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 3737
MSA University Campus 2
Images for MSA University " sites - building - landscape "

Recent Submissions

Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation
(Springer International Publishing AG, 2026-05-01) Joy Naoum; Revana Salama; Ali Hamdi
Oral cancer is frequently diagnosed at later stages due to its similarity to other lesions. Existing research on computer-aided diagnosis has made progress using deep learning; however, most approaches remain limited by small, imbalanced datasets and a dependence on single-modality features, which restricts model generalization in real-world clinical settings. To address these limitations, this study proposes a novel data-augmentation–driven multimodal feature-fusion framework integrated within a (Vision Recognition)VR-assisted oral cancer recognition system. Our method combines extensive data-centric augmentation with fused clinical and image-based representations to enhance model robustness and reduce diagnostic ambiguity. Using a stratified training pipeline and an EfficientNetV2-B1 backbone, the system improves feature diversity, mitigates imbalance, and strengthens the learned multimodal embedding. Experimental evaluation demonstrates that the proposed framework achieves an overall accuracy of 82.57% on 2 classes, 65.13% on 3 classes, and 54.97% on 4 classes, outperforming traditional single-stream CNN models. These results highlight the effectiveness of multimodal feature fusion combined with strategic augmentation for reliable early oral cancer lesion recognition and serve as a foundation for immersive VR-based clinical decision-support tools.
Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion Detection
(Springer International Publishing AG, 2026-05-01) Menna Elgabry; Ali Hamdi
This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet’s Jury Theorem (CJT). Unlike conventional ensembles that often rely on homogeneous architectures, our approach combines architecturally diverse small transformer-based large language models (sLLMs)—BERT, RoBERTa, DistilBERT, DeBERTa, and ELECTRA—each fully fine-tuned for emotion classification. To preserve error diversity, we minimize parameter convergence while taking advantage of the unique biases of each model. A dual-weighted voting mechanism integrates both global credibility (validation F1- score) and local confidence (instance-level probability) to dynamically weight model contributions. Experiments on the DAIR-AI dataset demonstrate that our credibility-confidence ensemble achieves a macro F1-score of 93.5%, surpassing state-of-the-art benchmarks and significantly outperforming large-scale LLMs, including Falcon, Mistral, Qwen, and Phi, even after task-specific Low-Rank Adaptation (LoRA). With only 595 M parameters in total, our small LLMs ensemble proves more parameter-efficient and robust than models up to 7B parameters, establishing that carefully designed ensembles of small, fine-tuned models can outperform much larger LLMs in specialized natural language processing (NLP) tasks such as emotion detection.
Nanozymes as Emerging Therapeutics for Asthma: A Redox-Responsive and Immunomodulatory Strategy
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-05-14) Manar T. El-Morsy; Nadine M. Askar; Ali Emad Khurkhash; Nagm Al-Din Mahrous; Yusuf Ahmed Elberry; Mohamed Ramadan Sayed; Norhan Ashraf Ahmed; Rowayda A. Ahmed; Yehia S. Mohamed; Sinclair Steele; Ahmad Ahmeda; Rudaynah Mohamed; Doaa S. R. Khafaga
Asthma is a chronic, etiologically diverse lung disease that contributes to worldwide morbidity and healthcare burdens. Although bronchodilators and corticosteroids remain the cornerstones of asthma treatment, their long-term use is associated with significant side effects. Furthermore, steroid resistance in severe asthma emphasizes the need for alternative therapeutic approaches. Nanotechnology has emerged as a viable alternative to these standard approaches, allowing for targeted, prolonged, and precise drug delivery. Nanozymes, or synthetic nanomaterials that imitate natural enzyme functions, are gaining popularity among nanomedicine platforms due to their redox-regulating and immunomodulatory properties. This review provides a comprehensive overview of the present landscape of nanozyme-based treatments for asthma, with a focus on carbon-based nanozymes, while discussing MOF-derived and single-atom nanozymes in terms of their physicochemical properties and potential applicability to airway inflammatory diseases. Moreover, we look at current advancements in nanozyme-enabled drug delivery systems, their biocompatibility profiles, and potential strategies for designing nanozyme therapies according to asthma endotypes. These findings establish nanozymes as a transformational and therapeutically promising platform for next-generation asthma treatment.
