes to Gram-negative bacteria found in the human body, it can make bacterial infections more difficult to treat.

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“…The antibiotic susceptibility pro les of the isolated pathogens were determined using the disk diffusion method following the guidelines set by the Clinical and Laboratory Standards Institute (CLSI) in 2020. A panel of commonly used antibiotics was tested against each isolate following (Ferdaus et al, 2019;Hossain et al, 2011) with some modi cations. The diameter of the inhibition zone was measured and interpreted according to CLSI guidelines to classify isolates as susceptible, intermediate, or resistant (Table 2).…”

Section: Methodsmentioning

confidence: 99%

High Prevalence of multidrug resistant common foodborne bacteria in raw beef and ready-to-eat samples from Dhaka City Corporations, Bangladesh

Fahim,

Muchemi,

Chowdhury

et al. 2024

Preprint

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Food safety in red meat value chain is a critical concern globally, with implications for both public health and economic stability. In the context of Bangladesh, a country with a burgeoning population and a significant reliance on red meat consumption, understanding the complexities of zoonosis, identifying the associated microbes, and addressing the AMR risk is paramount. This study investigates the prevalence of common foodborne pathogens and their multidrug resistance (MDR) patterns in raw beef and ready-to-eat (RTE) samples from Dhaka City Corporations, Bangladesh. Bacterial isolates from 60 raw beef and 60 RTE samples were identified using culturing and PCR-based molecular detection methods. The prevalence rates of E. coli, Salmonella spp., Shigella spp., S. aureus, Listeria spp., and Campylobacter spp. were determined, along with their antibiotic susceptibility profiles following CLSI guidelines. The study revealed that in raw beef samples, the prevalence of E. coli, Salmonella spp., Shigella spp., S. aureus, Listeria spp., and Campylobacter spp. were 25%, 46.67%, 66.67%, 96.67%, 66.67%, and 40%, respectively. In RTE samples, the occurrence rates were 30%, 3.33%, 15%, 25%, 21.67%, and 23.33%, respectively. Total viable and individual counts exceeded Bangladesh Food Safety Authority standards. Most isolates exhibited MDR, underlining the need for enhanced hygiene during beef processing and RTE preparation. The study addresses a knowledge gap, highlighting the public health risks linked with foodborne pathogens and the emergence of MDR. This research contributes to better food safety measures and awareness, safeguarding public health in urban settings like Dhaka, Bangladesh.

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Identification and Antibiogram Assay of Escherichia Coli Isolated From Chicken Eggs

Cited by 4 publications

References 13 publications

Distinction of Different Colony Types by a Smart-Data-Driven Tool

Distinction of Different Colony Types by a Smart-Data-Driven Tool

Effect of Gram-positive bacteria on antibiotic resistance in Gram-negative bacteria

High Prevalence of multidrug resistant common foodborne bacteria in raw beef and ready-to-eat samples from Dhaka City Corporations, Bangladesh

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