Background: This study aimed to compare the antibacterial activity, cytotoxicity, and fluoride release of 4 different glass ion-omer cements (GIC).Material/Methods: A total of 200 samples were prepared: Riva Silver, a silver-reinforced GIC; Equia Forte HT, a glass hybrid GIC; ChemFil Rock, a zinc-added GIC; and Ketac'" Molar Easymix. Using the agar diffusion test for antibacterial ac-tivity, 30 samples from each group were analyzed for Streptococcus mutans, Lactobacillus acidophilus, and Actinomyces naeslundii. The areas around the samples where no bacterial growth occurred were digitally mea-sured and recorded. For cytotoxicity analysis, the WST-1 test was performed on 10 samples from each group using the L929 mouse fibroblast cell line. The fluoride release property was evaluated using an ion-selective electrode method on 10 samples from each group.Results: The group that used Ketac'" Molar Easymix showed the lowest antibacterial activity against S. mutans, L. aci-dophilus, and A. naeslundii. In all 3 days of cytotoxicity testing, the group that used Riva Silver was found to be the least toxic material, while the group that used ChemFil Rock did not have viable cells after the 1st day. In all 4 materials, fluoride release values gradually increased since the first day, with Ketac'" Molar Easymix hav-ing the highest fluoride release.Conclusions: Of all the GICs tested, Ketac'" Molar Easymix demonstrated the least antibacterial activity despite having the highest fluoride release, while Riva Silver was found to be the least cytotoxic material.

BACKGROUND The use of artificial intelligence (AI) in dentistry has been increasing, leading to significant changes in diagnosis, treatment planning, and patient management. However, research on dental students' awareness, acceptance, and professional concerns regarding these technologies remains limited. This study aimed to examine dental students' perspectives on AI use, their professional expectations, and ethical concerns. MATERIAL AND METHODS A survey was conducted via Google Forms among students studying at dental faculties across Turkiye. The survey consists of 2 parts: (i) Demographic information (gender, faculty type, year of study, etc.), (ii) Questions on awareness of AI, professional implications, and ethical concerns. A 3-point Likert scale was used for measurement. The reliability and validity of the questionnaire were evaluated by Keiser-Meyer-Olkin test (0.837), Bartlett's test (p<0.001) and Cronbach's Alpha coefficient (Factor 1=0.831, Factor 2=0.600). RESULTS The majority of students (72.3%, p<0.05) believe that AI will lead to significant changes in the profession. While 64.5% expressed support for artificial intelligence-based diagnostic systems, 48.9% have reservations due to ethical concerns. CONCLUSIONS Dental students consider AI an essential tool for the future of the profession. However, there are uncertainties regarding ethical and professional transformation. This study suggests that educational programs should be developed to enhance awareness of AI.

Objectives: This meta-analysis aims to estimate the pooled prevalence of dental caries among children in Turkey by synthesizing national evidence from theses and peer-reviewed articles published between 2000 and 2024. Methods: Studies were retrieved from the YÖK Thesis Database and Google Scholar using keywords such as ‘Caries Prevalence’ and ‘Primary Tooth.’ PubMed was used to cross-check peer-reviewed articles. A total of 28 studies and 29 data points including 40,244 children aged approximately 2 to 15 years were included. PRISMA guidelines were followed. Heterogeneity was assessed using the I² index, and publication bias was evaluated with an Egger’s test and funnel plots. Results: The pooled prevalence of dental caries among Turkish children was 75.6% (95% CI: 70.8–79.8%). Substantial heterogeneity was observed (I² = 98.4%), but no significant publication bias was detected. Conclusions: Dental caries remains a significant public health issue among children in Turkey. Targeted preventive measures—such as nationwide dental screenings and school-based fluoride programs—are urgently needed.

Aim: Parents increasingly turn to platforms like YouTube to find information about their infants' health. While health professionals are the primary source of such information, the accessibility of medical content on the internet and the demand for remote healthcare services during the COVID-19 pandemic have made medical YouTube videos popular. In the field of pediatric dentistry, YouTube offers valuable insights into topics like oral hygiene, early childhood caries, and fluoride use. This study aimed to assess the content and quality of YouTube videos on teething symptoms in infants, identified as a highly searched topic on Google Trends. Methods: Content analysis, DISCERN reliability scoring, and five-point Global Quality Scale (GQS) assessment were conducted on a sample of 46 videos. Results were categorized by uploader gender, video source category, and video quality. Statistical analyses, including Kruskal-Wallis and Mann-Whitney U tests, along with correlation analysis, were performed to identify differences between videos and groups. Results: Among the video uploaders, 80.60% (n=38) were female, and 17.40% (n=8) were male. No statistically significant difference based on gender was found among video uploaders (p>0.05). Most videos were uploaded by non-healthcare professionals, health institutions, workers, and other YouTube channels. Mean view counts, likes, dislikes, and comments were determined for the examined videos. The quality of videos was evaluated using Global Quality Scale and DISCERN ratings, resulting in categorization as weak, moderate, and high quality based on Global Quality Scale scores. Conclusion: This study revealed varying quality in YouTube videos on teething symptoms in infants, with non-healthcare professionals' videos demonstrating lower quality. Nonetheless, reliable videos from pediatric dental experts can serve as valuable resources for parents with limited access to healthcare facilities, particularly during situations like the pandemic. Further research and support are needed in this area.

To evaluate the performance of a hybrid deep learning architecture, MaxViT-T, for fine-grained dental age estimation (DAE) and coarse-grained age-group classification using panoramic radiographs, and to interpret the model's decision-making process through Explainable AI (XAI). A dataset of n = 2,761 panoramic radiographs from patients aged 6-18 years was used. Two classification tasks were performed: a 13-class fine-grained age estimation and a 3-group coarse-grained age classification (age ranges 6-9, 10-13, and 14-18 years, approximating the early mixed, late mixed, and permanent dentition periods). The hybrid MaxViT-T model was compared against ten other architectures, including CNNs and Transformers, using 5-fold stratified cross-validation. Model interpretability was assessed using Grad-CAM and SHAP. MaxViT-T achieved the highest performance in both tasks. For the 13-class age estimation, it reached an accuracy of 0.4980 with a Mean Absolute Error (MAE) of 0.71 years. Tolerance-based accuracy increased to 84.9% and 96.1% within 1 and 2 years, respectively. For the 3-group age classification, the model achieved 91.13% accuracy and an AUC of 0.9669. XAI analyses confirmed that the model's attention shifted from deciduous teeth and erupting permanent teeth in younger ages to third molar development in older ages, predominantly focusing on the mandibular region. The hybrid MaxViT-T architecture achieved the highest accuracy for dental age estimation among the evaluated models, although its advantage over the leading CNN and Transformer architectures was not statistically significant. Its decision behavior aligned with established odontogenic developmental landmarks, supporting the biological plausibility of the learned features.