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

The aim of the research is the detection of heavy metals using (Inductively coupled Plasma ICP) for samples in Tigris river at intakes of water treatment plants Baghdad (Sharq dejla, Al-Wathba, Al-Wahda, and Al-Dora) and samples at Tigris banks near (Al-Adhamya, Al-Shuhda bridge and al-Jadrya).
All the recorded results were fitted with Iraqi standers No. 25 in 1967 for all samples with heavy metals (arsenic Ar, Cadmium Cd, Chromium Cr, Zinc Zn, Lead Pb, Copper Cu, Nickel Ni, Manganese Mn, Ferrous Fe) where all concentration were lower than standard values except Cadmium (0.01- 0.014) in plants intakes and (0.027- 0.048) in river samples while the standard value is (0.005).
Other tests such as chemical oxygen demand and oil &

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

In the present study, a total of 245 flour samples were collected from 49 mills on both sides of Baghdad city (Al- Karkh and Al- Resafa), during the period from 1/6 - 1/12/ 2015 to detect the prolportion of iron added to the flour samples. It is found that only 45% of mills produced flour contain the prescribed percentage of iron (30-60 ppm) while 51.9% of the mills produced flour at rate is less or much more than the prescribed percentage, while only 4.1% of the mills were not added iron to the flour.

The levels of lead (pb), copper (cu), cobalt (co) and cadmium (cd) were determined in different kinds of milk and the health risks were evaluated. The mean levels were 0.73±0.21, 0.06±0.01, 0.12±0.01 and 0.14±0.01 ppm for these metals respectively. The levels of pb and cu were found to be insignificant differences (p<0.05), whereas the levels of co and cd, were no significant differences (p>0.05). The dry and liquid kinds of milk were different significantly (p<0.05), whereas the original, was no significant differences (p>0.05). The values for all metals were more than one. The metals pb and cd were detected at highest concentrations in most dry and liquid milk samples.

This research shows the importance of Baghdad in the field of urban heritage and was demonstrated in the Ibn al-Fiqh al-Hamdani's book Baghdad the City of Peace in which he focused on many urban aspects and reviewing its historical importance by connecting them with modern events and the role they play in cultural and civilized construction which included: mosques, schools and markets etc. and the service providing institutions and other pillars of the Islamic cities through showing the development back then and its importance as an integral part of the urban and cultural heritage of the Islamic cities in general and Baghdad city in particular. 

In his book Baghdad the City of Peace, Ibn al-Faqih Hamadhaani