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A comparative immunohistochemical expression of cytokeratin 19 in odontogenic keratocyst, dentigerous, and radicular cysts
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Background: Odontogenic cysts are characterized by their sluggish growth and ability to enlarge, primarily affecting the oral and maxillofacial tissues. Timely diagnosis and treatment are crucial to prevent potentially serious consequences. The present study aimed to evaluate and compare the immunohistochemical expression of cytokeratin 19 in the epithelium of odontogenic keratocyst, dentigerous, and radicular cysts.

Methods: This study analyzed forty-five formalin-fixed, paraffin-embedded tissue blocks containing odontogenic cysts. The sample consisted of fifteen odontogenic keratocysts, fifteen dentigerous cysts, and fifteen radicular cysts. Immunohistochemical analysis was performed to assess the expression of the cytokeratin 19 epithelial marker in these samples. Statistical analyses were conducted using Statistical Package for Social Sciences version 26, employing the Chi-square test for comparative analysis of cytokeratin 19 expression among the odontogenic keratocyst, dentigerous cyst, and radicular cyst.

Results: This study showed that 80% of basal layer tissue samples in the odontogenic keratocyst group had negative cytokeratin 19 biomarker scores. In contrast, 60% of dentigerous cyst tissue blocks and 53.3% of radicular cyst tissue blocks were +1. This difference was statistically significant (P = 0.034). In comparison, between groups, there was no significant difference (P = 0.103) in CK19 expression in the surface and spinous layers.

Conclusion: The level of epithelial differentiation is correlated with cytokeratin 19 expression. The cysts with well-differentiated epithelium (radicular cyst and dentigerous cyst) express cytokeratin 19, whereas those with less well-differentiated epithelium (odontogenic keratocyst) exhibit low positives. Thus, it serves as a diagnostic tool for distinguishing these three lesions.

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Publication Date
Sat Jun 01 2024
Journal Name
Pakistan Journal Of Criminology
Artificial Intelligence Technology in the Field of Modern Forensic Evidence: Brain Fingerprinting as a Model
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Brain Fingerprinting (BF) is one of the modern technologies that rely on artificial intelligence in the field of criminal evidence law. Brain information can be obtained accurately and reliably in criminal procedures without resorting to complex and multiple procedures or questions. It is not embarrassing for a person or even violates his human dignity, as well as gives immediate and accurate results. BF is considered one of the advanced techniques related to neuroscientific evidence that relies heavily on artificial intelligence, through which it is possible to recognize whether the suspect or criminal has information about the crime or not. This is done through Magnetic Resonance Imaging (EEG) of the brain and examining

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Publication Date
Fri Jan 01 2016
Journal Name
Computational Intelligence And Neuroscience
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Publication Date
Wed Jan 01 2020
Journal Name
Energy Conversion And Management
Improved PCM melting in a thermal energy storage system of double-pipe helical-coil tube
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Publication Date
Sun Dec 27 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Estimation of Beta Two Microglobulins, Fetuin-A, Resistin Serum Level in Iraqi Multiple Myeloma Patients
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Multiple myeloma is hematological disease produces many complications in the bone, kidney, neural and other complications. The study aims to measure serum biomolecules like fetuin-A and resistin and determined the possibility to use these biomarkers as disease predictor. blood samples were isolated from 58 patients and 24 sex and age-matched control, serum then isolated, and proper ELISA kit then used to a determined level of B2 microglobulin, resistin, and fetuin-A. The result demonstrated significant increase in   B2 microglobulin, fetuin-A and resistin in patients compare to control (1.3470.714 vs. 0.9130.253), p = 0.000, (14.00310.352 vs. 9.2594.264), p= 0.005, (1.9673.595 vs. 0.6040.622), p = 0.009, respectively. &

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Publication Date
Mon Jan 01 2018
Journal Name
Ndt & E International
Porosity evaluation of in-service thermal barrier coated turbine blades using a microwave nondestructive technique
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Publication Date
Thu Oct 13 2022
Journal Name
Computation
A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Engineering
A Computational Fluid Dynamics Investigation of using Large-Scale Geometric Roughness Elements in Open Channels
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The hydraulic behavior of the flow can be changed by using large-scale geometric roughness elements in open channels. This change can help in controlling erosions and sedimentations along the mainstream of the channel. Roughness elements can be large stone or concrete blocks placed at the channel's bed to impose more resistance in the bed. The geometry of the roughness elements, numbers used, and configuration are parameters that can affect the flow's hydraulic characteristics. In this paper, velocity distribution along the flume was theoretically investigated using a series of tests of T-shape roughness elements, fixed height, arranged in three different configurations, differ in the number of lines of roughness element

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Publication Date
Thu Jun 01 2017
Journal Name
Journal Of The American Medical Directors Association
Comprehensive Literature Review of Factors Influencing Medication Safety in Nursing Homes: Using a Systems Model
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Publication Date
Thu Jul 01 2021
Journal Name
Solar Energy
A new approach for employing multiple PCMs in the passive thermal management of photovoltaic modules
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Publication Date
Sun Dec 27 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn: 1683 - 3597 , E-issn : 2521 - 3512)
Estimation of Beta Two Microglobulins, Fetuin-A, Resistin Serum Level in Iraqi Multiple Myeloma Patients
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Multiple myeloma is hematological disease produces many complications in the bone, kidney, neural and other complications. The study aims to measure serum biomolecules like fetuin-A and resistin and determined the possibility to use these biomarkers as disease predictor. blood samples were isolated from 58 patients and 24 sex and age-matched control, serum then isolated, and proper ELISA kit then used to a determined level of B2 microglobulin, resistin, and fetuin-A. The result demonstrated significant increase in   B2 microglobulin, fetuin-A and resistin in patients compare to control (1.3470.714 vs. 0.9130.253), p = 0.000, (14.00310.352 vs. 9.2594.264), p= 0.005, (1.9673.595 vs. 0.6040.622), p = 0.009, respectively.   These di

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