Association of vitamin B1/B6/B12 supplementation with sphingosine-1-phosphate signaling and its receptors in multiple sclerosis patients: relevance to LISPR1 and APOA1-AS
(Portland Press Ltd, 2026-05-21) Noha A. Mehana; Heba R. Ghaiad; Mohammed M. Nooh; Mai A. Amer; Lobna Talaat El-Ghoneimy; Maheera H. Safwat
MS is a lifelong autoimmune disorder striking the central nervous system (CNS). Despite the currently used disease-modifying therapies, patients are exposed to persistent neuropathy, pinpointing the need for supportive therapy. Neurotropic vitamins B1, B6, and B12 have been used to offer relief from immunological and neurological MS manifestations. The present study aimed to provide some mechanistic insights into the relationship of B1/B6/B12 vitamin supplementation with the development of MS regarding lipid metabolism and epigenetics. In this cross-sectional observational study, blood samples were obtained from 53 MS patients, including 25 patients, who had received daily vitamin B1/B6/B12 supplementation for over six months and 28 patients without supplementation. Plasma sphingosine-1-phosphate (S1P) and S1P receptor-1 (S1PR1) levels, lipid profile, and gene expression of ApoA1, sphingosine kinases 1&2 (SPHK1&2), S1PR1, as well as the lncRNAs APOA1-AS and LISPR1 were evaluated. Gene ontology and KEGG pathway enrichment analyses were conducted. Vitamin B1/B6/B12 supplementation was associated with a more favorable lipid profile. Supplemented patients also exhibited higher ApoA1 and lower APOA1-AS expressions compared with non-supplemented patients. Additionally, vitamin B1/B6/B12 supplementation was associated with lower expression levels of SPHK1, SPHK2, LISPR1, and S1PR1 and reduced circulating S1P concentrations. These findings imply significant associations between long-term vitamin B1/B6/B12 supplementation and alterations in lipid-related markers and sphingosine-associated signaling in MS patients. However, the observational design, selection bias, and small sample size limit causal inference and may not fully capture the heterogeneity of MS population. Besides, supplement adherence was self-reported and not objectively verified, and circulating vitamin levels were not measured.
Assessment of Accuracy and Orbital Volume Using Patient-Specific Titanium Implant Versus Patient-Specific Zirconia Implant for Orbital Floor Reconstruction in Blowout Fractures: A Randomized Clinical Trial
(Lippincott Williams and Wilkins, 2026-05-25) Ola Alaa El Morsy; Sameh Mekhemar; Mohamed Mounir; Shady El Assiouty; Samy Mounir
This randomized clinical trial aimed to compare patient-specific titanium and zirconia implants for orbital floor reconstruction with respect to reconstruction accuracy, orbital volume restoration, patient satisfaction, and cost-effectiveness. Twenty-four patients presenting with orbital blowout fractures underwent open reduction and internal fixation with orbital floor and/or medial wall reconstruction using patient-specific implants through a transconjunctival approach. Virtual surgical planning was performed by mirroring the contralateral intact orbit combined with digital fracture reduction. Postoperative evaluation included orbital volume measurements and 3-dimensional superimposition analysis to assess reconstruction accuracy. Patient satisfaction and implant manufacturing cost were also recorded. Both zirconia and titanium implants achieved comparable restoration of orbital volume and reconstruction accuracy, with no statistically significant differences between groups. Patient satisfaction scores were better in the zirconia group, although the difference was not statistically significant. Postoperative complications occurred only in the titanium group, including one case of globe restriction requiring partial implant removal and 2 cases suggestive of titanium hypersensitivity managed medically. Zirconia implants demonstrated a significantly lower manufacturing cost. Zirconia patient-specific implants provide anatomic and clinical outcomes similar to titanium implants, with a favorable cost profile, supporting their use as a viable alternative for orbital reconstruction.