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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    Students’ intention to use artificial intelligence in business courses: an extended TAM approach in a developing economy
    (Frontiers Media SA, 2026-07-08) Rehab EmadEldeen; Ahmed F. Elbayuomi; Marwa Farghaly; Mohamed Samy El-Deeb; Mohamed A. K. Basuony
    Introduction: This study examines students' adoption of Artificial Intelligence (AI) in business education through an extended Technology Acceptance Model (TAM), positioning AI as an intelligent and adaptive socio-technical system rather than a conventional digital tool. Methods: Drawing on data from 521 undergraduate business students in private universities in Cairo, Egypt, the study employs structural equation modelling to investigate the psychological, contextual, and cognitive factors of AI usage. Results: Findings reveal that perceived usefulness and perceived ease of use remain central drivers of students' attitudes toward AI; however, AI-specific factors (including job relevance, self-efficacy, and perceived resource availability) significantly shape these perceptions. Notably, the results highlight the importance of contextual constraints in developing economies, where institutional support and access to technological resources play a critical role in facilitating AI adoption. Discussion: By extending TAM to the context of AI-enabled learning, this study contributes to the literature by emphasizing the dual role of AI as both a technological tool and a cognitive partner in the learning process. The findings offer implications for the design of digitally enhanced curricula, particularly in emerging markets, and provide insights into how higher education institutions can better align with the demands of an AI-driven workforce.
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    A mechanically robust and moisture-retentive hydrocolloid dressing for chronic wound management
    (Nature Research, 2026-08-12) Laila Waled; Nour. H. S. Habib; Alaa Farid; Roaa Ibrahim; Noha Elnagar; Soha M. Kandil; Gehan Safwat; Mohamed Taha
    Due to their prolonged healing times and susceptibility to infection, chronic wounds pose a significant clinical challenge. In order to improve the treatment of chronic wounds, a silver-curcumin (Ag-Cur) hydrocolloid dressing was developed in this work. integrates nanotechnology with polymer-based wound dressings by using Ag-Curcumin nanoparticles (Ag-Cur NPs) as a nano filler, which were prepared and characterized using various techniques to confirm successful formation of stable silver nanoparticles with the acceptable properties, including surface Plasmon resonance (SPR), zeta potential (ZP), dynamic light scattering (DLS), Poly dispersive index (PDI), and HR-TEM. The results demonstrated the formation of Ag-Cur nanoparticles with a hydrodynamic size of 197.3 ± 0.709 nm and a zeta potential of -27.5 ± 0.603 mV. Morphological analyses via SEM and EDX revealed a porous structure with uniform nanoparticle distribution, while mechanical testing demonstrated superior tensile strength and a swelling ratio of 428.76%, surpassing commercial dressings. The hydrocolloid dressings shown high batch-to-batch reproducibility, and the Ag–Cur nanoparticles demonstrated acceptable long-term stability. Biological evaluations showed significant antimicrobial activity against pathogens such as Staphylococcus aureus, Escherichia coli, and Candida albicans, alongside potent anti-inflammatory effects (82.43% albumin denaturation inhibition at 1000 µg/mL) and substantial antibacterial, bactericidal, and anti-biofilm activity against MRSA, highlighting its promise as a multifunctional wound dressing for infection management and reducing biofilm-related healing delay. The sustained release profile, reaching 80% after 100 h, ensures prolonged therapeutic effects. Finally, with a CC50 of 470.06 ± 23.09 µg/mL, the Ag-Cur hydrocolloid demonstrated moderate cytocompatibility with HSF-1 cells. Additionally, Ag-Cur-HCD succeeds over both Blank-HCD and Ag-HCD in suppressing LPS-induced inflammation and oxidative stress in HSF-1 cells, as seen by the IL-1β, TNF-α, and NO levels. As a result, Ag-Cur-HCD considerably modulates important inflammatory and antioxidant pathways. These results suggest that the Ag-Cur hydrocolloid shows potential for future wound dressing applications. Future studies will focus on evaluating the formulation in models of infected and chronic wounds, as well as long-term safety and comparative efficacy assessments.
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    A machine learning approach to predict treatment response in myofascial pain patients receiving masseter trigger point injections
    (BioMed Central Ltd, 2026-08-10) Alshaimaa Ahmed Shabaan; Islam Kassem; Aliaa Ibrahium Mahrous; Inass Aboulmagd; Islam A. Amer; Ahmed Shaaban; Kareem Kamal Fathy; Reham Ragab; Mohamed Abd-El-Ghafour; Sally Ibrahim; Shaimaa Mohsen Refahee
    Background: Trigger point injection (TPI) therapy is widely used for masseter myofascial pain syndrome (MPS), yet outcomes vary substantially. Individualized prediction tools are lacking, often leading to trial-and-error treatment selection. Objective: To develop, externally validate, and generalize ensemble machine learning models for predicting composite treatment success following masseter TPI, and to deploy a web-based clinical decision support system (CDSS). Methods: This multicenter study included 1,181 patients with DC/TMD‑diagnosed masseter MPS treated with one of six injectable modalities. Baseline variables included pain (VAS), maximum mouth opening (MMO), and oral health‑related quality of life (OHIP-14). Composite treatment success was defined as simultaneous clinically meaningful improvement at 3 months: VAS reduction ≥ 2 points, MMO increase ≥ 5 mm, and OHIP-14 reduction ≥ 5 points. Random Forest (RF) and XGBoost models were trained on an internal cohort and externally validated on a geographically independent cohort. Performance was assessed using ROC‑AUC, precision‑recall AUC (PR‑AUC), calibration, decision curve analysis (DCA), and SHAP interpretability. Results: Overall composite success rate was 43.1%. Internal validation ROC‑AUC was 0.914 (RF) and 0.888 (XGBoost); external validation ROC‑AUC was 0.771 and 0.787, respectively. External PR‑AUC values were 0.759 (RF) and 0.761 (XGBoost). Both models showed good calibration and positive net benefit on DCA across clinically relevant thresholds. SHAP analysis identified baseline MMO, OHIP-14, age, pain intensity, and injectable modality as the most influential predictors, with consistent rankings across models and cohorts. The validated XGBoost model was deployed as a web‑based CDSS. Conclusions: Machine learning models demonstrated good ability to predict multidimensional treatment success following masseter TPI. Baseline MMO, OHIP-14, age, pain intensity, and injectable modality were the strongest outcome determinants. External validation, SHAP interpretability, and DCA support model robustness and potential clinical utility for personalized treatment planning in MPS. Clinical relevance: This tool may support treatment selection, reduce ineffective interventions, and improve patient outcomes in myofascial pain management.
