In this investigation, the effects of genistein (GEN) on the expression of steroidogenic genes such as steroidogenic acute regulatory protein (StAR), side-chain cleavage enzymes (P450scc) and cytochrome P450 aromatase (CYP19) were assessed. For this study, forty young female Sprague Dawley (SD) rats at aged 2-3 months (200±20 g) and forty aged female SD rats aged 10-12 months (490±20 g) were selected. Also, based on weight they were divided into a negative control group (NC), three different GEN dose groups, which received GEN of 15, 30, 60 mg/kg, and a positive control group (PC). The experiment lasted 30 days. Concentrations of serum hormones were determined by Enzyme-linked immunosorbent assay (ELISA). Gene and protein expressions of StAR, P450scc and CYP19 were determined by Real-Time PCR and western blot techniques. It was observed that 30-60 mg/kg GEN could increase the expression of androgen generating key enzymes in the young rat ovary. GEN also significantly increased progesterone and E2 levels in the serum of aged rats and reduced the levels of LH and FSH in the serum of both young and aged rats. Compared with young rats, the effect of GEN on the ovary of aged rats was stronger and a lower dose of GEN (15 mg/kg) showed an obvious effect on these indicators. GEN influenced both estrogen level and indicators associated with estrogen and androgen transformation processes, which indicates that GEN can impair the growth and maturation of the ovary.
In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.