Is it Necessary to initiate antibiotic therapy in children with pharyngitis?

Streptococcuscus Beta Hemolytic Group A is the most important pathogen causing which may lead to purulent and non purulent angina. Rheumatic fever is the most important complication that is the cause of 30% to 40% of cardiac disease and disablement. This study was performed to evaluate prevalence of Streptococcuscus Beta Hemolytic Group A and estimate role of clinical findings in children with Streptococcuscus angina diagnosis. antibiotic resistance was also assessed evaluated in the patients with bacterial pharyngitis. Throat culture was performed on 104 patients referred to Amiralmomenin hospital of Semnan at the age range of 5 to 15 years having angina without begin on antibiotic treatment after the completion of the questionnaire. A frequency of 1% have been assessed for Streptococcuscus Beta Hemolytic Group A, coagulase -positive Staphylococci and non-group A Streptococcuscus frequencies were 10.6% and 17.3% respectively. 100% of patients had pharyngeal erythema, 72% had fever, 55% had exudates and 52% had cervical adenopathy. The diagnosed Streptococcuscus was sensitive against penicillin, erythromycin and amoxicillin and resistant against cotrimoxazole. In examining Staphylococcus aureus antibiotic-resistance, only 40% of cases were sensitive to clindamycin and 40% were also sensitive to vancomycin. Very low frequency of group A Streptococcuscus has undermined the routine use of antibiotic and show that the clinical based diagnosis alone is not reliable and rational use of antibiotics requires the use of other diagnostic methods such as throat culture and rapid antigen test (RATs). Also in analyzing coagulase -positive Staphylococci antibiotic resistance, we can see increased cases of resistance against neomycin and clindamycin which indicates the necessity of rational treatment of patients afflicted by strep to coccal infections

Initial GCS and laboratory findings of patients with TBI are associated with the GOSE and mortality rate at one year

Background: To evaluate the relationship between presenting Glasgow Coma Scale (GCS) or laboratory data of patients with TBI and Extended Glasgow Outcome Scale (GOSE) and final outcome (deceased, survived) at one year.Methods: 74 patients (59 males and 15 females; mean age ±SD of 40±19years) who presented with TBI were entered into the study, and their GCS and laboratory data were recorded. After one year, GOSE level and final outcome were evaluated with 11 yes/no questions obtained from the patients or their first-degree relatives.Results: The patients with lower GCS on admission or day six, significantly had lower GOSE. Moreover, the lower the GCS in the first week of admission, the poorer the final outcome. Among laboratory data, the base deficit (BD) level of -6 or worse on admission was an indicator of mortality at one year. Hypernatremia was the only laboratory factor which predicted poor GOSE after a year. Furthermore, patients with serum hypernatremia, hyperkalemia, or high PTT levels on the first week of admission had poor final outcome.Conclusions: Presenting GCS and metabolic derangements are reliable indicators of long-term outcome and GOSE at one year.

A Simulation Study to Assess the Effect of the Number of Response Categories on the Power of Ordinal Logistic Regression for Differential Item Functioning Analysis in Rating Scales

Objective. The present study uses simulated data to find what the optimal number of response categories is to achieve adequate power in ordinal logistic regression (OLR) model for differential item functioning (DIF) analysis in psychometric research. Methods. A hypothetical ten-item quality of life scale with three, four, and five response categories was simulated. The power and type I error rates of OLR model for detecting uniform DIF were investigated under different combinations of ability distribution (θ), sample size, sample size ratio, and the magnitude of uniform DIF across reference and focal groups. Results. When θ was distributed identically in the reference and focal groups, increasing the number of response categories from 3 to 5 resulted in an increase of approximately 8% in power of OLR model for detecting uniform DIF. The power of OLR was less than 0.36 when ability distribution in the reference and focal groups was highly skewed to the left and right, respectively. Conclusions. The clearest conclusion from this research is that the minimum number of response categories for DIF analysis using OLR is five. However, the impact of the number of response categories in detecting DIF was lower than might be expected

