Krishnadevaraya College of Dental Sciences and Hospital

INTRODUCTION:Forensic age estimation is essential in clinical and legal settings, particularly in children, where reliable and non-invasive methods are required. Tooth eruption follows a relatively consistent developmental pattern and may serve as a practical indicator of chronological age during the mixed-dentition period. This study aimed to evaluate the relationship between cumulative tooth eruption status and chronological age in children and to develop a predictive model for age estimation. MATERIALS AND METHODS:A cross-sectional observational study was conducted on 120 children aged 6-13 years in the Department of Oral Pathology at Jawahar Medical Foundation's Annasaheb Chudaman Patil Memorial Dental College, Dhule. Chronological age was calculated in decimal years using verified birth records. Clinical examination of 28 permanent teeth (excluding third molars) was performed, and eruption status was scored as 0 (unerupted), 1 (partially erupted), or 2 (fully erupted), with cumulative scores calculated for each participant. Data were statistically analyzed. Normality was assessed using the Shapiro-Wilk test. Independent samples t-test, one-way analysis of variance (ANOVA) with Tukey's post-hoc analysis, Pearson's correlation, and linear regression were applied. Statistical significance was set at P < 0.05. RESULTS:The mean chronological age was 9.8 ± 2.3 years, and the mean eruption score was 32.6 ± 8.4. No significant sex differences were observed in age (p = 0.134) or eruption score (p = 0.174). A significant increase in eruption score across age groups was observed (p < 0.001). Post-hoc analysis confirmed significant differences among all age groups (p < 0.05). A strong positive correlation was observed between the eruption score and chronological age (r = 0.72, p < 0.001). Simple linear regression analysis demonstrated that the eruption score significantly predicted age (R² = 0.62, p < 0.001). CONCLUSION:Cumulative tooth eruption status shows a strong association with chronological age and serves as a reliable, non-invasive indicator for age estimation in children, with good discriminatory ability and practical applicability in clinical and forensic settings.

ABSTRACT Aim: The study aimed to compare and evaluate the penetration of epoxy resin-based sealers and calcium silicate-based sealers, both with and without adding chitosan nanoparticles (NPs). Methods: Eighty human mandibular premolars with a single canal were selected for the study. A standard root length of 15 mm was established for each tooth. The canals were instrumented using the ProTaper ® Gold NiTi system (Dentsply Maillefer, Switzerland) incrementally up to size F3 (30/09) and irrigated with 5 ml of 2.5% NaOCl followed by 5 ml of 17% EDTA. The sealers were divided into four groups: Group 1 (Adseal), Group 2 (Ceraseal), Group 3 (Adseal + 2% wt/vol chitosan NP), and Group 4 (Ceraseal + 2% wt/vol chitosan NPs). The samples were then obturated using size F3 gutta-percha using a single-cone technique with the respective sealers. About 0.1% weight Rhodamine B dye was added to assess sealer penetration to all groups. The samples were embedded in acrylic resin, sectioned at midroot level, and viewed under a confocal laser scanning microscope. Data analysis was performed using a one-way ANOVA test, followed by Tukey’s post hoc analysis, with a significance level set at P < 0.05. Results: The results demonstrated that sealer penetration at the midroot portion was significantly higher in Group 4 (Ceraseal + 2% wt/vol chitosan NPs), followed by Group 3 (Adseal + 2% wt/vol chitosan NPs), Group 2 (Ceraseal), and the lowest penetration observed in Group 1 (Adseal). Conclusion: Within the limitations of this study, it was concluded that Group 4 (Ceraseal + 2% wt/vol chitosan NPs) exhibited better sealer penetration than other groups.

Aim: To compare marginal adaptation of bulkfill low shrinkage composite in a class II cavity under scanning electron microscope. Materials and Methods: 52 extracted human molars were stored in the saline. Class II cavities were prepared extending 1mm below CEJ. The teeth were randomly assigned to 4 groups (n = 13). GROUP 1- Tetric N Ceram, GROUP 2- SDR, GROUP 3- Sonicfill, GROUP 4- Filtek bulkfill. All the cavities were then restored with the composite according to each manufacturer’s instructions and they were polished using flexible polishing discs. The teeth were sectioned labiolingually, mesial halves of the tooth samples were observed under SEM for marginal adaptation, and distal halves were thermocycled and observed under SEM. Results: Marginal adaptation seen in both SDR and sonicfill was better when compared to Tetric N Ceram and Filtek bulk fill and also all four experimental groups showed better enamel adaptation when compared to the adaptation in the cementum.

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. The Electric Vehicles charging should be performed in effective way. One of the significant challenges with widespread electric vehicle adoption is related to vehicle charging. Many potential EV drivers have range anxiety or don’t want to spend much time charging an EV battery on long trips. Although dynamic wireless charging may seem like something out of a science fiction movie, it could be a viable way to overcome vehicle charging issues. These wireless power transfer systems work while the vehicle is in motion, providing numerous benefits

Type 2 Diabetes Mellitus(T2DM) is a debilitating condition with a number of complications including those of the oral cavity which can further deteriorate patient's general and oral health related quality of life (OHRQoL). Machine Learning (ML) can help assign an individual's propensity to develop poor OHRQoL, given a set of variables, and at the same time identify the most important features contributing to this outcome. Previously inferential statistical methods have attempted to explain this, albeit with limited success. The aim of this cross sectional study is to determine the impact on OHRQoL in T2DM patients, and identify features most likely to be associated with this outcome and to compare ML and DL analytical methods with inferential statistics. Twelve-hundred T2DM patients were subjected to OHRQoL and demographic data questionnaires and WHO Oral Health Assessment form. K-means Clustering was performed to label individuals as having or not having an impact on OHRQoL. Class imbalance was addressed by undersampling of the majority class using informed subset selection. Further, using the collected data as input features we developed ML algorithms (Naive Bayes(NB), Random Forest(RF), Logistic Regression(LR), Kernel Support Vector Machine(SVM) and Artificial Neural Network(ANN)), to accurately classify individuals with or without poor oral health related quality of life (OHRQoL) and utilized SHapley Additive exPlanations (SHAP) analysis for feature importance. The best performing model was SVM (AUC=0.983; Sensitivity=1) for classifying the patients into into poor OHRQoL. SHAP values were highest for Age, Prosthetic Need, Tobacco use and years since onset of diabetes. Features closely related to diabetes, that is, periodontal pockets and loss of attachment were not identified as relevant by inferential statistics, but were deemed as important features associated with poor OHRQoL by SHAP analysis.