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bsj-7280
In vitro isolation and expansion of neural stem cells NSCs
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   Neural stem cells (NSCs) are progenitor cells which have the ability to self‑renewal and potential for differentiating into neurons, oligodendrocytes, and astrocytes. The in vitro isolation, culturing, identification, cryopreservation were investigated to produce neural stem cells in culture as successful sources for further studies before using it for clinical trials. In this study, mouse bone marrow was the source of neural stem cells. The results of morphological study and immunocytochemistry of isolated cells showed that NSCs can be produced successfully and maintaining their self‑renewal and successfully forming neurosphere for multiple passages. The spheres preserved their morphology in culture and cryopreserved to be a ready source for use in experiments as a model for neurological disorders.

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Publication Date
Fri Nov 01 2013
Journal Name
Romanian Biotechnological Letters
Elicitation of pseudomonas aeruginosa with live and dead microbial cells enhances phenazine production
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Publication Date
Thu Nov 21 2019
Journal Name
Al-kindy College Medical Journal
Investigate The Different Effect Of Nicotine On H460 And H441 Lung Cells Viability
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Background: Nicotine is the foremost chemical constituent responsible for addiction in tobacco products, in the non-ionized condition can be easily absorbed via epithelial tissue of the lung, the mouth, the nose  and across the skin

Objective:The study examines the harmful effect of   the nicotine which is an important component of cigarette in vitro.

Type of the study: Cross-sectional study.

Methods: Examines the harmful effect of   the nicotine which is an important component of cigarette in vitro by using two types of lung cancer cell lines (H460 TP53+/+, H441 TP53-/-).

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Publication Date
Wed Jan 01 2020
Journal Name
International Conference Of Numerical Analysis And Applied Mathematics Icnaam 2019
Investigate of TiO2 and SnO2 as electron transport layer for perovskite solar cells
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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
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Mobile Position Estimation using Artificial Neural Network in CDMA Cellular Systems
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Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through tha

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Mobile position estimation using artificial neural network in CDMA cellular systems
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Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

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Publication Date
Tue Dec 31 2024
Journal Name
Iraqi Geological Journal
Geomechanical Modeling and Artificial Neural Network Technique for Predicting Breakout Failure in Nasiriyah Oilfield
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Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It

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Publication Date
Sun Dec 30 2018
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of penetration Rate and cost with Artificial Neural Network for Alhafaya Oil Field
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Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

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Publication Date
Sun Mar 01 2020
Journal Name
Plant Archives
EVALUATION THE ACTIVITY OF SOME MEDICINAL PLANTS EXTRACTS AS PROMOTER ROOTING FOR STEM CUTTINGS OF ROSEMARY (ROSMARINUS OFFICINALIS L.)
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This study was conducted in the botanical garden, Department of biology, College of Science / Mustansiriyah University in spring season, where the starts from (15 February to 15 March, 2019). Under the natural environmental conditions in the greenhouse in order to evaluate the effectiveness of some plant extracts as a promoter for rooting the apical stem cutting of rosemary plants at different concentrations compared with the IBA growth regulator. Plant extracts are Parsley (Petroselinum crispum), Dill (Anethum graveolens) and date palm fruits (Phoenix dactylifera) were used with concentrations (0, 1.25, 2.5 g / l). The IBA concentration was (100 mg / L) with dipping time 24 hour for all treatments. The following measurements were taken aft

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
A Nonlinear MIMO-PID Neural Controller Design for Vehicle Lateral Dynamics model based on Modified Elman Neural Network
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This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

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