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Immunohistochemical Localization of Aspergillus and p53 in Human Lung Tissues

American j.urnal of Molecular Biology 2015年第4期5卷 117-123页

作者： Mohannad G. Al-Saghir Mosleh A. Alkhatatbeh ahed j. alkhatib Department of Biological Sciences Ohio University Zanesville Zanesville Ohio USA Department of Biological Sciences Jordan University of Science and Technology Irbid Jordan Department of Toxicology and Forensic Sciences Jordan University of Science and Technology Irbid Jordan

Aspergilli are filamentous fungi which can cause opportunistic infections in Acquired Immunodeficiency Syndrome (AIDS) patients. Aspergilli can be found in human tissues either in the form of spores or hyphae. p53 is a tumor suppressor gene located in the short arm of chromosome 17. It is a potent transcriptional regulator of genes which are involved in many cellular activities including cell cycle arrest, apoptosis and angiogenesis. A loss of tumor suppressor function of p53 is the most common event leading to the development of human cancers. The rate by which p53 has a homology between different species has been reported from human to other vertebrates, it has been reported that it is available within Drosophila melanogaster and C. elegans [1] [2]. The aim of this study is to check if p53 is localized within Aspergilli or not using immunohistochemical techniques and study the relationship between Aspergilli infection and p53 in human lung tissues. 45 different samples of lung tissues, diagnosed as being none tumor, were taken randomly during the year of 2003-2004 from the autopsy cases submitted to the forensic medicine center in Irbid, j.rdan. The sample group consisted of 12 females and 33 males. Labeled Streptavidin Biotin (LSAB) method and Mach-4 method were used to determine the Aspergilli infection and p53. The results show that the Aspergillus is presented in all used samples (100% of the infection) in the form of spores or hyphae and all infected samples have mutant p53 molecules (p53 was located in Aspergillus spores and hyphae). According to this study, it is safe to posit that the mutant p53 molecules may be used by Aspergillus for its multiplication. Seemingly it is a biological behavior of Aspergillus to produce p53. The fate of the p53 is questioned, is it going to interact with the human cells initiating cancer? Further experimental investigations are required to determine such pathway. In conclusion, this study shows that Aspergillus is a producin

关键词： Aspergilius p53 Immunohistochemical Lung Tissues

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Prediction of diabetes and hypertension using multi-layer perceptron neural networks

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International j.urnal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 120-137页

作者： Hani Bani-Salameh Shadi Malkhatib Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod ahed j.alkhatib Department of Software Engineering The Hashemite University ZarqaJordan Department of Computer Information System The Hashemite University ZarqaJordan Department of Legal Medicine School of Medicine JUSTJordan

Background:Diabetes and hypertension are two of the commonest diseases in the *** they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in ***:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these ***:This work used two online datasets which consist of data collected from 768 *** applied neural network algorithms to predict if the individuals have those two diseases based on some *** prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and ***:A model based on the Multi-Layer Perceptron Neural Network(MLP)was *** inputs of the network were the factors for each disease,while the output was the prediction of the disease’s *** model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of ***,a tool was implemented to help diagnose the diseases and to understand the ***:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for *** results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and *** shows MLPs effectiveness in early disease prediction.

关键词： Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools

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