Background:Nowadays, the treatment of burned patients is difficult because of the high frequency of infection with antibiotic resistance bacteria.Objectives:This study was conducted to evaluate the level of antibiotic resistance in Gram-negative bacteria and its relation with the existence of plasmid.Materials and Methods:The samples were collected from two hundred twenty hospitalized burned patients in Isfahan burn hospital during a three-month period (March 2012 to June 2012). The samples were isolated and the Gram-negative bacteria were identified using phenotypic method and API 20E System. Antibiotic susceptibility and plasmid profile were determined by standard Agar disc diffusion and plasmid spin column extraction methods.Results:Totally 117 Gram-negative bacteria were isolated, the most common were Pseudomonas aerugionsa (37.6%), P. fluorescens (25.6%), Acinetobacter baumanii (20/5%) and Klebsiella pneumoniae (7.6%), respectively. The isolates showed high frequency of antibiotic resistance against ceftazidime and co-amoxiclave (100%) and low frequency of antibiotic resistance against amikacin with (70%).The results indicated that 60% of the isolates harboured plasmid. On the other hand, the patients infected with A. baumanii and P. aeruginosa were cured (with 60% frequency) whereas, those infected with P. fluorescens were not cured. Hence, probably antibiotic resistance markers of A. baumanii and P. aeruginosa are plasmid mediated; however, P. fluorescens is chromosomally mediated.Conclusions:Based on our findings, P. aerugionsa is a major causative agent of wound infections and amikacin could be considered as a more effective antibiotic for treatment of the burned patients.

No abstract

No abstract

Memory corruption vulnerabilities remain a prevalent threat on low-cost bare-metal devices. Fuzzing is a popular technique for automatically discovering such vulnerabilities. However, bare-metal devices lack even basic security mechanisms such as Memory Management Unit. Consequently, fuzzing approaches encounter silent memory corruptions with no visible effects, making even discovery difficult. Once discovered, it is also essential to identify the type of observed vulnerability for applying mitigation. Both discovery and identification remain open challenges in the case of fuzzing firmware binaries. This paper addresses these problems by proposing an automated instrumentation technique that allows the observation of memory corruption vulnerabilities that are otherwise not observable and facilitates the automated identification of the observed vulnerability. Additionally, we surveyed state-of-the-art IoT fuzzers and analyzed their experimental methodologies. We found that existing approaches have fundamental problems that lead to incorrect or misleading results. To evaluate the effectiveness of IoT fuzzers, it is essential to determine the range and type of vulnerabilities that these fuzzers can discover. Thus, we propose the first ground-truth benchmark suite for IoT fuzzers that enables accurate and consistent evaluation of their vulnerability-finding performance. Our instrumentation framework's efficacy and efficiency in combination with state-of-the-art IoT fuzzers are assessed using the proposed benchmark.