Jorjani Biomedicine Journal

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Background & Objective: Inability to measure exact exposure in epidemiological studies is a common problem in many studies, especially cross-sectional studies. Depending on the extent of misclassification, results may be affected. Existing methods for solving this problem require a lot of time and money and it is not practical for some of the exposures. Recently, new methods have been proposed in 1:1 matched case–control studies that have solved these problems to some extent. In the present study we have aimed to extend the existing Bayesian method to adjust for misclassification in matched case–control Studies with 1:2 matching.

Methods: Here, the standard Dirichlet prior distribution for a multinomial model was extended to allow the data of exposure–disease (OR) parameter to be imported into the model excluding other parameters. Information that exist in literature about association between exposure and disease were used as prior information about OR. In order to correct the misclassification Sensitivity Analysis was accomplished and the results were obtained under three Bayesian Methods.

Results: The results of naïve Bayesian model were similar to the classic model. The second Bayesian model by employing prior information about the OR, was heavily affected by these information.

The third proposed model provides maximum bias adjustment for the risk of heavy metals, smoking and drug abuse. This model showed that heavy metals are not an important risk factor although raw model (logistic regression Classic) detected this exposure as an influencing factor on the incidence of lung cancer. Sensitivity analysis showed that third model is robust regarding to different levels of Sensitivity and Specificity.

Conclusion: The present study showed that although in most of exposures the results of the second and third model were similar but the proposed model would be able to correct the misclassification to some extent.

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Background and objectives: Bacterial antibiotic resistance is a major issue in the process of infectious disease treatments. The aim of this study was an evaluation of the antibacterial activity of Punica granatum flower extract against several gram-negative and positive clinical bacterial isolates.
 
Methods: An adequate dried flower of an endemic mature Punica granatum plant was used for extraction. The standard strain of several gram negative and positive bacteria was chosen for this study, as well as some distinguished clinical strains such as Pseudomonas aeruginosa and Enterococcus spp. In order to indicate the antibacterial effect of Punica granatum mature flower, well-diffusion method was done for each bacterium of the extraction of the flower, so that zone inhibitions can be reported. MIC and MBC test was done.
 
Results: Disc diffusion test was done and the greatest zone inhibition Shigella was 39 mm and then Salmonella typhimurium 13.1 mm. The lowest antibacterial effect of P. granatum extraction was gained on Proteus with 6 mm of zone inhibition. The Highest MIC and MBC effect was obtained from antibacterial evaluation on S. typhimurium and S. epidermidis.
 
Conclusion: the antibacterial activities of medicinal plants, pharmaceutical companies are just using medicinal plants in association with synthetic drugs in order to obtain better results. Setting up a more analytic test on medicinal plants same as HPLC test could be the next stage of this study in order to reach to a higher reliance of medicinal plants antibacterial activities qualification So That we could combine them with synthetic drugs and improve their efficiency.