Dental calculus is a significant contributor to periodontal disease, making its accurate identification crucial for effective prevention and treatment. Traditional methods for identifying dental calculus have the problems of strong subjectivity and low accuracy, especially when it comes to detecting calculus in the concealed subgingival areas, which cannot meet the demands of modern clinical practice. Recently, optical detection technology has attracted widespread attention due to its non-invasive and high-sensitivity characteristics. A variety of optical techniques have provided new ideas for the identification of dental calculus, such as polarization detection, optical coherence tomography (OCT), differential reflectometry, hyperspectral imaging (HSI), and fluorescence spectroscopy systems. Furthermore, artificial intelligence technology, particularly the combination of machine learning and deep learning with optical techniques, has significantly enhanced the level of automation and intelligence in the identification of dental calculus. This review provides the current mainstream methods for identifying dental calculus, compares and analyzes the advantages and disadvantages of each technology, and looks forward to the future development direction. This work aims to guide research and clinical application of dental calculus detection technologies.

The parotid gland is the anatomical site with the highest incidence of salivary gland tumours, and its complex anatomical structure places high demands on surgical precision.In this paper, we report a case of precise resection of a deep lobe tumour of the parotid gland by applying the surgical navigation (SN) system.With the SN system, complete preservation of important neurovascularity around the tumour was achieved intraoperatively, and the patient recovered well after surgery.This case confirms that the SN system has good clinical value in the resection of deep lobe tumour of parotid gland.

Cell membrane-coated nanoparticles (CMCNPs) are a class of nanoscale carriers enveloped by natural cell membranes, which efficiently preserve the abundant functional proteins and receptors on the surface of cell membranes, thereby integrating the biological properties of natural cell membranes and demonstrating excellent biocompatibility and precise targeting ability in the field of biomedicine. In treating various oral diseases, such as oral cancer and periodontitis, CMCNPs have shown great potential and provide new research directions for overcoming oral medicine problems, emerging as one of the forefront topics in current biomedical research. This paper focuses on CMCNPs, presenting a detailed review of their preparation techniques and research progress in treating oral diseases. This article focuses on CMCNPs and deeply explores relevant mechanisms, application results and existing challenges, aiming to provide scientific and precise references for the subsequent academic research and clinical translation of oral diseases such as oral cancer, periodontitis, and oral mucosal diseases.

Objective: To construct a survival prognosis prediction model based on gingival carcinoma patient data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States, and evaluate its effectiveness. Methods: Download relevant basic information, clinical information, and follow-up data of patients diagnosed with gingival carcinoma between 2004 and 2018 from the SEER database, and randomly divide the patients into a modeling group (70%) and a validation group (30%). Through univariate and multivariate Cox regression analysis, variables were screened to determine independent prognostic risk factors for gingival carcinoma patients, and a nomogram was constructed. Evaluate the prediction model from three perspectives: discrimination, calibration, and clinical applicability. X-Tile software was used for risk stratification of the gingival carcinoma patients. Results: This study included 3334 patients with gingival carcinoma, with 3-year, 5-year, and 10-year survival rates of 59.03%, 48.89%, and 30.69%, respectively. The concordance index (C-index) of the modeling group and validation group were 0.715 and the 0.716, respectively; The receiver operating characteristic (ROC) curve results showed that the area under the curve (AUC) for predicting the 3-year, 5-year, and 10-year overall survival rates of gingival carcinoma patients in the modeling group were 0.757, 0.759, and 0.771, respectively. In the validation group, they were 0.747, 0.765, and 0.784, respectively. The C-index and AUC of the nomogram were higher than those of the traditional TNM staging system (P<0.001). The calibration curve results indicate that the nomogram model has good predictive accuracy. The decision curve analysis (DCA) results show that the nomogram has good clinical application value and is superior to the traditional TNM staging system.Patients were divided into low-risk group (<116.47), medium-risk group (116.47-213.24), and high-risk group (≥213.24) based on the cutoff value of the total score in the nomogram. There was a statistically significant difference in survival rate among the three groups (P<0.001). Conclusion: Age, marital status, histological grading, T stage, N stage, surgical status, and radiotherapy status are factors affecting the survival of patients with gingival carcinoma. The nomogram prediction model constructed in this study can provide reference for predicting the prognosis of gingival carcinoma patients.