N-acetylcysteine as a novel methacrylate-based resin cement component: effect on cell apoptosis and genotoxicity in human gingival fibroblasts.

BACKGROUND: N-acetylcysteine (NAC) reduces the cytotoxicity and genotoxicity induced by monomers leached from dental composite resins. Herein, we investigated the effects of methacrylate-based resin cement used in dental implant restoration on apoptosis and genotoxicity, as well as the antiapoptotic and antigenotoxic capabilities of its component, NAC. METHODS: The antioxidant NAC (0.1 or 1 wt.%) was experimentally incorporated into the methacrylate-based dental resin cement Premier®. The Premier® + NAC (0.1 or 1 wt.%) mixture was subsequently immersed into Dulbecco's modified Eagle's medium for 72 h, and used to treat human gingival fibroblasts (HGFs). The viability of HGFs was determined using the XTT assay. The formation of deoxyribonucleic acid (DNA) double-strand breaks (DNA-DSBs) was determined using a γ-H2AX assay. Reactive oxygen species (ROS), apoptosis, necrosis, and cell cycles were detected and analyzed using flow cytometry. RESULTS: The eluate of Premier® significantly inhibited HGF proliferation in vitro by promoting a G1-phase cell cycle arrest, resulting in cell apoptosis. Significant ROS production and DNA-DSB induction were also found in HGFs exposed to the eluate. Incorporating NAC (1 wt.%) into Premier® was found to reduce cell cytotoxicity, the percentage of G1-phase cells, cell apoptosis, ROS production, and DNA-DSB induction. CONCLUSION: Incorporating NAC (1 wt.%) into methacrylate-based resin cement Premier® decreases the cell cytotoxicity, ROS production, and DNA-DSBs associated with resin use, and further offers protective effects against the early stages of cell apoptosis and G1-phase cell cycle arrest in HGFs. Overall, our in vitro results indicate that the addition of NAC into methacrylate-based resin cements may have clinically beneficial effects on the cytotoxicity and genotoxicity of these materials.

Autologous Transplantation Tooth Guide Design Based on Deep Learning.

BACKGROUND: Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated. PURPOSE: We aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline. STUDY DESIGN, SETTING, SAMPLE: This retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest images were extracted from 79 CBCT images. PREDICTOR VARIABLE: Autologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based design. MAIN OUTCOME VARIABLE: The main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation. The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline. ANALYSES: Data were analyzed using the Kruskal-Wallis test. Statistical significance was set at P < .05. RESULTS: In the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335 ± 0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (P = .688), and the standard deviation values did not differ among the 3 groups (P = .103). The deep learning-based design pipeline (0.017 ± 0.001 minutes) provided a faster assessment compared to the manual design pipeline by both senior (19.676 ± 2.386 minutes) and junior doctors (30.613 ± 6.571 minutes) (P < .001). CONCLUSIONS AND RELEVANCE: The deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability.

Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant's stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.

Temporomandibular disorders among medical students in China: prevalence, biological and psychological risk factors.

BACKGROUND: The purpose of the present study is to evaluate the prevalence of temporomandibular disorders (TMD) and their associated biological and psychological factors in Chinese university students. METHODS: A total of 754 students were included from Zunyi Medical University, each participant completed questionnaires and clinical examinations according to the Diagnostic Criteria for Temporomandibular Disorders. RESULTS: The overall prevalence of TMD was 31.7% among medical students. Subjects with TMD had a high prevalence of bruxism, empty chewing, unilateral chewing, chewing gum, anterior teeth overbite, anterior teeth overjet, depression, anxiety, and sleep disturbance. Moreover, sleep bruxism, empty chewing, unilateral chewing, anterior teeth overbite, depression, and anxiety were the strongest risk factors for TMD. CONCLUSIONS: Individuals with TMD have a high prevalence of psychological distress and oral parafunctional habits. Except for the psychological factors associated with TMD, bruxism, abnormal chewing, and malocclusion also shared similar risks for TMD.

Logistic regression analysis of the relationship between abnormal dental occlusion and temporomandibular disorders in lower grade college students / 口腔疾病防治

Objective @#To investigate the influence of abnormal occlusion factors on the incidence of temporomandibular disorders (TMDs) in junior college students and to provide an etiological basis for the prevention and treatment of TMDs.@*Methods @# We examined the temporomandibular joint (TMJ) and dental occlusion in 754 lower grade college students (male 354, female 400) at Zunyi Medical University (Zhuhai campus). A questionnaire was also administered. We analyzed the correlation between TMD and the other three factors (static abnormal occlusion, dynamical abnormal occlusion and oral parafunctional activity) from the perspective of multivariate unconditioned logistic regression and univariate unconditioned logistic regression with dummy variables.@*Results @#The prevalence of TMD was 31.7%. The incidence of TMD was significantly (P<0.05) associated with sleep bruxism (OR=2.070), clenching (OR=2.553), diurnal (OR=2.642) and anterior teeth overbite (OR=1.228). Univariate unconditioned logistics regression analysis by dummy variables was used to analyze the incidence of TMD at different deep overbites (mild, OR=1.558; moderate, OR=2.189; severe, OR=3.236; P<0.05). @*Conclusion@#The risk factors for TMD in lower grade college students included anterior teeth occlusion, sleep bruxism, clenching, and diurnal treatment. Worse deep overbite might increase the risk of TMD.