[Effect of Ferula gummosa ethanolic extract on osteogenesis in human mesenchymal stem cells]

Osteoporosis is one of the most prevalent disease in current century .Estrogen deficiency is the basic cause of osteoporosis in menopaused women. Hormone Replacement Therapy [HRT] can increase the risk of breast and ovary cancers. Medicinal plant are natural source of secondary metabolite and can a reliable source for treating osteoporosis. Ferula gummosa [Galbanum] has been used in traditional medicine since ancient time. This study is focused on determining the effect of Galbanum root ethanolic extract on osteogenesis progress in Human Mesenchymal Stem Cells [hMSCs]. The Bone Marrow hMSCs were seeded at 12 well plates and treated with different amount of Galbanum extracts [0.5 to 100 micro g/ml]. Extract cytotoxicity were measured using MTT method .Effect of extract on osteogenesis was evaluated in time interval 7 and 14 days using_ Alkaline Phosphatase_ enzyme activity method. The data analysis revealed a significant increase in cell proliferation in range of [0.5 to 5] micro g/ml after 24, 48 and 72 hour of treatment with galbanum extract. Analysis of result revealed a significant increase in alkaline phosphatase [ALP] enzyme activity in the range of 1 to 10 micro g/ml compared with control group. Ferula gumossa has been used in Iranian folk medicine for many years. Our in vitro study showed that Frula gummosa extract has osteoprotective effect

[Diagnosing voice disorders in children with cochlear implantation and hearing aids using artificial intelligence systems]

The purpose of this study is to quantify the voice disorders in children with cochlear implantation and hearing aids. Until now, quantifying voice disorders has been done subjectively by speech experts and it is for the first time that the preset study tends to run an objective experiment using signal processing features. 4 levels were considered to be qualify speech. Linear and nonlinear features were extracted from 5 Farsi words: "mashin', "mar', "moosh', "gav" and "mowz" uttered by 30 subjects and then put into hidden Markov classifiers. Classifier outputs then were fused together to have better accuracy. The main hypothesis of the study is to answer this question: Can we separate children into 4 levels based on their voice features? Voice features including "fundamental frequency, first formant, second formant, third formant, first to second formant ratio, third to second formant ratio, Rational Intensity, nasality, approximate entropy and fractal dimension were extracted from speech segments and then are were given to artificial decision making system [classifiers]. The results show that classifiers can separate 4 levels of voice disorders with the accuracy of 93.1%. Among the introduced features, first to second formant ratio and third to second formant ratio can be used directly to track voice recovery after using cochlear implantation or hearing aid. The output of this research study can act as a speaker independent system to help speech specialists with evaluating voice disorder recovery in children who fall in the same range of age