The main purpose of this investigation is to evaluate the concentrations of six essential metals (Na+, Mg2+, K+, Ca2+, Fe2+ and Zn2+) in saffron and a farm soil using the neutron activation analysis (NAA) as a nuclear spectrometry method. The stratified random sampling method was used here. The NAA results showed the well uptake of Mg2+, K+, Ca2+, Fe2+, and Zn2+ in saffron, which is lower than the toxicity range. Based on the contamination factor and geoaccumulation index, soil contamination levels were determined uncontaminated by Zn, moderately contaminated by Na+ and Fe2+, and strongly contaminated by Ca2+, K+, and Mg2+. Results of the contamination degree and pollution load index indicated moderately/strongly soil contamination and a moderate geometric mean of the contamination index. The Na+ enrichment factor (EF) showed a minimal man-made impact on sodium enrichment. Saffron cultivation has likely caused more accumulations of Mg2+, K+, Ca2+, and Fe2+, as well as a considerable deficiency of Zn2+ in the soil, based on EFs. The biological concentration factor showed a significant zinc accumulation by the corm of saffron. There was well translocation from corm to all the aerial tissues for K+. Also, sodium adsorption ratio, exchangeable sodium percentage, pH, and electrical conductivity evaluated the non-salinity level of soil in all saffron farms.
Objective(s): The study aims to assess the early detection of early detection of first degree relatives to type-II
diabetes mellitus throughout the diagnostic tests of Glycated Hemoglobin A1C. (HgbA1C), Oral Glucose Tolerance
Test (OGTT) and to find out the relationship between demographic data and early detection of first degree
relatives to type-II diabetes mellitus.
Methodology: A purposive "non-probability" sample of (200) subjects first degree relatives to type-II diabetes
mellitus was selected from National Center for Diabetes Mellitus/Al-Mustansria University and Specialist Center
for Diabetes Mellitus and Endocrine Diseases/Al-kindy. These related persons have presented the age of (40-70)
years old. A questio
Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration
... Show MoreThis case series aims to evaluate patients affected with post COVID‐19 mucormycosis from clinical presentation to surgical and pharmacological treatment to improve the disease prognosis.
This case series was conducted at a specialized surgery hospital in Baghdad Medical City for over 10 months. Fifteen cases who had mild to severe COVID‐19 infections followed by symptoms similar to aggressive periodontitis, such as mobility and bone resorption around the multiple maxillary teeth, were included in this case series.
This work deals with the preparation of a zeolite/polymer flat sheet membrane with hierarchical porosity and ion-exchange properties. The performance of the prepared membrane was examined by the removal of chromium ions from simulated wastewater. A NaY zeolite (crystal size of 745.8 nm) was prepared by conventional hydrothermal treatment and fabricated with polyethersulfone (15% PES) in dimethylformamide (DMF) to obtain an ion-exchange ultrafiltration membrane. The permeate flux was enhanced by increasing the zeolite content within the membrane texture indicating increasing the hydrophilicity of the prepared membranes and constructing a hierarchically porous system. A membrane contain
Al-Ruhbah region is located in the southwest of Najaf Governorate. A numerical model was created to simulate groundwater flow and analyze the water quality of the groundwater, by developing a conceptual model within the groundwater modeling system software. Nineteen wells were used, 15 for pumping and four for observation. A three-dimensional model was built based on the cross-sections indicating the geologic layers of the study area, which were composed of five layers. When a distance of 1,000 m between the wells was adopted, 135 wells can be operated simultaneously. These wells were hypothetically operated at 6, 12, and 18 h intervals, with a discharge of 200, 430, and 650 m