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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
Wed Jan 01 2025
Journal Name
Iv. International Rimar Congress Of Pure, Applied Sciences
A New Intrusion Detection Approach Based on RNA Encoding and K-Means Clustering Algorithm Using KDD-Cup99 Dataset
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Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the dis

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Publication Date
Sun Oct 01 2023
Journal Name
Baghdad Science Journal
Molecular detection by some virulence genes of Salmonella enterica subsp. enterica isolated from the stool of children with diarrhea
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Diarrhea is a real disease in childhood which could cause death. Therefore, this study was conducted to isolate Salmonella from 350 stool samples taken from children under five years in age, suffering from diarrhea during the period from March 2019 to March 2020 in Tikrit city / Iraq. The results showed the possibility to isolate ten isolates of Salmonella enterica subsp. Enterica, an infection rate, represents 2.875% of the total rate of patients who suffer from diarrhea. The virulence genes were investigated for ten isolates of S. enterica subsp. enterica, the result is that all isolates possessed the genes stn, invA, lpfA with an appearance percentage of 100%, whi

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Publication Date
Wed Jan 22 2020
Journal Name
Molecules
In Vivo and In Vitro Evaluation of the Protective Effects of Hesperidin in Lipopolysaccharide-Induced Inflammation and Cytotoxicity of Cell
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(1) Background: Plant flavonoids are efficient in preventing and treating various diseases. This study aimed to evaluate the ability of hesperidin, a flavonoid found in citrus fruits, in inhibiting lipopolysaccharide (LPS) induced inflammation, which induced lethal toxicity in vivo, and to evaluate its importance as an antitumor agent in breast cancer. The in vivo experiments revealed the protective effects of hesperidin against the negative LPS effects on the liver and spleen of male mice. (2) Methods: In the liver, the antioxidant activity was measured by estimating the concentration of glutathione (GSH) and catalase (CAT), whereas in spleen, the concentration of cytokines including IL-33 and TNF-α was measured. The in vitro expe

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Publication Date
Sat Jan 13 2018
Journal Name
Journal Of Engineering
Producing Coordinate Time Series for Iraq's CORS Site for Detection Geophysical Phenomena
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Global Navigation Satellite Systems (GNSS) have become an integral part of wide range of applications. One of these applications of GNSS is implementation of the cellular phone to locate the position of users and this technology has been employed in social media applications. Moreover, GNSS have been effectively employed in transportation, GIS, mobile satellite communications, and etc. On the other hand, the geomatics sciences use the GNSS for many practical and scientific applications such as surveying and mapping and monitoring, etc.

In this study, the GNSS raw data of ISER CORS, which is located in the North of Iraq, are processed and analyzed to build up coordinate time series for the purpose of detection the

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

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Publication Date
Wed Sep 07 2022
Journal Name
2022 Iraqi International Conference On Communication And Information Technologies (iiccit)
Construct an Efficient DDoS Attack Detection System Based on RF-C4.5-GridSearchCV
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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Sat Aug 31 2024
Journal Name
International Journal Of Intelligent Engineering And Systems
Credit Card Fraud Detection Using an Autoencoder Model with New Loss Function
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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Engineering
Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm
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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

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