Journal of Education for Pure Science-University of Thi-Qar

This study explained the protective effects of silymarin against liver toxicity induced by carbon tetrachloride (CCl4) in female rats. Silymarin, a natural herbal compound extracted from the Milk thistle (Silybum marianum) plant. The experiment included three groups, each consisting of six rats. The first group served as the negative control. The second group was exposed to CCl4 (CCl4 30 % dissolved in olive oil intraperitoneally (i.p) 3ml/kg body weight twice a week for four weeks to induce toxicity. The third group received a combination of silymarin100 mg/kg b.w and the CCl4 3ml/.kg b.w daily for Three weeks to assess its protective effects. In the conducted experiment, rats that received silymarin alongside CCl4 exhibited significant improvements(reduction) in liver enzyme toward the normal values (Alanine Aminotransferase ALT, Aspartate Aminotransferase AST, Alkaline Phosphatase ALP, and Gamma-Glutamyl Transferase GGT) levels and also exhibited significant improvements(reduction)in lipid profiles (Total Cholesterol (TC), Triglycerides (TG), Low-Density Lipoprotein (LDL) and Very Low-Density Lipoprotein (VLDL) and elevation in High-Density Lipoprotein (HDL), compared to those treated only with CCl4. While, they were significantly elevated in the CCl4 group compared to the control group, indicating the presence of liver injury. These findings indicate that silymarin may serve as a potential preventive option for reducing liver damage caused by CCl4. Conclusion: The silymarin demonstrates significant potential in protecting against CCl4-
Journal of Education for Pure Science- University of Thi-Qar
Vol.14, No. 4 (2024)
Website: jceps.utq.edu.iq Email:jceps@eps.utq.edu.iq
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induced liver damage, making it a viable candidate for further investigation and potential therapeutic use in liver disease management Additional research is needed to comprehensively understand silymarin's impact on lipid metabolism and liver injury.

The research focuses on chatbot interaction enrichment using reinforcement learning and user modeling. It aims to develop a personalized RL-based response generation framework for the optimization of satisfaction, engagement, and completion rates for the users. Anonymized historical interaction data from 500 users were collected to generalize user-profiles and contextual models. This was done by extracting features and observing and evaluating the control and experimental groups to test the efficacy of the working personalized system. Results indicated an overall increase of 35 percent in user satisfaction, 50 percent in session duration, and 25 percent in rates of completion of tasks against the traditional rule-based system. The results are very much in line with current literature on the improvements personalization can bring to the user's experience across many domains. The results from this study thus propose that personal AI systems powered with fine-grained models of users and reinforcement learning could obtain more engaging and efficient user interactions. The result has further-reaching implications for, for example, e-learning, customer service, and healthcare applications.