Cross-linking agents are incorporated into denture base materials to improve their mechanical properties. This study investigated the effects of various cross-linking agents, with different cross-linking chain lengths and flexibilities, on the flexural strength, impact strength, and surface hardness of polymethyl methacrylate (PMMA). The cross-linking agents used were ethylene glycol dimethacrylate (EGDMA), tetraethylene glycol dimethacrylate (TEGDMA), tetraethylene glycol diacrylate (TEGDA), and polyethylene glycol dimethacrylate (PEGDMA). These agents were added to the methyl methacrylate (MMA) monomer component in concentrations of 5%, 10%, 15%, and 20% by volume and 10% by molecular weight. A total of 630 specimens, comprising 21 groups, were fabricated. Flexural strength and elastic modulus were assessed using a 3-point bending test, impact strength was measured via the Charpy type test, and surface Vickers hardness was determined. Statistical analyses were performed using the Kolmogorov–Smirnov Test, Kruskal–Wallis Test, Mann–Whitney U Test, and ANOVA with post hoc Tamhane test (p ≤ 0.05). No significant increase in flexural strength, elastic modulus, or impact strength was observed in the cross-linking groups compared to conventional PMMA. However, surface hardness values notably decreased with the addition of 5% to 20% PEGDMA. The incorporation of cross-linking agents in concentrations ranging from 5% to 15% led to an improvement in the mechanical properties of PMMA.

This study evaluates the preference rates for smile designs created by professionals or by Artificial Intelligence (AI) among dentists, dentistry students, and laypeople. Four cases with symmetrical and asymmetrical features were selected based on the Facial Flow (FF) concept from the database of the Smile Designer app regarding anatomical facial points. Two smile designs were created for each selected case: one using Artificial Intelligence (AI) and one created manually. An online survey assessed participants’ preferences for the different smile designs. The chi-square test “Pearson’s and Fisher’s exact test (P)” was used to analyze the survey data. A total of 628 people completed the study. Dentists preferred the manually-created smile design for the first three cases. For Case 4, dentists who used the Smile Designer program preferred the manually-created design (55.88%), while those who did not use the program preferred the AI-generated design (55.84%). There was a significant difference in esthetic perception between dentists and dental students (p = 0.001) and between dentists and laypeople (p = 0.001) for Case 1, only between dentists and dental students (p = 0.003) for Case 2, and only between dentists and laypeople (p = 0.001) for Case 3. Furthermore, we found that females (p = 0.007) and orthodontists (p = 0.025) had a higher preference for the AI-generated design in this case compared to males and other dental specialties for Case 3. While age, education level, and clinical experience did not significantly impact dentists’ preference for manually-created or AI-generated smile designs (p > 0.05), our results suggest that there were some differences in preference for Case 3. Overall, our findings suggest that the use of AI-generated smile designs for symmetric faces is acceptable to both dentists and laypeople and can offer time-saving benefits for clinicians.

İstanbul Üniversitesi Yayın Projesi

This study aims to investigate the effects of digital literacy and self-esteem on dental students’ perceptions of professional competence and to provide recommendations for improving educational programs in dental schools. A mixed-methods, descriptive, and relational scanning design was employed, using a questionnaire comprising demographic information, the Rosenberg Self-Esteem Scale (RSES), the Digital Literacy Scale (DLS), and the Professional Competence Scale (PCS). The sample included 427 dental students in Istanbul, Turkey. Statistical analyses included Cronbach Alpha, Confirmatory Factor Analysis, Mann–Whitney U, Kruskal–Wallis, Spearman’s rho correlation, and the Generalized Linear Model (Logit Model). The majority of participants were female (65.6%), aged 21 or older (85.2%), and in their 4th grade of study (35.2%). There were no significant differences in PCS scores concerning gender, age, grade, type of residence, and residence location (p > 0.05). However, PCS scores were significantly higher among those who listed dentistry among their top five preferences, expressed high satisfaction with education, and demonstrated high professional interest (p < 0.05). A significant, positive correlation was observed between PCS and RSES (r = 0.398; p < 0.01), DLS (r = 0.404; p < 0.01), preference (r = 0.120; p < 0.05), education satisfaction (r = 0.298; p < 0.01), and occupational interest (r = 0.502; p < 0.05). Furthermore, the Logit Model analysis revealed that RSES (B = 0.290; p < 0.01), DLS (B = 0.258; p < 0.01), education satisfaction (B = 0.806; p < 0.05), and occupational interest (B = 3.825; p < 0.01) significantly influenced PCS. The findings underscore self-esteem and digital literacy’s integral role in shaping dental students’ perceptions of their professional abilities. Digital literacy is a substantial pillar supporting students’ professional competency. Coupled with a genuine interest in the field and bolstered self-confidence, digital literacy proves instrumental in enhancing students’ academic success in dentistry. By emphasizing and expanding upon digital literacy content within the curriculum, universities can further propel advancements in the dental domain. As such, it is pivotal for dental institutions to weave strategies into their educational fabric to elevate students’ self-esteem and digital proficiency.

This in vitro study aimed to evaluate the effects of resin type, layer thickness, and printing orientation on the surface and mechanical properties of 3D-printed occlusal splints fabricated using digital light processing (DLP) technology. Three commercially available splint resins (KeySplint Hard, Freeprint Splint 2.0, and V-Print Splint) were used to fabricate 180 rectangular specimens with two-layer thicknesses (50 µm and 100 µm) and three printing orientations (0°, 45°, 90°). Surface roughness (Ra, Rz), gloss, microhardness, flexural strength, and elastic modulus were measured. Statistical analysis was performed using robust ANOVA with Bonferroni correction. Resin type and printing orientation significantly influenced all surface and mechanical properties (p < 0.001), while layer thickness had a limited effect. Keystone resin exhibited the smoothest surface and highest gloss, whereas Freeprint resin showed the highest microhardness and elastic modulus. Printing at 45° generally enhanced flexural strength and provided more balanced mechanical performance. SEM analysis confirmed that surface morphology varied with orientation, correlating with profilometric and gloss measurements. Resin composition and printing orientation are critical determinants of the mechanical and surface performance of 3D-printed occlusal splints. Optimizing these parameters can improve durability, esthetics, and clinical functionality. All tested materials achieved clinically acceptable surface smoothness, supporting their suitability for intraoral use.