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Prevalence of Toxocara spp. in Cats and Detection of Intestinal Helminth Infections in Humans
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This study aimed to investigate the prevalence of intestinal helminth infections in humans and detect Toxocara spp. in cats, with a focus on assessing the impact of age and gender on infection rates. Traditional diagnostic methods have historically limited the accurate identification of helminth infections in humans. Analysis of 450 human stool samples revealed an overall helminth infection rate of 5.7% using conventional techniques. The specific infection rates were 0.4% for Strongyloides stercoralis, 0.6% for Schistosoma mansoni, 1.7% for Hymenolepis nana, and 2.8% for Ascaris lumbricoides. Notably, no infections were recorded in the 30–39 and ≥40-year age groups, while the highest infection rate (16.3%, P≤0.01) was observed in individuals aged 20–29 years. With respect to gender, males exhibited a significantly higher (P≤0.01) infection rate (7.5%) compared to females (4%). Additionally, human sera were tested serologically using indirect ELISA for IgG antibodies, with a positivity rate of 10.4%. Age-wise, no positive cases were recorded in the 20–29 year group, while positivity rates of 8% and 24% were found in the 30–39 and >40 year groups, respectively, showing a significant difference (P≤0.01). In terms of gender, females had a significantly higher (P≤0.01) seroprevalence (15.2%) than males (6%). In domestic and stray cats, the overall prevalence of Toxocara spp. was 12%, with a significantly higher (P≤0.01) infection rate in kittens compared to adult cats. This study revealed notable prevalence of intestinal helminths in humans and Toxocara spp. in cats, with age and gender influencing infection rates. The findings emphasize the need for improved parasite control and public health measures to reduce zoonotic risks.

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Publication Date
Thu Jan 01 2026
Journal Name
Computers, Materials & Continua
Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks
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Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective

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Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Advanced Pharmaceutical Technology & Research
The prevalence of undiagnosed thalassemia minor among primigravida pregnant women before 20 weeks of gestation
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ABSTRACT<p>Pregnancy-associated anemia is a widespread condition that can have varying impacts on both the mother and the developing fetus, depending on the level of hemoglobin in the mother’s blood and the stage of pregnancy at which the anemia occurs. In Iraq, 27% of all registered thalassemia cases are thalassemia minor. Pregnancy may aggravate mild undiagnosed thalassemia early in pregnancy. The objective of the study was to access the prevalence of undetected thalassemia minor in primigravida women who received prenatal care before 20 weeks and to compare the demographic and socioeconomic characteristic features between women with iron-deficiency anemia (IDA) and those without any anemia. A to</p> ... Show More
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Publication Date
Tue Jan 08 2019
Journal Name
Iraqi Journal Of Physics
Detection and interpretation of clouds types using visible and infrared satellite images
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One of the most Interesting natural phenomena is clouds that have a very strong effect on the climate, weather and the earth's energy balance. Also clouds consider the key regulator for the average temperature of the plant. In this research monitoring and studying the cloud cover to know the clouds types and whether they are rainy or not rainy using visible and infrared satellite images. In order to interpret and know the types of the clouds visually without using any techniques, by comparing between the brightness and the shape of clouds in the same area for both the visible and infrared satellite images, where the differences in the contrasts of visible image are the albedo differences, while in the infrared images is the temperature d

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Publication Date
Tue Sep 30 2014
Journal Name
J Clin Biomed Sci
Detection of EGFR Mutations in Bronchial Wash from Iraqi patients with nonsmall Cell Lung Cancer (NSCLC)
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Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

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Publication Date
Mon Sep 30 2024
Journal Name
Iraqi Journal Of Science
Overlapping Structure Detection in Protein-Protein Interaction Networks Using a Modified Version of Particle Swarm Optimization
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In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and har

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Publication Date
Sun Jan 01 2023
Journal Name
Bionatura
Detection of lukf-pv gene in Staphylococcus aureus isolated from pregnant women with Urinary tract infection
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Publication Date
Wed Nov 17 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Pearson coefficient matrix for studying the correlation of community detection scores in multi-objective evolutionary algorithm
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Publication Date
Mon Sep 15 2014
Journal Name
Journal Of Clinical And Biomedical Sciences
Detection of EGFR Mutations in Bronchial Wash from Iraqi patients with nonsmall Cell Lung Cancer (NSCLC)
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Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

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Publication Date
Sun Jul 01 2018
Journal Name
Computers And Electronics In Agriculture
Detection of charcoal rot (Macrophomina phaseolina) toxin effects in soybean (Glycine max) seedlings using hyperspectral spectroscopy
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Publication Date
Wed Jan 01 2025
Journal Name
Lecture Notes In Networks And Systems
Automated Detection of Dubas Bug Infestation in Palm Trees Using Deep Learning with Residual Neural Networks
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Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health

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