Assessing prevalence of antibiotic resistant microbes on fresh marketed vegetables of Aizawl city

Karuppasamy, C.V.; Lalsanglura, R; Lydia, M; Luke, Mobene Eneriene; Kiran, K. S.; Dipankar, B.

doi:10.21161/mjm.64614

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…The poor contrast transfer function seen in defocused single-particle images makes analysis of smaller complexes challenging. 7 Finally, there are issues of inhomogeneous local resolution, 8 radiation damage of acidic sidechains 9 and inherent disorder of large macromolecular complexes, which makes ab initio determination of accurate protein-ligand interactions intractable within the routinely available 3-5 Å EM models. 6,10 Therefore, applications of cryo-EM in the mainstream of drug discovery is still limited.…”

Section: Introductionmentioning

confidence: 99%

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

Vant

Lahey

Jana

et al. 2020

J. Chem. Inf. Model.

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Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands necessitates simultaneous fitting of the models into the density envelopes, exhaustive sampling of the ligand geometries, and most importantly, concomitant rearrangements in the sidechains to optimize the binding energy changes. In this article, we present a flexible-fitting pipeline where molecular dynamics flexible fitting (MDFF) is used to refine structures of protein-ligand complexes from 3-5 Å electron density data. Enhanced sampling is employed to explore the binding pockets rearrangements. To provide a model that can accurately describe the conformational dynamics of the chemicallydiverse set of small-molecule drugs inside MDFF, we use QM/MM and neural-network potential (NNP)/MM models of protein-ligand complexes, where the ligand is represented using the QM or NNP model and the protein is represented using established molecular mechanical force fields (e.g., CHARMM). This pipeline offers structures commensurate to or better than recentlysubmitted high-resolution cryo-EM or X-ray models, even when given medium to low-resolution data as input. The use of the NNPs makes the algorithm more robust to the choice of search models, offering a radius of convergence of 6.5 Å for ligand structure determination. The quality of the predicted structures was also judged by density functional theory calculations of ligand strain energy. This strain potential energy is found to systematically decrease with better fitting to density and improved ligand coordination, indicating correct binding interactions.

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Section: Introductionmentioning

confidence: 99%

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

Vant

Lahey

Jana

et al. 2020

J. Chem. Inf. Model.

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“…In non-diarrhoeic group, the E. coli from both pediatric patients from hospital and the community were detected which is very less than the previous reports in India [24,25]. This might be the possible source of transmission of this pathogen among children through mother, baby sitter or other members of the family [23].…”

Section: Discussionmentioning

confidence: 63%

“…The presence of E. coli from the population of the non-diarrhoeic patients also indicates their role as potent carrier of the organism, which may infect the other vulnerable population of the community [23]. In non-diarrhoeic group, the E. coli from both pediatric patients from hospital and the community were detected which is very less than the previous reports in India [24,25].…”

Section: Discussionmentioning

confidence: 82%

Detection of virulence genes in Shiga toxigenic Escherichia coli isolated from diarrhoeic and non-diarrhoeic pediatric patients in Ondo State, Nigeria

Olanrewaju

Onifade

2022

Microbes and Infectious Diseases

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Background: Shiga toxigenic Escherichia coli (STEC) is one of the major causes of intestinal infection, bloody and non-bloody diarrhoea and extra-intestinal complications in humans. It is therefore necessary to enumerate the virulence genes present in isolated STEC strains. Methods: This research investigated the prevalence of STEC and identified some virulence genes (stx1, stx2, and eaeA) from diarrhoeic and non-diarrhoeic pediatric patients of age 1 to 16 years. A total of 243 stool samples were collected from diarrhoeic and non-diarrhoeic pediatric patients (in ratio 43:200) in four different State Specialist Hospitals in Ondo State, Nigeria. Polymerase chain reaction (PCR) assay was used for detection of Shiga toxinproducing E. coli. The virulence genes (stx1, stx2, and eaeA) were detected by PCR assay. Results: Out of the 243 samples, 163 (67%) were positive for E. coli and specifically 6 of them were positive for E. coli O157 (3.7%). Exactly 50% of the 6 E. coli O157 contained eaeA while all the strains contained Stx1 and Stx2. The results obtained also revealed that 6 strains that were non-O157:H7 STEC also contained Stx1 and four strains contained Stx2 genes. Conclusion:The results obtained from this study revealed that Stx1 and Stx2 genes are of the genes responsible for the virulence of STEC among diarrhoeic and non-diarrhoeic pediatric patients.

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A New Evaluation Method for Antibiotic-Resistant Bacterial Groups in Environment

Watanabe¹,

Horinishi²,

Matsumoto³

et al. 2016

AiM

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In the present manuscript it was presented whether spreading of antibiotic resistant bacterial groups in environment could be monitored by our newly developed method by enumerating antibiotic resistant bacterial groups in various biological wastes and composts. Although the numbers were not so high, diverse kinds of colistin resistant bacteria (25 mg•L −1 ) were included in row cattle feces (1.78 × 10 4 MPN g −1 ) and cattle feces manure (>3.84 × 10 4 MPN g −1 ). Compost originated from leftover food (>44.8 × 10 4 MPN g −1 ) and shochu lee (>320 × 10 4 MPN g −1 ) included higher numbers of chlortetracycline resistant Pseudomonas sp., (25 mg•L −1 ), and row cattle feces included higher numbers of chlortetracycline resistant Enterobacteriacea (15.7 × 10 4 MPN g −1 ), which mostly consisted from Pantoea sp. or Xenorhobdus doucetiae. Numbers of multi drug resistant bacteria, resistant to 25 mg•L −1 of ciprofloxacin, streptomycin, chloramphenicol, and ampicillin, were the highest in row cattle feces (>143.6 × 10 4 MPN g −1 ), followed by cattle feces manure (4.19 × 10 4 MPN g −1 ), and shochu lee (0.36 × 10 4 MPN g −1 ), which included diverse kinds of bacterial group. The present results indicated that higher numbers of multi drug resistant bacteria were typically found in row cattle feces, and the method was found suitable to enumerate and identify them. These results suggested that the method might become their environmental risk evaluation method.

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Assessing prevalence of antibiotic resistant microbes on fresh marketed vegetables of Aizawl city

Cited by 3 publications

References 20 publications

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

Detection of virulence genes in Shiga toxigenic Escherichia coli isolated from diarrhoeic and non-diarrhoeic pediatric patients in Ondo State, Nigeria

A New Evaluation Method for Antibiotic-Resistant Bacterial Groups in Environment

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