Galala University (Arabic: جامعة الجلالة) is a national, non-profit Egyptian university located in Al Galala in Suez. The university includes 13 faculties in different fields of study. It was established in August 2020 by a decision of Abdel Fattah El-Sisi, President of Egypt.https://www.gu.edu.
Water pollution is a global challenge caused by industrial discharge, agricultural runoff, and insufficient wastewater treatment. Conventional treatment is widespread but faces challenges such as low removal efficiency, high costs, and secondary pollution. Sustainable, cost-effective adsorbents derived from agricultural byproducts, biomass, and minerals are promising alternatives but often exhibit low adsorption capacity, poor selectivity, and limited regeneration. Nanotechnology has improved these materials by increasing surface area, adding functional sites, and enhancing mass transfer. Reported nano-engineered biosorbents demonstrate adsorption capacities approaching 80–100 mg/g for several dyes, with removal efficiencies frequently exceeding 90–99
Investigating dentin strengthening effect of an aqueous extract of Nigella sativa seed (AEN), Er,Cr:YSGG laser (L), or their combination (AENL). Chemical analysis of AEN was carried out using gas chromatography-mass spectrometry (GC–MS) and inductively coupled plasma mass spectrometry (ICP-MS). Twenty extracted human posterior teeth were cut into 40 dentin samples. Samples were randomly distributed into 4 groups (n = 10) according to the prospective surface treatment (0.5 W power Er,Cr:YSGG Laser (L), AEN paste (AEN), AEN paste followed by laser (AENL), and light-cure calcium hydroxide (CH)). Dentin samples were artificially demineralized before surface treatments. Vickers microhardness was evaluated before demineralization (T0), after demineralization (T1), and after surface treatment (T2). Dentin samples were examined using scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDX). Data analysis of microhardness means and standard deviations was performed using repeated measure ANOVA and one-way ANOVA. GC–MS demonstrated high glycerol and fatty acid content. ICP-MS demonstrated phosphorus and calcium concentrations of 3000 and 2000 ppm respectively. (L), (AEN) and (AENL) increases the microhardness mean values back to basic levels of non-demineralized state when compared to (CH). (L) showed the highest microhardness mean values while (CH) showed the lowest values. After laser irradiation, (L) demonstrated Ca/P ratio identical to that of non-demineralized dentin (1.76). Both (L) and (AEN) could restore microhardness of demineralized dentin to normal levels. Combination treatment by the sequence in (AENL) did not demonstrate synergistic dentin strengthening effect. (CH) failed to restore the microhardness of demineralized dentin. Graphical abstract demonstrating the workflow of dentin samples preparation and testing procedures (created by BioRender)
The reliability of general-purpose multimodal large language models (LLMs) in oral histopathologic image interpretation remains incompletely characterized. To evaluate abstraction-level diagnostic competence, grading reliability, and structural error patterns of ChatGPT-5.2 in oral epithelial dysplasia histopathology. In this retrospective diagnostic accuracy study, ChatGPT-5.2 analyzed 200 digitized H E-stained oral mucosal images (100 OPMD; 100 normal) in a zero-shot setting. Model outputs were compared with consensus diagnoses from three expert oral pathologists. The primary outcome was abstraction-level agreement (κ) across predefined WHO-aligned morphologic domains. Secondary outcomes included binary diagnostic accuracy, grade-stratified sensitivity, grading discordance, feature-level error profiling, inter-run stability, and modeled clinical utility. Agreement declined monotonically across morphologic abstraction domains (κ: 0.85 coarse morphology; 0.42 architectural; 0.09 high-risk cytologic; P[ordered] ≈ 1.000). Binary classification achieved a sensitivity 87.0
Background:Diagnosing oral lesions from benign conditions to oral cancer remains challenging due to overlapping visual features and reliance on histopathology. Large language models (LLMs) can integrate textual and visual cues, but their diagnostic accuracy and clinical utility in real decision-making contexts remain uncertain. To systematically evaluate the diagnostic performance, clinical usefulness, and limitations of LLMs in identifying oral lesions. Methods:PubMed, CINAHL, Embase, Web of Science, and Google Scholar were searched to 20 July 2025. Eligible studies applied LLMs (e.g., ChatGPT, Gemini, DeepSeek, Copilot, Claude) for diagnosis or differential diagnosis of oral lesions using text, images, or multimodal inputs. Outcomes included diagnostic accuracy, agreement metrics, and qualitative assessments of explanation quality and clinical applicability. Risk of bias was assessed using an adapted QUADAS-2. Narrative synthesis was performed due to heterogeneity. Results:Seventeen studies (>1,200 cases) were included. Diagnostic accuracy ranged from 25%-96%, varying by model version, input modality, and lesion complexity. Multimodal inputs consistently improved performance, with Cohen's κ up to 0.85-0.90. Advanced models (GPT-4o, DeepSeek-R1, o1-preview) outperformed earlier versions and approached expert performance in some tasks, although specialists generally retained superior Top-1 accuracy. Clinical utility was highest when LLMs were used to structure differential reasoning, highlight red-flag features, and support communication, but limited in tasks requiring fine morphological interpretation or severity grading. Overall risk of bias was low to moderate. Conclusions:LLMs demonstrate variable diagnostic performance and context-dependent supportive utility as adjunctive tools in oral lesion assessment, particularly in multimodal settings. They should complement, rather than replace, expert clinical judgment. Future research should prioritize real-world workflow evaluation, standardized prompting strategies, and prospective clinical validation. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/view/CRD420251090315, identifier CRD420251090315.
The automated grading of clinical short-answer questions using large language models (LLMs) could alleviate faculty workload and improve the immediacy of feedback in dental education. However, evidence on the capacity of LLMs for rubric-based grading in dentistry remains limited. Therefore, this study aimed to compare the grading reliability and error patterns of two LLMs, ChatGPT-4 and the open-weight DeepSeek-3, against expert human evaluators. In a retrospective cross-sectional study with comparative validation design, we analyzed 2,358 short-answer responses from 262 undergraduate dental students (across nine clinical questions). All responses were analyzed, then human-graded by three calibrated subject-matter experts (SME) (intraclass correlation coefficient [ICC] = 0.84) to provide a reference. Each LLM was provided a 12-point analytic rubric to guide the grading, but was not provided any prior examples of the grading task (i.e., a zero-shot prompt). We assessed agreement using ICC, Pearson correlation, Cohen’s kappa, and mixed-effects models, and examined error tiers (≤ 1, 2–3, > 3 points) across Bloom’s levels and response styles. In this dataset, DeepSeek-3 obtained an ICC of 0.87 compared with ChatGPT-4 which obtained an ICC of 0.64. DeepSeek-3 matched exactly with human scores in 43.3
