Objective:To Evaluate of Estradiol and Prolactin hormones levels for Breast Cancer women in
Baghdad City.
Methodology: The current study was conducted on 60 breast cancer women and 40 apparently
healthy subjects to evaluate the levels of estradiol and prolactin "hormones in the serum" of
({premenopausal & postmenopausal}) breast cancer and healthy controle women. Estradiol and
prolactin hormones estimated for all cases by using the IMMULITE 2000 instrument that performs
chemiluminescent immunoassays results are calculated for each sample.Data were analysed using
SPSS-18.data of two groups was comparison by the student's t-test.
Results: The results showed a non significant""(P>0.05) elevation in the –mean- serum estradiol level
of the study (premenopausal &postmenopausal ) group (54.0 + 22.8 pg/ml),(31.6 + 9 pg/ml)
respectively compared to that of control (pre& postmenopausal ) group (47.0 + 22.3 pg/ml), (28.4 +
10.7 pg/ml) respectively.while a highly significant (P<0.01) elevation of serum prolactin (30.9 + 18.5
ng/ml) ,(20.9 +15.2 ng/ml) respectively was found in study (premenopausal &postmenopausal ) group
compared to healthy control (premenopausal &postmenopausal ) group (11.7 + 10.4" ng/ml"),(8.7 +
5.1 "ng/ml") respectively.The results of this study indicate that the elevation of estradiol and prolactin
hormones may implicate these hormones in the aetiology of" breast cancer"..
Recommendations: This learn lines recommends emphasizing the necessity of cooperation between
the Ministry of Health and the Ministry of Commerce and protecting them from dangerous behavior by
providing them with sufficient support and guidance to stay away from the hormonic products and
focusing on the extension programs in the protection of the community through educating them with
the guidance programs
Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
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Non-thermal or cold plasma create many reactive species and charged particles when brought into contact with plant extracts. The major constituents involve reactive oxygen species, reactive nitrogen species and plasma ultra-violets. These species can be used to synthesize biologically important nanoparticles. The current study addressed the effect of the green method-based preparation approach on the volumetric analysis of Zn nanoparticles. Under different operating conditions, the traditional thermal method and the microwave method as well as the plasma generation in dielectric barrier discharge reactor were adopted as a preparation approach in this study. The results generally show that the type of method used plays an important role in d
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In current study, the dye from flowers petals of Strelitzia reginae used for the first time to prepare natural photosensitizer for DSSC fabrication. Among five different solvents used to extract the natural dye from S. reginae flowers, the ethanol extract of anthocyanin dye revealed higher absorption spectrum of 0.757a.u. at wavelength of 454nm. A major effect of temperature was studied to increase the extraction yield. The results show that the optimal temperature was 70 °C and there was a sharp decrease of dye concentration from 0.827 at temperature of 70 °C to 0.521 at temperature of 90°C. The extract solution of flowers of S. reginae showed higher concentration in acidic media, especially at pH 4 (0.902). The
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