Dear Editor, According to data from the Global Burden of Disease, suicide-related deaths in the Eastern Mediterranean Region (EMR) doubled between 1990 and 2015 (1). Iran, situated in the EMR comprising 22 countries, has been the focus of several studies on poisoning-related mortality. The challenge lies in accuRATEly distinguishing between intentional and unintentional poisonings, a crucial factor for tailoring effective prevention stRATEgies.

We investigate the problem of estimating the ratio of subjects that share a particular characteristic in a population, but some degree of MISCLASSIFICATION is possible. The MISCLASSIFICATION probabilities are considered as the random variables, and we study a Bayesian approach to this problem. We propose a method to calculate the exact posterior density of the ratio, based on some prior distributions. We, then, study a method to determine the sample size, using average coverage criterion. We also investigate the effect of different prior distributions on the sample size.

Ataxia-telangiectasia is a rare primary immunodeficiency and multisystem DNA repair disorder, resulting from mutation in ataxia telangiectasia mutated (ATM) gene. The ATM protein plays a significant role in detecting DNA double-strand breaks (DSB), oxidative stress and other genetic stresses. The ATM can directly mention DNA ends in repair complexes and directly involve in the repairment of DSBs that are induced during T cell and B cell rearrangement. Therefore, increase in the level of serum IgM and mainly sinopulmonary recurrent infection, which is indistinguishable from hyper IgM syndrome, can be a symptom of some AT patients. AT patients with class-switched defect are more prone to severe infections, autoimmunity, and lymphoproliferative disorders. In this study an AT patient with characteristic features of hyper IgM phenotype and lymphoproliferation is investigated.

Background & Objectives: Thyroid is a vital gland, which affect all of the body oragans such as heart, digestive system, kidney and so on. The intention of this research is to decreas in wrong determination of normal thyroid gland from abnormal using boosting algorithm. This algorithm is a powerful method in diagnosis and prognosis. It iteratively grows base classifer on a sequence of reweighted datasets then takes a linear combination of consequencs and we hope improves accuracy at final.Material & Methods: A total of 103 patients’ data corrolated to November 2010 until November 2011 from Shoushtar salamat laboratory were analyzed for detemination thyroid gland state. Conventional decision trees and boosting decision trees were made for diagnosis normal thyroid gland from abnormal thyroid gland using R softwere vedersion 3.0.1.Results: Our findings revealed that for conventional decision trees MISCLASSIFICATION RATE, sensitivity and specificity with test set were 0.088, 0.91 and 0.92 respectively .However these figures considered by boosting desion trees were 0.029, 0.955 and 1 crrespondingly.Conclusion: The boosting decision trees had possibily superior sucsses in diagnosis normal tiroid gland ftom unnormal. So using boosting decisin trees propose in determination thyroid gland state.

Background: The main aim of the present study was to map the real high risk regions of gastric cancer (GC) using corrected data for MISCLASSIFICATION error in all Iranian provinces. Method: In this cross-sectional study, the data were extracted from the reports of Ministry of Health and Medical Education and previous studies on correcting GC registered data including 30 provinces in 2008. The information about socioeconomic factors was extracted from the statistical centers of Iran. To estimate the model parameters, the Bayesian approach was used with regards to spatial correlation due to adjacent effects. Results: The southern and northern provinces were introduced as high-risk regions and the central provinces were introduced as low-risk regions. The mean household income was inversely associated with the risk of GC. Conclusion: The real high-risk regions of GC in Iran are the north and south border provinces which should be considered by health policy makers. It is also necessary to correct misclassified registered data which can lead to seduction in health service allocation.

Evaluating the cutting RATE (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accuRATE enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.