Browsing by Author "Mahmoud, A"

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    The efficacy and underlying mechanism of phosphodiesterase-5 inhibitors in preventing cognitive impairment and Alzheimer pathology: A systematic review of animal studies
    (ELSEVIER, 2019-10) Nabil, N; Bassiony, M; Mahmoud, A; Yassen, M; Tarkhan, S; El Sayed, M; Riad, MS; Abo Elenain, O; Sobhy, A; Wagdy, O; El-Bakly, W
    Background: Alzheimer disease (AD) initially presents with cognitive decline that affects the affected individual's daily activities. Cognitive decline reversal represents an important medical need, where Phosphodiesterase-5 inhibitors (PDE-5Is) might play a role. Aim: This systematic review was performed to verify the efficacy of PDE-5Is in preventing cognitive impairment and to elucidate the underlying mechanism. Methods: Preclinical animal studies assessing the efficacy of PDE-5Is in preventing cognitive impairment and pathological changes by measuring A beta-42 beta 42 and p-Tau were included in the analysis. CAMARADES Checklist was used to assess study quality. Further, various signaling pathways in different studies were examined. Results and outcomes: Data of behavioral tests were extracted and a meta-analysis was conducted. Fifteen animal trials met the inclusion criteria, and all reported the prevention of cognitive deficits by PDE-5Is in Alzheimer's disease. A significant effect of PDE-5Is in increasing the time spent in the target quadrant was reported in four of seven studies using the water maze. Four studies showed significant improvement in contextual fear memory freezing time, and three studies showed improvement in the 14 unit maze number of errors. Conclusions: Cognitive decline in preclinical AD finds tauopathy has a more impact than A beta-42. This systematic review showed that PDE-5 inhibitors might help prevent cognitive impairment in AD, and while its mechanism of action is non-related to A beta-42, it might include decrease p-Tau, increase CREB and BDNF or suppressing apoptosis and inflammation. However, the efficacy of PDE-5 inhibitors in preventing cognitive impairment remains unclear due to various limitations, such as the small number of included studies, the high risk of bias, the lack of an integrated study design, and low reporting quality
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    A NOVEL OVERSAMPLING TECHNIQUE TO HANDLE IMBALANCED DATASETS
    (European Council for Modelling and Simulation, 06/01/2020) Mahmoud, A; Ali, F; El-Kilany, A; Mazen, S
    With the amount of data is growing extensively in different domains in the recent years, the data imbalance problem arises frequently. A dataset is called imbalanced when the data of a certain class has significantly more instances than that of other classes of the same dataset. This imbalanced nature of the data negatively affects the performance of a classifier since misclassification of data may cause data analysis results to be inaccurate and hence leads to wrong business decisions. This paper presents a study of the different techniques that are used to handle the imbalanced dataset, and finally proposes a novel oversampling technique to tackle the binary classification of imbalanced dataset problem. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther (Editors).