Browsing by Author "Fares, Mohamed"

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Adipocyte of Obese Breast Cancer Patients Is Characterized by The Overexpression of Caveolin-1 Protein/Mediator the Main Constituent of the Plasma Membrane Vesicles Caveolae That Contain Proteins Contribute to Breast Cancer Progression
(Egyptian Society of Biological Sciences, 2019) Saber, Aya; Abdelaziz Ibrahim, Sherif; Hosney, Mohamed; Taha Mohamed, Hossam; Fares, Mohamed; Sabet, Salwa; El-Shinawi, Mohamed; Mostafa Mohamed, Mona
Breast cancer (BC) is the second leading mortality cause due to poor survival rates compared to lung cancer all over the world. Recently, lifestyle increased obesity among the population globally. Since, the adipose tissues (AT) are the major contributor to the volume of the breast and adipocytes cells, which constitute AT are one of the major prominent cells play an effective role in cancer progression via releasing different mediators and adipokines. Thus, AT may display a crucial role in BC progression, especially in obese patients compared to non-obese patients, which characterized by increased AT. Interestingly, adipocytes are characterized by expressing caveolin-1 (Cav-1) protein. Cav-1 constitutes the lipid raft of caveola which contains different proteolytic enzymes inducing cancer metastasis. In this regard, the aim of the present study was to explore the level of expression of Cav-1 protein in the tissue specimen of 5 non-obese vs. 15 obese patients using immunohistochemistry (IHC) and immunoblotting techniques. Our finding demonstrates that the level of Cav-1expression was statistically significantly low in non-obese compared to obese BC patients (p < 0.05). Herein, our results revealed that the highest expression of Cav-1 in obese patients compared to non-obese (control) patients can be considered as a biomarker for BC patients.
Features Selection For Building An Early Diagnosis Machine Learning Model For Parkinson's Disease
(IEEE, 2016) Soliman, Abu Bakr; Fares, Mohamed; Elhefnawi, Mohamed M.; l-Hefnawy, Mahmoud
In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. The goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. Then we selected a good machine learning algorithm to detect which technique could help us by calculate the crossover scores for each technique. This research is based on a dataset which was created by Athanasius Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation. This target of these medical measurements is to find the Unified Parkinson's disease rating scale (UPDRS) which is the most commonly used scale for clinical studies of Parkinson's disease
Incidence of Human Cytomegalovirus in Breast Carcinoma Tissues is Associated with A Higher Expression of Growth Factor Receptor-Bound Protein 2
(The Egyptian Journal of Hospital Medicine, 2019) Fares, Mohamed; Taha Mohamed, Hossam; Abdelaziz Ibrahim, Sherif; Hosney, Mohamed; I. Rady, Mohamed; El-Shinawi, Mohamed; Mostafa Mohamed, Mona
Background: female mammary carcinoma is the second most common cancer incidence among women and the fifth most common leading cause of cancer death worldwide. Premenopausal young women are more frequently targeted by inflammatory breast cancer (IBC), which is the most lethal form of breast cancer. The human cytomegalovirus (HCMV) has been identified as one of the viral infection with a higher frequency in carcinoma tissues of IBC than in non-IBC. The adaptor protein growth factor receptor-bound protein 2 (Grb2), was found to be upregulated in HCMV-infected cells and play as crucial role in cancer progression. Objective: this study aimed to assess the expression level of Grb2 in carcinoma tissues of IBC and non-IBC with HCMV infection. Patients and Methods: overall, 135 female diagnosed with breast carcinoma were enrolled in this study. Using conventional and real time polymerase chain reaction (PCR), we determined the incidence of HCMV and assessed the expression level of Grb2 mRNA in the breast cancer tissue samples. Results: Grb2 mRNA was significantly upregulated in HCMV+ IBC higher than in HCMV+ non-IBC. According to the molecular subtype, Grb2 mRNA was significantly higher upregulated in breast carcinoma tissues of HCMV+ hormonal positive (HP) than in triple negative (TN) counterparts. Conclusion: HCMV infection is associated with a high expression of Grb2 mRNA in IBC and that HP HCMV+ mammary carcinoma tissues confer upregulated Grb2 mRNA, suggesting a potential role of HCMV infection in enhancing of Grb2 mRNA expression in breast cancer with HP.