Gram-positive enterococciare opportunistic and resistant to many antibiotics. This study aimed to investigate the presence of Enterococcus spp. in our community and whether these isolates are resistant to the macrolides class of antibiotics. Fifty isolates from 112 clinical samples were recognized as Enterococcus spp. and confirmed using Vitek-2 system. The current study found that 50/112 (44.6%) represented the total isolates, 38/50 (76%) of which were Enterococcus faecalis, while 12/50 (24%) were Enterococcus faecium, twenty (40%) isolates from root canals and 30 (60%) isolates from urine were isolated. The sensitivity of the enterococcal isolates to various macrolides (erythromycin, azithromycin and clarithromycin) antibiotics was determined by using the disk diffusion approach. Later, the minimum inhibitory concentrations (MICs) for erythromycin and the most resistant drug among this group were investigated using the agar dilution technique. And then molecular detection for mef gene was done using two specific primers via PCR technique. The current findings revealed high resistance rates to macrolide antibiotics which were reported in 21/50 (42%) of total isolates and at significant levels of MIC values for most isolates (57%). And as for the PCR results, it was negative for mef gene in all tested isolates.
The photodynamic inactivation against Methicillin-resistant Staphylococcus aureus using two different lasers, 532 nm diode pumped solid state laser (DPSS) in combination with safranin O and 650 nm diode laser in combination with methylene blue was investigated in the present work. A hundred swab samples were collected from patients with burn and wound infections admitted to two hospitals in Baghdad (Specialized Burns Hospital in Medical City and Al Imamein Al Jwadein Medical City Hospital) from December 2015 to February 2016 Antimicrobial susceptibility was performed by using Kirby- Bauer method. The irradiation experiments included four groups; a control group, a photosensitizer only group, a laser irradiation only group and a laser irr
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For sparse system identification,recent suggested algorithms are -norm Least Mean Square (
-LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named
-ZA-LMS,
In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.
Background: The Epstein-Barr virus (EBV) relates to the torch virus family and is believed to have a substantial impact on mortality and perinatal events, as shown by epidemiological and viral studies. Moreover, there have been documented cases of EBV transmission occurring via the placenta. Nevertheless, the specific location of the EBV infection inside the placenta remains uncertain. Methods: The genomic sequences connected to the latent EBV gene and the levels of lytic EBV gene expression in placental chorionic villous cells are examined in this work. A total of 86 placentas from patients who had miscarriage and 54 placentas from individuals who had successful births were obtained for analysis. Results: The research employed QPCR to dete
... Show MoreBiometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
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