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Dietary antioxidants including phenolic compounds are believed to be effective nutrients in the prevention of oxidative stress related disease. Pomegranate has been used for centuries in ancient cultures for its medicinal purpose and is widely acknowledged for antioxidant properties. The present study was designed to assess the effect of pomegranate fresh fruit consumption on the plasma antioxidant capacity. Thirty healthy volunteers were recruited for the study. Volunteers were randomly divided into three groups (pomegranate, vitamin E and water consumption). Blood samples were collected, after at least 12 hours overnight fast, the day before beginning supplementation period and the day after supplementation had finished. Total antioxidant capacity measurement by FRAP method and clinical laboratory test were performed for all volunteers in two selected times. The obtained data revealed that consumption of 100 grams pomegranate and vitamin E per day for ten days resulted in a significant rise (14.05%, 8.28%) plasma antioxidant capacity respectively, but this difference was not significant for water group.

Acute appendicitis is considered as one of the most prevalent diseases needing urgent action. Diagnosis of appendicitis is often complicated, and more precision in diagnosis is essential. The aim of this paper was to construct a model to predict acute appendicitis based on pathology reports. The analysis included 181 patients with an early diagnosis of acute appendicitis who had admitted to Shahid Modarres hospital. Two well-known neural network models (Radial Basis Function Network (RBFNs) and Multi-Layer Perceptron (MLP)) and logistic regression model were developed based on 16 attributes related to acute appendicitis diagnosis respectively. Statistical indicators were applied to evaluate the value of the prediction in three models. The predicted sensitivity, specificity, positive predicted value, negative predictive values, and accuracy by using MLP for acute appendicitis were 80%, 97.5%, 92.3%, 93%, and 92.9%, respectively. Maine variables for correct diagnosis of acute appendicitis were leukocytosis, sex and tenderness, and right iliac fossa pain. According to the findings, the MLP model is more likely to predict acute appendicitis than RBFN and logistic regression. Accurate diagnosis of acute appendicitis is considered an essential factor for decreasing mortality rate. MLP based neural network algorithm revealed more sensitivity, specificity, and accuracy in timely diagnosis of acute appendicitis. [ABSTRACT FROM AUTHOR]