Staphylococcal protein A (spa) typing of Staphylococcus aureus isolates causing nosocomial infections

BACKGROUND: Staphylococcal protein A (spa) typing is a typing method based on the DNA sequence analysis of staphylococcal protein A gene. The purpose of this study was to do molecular typing of Staphylococcus aureus isolated from patients in Toohid and Besat hospitals, Sanandaj, Iran, in 2014.METHODS: Clinical specimens were collected from hospitalized patients over a period of 1 year. Staphylococcus aureus isolates were identified using culture and biochemical standard methods based on the Clinical and Laboratory Standards Institute (CLSI) guideline. spa gene patterns in Staphylococcus aureus isolates were identified using spa-typing techniques.RESULTS: In total, 20 different patterns of spa gene were obtained in staphylococcus aureus isolates in this study, which included type t030 (6 cases), types t230, t459, and t701 (3 cases of each one), types t11332 and t304 (2 cases of each one), and types t325, t012, t1149, t1810, t197, t325, t7789, t808, t871, t937, t14896, t14913, t14928, and t14929 (1 case of each one). The highest prevalence belonged to types t030 (30.0%), and then, types t230, t459, and t701 (15.0% for each one). New types of t14896, t14913, t14928, and t14929 were identified during this study.CONCLUSION: There were some well-known patterns of spa types, and also we identified new types that should be studied more to qualify. Analysis of these patterns can improve insight to design nosocomial infection control programs

Flood Susceptibility Mapping Using Random Forest Machine Learning and Generalized Bayesian Linear Model

Today, the phenomenon of flooding is one of the most complex hazardous events that, more than any other natural disaster, causes deaths and finances every year in different parts of the world. Therefore, flood susceptibility mapping is the first step in a flood management program. The purpose of this study was to identify flood susceptible areas using two methods of random forest (RF) and Bayesian generalized linear model (GLMbayesian) machine learning in the Tajan watershed in Mazandaran province, Sari. Past flood distribution maps were prepared to predict future floods. Of the 263 flood locations, 80% (210 flood locations) was used for modeling and 20% (53 flood locations) was used for validation. Based on previous studies and surveying of the study area, 13 conditional factors were selected for flood zoning. The results showed that three factors of elevation (21.55), distance from the river (15.28) and slope (11.18) had the highest impact on flood occurrence in the study area, respectively. The results also showed that the AUC values for RF and GLMbayesian models were 0.91 and 0.847, respectively, indicating the superiority of the RF model and the accuracy of this model in flood susceptibility mapping in the study area. The highest flood susceptibility area in the RF model is in the very low class and the high class in the GLMbayesian model

Staphylococcal protein A (spa) typing of Staphylococcus aureus isolates causing nosocomial infections

BACKGROUND: Staphylococcal protein A (spa) typing is a typing method based on the DNA sequence analysis of staphylococcal protein A gene. The purpose of this study was to do molecular typing of Staphylococcus aureus isolated from patients in Toohid and Besat hospitals, Sanandaj, Iran, in 2014. METHODS: Clinical specimens were collected from hospitalized patients over a period of 1 year. Staphylococcus aureus isolates were identified using culture and biochemical standard methods based on the Clinical and Laboratory Standards Institute (CLSI) guideline. spa gene patterns in Staphylococcus aureus isolates were identified using spa-typing techniques. RESULTS: In total, 20 different patterns of spa gene were obtained in staphylococcus aureus isolates in this study, which included type t030 (6 cases), types t230, t459, and t701 (3 cases of each one), types t11332 and t304 (2 cases of each one), and types t325, t012, t1149, t1810, t197, t325, t7789, t808, t871, t937, t14896, t14913, t14928, and t14929 (1 case of each one). The highest prevalence belonged to types t030 (30.0%), and then, types t230, t459, and t701 (15.0% for each one). New types of t14896, t14913, t14928, and t14929 were identified during this study. CONCLUSION: There were some well-known patterns of spa types, and also we identified new types that should be studied more to qualify. Analysis of these patterns can improve insight to design nosocomial infection control programs

Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class

A school-based intervention to teach 3-4 grades children about healthy heart; The Persian Gulf Healthy Heart Project

BACKGROUND: Cardiovascular health promotion in children has the potential to reduce the risk of atherosclerosis in both the individual child and the population at large. It thus seems eminently reasonable to initiate healthful lifestyle training in childhood to promote improved cardiovascular health in adult life. AIMS: To test the hypothesis that a year long, classroom-based education for the third and fourth graders could change their knowledge scores about healthy heart. SETTINGS AND DESIGN: A randomized, controlled trial in elementary schools of Bushehr/Iran. METHODS AND MATERIALS: A total of 14 elementary schools, categorized by socioeconomic types and male and female setting were selected and randomized into control or intervention groups. Subjects were 1128 third and fourth graders, aged 9 to 10 years (49.1% boys and 50.9% girls). Over a course of 8 weeks, health educators and sport teachers of the elementary schools presented two hours sessions per week on heart function, nutrition, and exercise for healthy heart and living tobacco free for the intervention group. The education program was based on HeartPower! Program, an American Heart Association program. STATISTICAL ANALYSIS: Mann-Whitney U test and Wilcoxon matched-pairs signed rank test and Bonferroni correction for the two pair wise comparisons were used. RESULTS: Total heart knowledge at posttest was 25% correct higher in the intervention than in the control group (p<0.001). Difference in means of total healthy heart knowledge scores between control and intervention group increased from 1.43 points in baseline to 4.02 points in posttest (p<0.001). CONCLUSION: It can be concluded that the classroom-based cardiovascular health promotion had a significant effect on the heart healthy knowledge. Therefore, schools provide an excellent setting for introducing comprehensive healthy heart education and promotion of cardiovascular health to the general population

Molecular detection of ESBLs production and antibiotic resistance patterns in Gram negative bacilli isolated from urinary tract infections

Background: β-lactam resistance is more prevalent in Gram negative bacterial isolates worldwide, particularly in developing countries. In order to provide data relating to antibiotic therapy and resistance control, routine monitoring of corresponding antibiotic resistance genes is necessary. Aims: The aim of this study was the characterization of β-lactam resistance genes and its plasmid profile in bacteria isolated from urinary tract infection samples. Materials and Methods: In this study, 298 Gram negative bacteria isolated from 6739 urine specimens were identified by biochemical standard tests. Antimicrobial susceptibility testing was performed by the disk diffusion method. Extended-spectrum β-lactamase (ESBL)-producing strains were also detected by the double-disk synergy test. The presence of blaTEM and blaSHV genes in the strains studied was ascertained by polymerase chain reaction. Results: Of all Gram negative bacteria, Escherichia coli (69.1%) was the most common strain, followed by Klebsiella sp. (12.1%), Enterobacter sp. (8.4%), Proteus sp. (4.4%), Citrobacter (4%) and Pseudomonas sp. (2%). The most antibiotic resistance was shown to tetracycline (95.16%), nalidixic acid (89.78%) and gentamycin (73.20%) antibiotics. Among all the strains tested, 35 isolates (11.75%) expressed ESBL activity. The prevalence of TEM and SHV positivity among these isolates was 34.29%, followed by TEM (31.43%), TEM and SHV negativity (20.0%) and SHV (14.29%), respectively. Conclusions: Regular monitoring of antimicrobial drug resistance seems necessary to improve our guidelines in the use of the empirical antibiotic therapy