Antibiotic resistance and genetic diversity among Pseudomonas aeruginosa isolated from urinary tract infections in Iran.

Aims: To determine the antibiotic resistance and genetic diversity of Pseudomonas aeruginosa isolates. Methods: The antibiotic resistance, genetic diversity and the conjugate transformation among Pseudomonas aeruginosa collected from patients with urinary tract infection in Tehran, Iran, was investigated. Results: Antibiotic resistance against cefepime was seen in 51.74% of the isolates, followed by amikacin (47.76%). blaOXA-10 and blaVIM were the most prevalent extended-spectrum ß-lactamase and metallo-ß-lactamases genes, respectively. Five clusters (C1-C5) were obtained by pulse field gel electrophoresis and multilocus sequence typing revealed two strain types, ST235 and ST664. Conjugation detected blaOXA-48 and blaNDM genes were transferred to Escherichia coli K12. Conclusion: The resistance of P. aeruginosa to antibiotics is increasing, which highlights the need to determine the resistance patterns to design better treatment strategies.

Analysis of Energy Harvesting Enhancement in Piezoelectric Unimorph Cantilevers.

Environmental energy harvesting is a major operation in research and industries. Currently, researchers have started analyzing small-scale energy scavengers for the supply of energy in low-power electrical appliances. One area of interest is the use of piezoelectric materials, especially in the presence of mechanical vibrations. This study analyzed a unimorph cantilever beam in different modes by evaluating the effects of various parameters, such as geometry, piezoelectric material, lengths of layers, and the proof mass to the energy harvesting process. The finite element method was employed for analysis. The proposed model was designed and simulated in COMSOL Multiphysics, and the output parameters, i.e., natural frequencies and the output voltage, were then evaluated. The results suggested a considerable effect of geometrical and physical parameters on the energy harvesters and could lead to designing devices with a higher functional efficiency.

A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset.

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset.

A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2.

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.

First Study of Antimicrobial Activity of Ceftazidime-Avibactam and Ceftolozane-Tazobactam Against Pseudomonas aeruginosa Isolated from Patients with Urinary Tract Infection in Tehran, Iran.

PURPOSE: Pseudomonas aeruginosa causes complicated and/or nosocomial UTI. These infections are usually associated with severe and multi-drug resistant P. aeruginosa isolates. As there is no study about the activity of novel antibiotics ceftazidime-avibactam (CZA) and ceftolozane-tazobactam (C/T) against P. aeruginosa isolates in Iran, we aimed to evaluate for the first time the efficacy of these agents against P. aeruginosa isolated from patients with UTI in Iran. Then, the genetic diversity of the resistant isolates was assayed. METHODS: In this study, a total of 200 P. aeruginosa isolates were collected from patients with UTI in Tehran, Iran. Disk diffusion and Minimum Inhibitory Concentration (MIC) methods were applied to determine the resistance of the isolates to CZA, C/T, and the other antibiotics. Extended-spectrum ß-lactamases (ESBLs) and Metallo Beta Lactamase (MBL) production were assayed by Combination disk diffusion test (CDDT). Polymerase chain reaction (PCR) was carried out to detect the resistance genes, including beta-lactamases and carbapenemases genes. Finally, genomic analysis of the isolates was performed using the Pulse field gel electrophoresis (PFGE). RESULTS: Among the isolates, 16 (8%) were resistant to CZA and C/T that MIC confirmed it. The resistant isolates showed high resistance to the other classes of antibiotics. Among the resistant isolates, 31.2% and 75% were ESBL and MBL producers, respectively. The prevalence of blaOXA10, blaVIM, blaOXA48, blaOXA2 , and blaCTX-M was 100%, 50%, 31.2%, 25%, and 12.5%. Furthermore, two isolates (12.5%) harbored blaPER and blaNDM genes. The resistant isolates were grouped into 14 distinct pulsotypes and two shared pulsotypes were found. CONCLUSION: Ceftazidime-avibactam and ceftolozane-tazobactam showed high activity against the P. aeruginosa isolated from patients with UTI in Iran. The low rate of resistance to the antibiotics is also alarming and should be considered to avoid further spreading of the antibiotic resistance among the P. aeruginosa and the other bacteria.

Molecular detection of metallo-ß-lactamase genes, bla IMP-1, bla VIM-2 and bla SPM-1 in imipenem resistant Pseudomonas aeruginosa isolated from clinical specimens in teaching hospitals of Ahvaz, Iran.

BACKGROUND AND OBJECTIVES: Carbapenem resistant Pseudomonas aeruginosa is a serious cause of nosocomial infections. The main purpose of the study is to determine the prevalence rate of imipenem resistant Pseudomonas aeruginosa carrying metallo-ß-lactamase (MBL) genes. MATERIAL AND METHODS: 236 Pseudomonas aeruginosa isolates were collected from teaching hospitals of Ahvaz University of Medical Sciences during a period of 9 months in 2012. These strains were identified using conventional microbiological tests. The susceptibility of isolates to antibiotics were assessed using disk diffusion test. The IMP-EDTA combination disk phenotypic test was performed for detection of MBL producing strains. Finally, polymerase chain reaction (PCR) was performed to detect MBL genes, bla IMP-1, bla VIM-2 and bla SPM-1 in imipenem resistant strains. RESULTS: Out of 236 examined isolates, 122 isolates (51.4%) were resistant to imipenem. The IMP-EDTA combination test showed that among 122 imipenem resistant strains, 110 strains (90%) were phenotipically MBL producers. Additionally, the results of PCR method showed that 2 strains (1.6%) and 67strains (55%) of imipenem resistant Pseudomonas aeruginosa isolates contained bla VIM-2 and bla IMP-1 genes respectively. No SPM-1gene was found in the examined samples. CONCLUSION: Resistance of P. aeruginosa isolates to imipenem due to MBL enzymes is increasing in Ahavaz. Because of clinical significance of this kind of resistance, rapid detection of MBL producing strains and followed by appropriate treatment is necessary to prevent the spreading of these organisms.