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Seroprevalence and Molecular Detection of Human Parvovirus B19 in Beta Thalassemia Major Patients
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Beta thalassemia major (BTM) is a genetic disorder that has been linked to an increased risk of contracting blood-borne viral infections, primarily due to the frequent blood transfusions required to manage the condition. One such virus that can be transmitted through blood is the Human Parvovirus B19 (B19V). The aim of this study was to investigate the frequency and molecular detection of B19V. This study included 60 blood donors as controls and 120 BTM patients. B19V was identified by serology, which measured B19-IgG and B19-IgM antibodies. Nested Polymerase Chain Reaction (nPCR) was employed to target the VP1/VP2 structural proteins. The results showed that B19V seropositivity represents 27.5% (33 out of 120) in BTM patients, and only 8 out of 60 subjects represents (13.3%) in the control group (P-value 0.078). Notably, male patients exhibited a significantly higher prevalence of B19-IgM and B19-IgG antibodies, with 32% and 24% of males testing positive, respectively, compared to female patients. Elevated levels of Aspartate and Alanine Transaminase were observed with values of 51.94±50.09 and 46.81±50.20, respectively. Additionally, nPCR analysis detected B19V DNA in 4.16% (5 out of 120) of BTM patients, while no positive results were detected in the control group. Screening the blood and blood products for the virus in high-risk group can considerably reduce the prevalence. Preventive measures are required in such vulnerable population.

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Publication Date
Wed Sep 22 2021
Journal Name
Samarra Journal Of Pure And Applied Science
Toward Constructing a Balanced Intrusion Detection Dataset
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Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

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Publication Date
Thu Feb 28 2019
Journal Name
Multimedia Tools And Applications
Shot boundary detection based on orthogonal polynomial
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Publication Date
Sun Feb 02 2025
Journal Name
Engineering, Technology & Applied Science Research
Automated Glaucoma Detection Techniques: A Literature Review
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Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Mechanical Science And Technology
Damage detection in glass/epoxy composite structure using 8–12 GHz X-band
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Publication Date
Mon May 15 2017
Journal Name
Journal Of Theoretical And Applied Information Technology
Anomaly detection in text data that represented as a graph using dbscan algorithm
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Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

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Publication Date
Wed Aug 30 2023
Journal Name
Iraqi Journal Of Science
Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm
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     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

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Publication Date
Sat Jan 01 2022
Journal Name
Food Science And Technology
Study on herbicide residues in soybean processing based on UPLC-MS/MS detection
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Publication Date
Sat Sep 01 2018
Journal Name
2018 15th European Radar Conference (eurad)
Delamination Detection in Glass-Fibre Reinforced Polymer (GFRP) Using Microwave Time Domain Reflectometry
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Publication Date
Sun Dec 03 2017
Journal Name
Baghdad Science Journal
Inhibitory Effect of Bacteriophages Isolated from Sewage Water in the City of Kirkuk on some Types of Human Pathogenic Bacteria
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Most approaches to combat antibiotic resistant bacteria concentrate on discovering new antibiotics or modifying existing ones. However, one of the most promising alternatives is the use of bacteriophages. This study was focused on the isolation of bacteriophages that are specific to some of commonly human pathogens namely E. coli, Streptococcus pyogenes, Staphylococcus aureus, Proteus mirabilis, Pseudomonas aeruginosa, Salmonella spp. and Klebsiella pneumoniae. These bacteriophages were isolated from sewages that were collected from four different locations in Kirkuk City. Apart from S. pyogenes, bacteriophages specific to all tested bacteria were successfully isolated and tested for their effectiveness by spot test. The most effective

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Publication Date
Sun Jun 09 2019
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Status of CHADS2 and CHA2DS2-VASc Scores in Predicting Risk of Stroke and its Prevention in Iraqi Patients with Atrial Fibrillation
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Atrial fibrillation is associates with elevated risk of stroke. The simplest stroke risk assessment schemes are CHADS2 and CHA2DS2-VASc score. Aspirin and oral anticoagulants are recommended for stroke prevention in such patients.

The aim of this study was to  assess status of CHADS2 and CHA2DS2-VASc scores in Iraqi atrial fibrillation patients and to report current status of stroke prevention in these patients with either warfarin or aspirin in relation to these scores.

This prospective cross-sectional study was carried out at Tikrit, Samarra, Sharqat, Baquba, and AL-Numaan hospitals from July 2017 to October 2017. CHADS2

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