Journal of

Background and Aim: Foodstuffs additives are a general term for materials that are added to the foodstuffs in order to increase durability and to improve the appearance, composition, taste and food nutritional value. Colors are classified in this group and are added to foodstuffs in order to increase their attraction. The aim of this survey was to analyze status of added colors to the juice of black cherry and juice of barberry which are produced in Tehran City, capital of Iran.

Materials and Methods: Three haundred thirty six samples of dried sweets were randomly collected and analyzed from different areas of Tehran. First, the samples were de-colored by Clorhidric Acid, and then were analyzed after refining by Thin Layer Chromatography (T.L.C) method.

Results: Eighty nine percent from the total samples contained colors. Among chromatic samples, 62 samples (18.5%) out the total samples, contained artificial, non-edible colors and 237 samples (70.5%) from the total samples contained artificial and edible colors (for Industrial Producers) and 37 samples (11%), contained natural colors. Carmoisine color was detected more than added colors in juice of black cherry and juice of barberry.

Conclusion: Low costs, stability, Ph and similarity of artificial dyes with natural dyes motivate the producers for high utilization of these dyes without considering their possible hazards and/or their edible quality aspects.

Background and Aim: In recent years demand for a variety of cosmetic surgery, especially rhinoplasty has been increased in our country. Some research has shown that psychological factors influence the request for cosmetic surgery. Therefore, this study was performed to examine the dimensions of perfectionism.

Materials and Methods: The study was a causation - comparative study conducted in the spring and summer of 2010 in yasouj city. Fifty people undergone rhinoplasty surgery were compared with 50 subjects as controls. For the comparison of the dimensions and subscales of perfectionism, Frost multidimensional perfectionism scale was used. Data was analysed using SPSS software and dependent t test.

Results: Women using rhinoplasty were more than men. Most participants were single and between 26-30 years. The majority of the subjects had university education and their income level was between 7010000 to 9000000rials. There was significant difference in the Perfectionism subscale between the two groups in the Individual standards (P=0/001), order (P=0/001), concern about mistakes (P=0/001), parental criticism (P=0/001), doubt about action (P=0/013) and parental expectations (P=0/04). Generally the study showed significant difference in Perfectionism between the two groups(P=0/001).

Conclusion: People who had cosmetic rhinoplasty surgery were more perfectionists and were often negative perfectionists.

Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing complex medical data. Using artificial intelligence is common in diagnosing, treating and taking care of patients. Warfarin is one of the most commonly prescribed oral anticoagulants. Determining the exact dose of warfarin needed for patients is one of the major challenges in the health system, which has attracted the attention of researchers. The purpose of this study was to determine the exact dose of warfarin needed for patients with artificial heart valves using artificial neural networks (ANN).
Materials and Methods: A total of 9 multi-layer perceptron ANNs with different structures were constructed and evaluated based on a dataset including 846 patients who had referred to the PT clinic in Tehran Heart Center in the second half of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated. All simulations including data preprocessing and neural network designing were done in MATLAB environment.
Results: The effectiveness of ANNs was evaluated in terms of classification performance using 10-fold cross-validation procedure and the results showed that the best model was a network that had 7 neurons in its hidden layer with an average absolute error of 0.1, turbulence rate of 0.33, and regression of 0.87. 
Conclusion: The achieved results reveal that ANNs are able to predict warfarin dose in Iranian patients with an artificial heart valve. Although no system can be guaranteed to achieve 100% accuracy, they can be effective in reducing medical errors.