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Anatomy education faces challenges in providing engaging, interactive, and collaborative learning experiences, particularly in understanding complex three-dimensional structures and maintaining long-term knowledge retention. Although virtual reality (VR) has shown promise in addressing spatial comprehension challenges, questions remain regarding its effectiveness in supporting collaborative learning and sustained knowledge retention. This longitudinal study examined Collaborative Immersive Virtual Anatomy Laboratory (C-IVAL), an innovative VR platform designed to enhance traditional anatomy learning through integrated collaborative features, immersive technology, and serious game elements. We conducted an experimental study with 65 medical students to evaluate their knowledge acquisition and technology acceptance compared to its non-collaborative predecessor, the Immersive Virtual Anatomy Laboratory (IVAL). Our evaluation framework combined quantitative assessments (knowledge tests, comprising pre-test, immediate post-test, and delayed post-test) with Technology Acceptance Model (TAM) analysis. Knowledge assessment revealed significant cognitive improvements, with mean knowledge scores increasing from 2.48 to 3.94 in the immediate post-tests, while long-term retention of anatomy knowledge showed sustained engagement for over two months. Importantly, C-IVAL demonstrated significantly higher scores across all TAM dimensions than the non-collaborative IVAL system, particularly for perceived usefulness and intention to use. Post-session feedback analysis showed 73.8% positive responses, highlighting enhanced social presence, immersive engagement, and effective collaboration, with 26.2% of constructive feedback focusing on system refinement and feature enhancement. This study contributes to the understanding of the effectiveness of collaborative features in virtual reality education by offering insights into designing and implementing virtual learning environments that enhance both knowledge retention and user acceptance.

Maize (Zea mays) is a critical crop for global food security and economic stability. However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. These models also suffer from high computational complexity when deeper layers are introduced to handle fine-grained details. Transformer-based models, on the other hand, provide long-range dependencies but come with significant computational overhead, limiting their use in real-time agricultural applications. To overcome these challenges, we propose MSCPNet, a novel architecture that combines a truncated MobileNetV2 backbone with a Multi-Scale Convolutional PoolFormer block. The truncated backbone ensures that only essential layers for general feature extraction are retained, enhancing the model’s adaptability across domains. The Multi-Scale Convolutional PoolFormer block captures both local and global dependencies through parallel convolutional branches of varying kernel sizes, while the PoolFormer module efficiently handles feature aggregation without the heavy computational cost associated with traditional attention mechanisms. This design allows the model to balance computational efficiency and high accuracy, making it highly suitable for real-time maize disease detection. Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. Furthermore, the model was evaluated on the PlantVillage dataset for tomato leaf disease classification, where it achieved an accuracy of 99.32%, precision of 99.32%, recall of 99.33%, F1-score of 99.32%, and an MCC of 0.9925. These results demonstrate the effectiveness and efficiency of MSCPNet in disease classification across different domains.

Abstract Background The WHO considers anemia in pregnancy a severe public health issue when prevalence surpasses 40%. In response, we conducted a systematic review and meta-analysis to examine anemia among pregnant women in Egypt, focusing on its prevalence, determinants, and associated complications. Methods We conducted a systematic literature search for studies published between January 1, 2010, and August 18, 2024, to identify studies from Egypt reporting on anemia in pregnant women, including its prevalence, associated determinants, and complications. A meta-analysis was conducted using a random-effects model to estimate pooled prevalence, odds ratios (OR), and standardized mean differences (SMD). Sensitivity analyses and publication bias were performed. All statistical analyses were conducted using R software. Results Eighteen studies met the eligibility criteria with a total sample size of 14,548. The overall prevalence of anemia among pregnant women was 49% (95% CI: 42–57), with no significant difference between Upper and Lower Egypt (P = 0.66). The sensitivity analysis demonstrated the absence of influential outliers and Egger’s test indicated no evidence of publication bias (P = 0.17). Anemia prevalence was significantly higher in the third trimester (65%) compared to the second trimester (47%) (P = 0.03). Among anemic pregnant women, most cases were mild (47%) and moderate (47%). The determinants of anemia among pregnant women included being over 30 years old (OR: 1.95), residing in rural areas (OR: 1.76), illiteracy (OR: 1.93), birth spacing