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    Efficacy of adjunct Nd-YAG laser (1064 nm) to non-surgical periodontal therapy: a systematic review and meta-analysis of randomized controlled trials
    (Springer Nature, 2026-08-06) Ahmed Atya; Ahmed Tawfik; Rim Waly; Karim Abdelazim; AlShimaa Ibrahim Abd El Aziz Elsandoby; Al-Hassan Soliman Wadan; Ahmed Elkoumi
    Background: The Neodymium-doped Yttrium Aluminum Garnet (Nd: YAG) laser is proposed as an adjunct to scaling and root planing (SRP) to enhance bacterial reduction and healing in periodontitis. This study aims to evaluate the efficacy of Nd: YAG laser therapy (1064 nm) compared with SRP alone in improving clinical periodontal outcomes. Methods: This systematic review adhered to PRISMA 2020 guidelines and was prospectively registered with PROSPERO. We included only randomized controlled trials (RCTs) comparing SRP with adjunctive Nd: YAG laser therapy to SRP alone in patients with periodontitis. The primary outcomes were pocket depth (PD), plaque index (PI), bleeding on probing (BOP), and clinical attachment loss (CAL) assessments at baseline and after three months. Secondary outcomes included gingival cervical fluid (GCF) at baseline and after three months. Risk-of-bias assessment (using ROB-2) was performed independently by multiple reviewers. All statistical analyses were performed using R (version 4.4.3). The certainty of evidence for all outcomes was evaluated using the GRADE approach. Results: The systematic review and meta-analysis included 22 randomized controlled trials published between 2010 and 2025, totaling 776 patients. Meta-analysis demonstrated that adjunctive ND: YAG laser with SRP significantly improved PD reduction compared with SRP alone (MD = − 0.46 mm; 95% CI −0.82 to − 0.10; p = 0.0117), Subgroup analysis showed that split-mouth RCTs had a larger effect (MD = − 0.66 mm) than parallel-group RCTs (MD = − 0.20 mm), but the difference between subgroups was not significant. No statistically significant benefits were observed for PI (MD = − 0.03; 95% CI −0.19 to 0.13; p = 0.63), CAL (MD = − 0.04; 95% CI −0.21 to 0.13; p = 0.61), GCF volume (MD = − 0.20; 95% CI −0.45 to 0.06; p = 0.097), or BOP (MD = − 7.88; 95% CI −19.23 to 3.46; p = 0.17). Conclusion: Adjunctive Nd: YAG laser therapy provides a modest, statistically significant benefit in reducing periodontal pocket depth beyond conventional therapy alone. However, it does not demonstrate superior efficacy in improving clinical attachment levels or plaque control. Consequently, while beneficial for pocket reduction, current evidence does not support its routine universal use as a substitute for standard mechanical debridement.
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    Transformative Approaches to Wheat Disease Detection: An Efficient Deep Learning Framework for Enhanced Crop Resilience
    (University of Bahrain, 2026-06-30) Seifeldin A. Ismail; Yahia M. Anas; Nermin K. Negied
    Wheat is the third most consumed grain globally and a critical staple food for billions of people; yet its production is severely impacted by fungal diseases that cause annual yield losses of 15% to 20%, posing a direct threat to global food security and agricultural economics. Traditional methods of disease detection, reliant on manual field inspections, are inefficient, labour-intensive, require expertise that is not always available, and are prone to subjectivity—motivating the need for automated, reliable, and scalable solutions for early and accurate wheat disease identification. This study presents a controlled comparative evaluation of four CNN-based pre-trained architectures—DenseNet121, ResNet50, VGG19, and InceptionV3—for wheat disease classification using the Large Wheat Disease Classification Dataset (LWDCD2020), employing targeted data augmentation strategies to improve model robustness and generalizability without overfitting. The results reveal that InceptionV3 achieved the highest performance with a Macro F1-score of 96.75%, outperforming all other proposed models and existing approaches in the literature, while requiring only ten training epochs compared to 50–200 epochs in prior studies. The introduction of the Revolutions Per Inference (RPI) metric further quantifies the efficiency–accuracy trade-off, demonstrating InceptionV3’s practical suitability for deployment in resource-constrained agricultural environments.