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Author: search.filters.author.Asrar RashidHas files: Yes

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    Evaluating gene expression patterns for NF-κB1, TNF, and VEGF A& VEGF B in a mouse model of SARS-CoV-2 infection
    (Elsevier BV, 2025-06)
    Wael Hafez
    ;
    Asrar Rashid
    ;
    Feras Al-Obeidat
    ;
    Nouran Hamza
    ;
    Muneir Gador
    Introduction: The coronavirus disease (COVID-19) pandemic has encouraged extensive research into its pathophysiology, specifically the role of biomarkers in disease progression. Although TNF, NF-κB1, VEGF-A, and VEGF-B play fundamental roles in vascular development and the infection response, their precise involvement in COVID-19 remains unclear. We aimed to evaluate and synthesize TNF, NF-κB1, VEGF-A, and VEGF-B gene expression patterns in a mouse model of SARS-CoV-2 infection to understand their involvement in disease pathogenesis. Methods: Gene datasets available on the open-source Gene Expression Omnibus (GEO) platform were extracted from eleven specific datasets: GSE68220, GSE51387, GSE49262, GSE51386, GSE50000, GSE40824, GSE33266, GSE50878, GSE40840, GSE49263, and GSE40827. We used R 4.3.2 software in this analysis. Results: A Substantial changes in the expression of VEGFA, VEGFB, TNF-, and NF-κB1 were observed. Upregulation of TNF- and NF-κB1 implies a strong inflammatory response, consistent with their established involvement in inflammation. Conversely, VEGFA and VEGFB showed a pattern of downregulation, suggesting alterations in the vascular and endothelial functions. Conclusion: Substantial changes in TNF, NF-κB1, VEGFA, and VEGFB gene expression were observed During SARS-CoV infection, indicating their interconnected roles in disease pathogenesis. These findings improve our understanding of the molecular basis of COVID-19 vascular complications and will guide future research and therapies.
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    Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis
    (Frontiers Media SA, 2025-01-17)
    Feras Al-Obeidat
    ;
    Wael Hafez
    ;
    Asrar Rashid
    ;
    Mahir Khalil Jallo
    ;
    Munier Gador
    Background: Leukemia is the 11th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. Aim: To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Methods: Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used in the studies. Accuracy and sensitivity were the primary outcome measures. Results: Ten studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). The common- and random-effects models had accuracies of 1.0000 [0.9999; 1.0001] and 0.9557 [0.9312, and 0.9802], respectively. The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. Studies have shown substantial variations in accuracy and sensitivity, as shown by the Q values and I2 statistics. Conclusion: Our systematic review and meta-analysis found an overall high accuracy and sensitivity of AI models in correctly identifying true-positive AML cases.