HPV type 16, 18, and co-infection of both types showed high prevalence in oral squamous cell carcinoma.The prevalence of HPV type 18 was found to be higher than HPV type 16 and co-infection in oral leukoplakia. It was observed that the tongue and palate lesions in the oral squamous cell carcinoma patients showed high prevalence of HPV type 16, type 18, and co-infection compared with other sites.

Background In individuals with nasal septal deviation (NSD), compensatory hypertrophy of the nasal turbinates occurs as a protective mechanism of the nasal passage from dry and cold air. NSD associated nasal turbinate hypertrophy is usually recurrent, requiring repetitive imaging. Therefore, a multiplanar imaging modality with a low radiation dose is best suited for long-term follow-up of this condition. This study aimed to evaluate the association of width of inferior turbinates and presence of concha bullosa with the degree of NSD using Cone beam computed tomography (CT). Methods The CBCT scans of 100 patients with NSD were selected as per convenience sampling and were evaluated by two maxillofacial radiologists. The width of the non-hypertrophied inferior turbinate (NHT) on the convex side of the NSD, and hypertrophic inferior turbinates (HT) on the concave side of the NSD were measured at three locations. The septal deviation angle (SDA) and the presence of concha bullosa (CB) were determined. Results A significant difference was observed in the anterior, middle, posterior, and mean widths between HT and NHT (p < 0.001). There was a significant difference in the widths of the HT and NHT among different types of NSD. A strong positive correlation (r = 0.71, p < 0.001) was found between SDA and the mean width of the HT. Age (P = 0.71) and gender (P = 0.65) had no significant difference among different types of NSD. Regression analysis revealed that the presence of CB (p = 0.01) and middle width of the HT (p < 0.001) are significant predictors of SDA and type of NSD. Conclusion The results of the present study reveal that the middle width of the HT and the presence of CB influence the degree of NSD. The present study results recommend the use of CBCT as a substitutive low radiation dose imaging modality for evaluation of NSD, CB, and associated inferior turbinate hypertrophy.

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The present research evaluates how E-learning environment, E-learning adoption, Digital readiness, and Students attitudes towards E-learning, affect Academic achievement. The study focuses on a much-neglected cultural context, Gulf Cooperation Council countries (GCC), since Student’s readiness as well as institutions and professors’ endowments greatly varied within countries and among universities. The study further incorporates Instructors attitudes and evaluates the mediation effect of Academic engagement on Academic achievement. The methodology relies on Partial Least Squares structural equation modelling (PLS-SEM). The research findings emphasize the role of E-learning environment, Digital readiness, Academic engagement, students as well as instructors E-learning attitude as the decisive factors that determine students’ Academic achievement. This implies that institutions who adapt to a changing environment by aligning students and instructors’ goals to develop a positive and supportive E-learning environment, will foment Academic engagement and promote students’ Academic achievement.

Dental implants have become increasingly important in daily dental offices. The degree of pain and discomfort experienced during a surgical procedure varies from one patient to another. Using advanced machine learning algorithms to predict pain, the dentist and the patient would make more informed decisions about the treatment. This study aims at Predicting postoperative discomfort using an AI-based multi-linear regression model. The functional parametric association between the eight parameters (age, sex, and operating technique) and the patient's postoperative pain was established following implant surgery. The output was normalized information regarding both incidence and severity of immediate discomfort post-implant surgery. To enhance the generalization ability of the multiple linear regression (MLR) model and avoid overfitting, 825 cases were provided as the training set, while 207 cases were given for data authentication. In addition, 45 samples were used as controls to determine the model's prediction accuracy. Evaluation of the given model reveals a Root Mean Squared Error of 0.108477788. This prototype predicted AI model postoperative pain following implant surgery with 89.6 % accuracy. Finally, this AI model exhibited clinical viability and utility in predicting postoperative pain after surgery.