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Articles by N. Karimi
Total Records ( 2 ) for N. Karimi
  S. Hasani- Ranjbar , H. Vahidi , S. Taslimi , N. Karimi , B. Larijani and M. Abdollahi
  Several drugs may increase blood prolactin concentration. Dopamine receptor antagonists are one of the most common causes of hyperprolactinemia. To reduce happening of hyperprolactinemia, some medicinal plants have been traditionally used. This review focuses on the efficacy of effective herbal medicines in the management of human drug-induced hyperprolactinemia. PubMed, Scopus, Web of science, Cochrane library database were searched for any relevant studies that investigated the effect of herbal medicines on drug induced hyperprolactinemia up to May 2010. The inclusion criteria were clinical trials studied efficacy of herbal medicines in drug-induced hyperprolactinemia. Among different compounds, four herbal supplements including Shakuyaku-kanzo-to (TJ-68), Peony-Glycyrrhiza Decoction (PGD), Zhuangyang capsule, Tongdatang serial recipe (TDT) were found clinically effective and safe in management of drug-induced hyperprolactinemia. Although, the quality of included clinical trials was low not allowing us to conduct a meta-analysis but positive results on efficacy (TJ-68), (PGD), Zhuangyang capsule and (TDT) cannot be ignored. Interestingly compounds with prolactin-suppressive effects have a number of diterpenes mainly clerodadienols that seem almost identical for their efficacy. Further studies to isolate and characterize constituents of the effective herbs are needed to reach novel therapeutic and more effective agents.
  M. Amiri , N. Karimi and S.F. Jamshidi
  This study presents a methodology for solving multi-response optimization problems. Since goal programming method considers decision maker`s comments objectively, it has special significance; but using this method in large and complex problems alone can`t be, so effective, thus it would be a better idea to use a metaheuristic method. The proposed method is a combination of simulation approach, fuzzy goal programming, genetic algorithm and local search algorithm. This method will use firstly simulation to generate required inputs, secondly fuzzy goal programming to model the problem and finally genetic local search algorithm for problem optimization. At the end we will show the performance of this method by numerical example and designed experiments.
 
 
 
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