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Background/purpose: Oral health is related to general health and a person's overall well-being. The aim of the present study was to explore the association between oral health status and bite force among young adults. Materials and methods: Maximum bite force (MBF) was measured using Dental Prescale II in conjunction with a pressure-sensitive film and bite force analyzer in 40 young adults aged 20 to 40. Supragingival dental plaque was collected and cultured. Plaque weight, pH, and colony counts were assessed. The decayed, missing, and filled teeth index (DMFT) and body mass index (BMI) were recorded. Results: Bite force was negatively correlated with the number of missing teeth and the sum of missing and filled teeth. When the filled-to-remaining-teeth ratio (F/R ratio) was less than 8%, the bite force was significantly higher compared to an F/R ratio of 8-25%. Additionally, the amount of total bacteria was positively correlated with total bite force, and the quantity of Streptococcus mutans (S. mutans) along with total bacteria was positively correlated with bite force in the molar region (∗P < 0.05). The molar region predominantly contributed to bite force. Conclusion: Elevated levels of cariogenic bacteria may increase the risk of tooth loss, subsequently leading to reduced bite force. This reduction in bite force can further impact the efficiency of chewing function and, consequently, the quality of life. An F/R ratio above 8% could be easily calculated clinically and could serve as a guide to identify patients, particularly young adults, at risk of reduced bite force.
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Background/purpose: Artificial Intelligence (AI) can optimize treatment approaches in dental healthcare due to its high level of accuracy and wide range of applications. This study seeks to propose a new deep learning (DL) ensemble model based on deep Convolutional Neural Network (CNN) algorithms to predict tooth position, detect shape, detect remaining interproximal bone level, and detect radiographic bone loss (RBL) using periapical and bitewing radiographs. Materials and methods: 270 patients from January 2015 to December 2020, and all images were deidentified without private information for this study. A total of 8000 periapical radiographs with 27,964 teeth were included for our model. AI algorithms utilizing the YOLOv5 model and VIA labeling platform, including VGG-16 and U-Net architecture, were created as a novel ensemble model. Results of AI analysis were compared with clinicians' assessments. Results: DL-trained ensemble model accuracy was approximately 90% for periapical radiographs. Accuracy for tooth position detection was 88.8%, tooth shape detection 86.3%, periodontal bone level detection 92.61% and radiographic bone loss detection 97.0%. AI models were superior to mean accuracy values from 76% to 78% when detection was performed by dentists. Conclusion: The proposed DL-trained ensemble model provides a critical cornerstone for radiographic detection and a valuable adjunct to periodontal diagnosis. High accuracy and reliability indicate model's strong potential to enhance clinical professional performance and build more efficient dental health services.
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Background: Dental caries and periodontal disease remain the most prevalent oral health problems in the world. Chewing xylitol gum may help reduce the risk of caries and periodontitis for dental health benefits. However, little evidence has shown healthy food estimation by sequencing 16S rDNA in oral microbial communities. This study investigated the clinical effect of xylitol chewing gum on dental plaque accumulation and microbiota composition using the PacBio full-length sequencing platform in 24 young adults (N = 24). The participants were randomly assigned to xylitol chewing gum and control (no chewing gum) groups. Participants in the chewing gum group chewed ten pieces of gum (a total of 6.2 g xylitol/day). Dental plaque from all teeth was collected for weighing, measuring the pH value, and analysis of microbial communities at the beginning (baseline, M0) and end of the 2-week (effect, M1) study period. Results: The results suggested a 20% reduction in dental plaque accumulation (p < 0.05) among participants chewing xylitol gum for 2 weeks, and the relative abundance of Firmicutes (a type of pathogenic bacteria associated with caries) decreased by 10.26% (p < 0.05) and that of Bacteroidetes and Actinobacteria (two types of pathogenic bacteria associated with periodontitis) decreased by 6.32% (p < 0.001) and 1.66% (p < 0.05), respectively. Moreover, the relative abundance of Fusobacteria was increased by 9.24% (p < 0.001), which has been proven to have a higher proportion in dental plaque of healthy adults. However, the dental plaque pH value stayed in a healthy range for the two groups. Conclusion: In conclusion, chewing xylitol gum would benefit cariogenic and periodontal bacterial reduction in the oral cavity, which could help to prevent the diseases related to these bacteria.
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Alzheimer's disease (AD) is a neurodegenerative disease that usually affects older individuals. Owing to the higher incidence of root caries and missing teeth in elderly individuals, the bacteria involved in these dental concerns might potentially deteriorate their cognitive function. Altered microbiota in the oral cavity may induce neuroinflammation through migration from the oral cavity to the brain. However, the correlation between the composition of the oral microbiota and neurodegenerative disease remains unclear. In this study, we evaluated sequence to determine the relative abundance and diversity of bacterial taxa in the dental plaque of elderly patients with AD and controls. Oral samples; the DMFT index; and other clinical examination data were collected from 17 patients with AD and 18 normal elderly individuals as the control group. Patients with AD had significantly more missing teeth and higher dental plaque weight but lower microbial diversity than controls. Significantly increased numbers of Lactobacillales, Streptococcaceae, and Firmicutes/Bacteroidetes and a significantly decreased number of Fusobacterium were observed in patients with AD. In conclusion, using the PacBio single-molecule real-time (SMRT) sequencing platform to survey the microbiota dysbiosis biomarkers in the oral cavity of elderly individuals could serve as a tool to identify patients with AD.