Abstract\nObjective: To investigate serum and follicular fluid (FF) HIF-1α levels in nonobese, nonhyperandrogenic patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization (IVF), in addition to IVF outcomes. \nMaterials and Methods: A prospective sequential cross-sectional study carried at a Research and Education Hospital. In total, 160 patients undergoing IVF treatment were included in the study: 80 patients diagnosed with PCOS according to the Rotterdam criteria (group I, study) and 80 patients with the etiology of male factor infertility (group II, control). \nResults: There were statistically significant between-group differences in serum estradiol (E2) levels on the day of hCG administration (2,377.00 ± 733.23 versus 1,931.3 ± 1,010.69), the total gonadotropin dose required (2,000.63 ± 1,051.87 versus 1.134.69 ± 286.45), and the total number of retrieved oocytes (8.60 ± 2.06 versus 11.05 ± 4.39) (P < 0.05). There was also a statistically significant between-group difference in serum and FF HIF-1α levels on the day of oocyte retrieval (0.21 + 0.06 versus 0.17 + 0.04, P = 0.001; 0.09 + 0.05 versus 0.06 + 0.03 P = 0.007; respectively). \nConclusions: In a selected population of nonobese, nonhyperandrogenic PCOS patients, there was a significant difference in HIF-1α levels of the PCOS group versus those of the control group. Further studies are needed to determine the effects of HIF-1α in women with PCOS and to develop a new marker to monitor treatment outcomes.\nKeywords: Hypoxia-inducible factor 1 alfa, in vitro fertilization, polycystic ovary syndrome
A hybrid algorithm is an algorithm that combines\ntwo or more algorithms that solve a similar problem. This paper\nintroduces a hybrid spatial data structure called hybrid tree\nto reduce the time for retrieved data from GIS map that is\nstored in a cloud. The hybrid spatial data structure combines two\ndata structure: Quad-tree and KD-Tree. The hybrid algorithm is\ndesigned to reduce the times for retrieving points,lines, and the\npolygons that are used in a GIS map through the insert; search;\nor delete operations. The experimental results tests ten points,\nten lines, and ten polygons in different area in GIS map. The\nresults are compared with other algorithms such as (KD-Tree,\nand Quad-Tree). A reduction in retrieving time by 67.47% as\ncompared to KD-Tree, and 51.3% as compared to Quad-Tree is\nachieved from GIS map that stored in the cloud.
In this paper I will present two different genetic and ant colony algorithms for solving a classic computer science problem: shortest path problems. I will first give a brief discussion on the general topics of the shortest path problem, genetic and ant colony algorithms. I will conclude by making some observations on the advantages and disadvantages of using genetic and ant colony algorithms to solve the shortest path problem and my opinion on the usefulness of the solutions and the future of this area of computer science