Objective: To compare the influence of coronavirus disease 2019 (COVID‐19) pandemic on the student reported outcomes among dental students reading/studying different courses offered in the only dental school in Malta.Materials and methods: An anonymous questionnaire consisting of 13 closed‐ended and open‐ended questions was sent to all students (n = 97) at the Faculty of Dental Surgery at the University of Malta. Emergent themes from open‐ended questions were identified and tallied. Since data were classified as categorical ranks, nonparametric tests were used to compare variables among the different courses, years of study, preclinical/clinical students, and gender categories.Results: Dental technology students were significantly less stressed about contracting COVID‐19 (P = 0.005) and regarding lack of preparation due to inability to physically go to the library (P = 0.019). Female students reported significantly more anxiety for the following factors: “feeling anxious all the time” (P = 0.033), “worried about contracting COVID‐19′” (P = 0.012), “worried that a family member may contract COVID‐19′” (P = 0.048), “more anxious about exams this year because of the challenges we are facing” (P = 0.029), and “concern about losing manual dexterity skills” (P = 0.038). Qualitative data gathered show considerable stress reported by students regarding changes in examination processes and formats and lack of timely communication.Conclusion: Students were greatly affected by the COVID‐19 pandemic and this caused fear of losing their manual dexterity skills, anxiety related to its consequences on their long‐term plans, and anxiety related to the examinations. Dental schools in turn should adapt rapidly and customize changes that are specific to the individual student cohort and their stage of training.

Granular cell tumor (GCT) is a rare neoplasm that can appear in any site of the body, but most are located intraorally. Its histogenetic origin remains unclear. This report analyzes the immunoprofile of 15 cases of granular cell tumors, occurring in 13 women and 2 men and the lesions were located on the tongue or upper lip. Patient age ranged from 7 to 52. The patients demographic data and the cytological and architectural features of the lesions were analyzed in oral GCTs (n=15). The lesions were also submitted to a panel of immunohistochemical stains with antibodies against S-100, p75, NSE, CD-68, Ki-67, Synaptofisin, HHF-35, SMA, EMA, Chromogranin, Progesterone, Androgen and Estrogen. Among the fifteen cases analyzed, the most common location was the tongue (84.6%). Histologically, the tumors exhibited cellular proliferation composed mainly by polygonal cells presenting an abundant granular eosinophilic cytoplasm. The nuclei were central, and the cell membranes were moderately clear. No mitotic figures were observed. The immunohistochemical analysis showed positivity in all cases for S-100, p75, NSE and CD-68, and no immunoreactivity for Ki-67, Synaptofisin, HHF-35, SMA, EMA, Chromogranin, Progesterone, Androgen and Estrogen. The immunoprofile of granular cell tumors showed nerve sheath differentiation--lending support to their neural origin--and helping to establish a differential diagnosis between this lesion and other oral granular cell tumors, whether benign or malignant.

Dentists could fail to notice periapical lesions (PLs) while examining panoramic radiographs. Accordingly, this study aimed to develop an artificial intelligence (AI) designed to address this problem. Materials and methods: a total of 18618 periapical root areas (PRA) on 713 panoramic radiographs were annotated and classified as having or not having PLs. An AI model consisting of two convolutional neural networks (CNNs), a detector and a classifier, was trained on the images. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or not-PL using a fine-tuned CNN. The classifier was trained and validated on a balanced subset of the original dataset that included 3249 PRAs, and tested on 707 PRAs. Results: the detector achieved an average precision of 74.95%, while the classifier accuracy, sensitivity and specificity were 84%, 81% and 86%, respectively. When integrating both detection and classification models, the proposed method accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Conclusion: a two-stage CNN model consisting of a detector and a classifier can successfully detect periapical lesions on panoramic radiographs.

Choi IG, et al. Imaging Sci Dent. 2018 Jun;48(2):79-86. https://doi.org/10.5624/isd.2018.48.2.79