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bijps-362
Formulation and Evaluation of Ezetimibe Nanoparticles
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The aim of this study is to formulate and evaluate ezetimibe nanoparticles using solvent antisolvent technology. Ezetimibe is a practically water-insoluble drug which acts as a lipid lowering drug that selectively inhibits the intestinal absorption of cholesterol and related phytosterols. Ezetimibe prepared as nano particles in order to improve its solubility and dissolution rate.

Thirty formulas were prepared and different stabilizing agents were used with different concentrations such as poly vinyl pyrrolidone (PVPK-30), poly vinyl alcohol (PVA), hydroxy propyl methyl cellulose E5 (HPMC), and poloxamer. The ratios of drug to stabilizers used to prepare the nanoparticles were 1: 2, 1:3 and 1:4.

The prepared nanoparticles were evaluated for particle size, entrapment efficiency, dissolution study, Fourier transform infrared spectroscopy, differential scanning calorimetry, and atomic force microscopy. The percentage of drug entrapment efficiency of F1-F30 was ranged from 85% ± 1 to 98 % ± 1. On the other hand dissolution rate increasing as the particle surface area is increase due to reduction of particle size to the nano range.

The results showed that poly vinyl pyrrolidone (PVPK-30) was found to be the best stabilizer.

 Keywords: Ezetimibe, Nanoparticles, Particle Size, poly vinyl alcohol.

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Publication Date
Fri Nov 01 2013
Journal Name
Journal Of Engineering
Strengthening and Closing Cracks for Existing reinforced Concrete Girders Using External Post-Tensioning
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
the relationship btwen serum sialic and humoral immune response in patients with sthma
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forty-six patients with asthma were tested for the scrum levels of total sialic and diffrental the results reveled a significant increased in the scra of asthmatic patients

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Publication Date
Tue Feb 04 2025
Journal Name
Journal Of Communicable Diseases
Correlation between MicroRNA-155 Expression and Viral Load in Severe COVID-19 Patients
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Background: The SARS-CoV-2 virus causes COVID-19, a respiratory syndrome. It causes inflammation and damages several organs in the body. miRNAs play a role in regulating the infection resulting from SARS-CoV-2. MicroRNA-155, a kind of microRNA linked to viral defences, can affect the immune responses during COVID-19. Objectives: Examination of the involvement of microRNA-155 in the development and severity of COVID-19, as well as finding the correlation between microRNA-155 and viral load (copies/mL) in severe cases of the disease. Materials and Method: A case-control research study was performed between October 2022 and June 2023. It included a cohort of 120 hospitalised individuals with severe cases of COVID-19, together with 115 individu

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Publication Date
Wed Aug 28 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Publication Date
Tue Oct 27 2020
Journal Name
Proceedings Of The 2020 2nd International Conference On Sustainable Manufacturing, Materials And Technologies
The complex dynamic in three species food webmodel involving stage structure and cannibalism
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A three species food web model involving a stage structure and cannibalism in the top predator species is proposed and studied. It is assumed that the prey species growth logistically in the absence of predator and the predation process occurred according to theLotka-Volterra functional response. The existence, uniqueness and bounded-ness of the solution of the model are investigated. The local and global stability conditions of all possible equilibrium points are established.The persistence conditions of the model are also determined. The local bifurcation near each of the equilibrium points is analyzed. The global dynamics of the model is investigated numerically and compared with the obtained analytical results. It is observed that the p

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Publication Date
Wed Aug 28 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Publication Date
Fri Jan 01 2021
Journal Name
Materials Today: Proceedings
Response surface methodology: A review on its applications and challenges in microbial cultures
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Publication Date
Thu Jan 30 2020
Journal Name
Neuroquantology
Electronic Transfers and (NLO) Properties Predicted by AB Initio Methods with Prove Experimentally
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Hartree-Fock (HF) method relies in the calculations of nonlinear optical properties (NLO) for benzoic acid molecule. Also, another theoretical study is conducted by using the TD-DFT Density Functional Theory through B3LYP/High Base Set 6-311++G (2d,2p) on Gaussian program09. Moreover, an experimental study has been done to obtain the electrons spectrum for benzoic acid with and without ethanol. While the experimental study is done by using UV/VIS. spectrophotometer. Energy gap values of electronic transition between HOMO and LUMO is obtained from theoretical and experimental results. Consequently, the theoretical result for determining the energy gap calculated from EHOMO-LUMO wasvery close to the results of UV / VIS. spectrum. A theoretica

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Publication Date
Wed May 10 2023
Journal Name
Dermatology Research And Practice
Interleukin-15 and Tumor Necrosis Factor-α in Iraqi Patients with Alopecia Areata
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Background. Alopecia areata (AA) is a common form of noncicatricial hair loss of unknown cause, affecting 0.1-0.2% of the general population. Most evidence supports the hypothesis that it is disease of the hair follicle of autoimmune nature mediated by T-cells, with important cytokine role. Objective of the Study. The objective of this study is to study the association and changes in serum levels of interleukin-15 (IL-15) and tumor necrosis factor-α (TNF-α) in patients with AA in relation to the type, activity, and disease duration. Patients and Methods. Thirty-eight patients with AA and 22 individuals without the disease as controls were enrolled in this case-controlled study conducted in the Department of Dermatology in the Al-K

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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