BACKGROUND: The present analysis focuses on phenotypic and genotypic characterizations of efflux-mediated erythromycin resistance in Streptococcus pneumoniae due to an increase in macrolide resistance in S. pneumoniae worldwide. METHODS: We investigated the prevalence of efflux-mediated erythromycin resistance and its relevant genetic elements from 186 specimens of S. pneumonia isolated from clinical and normal flora from Tehran, Iran. The presence of erythromycin resistance genes was tested by PCR with two sets of primers, specific for erm(B) and mef(A/E), and their genetic elements with tetM, xis, and int genes. Isolates were typed with the BOX PCR method and tested for resistance to six antibiotics. RESULTS: Antibiotic susceptibility tests revealed that 100% and 47% isolates were resistant to tetracycline and erythromycin, respectively. The erythromycin and clindamycin double-disc diffusion test for macrolide-lincosamide-streptograminB (MLSB) resistance phenotype showed 74 (84%) isolates with the constitutive MLSB phenotype and the remaining with the M phenotype. BOX PCR demonstrated the presence of 7 types in pneumococci with the M phenotype. Fourteen (16%) isolates with the M phenotype harbored mef(A/E), tetM, xis, and int genes. CONCLUSIONS: The present results suggest dissemination of polyclonal groups of S. pneumoniae with the M phenotype carrying resistance genes attributed to transposon 2009.
Anti-Bacterial Agents/*pharmacology ; Bacterial Proteins/genetics ; DNA, Bacterial/metabolism ; Drug Resistance, Multiple, Bacterial/*genetics ; Erythromycin/*pharmacology ; Genotype ; Humans ; Microbial Sensitivity Tests ; Phenotype ; Pneumococcal Infections/microbiology/pathology ; Polymerase Chain Reaction ; Streptococcus pneumoniae/*drug effects/*genetics/isolation & purification ; Tetracycline/pharmacology

BACKGROUND: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. METHODS: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. RESULTS: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. CONCLUSION: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.
Human Engineering* ; Noise ; Passive Cutaneous Anaphylaxis ; Principal Component Analysis

BACKGROUND: Resilience engineering (RE) is a new paradigm that can control incidents and reduce their consequences. Integrated RE includes four new factors—self-organization, teamwork, redundancy, and fault-tolerance—in addition to conventional RE factors. This study aimed to evaluate the impacts of these four factors on RE and determine the most efficient factor in an uncertain environment. METHODS: The required data were collected through a questionnaire in a petrochemical plant in June 2013. The questionnaire was completed by 115 respondents including 37 managers and 78 operators. Fuzzy data envelopment analysis was used in different α-cuts in order to calculate the impact of each factor. Analysis of variance was employed to compare the efficiency score means of the four above-mentioned factors. RESULTS: The results showed that as α approached 0 and the system became fuzzier (α = 0.3 and α = 0.1), teamwork played a significant role and had the highest impact on the resilient system. In contrast, as α approached 1 and the fuzzy system went toward a certain mode (α = 0.9 and α = 1), redundancy had a vital role in the selected resilient system. Therefore, redundancy and teamwork were the most efficient factors. CONCLUSION: The approach developed in this study could be used for identifying the most important factors in such environments. The results of this study may help managers to have better understanding of weak and strong points in such industries.
Analysis of Variance* ; Plants* ; Surveys and Questionnaires