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The Effect of Thymosin Beta 4 on Developing Dental Tissue (Experimental Study on Rats)
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Background: Odontogenisis is a complex process controlled by dynamic and reciprocal interactions that regulated by various molecules. Thymosin β4 is a small bioactive peptide with wide spectrum biological effects on much cell types. The present study was designed to highlight the effect of synthetic exogenous Tβ4 on developing dental tissue of the upper central teeth of rats, by histological examination and immunohistochemical evaluation of TGFβ1. Materials and method: Thirty six Albino Wister pregnant rat 18control group received intraperitoneal injection of normal saline and the others are experimental group received 50µg/300µl of Tβ4 injection. The animals were sacrificed at periods 16th and 18th day I.U.L and one day post natal, as six animals for each period. Histological and immunohistochemical evaluation for expression of TGF β1 in dental tissue of upper central teeth of the rat were done Results: In-vivo results showed that experimental group had accelerated stages of tooth development with acceleration in deposition of dental hard tissue (enamel and dentin) with high positive expression of TGF β1by enamel organ, dental papilla and dental sac cells. Conclusion: these data suggest synthetic exogenous Tβ4 act as bioactive initiator enhances tooth development by stimulating proliferation and differentiation of both epithelial and mesenchymal cells.

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Publication Date
Fri Mar 10 2017
Journal Name
Superconductor Science And Technology
Conceptual designs of conduction cooled MgB<sub>2</sub> magnets for 1.5 and 3.0 T full body MRI systems
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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Insulin Like Growth Factor Binding Protein 7 as a Novel Diagnostic Marker in Sera of Iraqi Patients with Acromegaly
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The purpose of this study was to measure serum levels of insulin-like growth factor-binding protein (IGFBP7), Insulin-like Growth Factor 1 (IGF-1), Growth Hormone (GH), Interleukin 6 (IL-6) and insulin in acromegaly patients and healthy controls. The acromegaly group had 60 patients, while the population group had 30 people who had never had acromegaly before. The concentration of IGFBP7, IGF-1, GH, IL-6, and insulin were determined. The results of the present study indicate that IGFBP7 level in the acromegaly group was significantly lower (1.690.07 ng/mL vs. 2.740.12 ng/mL, respectively, p = 0.001). IGF-1, GH, IL-6, and insulin concentrations were also significantly higher in acromegaly patients. The diagnostic accuracy (2.194) was exce

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Publication Date
Sun Jan 01 2023
Journal Name
Materials Today: Proceedings
Preparation, characterization and preliminary cytotoxic evaluation of 6-mercaptopurine-coated biotinylated carbon dots nanoparticles as a drug delivery system
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Publication Date
Thu Nov 19 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
CFD Simulation of Velocity Distribution in a River with a Bend Cross Section and a Cubic Bed Roughness Shape
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Publication Date
Fri Jan 01 2021
Journal Name
Aip Conference Proceedings
Preparation and characterization of biomass-alumina composite as adsorbent for safranine-o dye from aqueous solution at different temperatures
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Publication Date
Sun Mar 24 2024
Journal Name
Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )
Correlation between Therapeutic Drug Monitoring of Infliximab Serum Trough Levels and other Biomarkers in Iraqi Patients with Crohn's Disease
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Background: Inflammatory bowel disease (IBD) is a collection of chronic, recurrent inflammatory illnesses of the gastrointestinal system, including Crohn's disease (CD). Infliximab is one of the biological medications used to treat CD. Therapeutic drug monitoring has evolved as a treatment in IBD, aiming to optimize benefit while meeting more demanding, objective end criteria. Objective: To determine the achievement of target trough level (TL), develop anti-drug antibodies (ADAs) to infliximab, assess response to therapy, and study TL relations with different variables. Methods: The present study was cross-sectional and conducted from May 2022 to November 2022. It included 40 CD patients allotted into 2 groups: group 1 patients ach

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Publication Date
Tue Jun 01 2021
Journal Name
Swarm And Evolutionary Computation
A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
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Publication Date
Thu Feb 06 2020
Journal Name
Scientific Reports
Waste foundry sand/MgFe-layered double hydroxides composite material for efficient removal of Congo red dye from aqueous solution
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Abstract<p>We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure</p> ... Show More
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Publication Date
Wed Jun 28 2023
Journal Name
Engineering Applications Of Computational Fluid Mechanics
Comprehensive analysis of melting enhancement by circular Y-shaped fins in a vertical shell-and-tube heat storage system
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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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