Browsing by Author "Ayman, Mohamed"

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    THE BACILLUS CALMETTE-GUéRIN DERIVED PURIFIED PROTEIN (PPD) POTENTIATES IN-VITRO ANTI-CANCER ACTIVITY OF CERASTES CERASTES SNAKE VENOM IN COLON AND PROSTATE CANCER CELLS
    (Inventi, 2017-06) Sabatier, Jean-Marc; Shebl, Rania I; Bakkar, Ashraf; Mohamed, Aly Fahmy; Ayman, Mohamed
    Prostate and colon cancer represent a major health problem worldwide. In the present study, we evaluated the anticancer properties and cytotoxicity of Cerastes-cerastes (CC) snake venom on colon (Caco-2) and prostate (PC-3) cancer cells after their pretreatment with variable concentrations of Bacillus Calmette-Guérin (BCG) derived purified protein derivative (PPD). We monitoned the cell cycle arrest profile and specific cellular apoptosis markers (i.e. pro- and anti-apoptotic genes P53, Bax and Bcl-2 in CC- and BCG/PPD-pretreated cells using real time PCR. The cytotoxicity was determined by using MTT assay. Our data show that 24 h-treatment of cancer cells with CC venom induced a concentration-dependent cytotoxicity with IC50 values of 60 (Caco-2 cells) and 81 (PC-3 cells) μg/ml. Interestingly, addition of BCG/PPD at 25 and 50 μg/ml markedly increased the CC venom-induced toxicity on cancer cells, with IC50 values of 1.04 and 0.59 μg/ml for Caco-2 (up to 102-fold increase) or 2.78 and 0.70 μg/ml for PC-3 cells (up to 116-fold increase). By analyzing the cell cycle arrest and related gene expression pattern, the main phase of cell cycle arrest was found to be G2/M in both cell lines. An S-phase arrest was also observed in PPD pretreated colon Caco-2 cell line to a greater extent than that observed in cells only treated with CC venom. Up regulation of proapoptotic and down regulation of anti-apoptotic genes in PPD pretreated cells were significantly enhanced as compared to cells treated with CC venom alone. In this study, we suggest that PPD -via its synergistic action with the CC venom-might be used as an enhancer of the anti-cancer properties of CC venom.
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    Deep Learning-Based Alzheimer’s Disease Classification: An Experimental Study
    (IEEE, 2023-07) Ayman, Mohamed; Darwish, Farah; Mohammed, Tukka; Mohammed, Ammar
    Alzheimer's disease poses a significant challenge to healthcare professionals due to its prevalence in dementia cases. Accurate and timely diagnosis is essential for effective management of patients. Magnetic resonance imaging (MRI) has emerged as a vital tool for diagnosing Alzheimer's disease. This paper evaluates the effectiveness of image classification models in detecting Alzheimer's disease using MRI images, with four categories of Alzheimer's disease ranging from no dementia to very mild, mild, and moderate dementia. The study employs and fine-tunes different CNN-based model including VGG16, Inception, and ResNetV2. In order to ensure greater reliability and robustness of the results obtained, we employ cross-validation during the experimentation phase, with different test splits. The experimental results demonstrate that fine-tuning VGG16 yields the highest accuracy of 98.810%. These findings suggest that further optimization and refinement of these models may lead to enhanced accuracy in MRI-based Alzheimer's disease diagnosis, potentially revolutionizing how this condition is managed.