Abstract Glycogen Storage Disease Type Ib (GSD-Ib) is a rare autosomal recessive metabolic disorder caused by mutations in SLC37A4, leading to a deficiency in glucose-6-phosphate translocase. This disorder is characterized by impaired glycogenolysis and gluconeogenesis, resulting in clinical and metabolic manifestations. We report a three-month-old Moroccan female patient presenting with doll-like facies, hepatomegaly, dysmorphic features, and developmental delays. Laboratory analysis revealed hypoglycemia, elevated triglyceride levels, hypercalcemia, and neutropenia. Genetic testing confirmed a homozygous pathogenic variant in SLC37A4 and a heterozygous variant of uncertain significance in TBX1. Initial management included a lactose-free and galactose-free diet, multivitamin supplementation, and granulocyte colony-stimulating factor (G-CSF) therapy to address neutropenia. A novel aspect of this case involves hypercalcemia as an unusual finding in GSD-Ib and the co-occurrence of a variant in the TBX1 gene, which is not typically associated with the disease but may contribute to the patient’s clinical presentation. These findings add a new dimension to our understanding of GSD-Ib and suggest potential avenues for future research to elucidate these genetic interactions and their impact on clinical outcomes.

Hydraulic motion platforms are widely utilized in various environments due to their capacity to bear heavy loads and provide long actuator strokes. The predominant control system employed for hydraulic motion platforms is the electro-hydraulic servo system (EHSS) due to its high accuracy and robustness. However, compared to other types of hydraulic valves, the electro-hydraulic servo valve (EHSV) is less durable. An alternative system to EHSS is the electro-hydraulic proportional system (EHPS), which employs an electro-hydraulic proportional valve (EHPV) instead of the EHSV. The EHPV resolves the durability issue at the expense of accuracy and system error. The optimization of durability and accuracy is the core of this study. The tracking behavior and response of both Three degrees of freedom (3 DOF) motion platforms using EHSS and EHPS are investigated across different frequency ranges.

This study utilizes density functional theory (DFT) with the Tran–Blaha modified Becke–Johnson (TB-mBJ) exchange potential in the WIEN2k framework to comprehensively investigate the structural, electronic, elastic, and optical properties of Rb2TlIrF6 and Rb2TlRhF6 double perovskite compounds. Structural stability assessments affirm that both materials maintain their integrity under standard conditions, demonstrating robust frameworks. The calculated elastic constants reveal mechanical stability, elasticity, and directional anisotropy, emphasizing their resilience and adaptability in varied environments. Electronic band structure calculations categorize Rb2TlIrF6 and Rb2TlRhF6 as direct bandgap semiconductors, with respective bandgaps of 2.61 eV and 1.94 eV at the L–L symmetry points, underscoring their potential in optoelectronic applications. The optical properties were analyzed through key parameters: refractive index n(ω), optical conductivity σ(ω), reflectivity R(ω), absorption coefficient α(ω), and extinction coefficient k(ω). Additionally, the real and imaginary parts of the dielectric function ∊1(ω) and ∊2(ω) were evaluated, indicating effective light interaction and absorption properties in the visible to UV range. These findings suggest that Rb2TlIrF6 and Rb2TlRhF6 exhibit advantageous optoelectronic characteristics suitable for semiconductor and next-generation electronic devices.

Background/Objectives: Dermatomyositis (DM) is a rare idiopathic inflammatory myopathy characterized by muscle weakness and typical cutaneous rash. Dermatomyositis-specific antibodies, such as anti-TIF1γ, anti-SAE, anti-Mi2, anti-MDA5, and anti-NXP2, have been associated with specific clinical phenotypes. Our study aimed to describe the clinical profile of Moroccan patients with DM and clinical associations with myositis-specific antibodies. Methods: We recruited 54 adult patients with DM according to the Bohan and Peter criteria, admitted to the internal medicine and dermatology departments of the University Hospital Center Ibn Rochd of Casablanca from January 2020 to December 2023. Testing for myositis-specific autoantibodies (MSAs) was conducted using an Immunodot assay. Statistical analysis was performed using the Chi-square test. Results: Among our patients, 74% were female. The mean age of the patients at the time of diagnosis was 45.8 years (±12.95 years). The main clinical manifestations were a V-neck sign (70.4%), myalgia (70.4%), Gottron’s papules (68.5%), heliotrope rash (63%), arthritis/arthralgia (48.1%), proximal muscle weakness (68.5%), periungual erythema (46.3%), and dysphagia (59.3%). Of the 54 patients, 37 (68.5%) showed dermatomyositis-specific antibody positivity. The most frequently found autoantibody was anti-Mi2 (22.2%), followed by anti-TIF1γ (14.8%), anti-NXP2 (9.2%), anti-MDA5 (7.4%), and anti-SAE (7.4%). The association between clinical manifestations and MSAs showed that anti-TIF1γ antibodies were associated with the V-neck sign (p < 0.05), and the MSA-negative group was protected from periungual erythema (p < 0.05). No other significant association was found. Conclusions: This study shows the autoantibody profile of Moroccan patients with DM and the associations of MSAs with clinical manifestations.