Propolis is a potent anti-microbial and natural anti-inflammatory by-product obtained from the beehive. Studies have demonstrated the superior biocompatibility and anti-microbial properties of propolis as compared to calcium hydroxide. However, its effect on postoperative endodontic pain is unknown. Therefore, this study aimed to investigate the impact of Chinese propolis paste as an intracanal medicament on postoperative endodontic pain intensities compared with calcium hydroxide (control) at different time intervals in necrotic teeth with periapical radiolucency. Eighty patients with single-rooted necrotic teeth with visible periapical radiolucency were recruited and randomly allocated to either the calcium hydroxide or propolis groups. After chemo-mechanical preparation and intracanal medicament insertion, patients were given the VAS (visual analogue scale) to record pain scores. Inter-group data were compared and analyzed using two-way repeated measure ANOVA (Bonferroni test). A p-value of < 0.025 was considered significant. In total, >78% of the patients experienced no or only mild post-operative pain in both the groups at all time intervals, without any significant difference in pain scores between the two groups (p > 0.025). An overall flare-up rate of 14.8% was found. The results suggest that either of these medicaments can be used as an inter-appointment medication for the prevention of postoperative pain in necrotic cases.

To evaluate the thoughts and feelings of dentists viewing posted clinical cases (PCCs) on various social media (SM) platforms. A questionnaire was developed, validated and its reliability was established. It was distributed (in 2022) to 355 dentists who had graduated at least 2 years prior. It had three sections consisting of 20 items pertaining to general information, SM usage, and feelings. Descriptive data were reported using percentages. The chi-squared test was used for inferential analysis. P ≤ 0.05 was considered statistically significant. The response rate was 92%. The majority of respondents were non-post-graduates (63%), and aged ≤40 years (90%) with clinical experience 15 years) shared their clinical work significantly more than their counterparts (p < 0.05). Most of the dentists did not feel motivated after viewing the PCCs (41%), especially those aged >40 years (p = 0.037), and thought that viewing the PCCs was the reason for personal dissatisfaction (50%). They had a belief that they possess skills (36%) and knowledge (42%), but not the necessary equipment to replicate the PCCs (39%). The majority of dentists felt that viewing the PCCs on SM can cause personal dissatisfaction and lack of motivation. They had a belief that they possess the skills and knowledge to replicate the PCCs, but lack equipment. Investing resources in clinical workshops, mentorship, and the procurement of modern equipment early in the career of dentists may improve their mental well-being, satisfaction, and quality of treatment delivered to patients.

Objectives: To compare EDTA and Green Tea extracts as Root canal chelators on the basis of apicalseal of tooth root achieved.Methods: This Quasi experimental study was conducted on single canal decoronated teeth of average 14mm length (n=20). Step back preparation was done with MAF of 40 # k file. Between every file change root canals were irrigated in group A with 5.25% NaOCl and the finishing rinse was done with 17% EDTA, in group B, between each file change root canals was irrigated with 5.25% NaOCl and the finishing rinse was done with 10% green tea extract. Green tea extract was prepared by boiling 15 g of green tea powder in 150 ml distilled water at 100°C, yielding 15 ml extract. Both groups were obturated. Application of nail varnish was done to the decoronated teeth leaving 2mm of apex. The teeth were immersed in 2% methylene blue dye solution for a week and the left to dry for 24 hrs. The teeth were vertically split and dye penetration was checked by mm scale.Result: A total of 100 samples were included in the study, with 50 samples allocated to each of the two irrigation protocol groups: Group A (EDTA) and Group B (Green tea extract). Dye penetration was significantly lower in Group A (mean = 4.32 mm, SD = 1.64) compared to Group B (mean = 5.74 mm, SD = 1.43). Independent samples t-test showed this difference to be statistically significant (p =0.000).Conclusion: Green tea extracts was demonstrated less efficacious than EDTA in achieving apical seal with a statistically significant difference. However, green tea extracts have demonstrated promising chelating, anti-inflammatory and antimicrobial properties further preclinical and clinical studies are needed to determine optimal formulation, ideal concentration, application protocols, to validate its clinical effectiveness

1.1 Background: Artificial intelligence has several benefits, particularly in the field of oral and maxillofacial radiology.Artificial intelligence allows for the early diagnosis of osteoarthritis of the temporomandibular joint, perhaps improving the prognosis. 1.2 Objective: This work uses artificial intelligence to segment the temporomandibular joint (TMJ) using cone beam computed tomography (CBCT) sagittal images and categorize temporomandibular joint (TMJ) osteoarthritis. 1.3 Methods: In this work, we assess the performance of an artificial intelligence model called YOLOv5 architecture in TMJ segmentation and osteoarthritis classification using 2000 sagittal sections (500 photos of healthy, 500 photographs of erosion, 500 images of osteophytes, and 500 images of flattening) derived from CBCT DICOM images of 290 patients. 1.4 Results:For the categorization of TMJ osteoarthritis, the model's sensitivity, accuracy, and F1 scores are 1, 0.7678, and 0.8686, respectively. The accuracy of categorization is 0.7678. The categorization model predicts that 88% of joints will be healthy, 70% will be flattened, 95% will have erosion, and 86% will have osteophytes. For TMJ segmentation, the YOLOv5 model's sensitivity, accuracy, and F1 score are 1, 0.9953, and 0.9976, respectively. The TMJ segmentation model's AUC score is 0.9723. Furthermore, the model's TMJ segmentation accuracy is 0.9953. 1.5 Conclusion: The study's artificial intelligence model functions as a time-saving and convenient diagnostic aid for doctors, enabling good outcomes in the segmentation of the mandible and the categorization of osteoarthritis.