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The Significance Of Maternal Total Serum Homocysteine Level In Iraqi Mothers Who Had Previous Babies With Neural Tube Defects
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Background: Neural tube defects (NTDs) are said to be inherited in a multifactorial fashion, i.e. genetic-environmental interaction. Maternal nutritional deficiencies had long been reported to cause NTDs, especially folate deficiency during early pregnancy. More attention had been paid to the exact mechanism by which this deficiency state causes these defects in the developing embryo. The most significant of all researches was that connecting reduced folate and increased homocysteine level in maternal serum on one hand and the risk of developing a NTD baby on the other hand. Objectives : to determine the significance of homocysteine level in Iraqi mothers who gave birth to babies with NTDs as compared to normal controls. Patients, Materials and Methods: Fifty Iraqi women having babies born with NTDS, referred to the genetic clinic in Baghdad Teaching Hospital, were included in this study (the study group) as well as 37 healthy women having normal children (the control group). This study was conducted from November, 2002 till October, 2003. Analysis of total serum homocysteine level for all women was done using a computerized HPLC system. Results : the age of women in both groups was comparable (mean+SD in the study group was 26.2+5.14 years vs. 26.3+4.57 years in the controls). Among the study group, 4 (8%) had normal tHcy level; 44 (88%) had mildly elevated level, and only 2 (4%) had moderately elevated tHcy level, while all (100%) women in the control group had their tHcy level within normal level. This difference was statistically highly significant (p<0.001). Conclusions : Women become at an increased risk of delivering a baby with NTD when having an elevated tHcy level in their sera, and that tHcy level is an important marker in maternal serum that is associated significantly with pregnancy outcome.

Publication Date
Fri Jan 01 2021
Journal Name
Int. J. Agricult.
FORECASTING THE EXCHANGE RATES OF THE US DOLLAR AGAINST THE IRAQI DINAR USING THE BOX-JENKINS METHODOLOGY IN TIME SERIES WITH PRACTICAL APPLICATION
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The goal of the study is to discover the best model for forecasting the exchange rate of the US dollar against the Iraqi dinar by analyzing time series using the Box Jenkis approach, which is one of the most significant subjects in the statistical sciences employed in the analysis. The exchange rate of the dollar is considered one of the most important determinants of the relative level of the health of the country's economy. It is considered the most watched, analyzed and manipulated measure by the government. There are factors affecting in determining the exchange rate, the most important of which are the amount of money, interest rate and local inflation global balance of payments. The data for the research that represents the exchange r

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Scopus
Publication Date
Tue Jan 01 2019
Journal Name
Energy Procedia
The effect of the activation functions on the classification accuracy of satellite image by artificial neural network
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Publication Date
Fri Sep 30 2016
Journal Name
Al-khwarizmi Engineering Journal
Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
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The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.

Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio

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Publication Date
Sun Dec 01 2013
Journal Name
Baghdad Science Journal
Detection of Anti-Helicopacter pylori IgG Antibody, Anti-GAD antibody, Anti-Beta islets cell in a sample of serum with type-2 Diabetes mellitus patients
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We have investigated twenty five patients with type-2 diabetes mellitus aged (35-60) years and fifteen healthy persons as control group to detect Anti-Helicobacter pylori IgG antibody. All studied groups were carried out to measure fasting blood sugar, anti- Glutamic acid decarboxylase (GAD), anti-? islets cells antibody by IFAT, Anti-H. pylori IgG antibody by ELISA technique. There was significant elevation in the concentration of fasting blood sugar than in control group (P < 0.05), the patients had negative results for anti-GAD antibody and anti- ? islets cells antibody, there were significant differences (P < 0.05) of anti-H. pylori IgG antibody in 28 % of patients had type-2 diabetes than control group. This lead to suggestion that typ

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Crossref
Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Sun Jun 07 2015
Journal Name
Baghdad Science Journal
Determanation of Several Biochemical Parameters in Sera of Iraqi Patients with type 2 Diabetes
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Diabetes mellitus, or simply diabetes, is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced. This high blood sugar produces the classical symptoms of polyuria (frequent urination), polydipsia (increased thirst), and polyphagia (increased hunger). The aim of the study is to measure several biochemical parameters in T2DM patients and the effect of these parameters in development the disease. Laboratory investigations including Ceruloplasmin , fasting blood glucose (FBG) , malondialdehyde (MDA), serum protein, uric acid, and protein electrophoresis have been measured in patients with type

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Crossref
Publication Date
Sun Sep 24 2023
Journal Name
Arab World English Journal
The Correlation between EFL Learners’ Academic Intelligence and the Level of Productive Skills
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Communicating effectively by gaining productive skills in a classroom setting is one of the critical goals of learning the English language. The current study was conducted to explore the correlation of EFL learners’ level of academic intelligence with their productive skills. The study tries to find an answer to what is the correlation between EFL learners’ academic intelligence and level of production skills. The study population represents EFL students at the departments of English language of the Iraqi Colleges of Education for the academic year (2022-2023). The sample includes 310 EFL students selected from the 3rd year of the Department of English of the College of Education, Ibn-Rushd for Human Sciences/University of Bagh

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Publication Date
Thu Dec 29 2016
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Solvent Extraction of Phenolic Compounds and Determination Total Phenolic Content from Teucrium Polium Plant
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       A direct solvent extraction was used for the determination of phenolic compounds from Teucrium polium . Methanol, ethanol, iso-propanol, butanol and D.W  were used as an extractants and study  the affecting extraction factors including : the type and solvent concentration, temperature, extraction time and number of batch extraction, by  using Folin-ciocalteu method to determine the Total Phenolic Content (TPC) and measurement of the absorbance at λmax 765nm. The accuracy and precision of this method were determined by preparing laboratory samples of Gallic acid , the results showed relative errors ranging from ±0.5 – 1.49 % and the standard deviation equal to 1.2

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Publication Date
Thu Jun 30 2011
Journal Name
Al-khwarizmi Engineering Journal
Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
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The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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