Cementodentinal Tear Associated with a Periodontal- Endodontic Combined Lesion: A Case Report with a 14-Month Follow-up.

This case report describes the diagnosis and multidisciplinary treatment of a clinically indiscoverable cementodentinal tear associated with a periodontal-endodontic combined lesion. The tear site was located at the palatal root surface of the maxillary left canine. Due to its position and concomitant periapical periodontitis, it was not noticed at the initial visit until a 3D CBCT examination was conducted. Through combined endodontic-periodontal therapy (which included root canal therapy, root debridement, and periodontal flap surgery), the tear fragment was removed, and the periapical lesion healed gradually. A histologic examination confirmed the definitive diagnosis of a cementodentinal tear. After 14 months, the periodontal and endodontic status of the maxillary left canine were stable. According to these results, CBCT examination and multidisciplinary cooperation seem to be effective and necessary for the diagnosis and treatment of such clinically indiscoverable cementodentinal/cemental tears.

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study.

BACKGROUND: The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers' clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. METHODS: We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. FINDINGS: 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973-0·991), sensitivity of 94·9% (0·915-0·978), and specificity of 88·7% (0·845-0·926) on the internal validation dataset (n = 401), and an AUC of 0·935 (0·910-0·957), sensitivity of 89·6% (0·847-0·942) and specificity of 80·6% (0·757-0·853) on the external validation dataset (n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988-0·999), sensitivity of 97·4% (0·932-1·000) and specificity of 93·5% (0·882-0·979) in detecting early-stage OCSCC. On the clinical validation dataset (n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902-0·943] vs 92.4% [0·912-0·936]), sensitivity (91·0% [0·879-0·941] vs 91·7% [0·898-0·934]), and specificity (93·5% [0·909-0·960] vs 93·1% [0·914-0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855-0·885], sensitivity of 83·1% [0·807-0·854], and specificity of 90·7% [0·889-0·924]) and the average non-medical student (accuracy of 77·2% [0·757-0·787], sensitivity of 76·6% [0·743-0·788], and specificity of 77·9% [0·759-0·797]). INTERPRETATION: Automated detection of OCSCC by deep-learning-powered algorithm is a rapid, non-invasive, low-cost, and convenient method, which yielded comparable performance to that of human specialists and has the potential to be used as a clinical tool for fast screening, earlier detection, and therapeutic efficacy assessment of the cancer.

MicroRNA-181b-5p modulates tumor necrosis factor-α-induced inflammatory responses by targeting interleukin-6 in cementoblasts.

Tooth cementum is a bone-like mineralized tissue and serves as a microbial barrier against invasion and destruction. Cementum is also responsible for tooth stability and defending pulp from outside stimuli, which is formed by cementoblasts. Although it is crucial for periodontal and periapical diseases, the mechanisms underlying the pathophysiological changes of cementoblasts and their inflammatory responses remain unclear. MiR-181b is found to modulate vascular inflammation and endotoxin tolerance. In this study, miR-181b-5p was downregulated in tumor necrosis factor-α (TNF-α)-stimulated cementoblasts, whereas proinflammatory molecules increased. The mouse periapical lesions have similar results, which imitate an inflammatory environment for cementoblasts in vivo. The bioinformatics analysis and dual luciferase reporter assay suggested that miR-181b-5p targeted interleukin-6 (IL-6). Overexpressing miR-181b-5p negatively regulated IL-6 and proinflammatory chemokine. Western blot analysis and luciferase activity reporter assay verified that miR-181b-5p weakened the NF-κB activity. Hence, miR-181b-5p moderated proinflammatory chemokine production by targeting IL-6 in cementoblasts and NF-κB signaling pathway was involved. Furthermore, miR-181b-5p promoted cementoblast apoptosis, which may enhance the resolution of inflammation. Overall, our data revealed that miR-181b-5p was a negative regulator of TNF-α-induced inflammatory responses in cementoblasts.