Pseudomonas aeruginosa, a ubiquitous environmental organism, is a difficult-to-treat opportunistic pathogen due to its broad-spectrum antibiotic resistance and its ability to form biofilms. In this study, we investigate the link between resistance to a clinically important antibiotic, imipenem, and biofilm formation. First, we observed that the laboratory strain P. aeruginosa PAO1 carrying a mutation in the oprD gene, which confers resistance to imipenem, showed a modest reduction in biofilm formation.We also observed an inverse relationship between imipenem resistance and biofilm formation for imipenem-resistant strains selected in vitro, as well as for clinical isolates.We identified two clinical isolates of P. aeruginosa from the sputum of cystic fibrosis patients that formed robust biofilms, but were sensitive to imipenem (MIC≤2 μg/ml). To test the hypothesis that there is a general link between imipenem resistance and biofilm formation, we performed transposon mutagenesis of these two clinical strains to identify mutants defective in biofilm formation, and then tested these mutants for imipenem resistance. Analysis of the transposon mutants revealed a role for previously described biofilm factors in these clinical isolates of P. aeruginosa, including mutations in the pilY1, pilX, pilW, algC, and pslI genes, but none of the biofilmdeficient mutants became imipenem resistant (MIC≥8 μg/ml), arguing against a general link between biofilm formation and resistance to imipenem. Thus, assessing biofilm formation capabilities of environmental isolates is unlikely to serve as a good predictor of imipenem resistance. We also discuss our findings in light of the limited literature addressing planktonic antibiotic resistance factors that impact biofilm formation.
The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co
... Show MoreBiosynthesis of nanoparticles has received considerable attention due to the growing need to develop environmentally benign nanoparticle synthesis processes that do not use toxic chemicals. Therefore, biosynthetic methods employing both biological agents such as bacteria and fungus or plant extracts have emerged as a simple and a viable alternative to chemical synthetic and physical method .It is well known that many microbes produce an organic material either intracellular or extracellular which is playing important role in the remediation of toxic metals through reduction of metal ions and acting as interesting Nano factories. As a result, in the present study Ag NPs were syn
... Show MoreIn this work, the study of
Biosorption is an effective method to remove toxic metals from wastewaters. In this study biosorption of lead and chromium ions from solution was studied using Citrobacter freundii and Citrobacter kosari isolated from industrial wastewater. The experimental results showed that optimum grwoth temperature for both bacteria is 30oC and the optimum pH is 7 &6 for C. freundii and C. kosari respectively. While the optimum incubation period to remove Pb and Cr for C. freundii and C. kosari is 4 days and 3days respectively. Also the biosorption of Pb and Cr in mixed culture of bacteria and mixed culture of Pb and Cr was investigated. Result indicate that uptake of Cr and Pb for C.freundii, C. kosari and in mixes culture of both bacteria is 58%, 53%
... Show MoreKinetics and mechanism studies of oxidation of some α-amino acids (Proline, Arginine, Alanine) (AA) by N-Bromosuccinimide (NBS) by using conductivity method was carried out. The kinetic study showed that the reaction was first order with respect to NBS and AA. The effect of addition of HClO4 to the reaction was negative on the rate of reaction. The reaction was carried out at different temperatures in which * * * , S , G were calculated. The rate of reaction of AA was as follows: Proline > Arginine > Alanine
In this work, pure and doped Vanadium Pentoxide (V2O5) thin films with different concentration of TiO2 (0, 0.1, 0.3, 0.5) wt were obtained using Pulse laser deposition technique on amorphous glass substrate with thickness of (250)nm. The morphological, UV-Visible and Fourier Transform Infrared Spectroscopy (FT-IR) were studied. TiO2 doping into V2O5 matrix revealed an interesting morphological change from an array of high density pure V2O5 nanorods (~140 nm) to granular structure in TiO2-doped V2O5 thin film .Transform Infrared Spectro
... Show MoreIn this work, lead oxide nanoparticles were prepared by laser ablation of lead target immersed in deionized water by using pulsed Nd:YAG laser with laser energy 400 mJ/pulse and different laser pulses. The chemical bonding of lead oxide nps was investigated by Fourier Transform Infrared (FTIR); surface morphology and optical properties were investigated by Scanning Electron Microscope (SEM) and UV-Visible spectroscopy respectively, and the size effect of lead oxide nanoparticles was studied on its antibacterial action against two types of bacteria Gram-negitive (Escherichia coli) and Gram-positive (Staphylococcusaurus) by diffusion method. The antibacterial property results show that the antibacterial activity of the Lead oxide NPs was
... Show MoreThe logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables. The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.
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