Ghrelin Status and Lipid Profile in Obese Women from Southern Gaza Strip

Background: Ghrelin is a novel hormone consisting of 28 amino acids, which causes weight gain by increasing appetite, food intake (hunger hormone), decreases fat utilization and increases fat accumulation. Therefore, assessment the status of ghrelin hormone in obesity could constitute a promising therapy of obesity. Objective: To asses ghrelin status and some biochemical parameters in obese women from Southern Gaza Strip. Materials and Methods: This case-control study comprised 94 obese women (mean body mass index, BMI=35.1±4.6 Kg/m2) and 94 healthy normal weight women (mean BMI=22.6±1.8 Kg/m2). Questionnaire interview was applied. Serum ghrelin, cholesterol, triglycerides, high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) were determined. Data were analyzed using SPSS version 18.0. Results: The mean ages of controls and cases were 28.7±6.2 and 29.6±6.3 years, respectively. Obesity was more frequent among married, less educated, unemployed women as well as among women with family history of obesity compared to single, highly educated, employed women and women without family history of obesity (P=0.000). Drinking soft drink, not doing exercise and sedentary life style were risk factors of obesity (P=0.039, P=0.037 and P=0.002). The mean level of serum ghrelin was significantly decreased in cases compared to controls (1060±646.8 pg/ml vs. 1473.4±690.7 pg/ml, % difference=32.6 and P=0.005). The average levels of triglycerides and LDL-C were found to be significantly higher in cases (141.8±36.5 and 89.5±32.1 mg/dl) compared to controls (114.8±48.2 and 75.9±31.4mg/dl) with % differences of 21.0 and 16.4% and P=0.004, P=0.049, respectively. When related to sociodemographic characters of the study population, ghrelin showed lower levels in married, less educated, unemployed women as well as in women with family history of obesity (P>0.05). Similar trend in ghrelin levels were found in women who did not do exercise and drunk soft drink. Life style revealed that the less active life style, the lower the level of ghrelin. This positive relationship was significant (P=0.015). The Pearson correlation test showed negative correlations between ghrelin levels and cholesterol, triglyceride and LDL-C levels and a positive correlation with HDL-C (P>0.05). Concerning BMI, there was a negative signifigant correlation between BMI and ghrelin levels (r=-0.279, P=0.009). Conclusions: Serum ghrelin was significantly lower in obese women compared to normal weight women. The less active life style, the lower the level of ghrelin. There was a negative signifigant correlation between BMI and ghrelin levels

Arabic Text Classification Using Learning Vector Quantization

Text classification aims to automatically assign document in predefined category. In our research, we used a model of neural network which is called Learning Vector Quantization (LVQ) for classifying Arabic text. This model has not been addressed before in this area. The model based on Kohonen self organizing map (SOM) that is able to organize vast document collections according to textual similarities. Also, from past experiences, the model requires less training examples and much faster than other classification methods. In this research we first selected Arabic documents from different domains. Then, we selected suitable pre-processing methods such as term weighting schemes, and Arabic morphological analysis (stemming and light stemming), to prepare the data set for achieving the classification by using the selected algorithm. After that, we compared the results obtained from different LVQ improvement version (LVQ2.1, LVQ3, OLVQ1 and OLVQ3). Finally, we compared our work with other most known classification algorithms; decision tree (DT), K Nearest Neighbors (KNN) and Naïve Bayes. The results presented that the LVQ's algorithms especially LVQ2.1 algorithm achieved high accuracy and less time rather than others classification algorithms and other neural networks algorithms