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Your search keyword '"A. S. Amein"' showing total 7 results
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7 results on '"A. S. Amein"'

1. Malva parviflora seed oil; Isolation, gas chromatographic profiling and its cardioprotective activity against myocardial infraction in animal model

Author

Mohammed A. Hussein, Mohammad Shahidul Islam, Ali A. Ali, Mohamed S Mansour, Mohamed Bondok, Mohamed A. Salem, Ahmed S. Amein, and Heba A. ElGizawy

Subjects
MPSO, miRNA-140-5p, miRNA-208b, Cardiac Injury, Antioxidant, Inflammatory Mediators, Science (General), Q1-390
Abstract

Myocardial infarction (MI), one of the most perilous types of cardiac illness, carries a significant burden of mortality and morbidity. This study aims to shed light on the impact of Malva parviflora seed oil (MPSO) on plasma cardiac function tests, levels of cardiac inflammatory mediators, and the expression of cardiac miRNA140-5p and miRNA208b genes in a rat model of MI induced by isoproterenol (ISO). The methods involved the extraction of MPSO using hexane, with the determination of fatty acid contents accomplished through GC spectrometry. Adult albino rats, weighing 185 ± 7 g, were divided into five groups (n = 6): normal control rats, ISO-treated rats, ISO-treated rats with MPSO (157 and/or 314 mg/kg, respectively), and ISO-treated rats with omega (100 mg/kg, respectively) for a duration of four weeks. The results revealed that Malva parviflora seeds yielded 3.3 gm/100 dry seeds. Among the nine fatty acid components identified, coriolic acid was the most abundant (31.60 %), followed by pentadecanoic acid (30.05 %). The cardioprotective potential of MPSO was assessed in rats subjected to ISO-induced cardiac injury. Following 24 h of ISO treatment, rats displayed elevated levels of plasma cardiac troponin I (cTnI), troponin T (cTnT), lactate dehydrogenase (LDH), and B-Type Natriuretic Peptide (BNP), as well as cardiac BcL-2, P53, toll-like receptor 4 (TLR4), NF-κB (Nuclear factor kappa-light-chain-enhancer of activated B cells), thiobarbituric acid reactive substances (TBARs), reduced glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), miRNA-140-5p, and miRNA-208b gene expression. Histopathological examination confirmed cardiac injury in ISO-treated rats. Furthermore, MPSO mitigated the elevation of cardiac enzymes and TBARS, as well as cardiac inflammatory mediators, while concurrently downregulating the expression levels of miRNA-140-5p and miRNA-208b genes. Conversely, the enhancement of cardiac GSH, SOD, and CAT activity demonstrated the antioxidant capabilities of MPSO against ISO-induced cardiac injury. Histopathological findings underscored MPSO's protective effect on cardiac tissue against oxidative damage in ISO-treated rats. In nutshel, the findings of this study unveil the cardioprotective and free radical scavenging attributes of MPSO in rats with ISO-induced cardiac damage. MPSO appears to provide cardiac protection against free radicals and inflammation induced by xenobiotics, potentially owing to its rich content of polyphenols, flavonoids, polyunsaturated fatty acids (PUFAs), and cyclopropenoid fatty acids.

Published
2024
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2. SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Author

Mohammed Elwan, Ahmed S. Amein, Aiman Mousa, Abdelmoty M. Ahmed, Belgacem Bouallegue, and Abdelhameed S. Eltanany

Subjects
Article Subject, Computer Networks and Communications, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Information Systems
Abstract

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

Published
2022

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