Publications by authors named "Ola Sayed Ahmed"

Aim: Molecular characterization of the CD133+ stem cells associated with hepatocarinogensis through identifying the expression patterns of specific microRNAs (miRNAs).

Methods: We investigated the expression pattern of 13 miRNAs in purified CD133+ cells separated from the peripheral blood of healthy volunteers, chronic hepatitis C (CHC), liver cirrhosis (LC) and hepatocellular carcinoma (HCC) patients a long with bone marrow samples from the healthy volunteers and the LC patients using custom miScript miRNA PCR array.

Results: The differential expression of the 13 studied miRNAs in CD133+ cells separated from the HCC patients' peripheral blood compared to the controls revealed that miR-602, miR-181b, miR-101, miR-122, miR-192, miR-125a-5p, and miR-221 were significantly up regulated (fold change = 1.

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The identification of new high-sensitivity and high-specificity markers for hepatocellular carcinoma (HCC) is essential. We aimed at identifying serum microRNAs (miRNAs) as potential biomarkers for early detection of HCC on top hepatitis C virus (HCV) infection. We investigated serum expression of 13 miRNAs in 384 patients with HCV-related chronic liver disease (192 with HCC, 96 with liver cirrhosis (LC), and 96 with chronic hepatitis C (CHC)) in addition to 96 healthy participants enrolled as a control group.

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Aim: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).

Methods: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data.

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Background: Changes in DNA methylation patterns are believed to be early events in hepatocarcinogenesis. A better understanding of methylation states and how they correlate with disease progression will aid in finding potential strategies for early detection of HCC. The aim of our study was to analyze the methylation frequency of tumor suppressor genes, P14, P15, and P73, and a mismatch repair gene (O6MGMT) in HCV related chronic liver disease and HCC to identify candidate epigenetic biomarkers for HCC prediction.

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