Antibacterial effects of Scrophularia striata seed aqueous extract on staphilococcus aureus

Plant-based drugs are regarded promising recently. Scrophularia has been shown various biological activities such as antimicrobial, antitumor and anti-inflammatory properties. According to pervious researches, bacteria are becoming resistant to some kinds of antibiotics, so it is prominent to find more reliable sources against them. Here Staphylococcus aureus as a common type of Gram- positive bacteria has been chosen for this in vitro study. After culturing this bacterium, it was treated with various dosages of Scrophularia striata seed aqueous extract and tetracycline, and then its antibacterial effect was assessed by spectrophotometery method based on bacteria population alteration after 24h incubation. In order to investigate probable side effects of the seed extract its effect on human fibroblast cells has been studied. MTT assay was applied for cell survival determination of human fibroblast cells after 24h. Findings indicate that bacterial population has been declined between 1 to 20 micro g mL-1 dosages of the extract, which 5 micro g mL-1 is the most effective dosage without containing any cytotoxic effect on human fibroblast cells. In fact extract not only has no antiproliferation properties but also has evoked cell profileration, so it can be consider as a cell growth factor. Tetracycline, on the contrary, showed its potent impact merely in highest dosages with noticeable side effects. It can be concluded that, Scrophularia striata could be possibly a promising antibiotic which shows significant antibacterial properties, and with lesser side effects

How to test normality distribution for a variable: a real example and a simulation study

Many commonly used statistical methods require that the population distribution be nearly normal. Unfortunately, in some papers the one-sample Kolmogorov-Smirnov test has been used for testing normality while the assumptions of applying this test are not satisfied. To conduct this test, it is assumed that the population distribution is fully specified. In practical situation where the mean and SD of population distribution is not specified in advance, one can use a modification of the K-S test for checking the normality assumption which is called, Lilliefors test. In this paper, we explain the method of computing this test with some common statistical softwares such as SPSS, S-PLUS, R and StatXact and utilize a dermatology dataset from Skin Research Center of Shohada-e-Tajrish hospital to illustrate how the use of the one-sample K-S [with the mean and SD estimated from the sample] instead of its modification can be misleading in practice. We also use Monte Carlo simulation to compare the approximate power of the one-sample K-S test [with the estimated population mean and SD] with Lilliefors test in some common specified continuous distributions. The result indicates that one should not use the one-sample K-S test for assessing the normality assumption in practical situation