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 3788
MSA University Campus 2
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Recent Submissions

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.
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.
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.
Whole genome sequencing of ceftolozane/tazobactamresistant, XDR Pseudomonas aeruginosa ST773 in hospitalized critically ill infants and young children with ventilatorassociated pneumonia
(Frontiers Media SA, 2026-07-10) Samira M. Hamed; Amira F.A. Hussein; Moshira H. Ezz El Arab; Mohamed H. Al-Agamy; Mohammed Aufy; Mohamed Abdelmoteleb; Mai M. Zafer
Introduction: Ventilator-associated pneumonia (VAP) caused by Pseudomonas aeruginosa poses a major therapeutic challenge for critically ill infants and young children. Methods: In this study, we assessed the antimicrobial susceptibility profiles of 42 P. aeruginosa isolates recovered from neonatal and paediatric intensive care unit (NICUs and PICU) patients with VAP between March and September 2021. Five isolates (11.9%) exhibited an extensively drug-resistant (XDR) phenotype and were resistant to both ceftazidime–avibactam and ceftolozane–tazobactam. These five isolates were subjected to whole-genome sequencing (WGS) and comparative genomic analyses. Results: All five isolates belonged to serogroup O11 and sequence type ST773. Nevertheless, WGS-based phylogenetic analyses, such as core genome MLST and SNP-based phylogeny, showed that the isolates were non-clonal and had a closer genetic relationship to previously identified ST773 strains from Egypt and Germany. Several multidrug efflux systems and a broad range of acquired antimicrobial resistance genes, such as blaNDM-1, rmtB4, tet(G), and flor2, carried on a conserved integrative conjugative element previously reported in ST773, were found by genomic analysis. Furthermore, on a genomic island inserted downstream of the glmS gene, a class 1 integron containing qnrVC1, aadA11, qacEΔ1, and sul1 was found. All strains possessed the same quinolone resistance-determining region alterations (gyrA T83I and parC S87L) and the same array of virulence-associated genes linked to motility, secretion systems, iron acquisition, quorum sensing, and toxin production. Discussion: This study reports the identification of non-clonal, XDR P. aeruginosa ST773 isolates associated with VAP in critically ill infants and young children. Despite their non-clonal nature, the isolates shared key features, including mobile genetic elements carrying important resistance genes and a consistent virulence gene profile. These findings highlight the clinical significance of this sequence type and raise concerns about its potential impact in the NICU and PICU settings. Continuous genomic surveillance, along with improved antimicrobial stewardship and stricter infection control practices, remains essential to limit its spread.
Modeling and Simulation of Cyber Epidemics Using a Delayed Variable-Order Fractional Framework
(John Wiley and Sons Inc, 2026-07-31) Mohamed Khalil; Anas Arafa; Ahmad Almutlg; Amaal Sayed
In today’s digital era, cyber epidemics are evolving rapidly. They pose a growing global threat. A cyber epidemic involves malware that spreads across connected networks. In this paper, we propose a delayed variable-order fractional model to study the spread of malware. The model incorporates time-varying memory effects through Caputo’s variable-order fractional derivative. It also accounts for delayed antivirus response via a time-delay term. The system is solved numerically using a predictor–corrector scheme. To the best of our knowledge, this work presents the first delayed variable-order fractional cyber epidemic model that integrates both time-dependent memory and time-delay effects. The results indicate that as the fractional order decreases, the system dynamics become slower and more stable, with lower infection levels. In addition, the variable-order model provides a more flexible description of evolving memory effects in cyber epidemic systems.