Background: Dialysis is in common use to treat patients
with end stage renal failure .However longstanding dialysis
harboring some cellular changes in various body fluids.
This study was conducted in order to detect these changes
in urine.
Objective: The study was conducted to detect cellular
changes in urine of patients with longstanding dialysis.
Method: Fifty-three urine samples were examined
cytologically obtained from patients with longstanding
dialysis during 6 months period. Freshly voided midstream
urine samples were taken . Samples were centrifuged and 2
to 3 drops of sediments were smeared on 2 glass slides and
fixed in 95% ethyl alcohol then stained with Hand E stain
to be evaluated.
Results: Gross physical examination of all urine samples
were neither purulent nor hemorrhagic .Microscopically
there was increased exfoliation of urothelial cells except in
9 cases. No significant cytological atypia were seen in all
urine samples. Excessive exfoliation in the absence of
significant inflammation, hemorrhage and cytological
atypia compared with control group.
Conclusion: The study revealed that some cytological
changes do occur in the urothelial cells of patients with
longstanding dialysis. These changes need further attention
and study to disclose their causes.
Flame atomic absorption spectrophotometer (FAAS) was used in this study to determine the concentrations of heavy metals such as Ca, Fe, Mn, Cd, Co, Cr, Ni, Cu, Pb and Zn in some food additives of Iraq. The order of metal contents in food additives was found to be Ca ˃ Mn ˃ Fe ˃ Cu ˃ Zn ˃ Pb ˃ Cr ˃ Ni ˃ Co ˃ Cd. The concentration level of each metal was compared with that recommended by food agriculture organisation (FAO) and world health organisation (WHO). Calibration curves were linear for all standard solutions of heavy metals in the range starting from 0.02-0.4 mg/kg for Cd to 11-100 mg/kg for Ca. The correlation coefficients values (R2) of calibrations were investigated and ranged from 0.9971 for Cr to 0.9999 for Ca. Th
... Show MoreThis review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.
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The pre-mixed ultra-high performance concrete which manufactured by Lafarge.
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... Show MoreThe purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tend
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