Browsing by Author "Ahmed Samir"

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    Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques
    (Nature Publishing Group, 2024-11-04) Ahmed Samir; Amira Abdeldaim; Ammar Mohammed; Asmaa Ali; Mohamed Alorabi; Mariam M. Hussein; Yasser Mabrouk Bakr; Asmaa Mohamed Ibrahim; Ahmed Samir Abdelhafiz
    Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among at-risk populations. The objective of this study was to assess the utility of machine learning in predicting HCC by analyzing the combined expression of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152, LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis. Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60 to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5 expression ratio significantly correlated with increased mortality risk. The integration of lncRNA biomarkers with conventional laboratory data within a machine learning framework demonstrates significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and explore a wider array of lncRNAs.
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    Analytical-Quality-by-Design Fluoroprobes for Quantitation of Entecavir and Penciclovir in Spiked Human Plasma and Content Uniformity Testing: Insights of DNA Mismatching, Three-Colors Assessment and Sustainability Profiling
    (John Wiley and Sons Ltd, 2025-03-03) Sarah S. Saleh; Ahmed Samir; Omnia A. El-Naem
    Entecavir and penciclovir are nucleoside-analog antiviral drugs structurally related to guanine that act by inhibiting the active viral replication process. Through this study, the quantitation of both drugs was carried out using two fluorescent probes, referred to as fluoroprobes. The first type was prepared by the addition of surfactants such as Tween 80 and sodium dodecyl sulfate (micelle-enhanced fluoroprobes), while the second type includes the formation of a tertiary complex of drug-terbium-DNA (Tb-DNA fluoroprobes). The preparation of the fluoroprobes was optimized using analytical quality by design (AQbD) via I-optimal design. A positive effect of the selected antiviral drugs on DNA mismatching was observed. The analytical procedures were validated according to ICH guidelines with a linearity range of 2.0–40.0 μM and 25.0–300.0 nM for micelle-enhanced and Tb-DNA fluoroprobes, respectively. The analytical procedures were evaluated in compliance with the three-color (GBW) assessments: greenness (using AGREE and ComplexGAPI metrics), blueness (using the BAGI tool), and whiteness (using the RGB algorithm). The sustainability profiles were established using the efficient-valid-green (EVG) framework. Both types of fluoroprobes were successfully applied to quantify entecavir and penciclovir in content uniformity testing and spiked human plasma as a simpler and cheaper alternative to hyphenated analytical techniques.