MSA Repository "MSAR"

MSAR University's Digital Repository is a documentation and digitization of all university outcomes that are of effective value in the scientific and academic community and reflects the university's image, work, and effective contribution to society Through MSAR Digital Repository, the university managed to collect, store, archive and publish digital content - including documents, audio files, images and data sets - all in a safe place. MSAR is one of the strongest University Digital Repositories in Egypt and documented in the DSPACE community with its latest versions.

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MSA Journals 140
A Full content for MSA university Faculties Journals
MSA Theses 5
A digital collection of MSA University postgraduate theses, including PhD and Master’s theses, organized by academic degree and faculty.
MSA University Academic Graduation Projects 1153
A Full content for msa university Distinguished Graduation Projects Yearbook
MSA University Academic Research 3782
MSA University Campus 2
Images for MSA University " sites - building - landscape "

Recent Submissions

Induction of differential cytotoxicity and ROS-driven severe genomic DNA damage and intrinsic mitochondrial apoptosis by erbium oxide nanoparticles in p53-compromised human NSCLC cells
(Springer Science and Business Media Deutschland GmbH, 2026-07-23) Hanan R. H. Mohamed; Alaa H. Elsewedy; Shahd Mosaad; Aya A. Osman; Habiba M. Zaki; Mayada E. Borai; Ayman Diab; Gehan Safwat
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, with current therapeutic strategies limited by systemic toxicity, drug resistance, and poor tumor selectivity. These limitations highlight the urgent need for safer, targeted therapeutic strategies. In this context, erbium oxide nanoparticles (Er2O3NPs) have attracted attention in biomedical research due to their unique physicochemical characteristics, yet their potential as anticancer agents against NSCLC remains fully unexplored. This study was consequently conducted to assess the anticancer efficacy and underlying mechanisms of Er2O3NPs in human A549 NSCLC cells, alongside with evaluating their safety in normal WI-38 lung fibroblasts. Cytotoxicity was determined using the MTT assay, and genomic DNA integrity was assessed via the alkaline Comet assay. Oxidative stress and mitochondrial function were evaluated through 2′,7′-dichlorodihydrofluorescein diacetate (2,7-DCFH-DA) and Rhodamine-123 staining, respectively. Apoptotic induction was analyzed using DAPI nuclear staining and chromatin diffusion assays, while qRT-PCR quantified the expression of apoptosis- and mitochondria-related genes. Er2O3NPs exhibited concentration-dependent cytotoxicity in A549 cells (IC50 = 93.46 µg/ml) and lower toxicity in WI-38 fibroblasts (IC50 = 147.5 µg/ml), yielding a selectivity index of 1.58. Mechanistically, treating A549 cells at the IC50 concentration for 72 h induced severe genomic DNA damage, excessive ROS generation, and pronounced mitochondrial membrane depolarization. DAPI staining revealed characteristic apoptotic nuclear changes, including chromatin condensation and fragmentation, while the chromatin diffusion assay demonstrated extensive DNA dispersion indicative of advanced apoptotic DNA degradation. Moreover, qRT-PCR analysis showed significant downregulation of p53, Bcl-2, and ND3 gene expression. Given the inherent mutant p53 status of A549 cells, this transcriptional downregulation indicates that Er2O3NPs bypass canonical wild-type p53 transcriptional activation, triggering instead an ROS-driven intrinsic mitochondrial apoptotic signaling pathway. In conclusion, Er2O3NPs induce preferential cytotoxicity in NSCLC cells through ROS-mediated severe genomic DNA damage and intrinsic mitochondrial dysfunction within a p53-compromised genetic background. These findings suggest that Er2O3NPs hold potential as a nanotherapeutic candidate for NSCLC. However, further in vivo studies, targeted delivery systems, and direct protein-level evaluations are warranted to fully clarify these mechanisms and advance their clinical applicability.
Exploratory Machine Learning Predictors of Financial Performance: Evidence from Listed Egyptian Fintech Ventures
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-07-16) Doaa Mohamed Salman; Sherif El-Halaby; Andriy Stavytskyy; Ganna Kharlamova; Amal Gamil
This study provides an exploratory predictive analysis to examine how different dimensions of digital infrastructure - capital market development, digital payment adoption, e-commerce penetration, and market volatility -predict the financial performance metrics of fintech ventures in Egypt. Using panel data from ten fintech ventures listed on the Egyptian Stock Exchange over the period 2017–2023, the research employs Random Forest machine learning algorithms alongside Logistic Regression as a baseline comparator. Feature importance analysis identifies the most significant predictors of profitability across four performance metrics: gross revenue, sales growth, gross margin, and net profit margin. This study employs Random Forest with five-fold cross-validation. Hyperparameters were optimized via grid search, and feature importance scores are reported with cross-validation standard deviations. To address panel structure concerns, we additionally employ leave-one-firm-out cross-validation. All findings reflect predictive associations only; no causal claims are made due to potential reverse causality. Findings show that capital market development emerges as the most important predictor across all profitability metrics, accounting for 45% of feature importance for net profit margin and 42% for gross revenue (mean importance across five folds; SD = 0.07–0.08). Digital payment adoption exhibits a paradoxical dual association—positively associated with revenue and margins through operational efficiency (38% importance for gross margin; SD = 0.08) while negatively associated with sales growth (22% importance; SD = 0.10). Gross online sales show limited predictive efficacy, affecting only gross margin. Market volatility correlates solely with sales growth. Random Forest consistently outperforms Logistic Regression across all models, with accuracy rates ranging from 68% to 76% (compared to a chance level of 50% and a majority-class baseline of 52–58%). Due to the limited sample of 70 firm-year observations, these findings must be interpreted as strictly exploratory and hypothesis-generating; they apply uniquely to publicly listed fintech firms on the Egyptian Stock Exchange and cannot be generalized to private, early-stage, or unlisted fintech startups without further empirical validation.
