Aims of this research to determine asbestos fibers levels in surrounding air of some crowded sites of Baghdad city were monitored in summer 2020. Collection of samples was conducted by directing air flow to a mixed cellulose ester membrane filter mounted on an open‑faced filter holder using sniffer a low flow sampling pump, samples of air were collected from five studied areas selected in some heavy traffic areas of Baghdad city, (Al-Bayaa and Al-Shurta tunnel, Al-Jadriya, and Al-Meshin commercial complex, control), then analyzed to determine concentrations of asbestos fibers. Counting of asbestos on the filters was carried out through using both scanning electron microscope SEM and an energy dispersive X‑ray system EDS to count

Three isolated bacteria were examined to remove heavy metals from the industrial wastewater of the Diala State Company of Electrical Industries, Diyala-Iraq. The isolated bacteria were identified as Pseudomonas aeruginosa, Escherichia coli and Sulfate Reducing Bacteria (SRB). The three isolates were used as an adsorption factor for different concentrations of Lead and Copper (100, 150, and 200 ppm.), in order to examine the adsorption efficiency of these isolates. In addition, the effect of three factors on heavy metals adsorption were examined; temperature (25, 30, and 37 ?C), pH (3 and 4.5) and contact time (2 and 24 hrs). The results showed that the highest level of lead adsorption was obtained at 37 ?C by E. coli, P, aerugenosa and

Objectives: To study the spectrum and classification of ATP7B variants in Iraqi children with Wilson disease by direct gene sequencing with clinical correlation. Methods: Fifty-five unrelated children with a clinical diagnosis of Wilson disease (WD) were recruited. Deoxyribonucleic acid was extracted from peripheral blood samples, and variants in the ATP7B gene were identified using next-generation sequencing. Results: Seventy-six deleterious variants were detected in 97 out of 110 alleles of the ATP7B gene. Thirty (54.5%) patients had 2 disease-causing variants (15 homozygous and 15 compound heterozygous). Twelve (21.8%) patients had one disease-causing variant and one variant of uncertain significance (VUS) with potential pathogenicity. T

The objective of the present work is to measuring the concentration of heavy elements (Pb, Cd, Zn, As) in Baghdad's soil city and indication to the probable sources of pollution as well as comparing the concentration of heavy elements with local and international ranges. The Sampling and analyzing conducted in the present work included ( 15 ) Samples from Baghdad city ( three samples for each location ).The rates of heavy elements in soil samples were as following:. Pb ( 67.5 ) ppm, Cd ( 4.11 ) ppm , Zn ( 77.9 ) ppm , As ( 4.64 ) ppm. According to the results, we find increasing in the concentrations of the heavy elements ( Pb, Cd, Zn ) in soils and decreasing in ( As ).We conclude that the main reason behind the in

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

This work focused on anthropogenic influences of the trace metals distribution in the soils of Kirkuk city. Sequential extraction technique was used to determine the distribution of the chemical fractions of Ag, Cd, Co, Cu, Ni, Pb, Zn, As, Cr and V in soil of Kirkuk city. This area is affected mainly by burning oil trash. Results show that these heavy metals were primarily restricted to surface horizons and mostly associated with the residual fraction (28.8 – 50%). The remnant fractions (13.8 – 33.1%) linked to the organic matter, 7.9 – 27.2% was bound to Fe-Mn oxide, 0.7 – 27.9 was bound to carbonate. Only a small amount of the total metals in the soil is exchangeable (0.5 – 4.2%) and water soluble (0 – 4.1%) fractions.

There are many aims of this book: The first aim is to develop a model equation that describes the spread of contamination through soils which can be used to determine the rate of environmental contamination by estimate the concentration of heavy metals (HMs) in soil. The developed model equation can be considered as a good representation for a problem of environmental contamination. The second aim of this work is to design two feed forward neural networks (FFNN) as an alternative accurate technique to determine the rate of environmental contamination which can be used to solve the model equation. The first network is to simulate the soil parameters which can be used as input data in the second suggested network, while the second network sim