Machine learning tools to improve nonlinear modeling parameters of RC columns

Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental data sets: identifying the most likely mode of failure of structural components, and implementing data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It was found that machine learning regression models and machine learning black-boxes were more accurate than current provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized Linear Regression was the most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black-box models evaluated, namely the Gaussian Process Regression and the Neural Network (NN), provided the most accurate estimates of MPs a and b. The NN model was the most accurate machine learning tool of all evaluated. A multi-class classification tool from the Scikit-learn machine learning library correctly identified column mode of failure with 79% accuracy for rectangular columns and with 81% accuracy for circular columns, a substantial improvement over the classification rules in ASCE 41-13

Distribution of socioeconomic factors among the new patients of skin cancer in Iran

Background: The global burden of cancer due to population growth and aging, and various environmental factors is increasing. Skin cancer is the most common cancer among Iranians and among men, is more common. There is strong evidence from Industrialized and less developed countries that cancer incidence and survival is related to socioeconomic factors. The aim of this study was to investigate the relationship between socioeconomic variables including Human Development Index, unemployment rate and Urbanization ratio with the incidence of skin cancer in Iran. Method: The panel data were for 30 provinces for 6 years) 2007 to 2012(. Data of socioeconomic factors were collected from the Statistical Center of Iran and the data related to the incidence of cancer were collected from the reports on cancer registry of Health and Medical Education Ministry. For data analysis Stata11th version was used. Result: There is no relation between unemployment and the incidence of skin cancer. There is negative relationship between urbanization and incidence of skin cancer in both sexes. There is negative relation between HDI and the incidence of skin cancer in both sexes. Conclusion: Among the three variables selected in this study, the human development index and the urbanization, influenced on the cancer incidence. Therefore, in order to prevent skin cancer, paying attention appears to be necessary for policymakers. Key words: Socioeconomic Factors, Skin Neoplasm, Ira

Characterization of Effective Native Lactic Acid Bacteria as Potential Oral Probiotics on Growth Inhibition of Streptococcus mutans

Background and Objective: Probiotics' effects on harmful oral bacteria have been verifed. As antibiotic resistance becomes a major problem, searching for novel potential species is important. The objective of this study was to select novel safe strains of lactic acid bacteria with potentials as oral probiotics. Furthermore, ability of these strains to suppress growth and attachment of Streptococcus mutans as the most important cariogenic bacteria in tooth decay was investigated. Material and Methods: Initial identification tests, including Gram staining and catalase and oxidase tests, were carried out on 22 strains of lactic acid bacteria isolated from Iranian traditional dairy products. Safety of the strains was assessed using hemolysis test and antibiotic resistance assessment. Strains were then assessed for probiotic characteristics such inhibition of Streptococcus mutans growth, tolerance to lysozyme enzymes and ability of adhesion as well as ability of decreasing Streptococcus mutans adhesion. Selected strains were identified using16S rRNA molecular method. Results and Conclusion: Of all strains, four strains with the optimal probiotic characteristics were selected. These included one Lactobacillus brevis, one Lactobacillus casei and two Lactobacillus paraceasei. These four strains showed strong antimicrobial characteristics against Streptococcus mutans, were resistant to oral lysozyme enzymes and included high adhesion abilities to polystyrene wells. Furthermore, they decreased Streptococcus mutans attachment; thus, biofilm formation by this bacterium was prevented. These strains were recognized as safe strains since they were approved in assessments of antibiotic susceptibility and hemolytic activity. Therefore, these four strains are suggested as oral probiotics. Conflict of interest: The authors declare no conflict of interest

Predicting the price of second-hand vehicles using data mining techniques

The electronic commerce, known as “E-commerce”, has been boosted rapidly in recent years, and makes it possible to record all information such as price, location, customer’s review, search history, discount options, competitor’s price, and so on. Accessing to such rich source of data, companies can analyze their users’ behavior to improve the customer satisfaction as well as the revenue. This study aims to estimate the price of used light vehicles in a commercial website, Divar, which is a popular website in Iran for trading second-handed goods. At first, highlighted features were extracted from the description column using the three methods of Bag of Words (BOW), Latent Dirichlet Allocation (LDA), and Hierarchical Dirichlet Process (HDP). Second, a multiple linear regression model was fit to predict the product price based on its attributes and the highlighted features. The accuracy index of Actuals-Predictions Correlation, the min-max index, and MAPE methods were used to validate the proposed methods. Results showed that the BOW model is the best model with an Adjusted R-square of 0.7841