A systematic stepwise optimization framework for rapid multi-analyte UPLC–MS/MS plasma analysis with integrated sustainability assessment
(Nature Research, 2026-07-28) Amira E.Abd-Elnabi; Amr M. Mahmoud; Dina A. El Mously; Omnia A. El-Naem
A stepwise optimization guidance model for rapid multi-analyte UPLC–MS/MS is proposed to report recurring limitations in bioanalytical method development. The model involves six stages: (1) physicochemical profiling, (2) extraction strategy selection, (3) chromatographic optimization, (4) mobile phase selection, (5) method validation, and (6) greenness evaluation. The applicability of this framework was demonstrated by determining Rifaximin, Ciprofloxacin, and Fluconazole in spiked human plasma, using Ibuprofen as an internal standard. Physicochemical profiling guided the selection of ionization mode and prediction of solubility behavior. Based on the model recommendations, liquid–liquid extraction using dichloromethane was selected to achieve effective analyte recovery. Chromatographic optimization and mobile phase selection through the proposed framework supported the use of rapid isocratic separation on a C18 column using a methanol/water mixture (95:5, v/v) as mobile phase. The method validation, performed as an integral stage of the workflow according to regulatory guidelines, demonstrated exceptional linearity (r² ≥ 0.999). The final stage of the model involved evaluating environmental applicability using AGSA and EPPI analyses. The proposed sequential framework is intended to serve as a methodological reference for analysts employing LC–MS/MS platforms, to establish a structured and reproducible analytical workflow that advances ecological sustainability in multi-analyte LC–MS/MS bioanalytical applications.
Effect of core stabilization on lumbosacral angle in centrally obese adolescents: A randomized controlled trial
(Churchill Livingstone, 2026-07-28) Marian M. Shafeek; Ahmed S. Ali; Hanan Hosny M. Battesha; Noha Aly Mohamed; Ahmed Mahmoud Hamed Hasnin; Sami kamal Mohamed Elgendy; Ibrahim M. Abdelhakim
Objective: To assess the effects of core stabilization exercises (CSE) on the lumbosacral angle (LSA), low back pain (LBP), and functional disability compared to conventional physical therapy in adolescents who are centrally obese. Design: Randomized controlled study. Methods: Forty adolescents with central obesity and chronic LBP were randomly assigned to either an experimental group participating in a structured CSE program or a control group receiving conventional physical therapy. Participants were randomly assigned to either group using concealed allocation through sealed opaque envelopes. Treatment sessions were 45 min, three times weekly for 12 weeks. The primary outcome measure was LSA, and Secondary measurements included the Functional Disability Inventory (FDI) to measure functional disability and the Visual Analog Scale (VAS) to determine the intensity of LBP. Both groups were tested before and after treatment. Results: Both groups showed significant post-treatment improvements (p < 0.001). The experimental group showed greater improvement in LSA and FDI than the control group, with profound and very large effect sizes, respectively. No significant between-group difference was found for VAS, with a negligible effect size. Conclusion: CSE and conventional therapy effectively manage chronic LBP in centrally obese adolescents, with stabilization programs significantly better at correcting the LSA and reducing disability.
Electrochemical Sensor Based on Ferric Oxide/ Reduced Graphene Oxide Nanocomposite for Linezolid Determination
(Institute of Physics, 2026-07-24) Amira E. Abd-Elnabi; Amr M. Mahmoud; Omnia A. El-Naem; Dina A. El Mously
An innovative electrochemical sensor, constructed on a ferric oxide-reduced graphene oxide (Fe2O3/RGO) nanocomposite-modified carbon paste electrode, was developed for the determination of linezolid (LNZ) in pharmaceutical formulations. LZN is a critically important oxazolidinone antibiotic widely used to treat severe infections caused by multidrug-resistant Gram-positive pathogens. Owing to its narrow therapeutic index and the risk of dose-related adverse effects during prolonged therapy, sensitive and reliable analytical methods for its determination are essential to support quality control and therapeutic drug monitoring. The morphology and electrochemical behavior of the fabricated Fe2O3/RGO nanocomposite were evaluated. Cyclic voltammetry and differential pulse voltammetry were used to investigate the electrochemical behavior of LNZ at the modified electrode. Optimal analytical performance was accomplished systematically by adjusting different DPV experimental parameters. Under the optimized conditions, a linear response was revealed over the concentration range of 0.4–7.0 µM, with a limit of detection of 0.152 µM and a limit of quantification of 0.461 µM was exhibited by the sensor. This method was successfully employed to the quantification of LNZ in pharmaceutical dosage forms, producing accurate and reproducible results. The developed sensor establishes high sensitivity, good repeatability, and satisfactory analytical performance, confirming its suitability for routine pharmaceutical analysis.