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Prediction of maternal diabetes and adverse neonatal outcome in normotensive pregnancy using serum uric acid
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Diabetes mellitus, with adverse neonatal events are challenging issues to all obstetricians and pediatricians, where uric acid could play a vital role. We aimed to assess the relationship and prognostic benefits of serum uric acid measured at about 20 weeks’ gestation in normotensive pregnancy, with subsequent maternal diabetes, and neonatal complications. All singleton normotensive pregnant women with normal blood glucose, serum creatinine, and weight before pregnancy, whom attended Medical City Hospital, Department of Obstetrics and Gynecology in Baghdad, were involved and regarded as the case group, on the condition that their serum uric acid measured at 20 weeks’ gestation > 3 mg/dl, but if ≤ 3 mg/dl, they would be registered as a control group. A complete follow up was performed regularly during pregnancy, and after delivery; regular assessments of maternal blood glucose were done up to one year. Maternal diabetes mellitus (DM), small for gestational age (SGA) neonates, and preterm delivery (PD) constituted (27.59%), (43.60%), and (1.97%), respectively in case group which had significantly included maternal DM and SGA (P <0.001). Also, elevated mid-pregnancy serum uric acid was strongly associated (P <0.0001) with maternal DM (5.86 ± 0.69) and SGA (4.78 ± 0.34). Cut-off values of uric acid of 4.76 mg/dl were best associated with maternal DM, while 4.33 mg/dl with SGA. In conclusion, the cut-off points of 4.76 and 4.33 mg/dl of maternal mid- normotensive pregnancy serum uric acid have the potential ability to predict Maternal DM and SGA, respectively.

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Publication Date
Wed Mar 29 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Possible Cardiac Adverse Effects Induced by Therapeutic Doses of Ciprofloxacin in Juvenile Rats
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Ciprofloxacin is widely used in treating adults infected with Gram-negative bacteria. It is contraindicated in children, growing adolescents and during pregnancy due to joint toxicity. Its toxicity concerning other organs needs to be clarified. Thus, this study was designed to study the possible cardiac damage induced by two selected doses of ciprofloxacin in juvenile rats.Eighteenth healthy juvenile rats (4 weeks old and their weight 30 ± 2 gm) were utilized in this study and divided into three groups. Group-I control; group II and group III, respectively injected IP with 25 mg/kg and 50 mg/kg ciprofloxacin every 12 hours for one week. Serum enzymes activities alanine aminotransferase (ALT), aspartate aminotransferase  (AST), cr

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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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Publication Date
Wed Dec 01 2021
Journal Name
Civil And Environmental Engineering
Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
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Abstract<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo</p> ... Show More
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Publication Date
Thu Nov 29 2018
Journal Name
Al-khwarizmi Engineering Journal
Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
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Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

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Publication Date
Tue Aug 01 2017
Journal Name
Veterinary World
Therapeutic uses of epicatechin in diabetes and cancer
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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
Lossy Image Compression Using Wavelet Transform, Polynomial Prediction And Block Truncation Coding
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Publication Date
Tue Sep 08 2020
Journal Name
Al-kindy College Medical Journal
Outcome of En Bloc Resection of Osteoidosteoma
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Background: Osteoid osteomaa benign tumor is unusual before the age of 5 or after age 30 and is more prevalent in men. The main symptom is pain, which is typically severe and responsible for nocturnal awakenings. The conditons usually diagnosed through radiological imagine and confirmed by Histopathology.
Objectives: To assess the effectiveness and the complications that had been risen during the surgical procedure of osteoid osteoma using en bloc resection.
Methods: (10) Patients diagnosed with osteoid osteomawere treated with enbloc surgical reseaction were included in this study.the study took place at Al Yarmouk teaching hospital.the from April 2017-october 2018 and included 10 patients..(7) male, (3) females.The mean age of th

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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Prediction of consolidation due to dewatering by using MATLAB software
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Prediction of COVID 19 Disease Using Feature Selection Techniques
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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in </p> ... Show More
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