Background: Currently, the demands for restorations have increased considerably; thus, improvements and modifications have been made in dental composite technologies especially using materials that have been claimed to improve color stability.Objective: This study aimed to determine the effect of various solutions on the color stability of different restorative materials in vitro study.Methods: This study used three types of esthetic restorative materials. The samples comprised 45 discs, including 15 micro-hybrids, 15 nano-hybrids, and 15 glass ionomers). These discs were immersed in various beverages an hour a day for one month at room temperature. The color stability was measured using a spectrophotometer before/after immersion (days 7 and 30).Results: We realized a significant difference in color change with Coca-Cola and tea-milk solution after seven days and 30 days (p < 0.05). However, no significant difference was found in the samples immersed in DM after seven days and 30 days (p > 0.05). The highest value of lightness (∆L) and stainability (∆a) was seen in micro-hybrid after 30 days of immersion in tea-milk solution (− 12.16 ± 1.74 and 11.4 ± 3.82, respectively), while most samples had a positive ∆b value.Conclusion: After one month, the tea-milk solution affected the color stability of all used restorative materials. In addition, micro-hybrid had higher color stability than nano-hybrid and glass ionomer.

Objective: Cone beam computed tomography (CBCT) may be recommended as a dose-sparing technique and 3D imaging that provides the clinician more details for examining the maxillary sinus and its relation to the root apices of upper posterior teeth, as compared with standard medical computed tomography. Methods: A total of (340) CBCT images were examined, images were analyzed by using (Galileos Viewer Software Version 1.9, Dentsply). The distance was measured from the molar and premolar teeth; the closest tooth to the sinus was considered for both sides using the software measuring tool. Results: A total of 340 CBCTs were examined. One hundred and forty-seven CBCTs were included that matched the criteria. After examination of the CBCTs, the most common relation between the roots of the upper posterior teeth with the maxillary sinus was Type 2: the apices of the upper posterior teeth touching the sinus floor. The result shows no statistical difference between the age groups (p value = 0.135); also, the relationship between sex and the relation of the teeth with the maxillary sinus was not significant (p value = 0.075). Conclusions: The most common relation between the apices of maxillary posterior teeth and the maxillary sinus was close contact. We also concluded that the use of CBCT is essential in diagnosing the type of relation, especially during procedures where oro-antral communication and trauma to the maxillary sinus is possible.

Background/Objectives: Silicone elastomers are widely used in maxillofacial prostheses, but their service life is typically limited to 6–24 months due to progressive degradation. Reinforcement with zirconium silicate (ZrSiO₄) nanoparticles has been proposed to improve durability, yet evidence on their long-term performance under storage remains limited. This study evaluated the effect of two years of dark storage on the mechanical properties of room-temperature-vulcanized (RTV) silicone reinforced with 1.5 wt% ZrSiO₄ nanoparticles. Materials and Methods: Zirconium silicate (ZrSiO₄) nanoparticles at 1.5 wt% were incorporated into A-2186 RTV silicone specimens, which were randomly divided into two equal groups: baseline specimens stored for 24 h and aged specimens stored for 24 months under dark conditions. Mechanical properties were assessed by measuring tensile strength, elongation at break, tear resistance, and Shore A hardness in accordance with standardized protocols. Fourier transform infrared (FTIR) spectroscopy was performed to verify the structural characteristics of the ZrSiO₄ nanopowder. Statistical analysis was conducted using independent-samples t-tests, with significance set at p < 0.05. Results: After 24 months of dark storage, tensile strength and elongation at break decreased significantly (p < 0.05), indicating reduced elasticity. Tear resistance and hardness showed slight but non-significant reductions. FTIR confirmed the preservation of ZrSiO₄ structural features. Conclusions: Dark storage selectively reduced reinforced silicone’s tensile and elongation properties, while tear resistance and hardness remained relatively stable. ZrSiO₄ nanoparticles provided partial reinforcement, enhancing stability but not entirely preventing RTV silicone aging.

Background: Accurate radiographic tooth/root length estimation can support endodontic treatment planning, but panoramic radiographs are affected by magnification and regional distortion. This retrospective model-development study evaluated the accuracy of panoramic radiographic measurements against cone-beam computed tomography (CBCT)-derived reference measurements and developed regression-based correction equations for estimating tooth/root length from panoramic measurements. Methods: The study included 156 patients with paired orthopantomogram (OPG) and CBCT records. A balanced tooth/root-level dataset of 1,300 paired observations was created, with 100 observations in each of 13 tooth/root categories. CBCT-derived tooth/root lengths were used as the reference measurements. Two calibrated investigators performed repeated measurements, and intra- and inter-examiner reliability were assessed using intraclass correlation coefficients. Paired comparisons, linear regression modelling, mean absolute error, root mean square error, clinical threshold analysis, and Bland-Altman agreement analysis were performed. Results: The overall mean OPG measurement was 20.33 ± 3.04 mm, whereas the mean CBCT-derived reference measurement was 21.63 ± 2.75 mm. Uncorrected OPG measurements significantly underestimated tooth/root length by -1.30 mm. The overall regression model showed a positive relationship between OPG and CBCT-derived measurements and explained 48.9% of the variance. Regression-based correction reduced the apparent mean absolute error within the present dataset from 2.00 mm to 1.56 mm using the overall equation. For the category-specific equations used in the desktop application, 53.6% of corrected predictions were within ± 1.0 mm and 83.8% were within ± 2.0 mm of the CBCT-derived reference measurements. Conclusions: Panoramic radiographs generally underestimated CBCT-derived tooth/root length, with distortion varying by tooth/root category. The correction model reduced apparent measurement error within the study dataset, but no independent external validation was performed. Therefore, the equations should be regarded as exploratory preoperative estimation tools pending patient-level internal validation and independent external validation. Clinical relevance statement: Regression-based correction of panoramic radiographic measurements may improve preoperative estimation of tooth/root length, particularly in regions with greater panoramic distortion. The model may support endodontic planning, but it should be used only as an adjunctive radiographic support tool and not as a replacement for established clinical working-length determination methods.

Traditional lecture-based teaching remains widely used in undergraduate dental education, but it may provide limited opportunities for student engagement,

This study aimed to design a desktop application that implements machine learning algorithms to predict dental treatment time durations, assess the accuracy of the model, and assess its clinical efficiency. The Python programming language was used to develop software that uses Machine Learning and Google SerpApi service for the prediction process. The sample consisted of 2750 records, 2500 records for training, and 250 records for testing the model. Spearman correlation test result was (r (250) = 0.96, p = < 0.001), the R2 value was (0.97), which means that the actual durations can predict 97.32% of the change in predicted durations, and the Mean Absolute Error metric, yielding a result of 2.6432 min. Age and sex of participants showed no statistically significant effect. The application of Machine Learning is promising in dentistry and the medical field to help regulate the workflow. The integration of the Google SerpApi service was successful and can be helpful in cases without training data. Also, the availability of electronic patient records is necessary in all medical facilities. Finally, Python is a powerful tool in designing software that implements Machine Learning algorithms.