Evaluation of the antibacterial effect of nickel oxide nanoparticles against bacteria involved in dental caries

Tooth decay is one of the most common diseases in the oral cavity and is one of the most widespread diseases in the human population. This study aimed to determine the antibacterial effect of nickel oxide nanoparticles against bacteria involved in tooth decay. In this study, the disk diffusion method was used to determine the antibiotic susceptibility and the microdilution broth method was used to determine the minimum inhibitory concentration (MIC). Nanoparticles were also synthesized in two molecular size (A: 8.1 and B: 12 nm) by the sol-gel method. The MIC of the first nanoparticle for Streptococcus sanguinis and Streptococcus mutans was 31.25 and 125 μg/ml, respectively. The MIC of the second nanoparticle for S. sanguinis was 125 μg/ml. In the case of S. mutans up to a concentration of 500 μg/ml, no growth inhibition was observed. The results showed that nickel oxide nanoparticles have acceptable antibacterial properties against S. mutans and S. sanguinis, which can be used in dental materials to prevent dental caries. However, this requires the determination of cellular toxicity and its side effects in future studies.

The Fear of COVID-19 Scale: A meta-Analytic structural equation modeling approach

The widespread administration and multiple validations of the Fear of Covid-19 Scale (FCV-19S) in different languages have highlighted the controversy over its underlying structure and the resulting reliability index. In the present study, a meta-analysis based on structural equation modeling (MASEM) was conducted to assess the internal structure of the 7-item, 5-point Likert-type FCV-19S version, estimate an overall reliability index from the underlying model that best reflected the internal structure (one τ-equivalent factor, one congeneric factor, or two-factor models), and perform moderator analyses for the model-implied inter item correlations and estimated factor loadings. A Pearson inter-item correlation matrix was obtained for 48 independent studies, from which a pooled matrix was calculated following a random-effects multivariate meta-analysis. The results from the one-stage MASEM analysis showed that the two-factor model properly fitted the pooled matrix, while the τ-equivalent and congeneric one-factor models did not. Even though, the use of a bifactor model exhibited the predominance of the general factor over the domain-specific ones. High omega coefficients were obtained for the entire scale (.91) and the psychological (.83) and physiological (.83) symptoms subscales. Moderator analyses evidenced an increase in the estimated factor loadings, as well as in the reliability of the FCV-19S, when the standard deviation of the total scores increased and when the FCV-19S was administered to specific (vs. general) populations. The FCV-19S can be therefore considered as a highly related two-factor scale whose reliability makes it suitable for applied and research purposes.2022-2

Evaluation of the validity and reliability of the Persian version of Iowa Satisfaction with Anesthesia Scale in Iran

Introduction: The interaction between the doctor and the patient achieved when the physician is able to communicate effectively with the patient. Iowa Satisfaction with Anesthesia Scale is a tool for this purpose. Given that ISAS is originally in English and understudy in Iran, we decided to translate this scale into Persian and evaluate its validity and reliability.Purpose: Our aim in this study is translating, validity and reliability of ISAS.Methods: This study is an observational study with a correlational design that adopts an analytical approach. The population consisted of all patients undergoing surgery at Imam Reza Hospital out of whom 162 patients met the inclusion criteria, i.e. aged 18 years and above, transferred from the OP to ICU, exposed to general anesthesia and signed the consent form.Results: Patients completed the questionnaire in 5 minutes. The mean (maximum-minimum) age of the patients was 57.39 (18-87). As for gender, 102 (63%) of patients were male and 56 (34.6%) were female. About 4 (2.5%) of the data went missing. Cronbach's Alpha was 0.85. The correlation analysis showed that Iowa scale was significantly correlated with RP (P = 0.007), BP (P = 0.002), RE (P = 0.007) and GH (P = 0.012). The PSQ questionnaire had a significant correlation with the LOWA questionnaire (p < 0.001).Conclusion: Validity and reliability of the Persian version of Iowa Satisfaction with Anesthesia Scale (ISAS) in Iran are excellent