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Methionine challenge test: methyl mercaptan (CH3SH) response in periodontitis

2026-03-25T03:04:26Z

Title: Methionine challenge test: methyl mercaptan (CH3SH) response in periodontitis Authors: Choi, Yiseul; Song, Yoolbin; Kim, Sooyeon; Park, Wonse Abstract: Halitosis, frequently associated with volatile sulfur compounds (VSCs) produced by oral microbiota, affects a large proportion of adults. Among VSCs, methyl mercaptan (CH3SH) is a critical biomarker for periodontitis-related halitosis due to its strong correlation with periodontal pocket depth and attachment loss. This study investigated the utility of a methionine challenge protocol to selectively stimulate CH3SH production and enhance the standardization of oral air-based screening for periodontal disease. Thirty adults were enrolled and divided equally into control and periodontitis groups. Mouth air samples were collected from oral cavity air using a straw-based sampling method connected to a portable gas-sensing device, which continuously monitored VSCs, including CH3SH and hydrogen sulfide (H2S), across eight time points. Participants underwent an 8 h fast prior to baseline oral air collection, followed by standardized toothbrushing. After a 60 min rest period, they swilled with a methionine solution, with oral air samples collected immediately after and at 10 min intervals for 40 min. Both groups showed increased CH3SH levels following methionine stimulation, with the periodontitis group exhibiting a significantly greater increase from pre- to post-stimulation (p < 0.001) and higher cumulative exposure (p < 0.001). In contrast, H2S levels remained consistently elevated in the periodontitis group but did not fluctuate significantly over time. Furthermore, correlations between CH3SH and H2S decreased immediately post-stimulation and gradually recovered in the periodontitis group. These findings indicate that the methionine challenge effectively induces CH3SH production linked to periodontal dysbiosis, supporting its potential as a non-invasive screening and indicator tool for the presence of periodontitis, rather than for staging disease severity. The protocol offers a promising approach to improve diagnostic accuracy while minimizing variability related to oral hygiene. (The study is registered with the Clinical Research Information Service under number KCT0010328.).

Shotgun metagenomic analysis of the tongue-coating microbiome reveals oral microbes and their functions in older adults with dementia

2026-03-31T01:38:04Z

Title: Shotgun metagenomic analysis of the tongue-coating microbiome reveals oral microbes and their functions in older adults with dementia Authors: Cha, Jun Hyung; Jeong, Sol-Ah; Ye, Byoung-Seok; Lee, Insuk; Jung, Bock-Young Abstract: IntroductionDementia poses a growing burden in the aging population, prompting the search for noninvasive biomarkers for early detection.Materials and methodsWe performed shotgun metagenomic sequencing of tongue-coating samples from older adults with dementia (n = 30) and cognitively healthy controls (n = 28) to identify oral microbiome signatures.ResultsThe analysis revealed distinct microbial compositions associated with dementia, including an enrichment of Veillonella parvula in dementia patients, whereas Lautropia dentalis was more abundant in healthy controls. We also identified functional alterations in the microbiome in the dementia group, including increased abundance of the histidine degradation and biotin biosynthesis pathways, whereas ubiquinol biosynthesis was more abundant in the healthy control group. The abundance of several microbial taxa and metabolic pathways were correlated with scores on the Korean Mini-Mental State Examination 2nd edition (K-MMSE), a clinical assessment of dementia severity. Prevotella pleuritidis, Actinomyces sp., Leptotrichia buccalis, and Leptotrichia sp. were positively correlated, whereas Oribacterium parvum was negatively associated with K-MMSE scores. Among the metabolic pathways, glutamine/glutamate biosynthesis was positively correlated with cognitive performance.ConclusionsThese results suggest that specific oral taxa and their metabolic functions are associated with cognitive status and may reflect underlying neurodegenerative processes.

Mapping the knowledge landscape of mobile teledentistry: A bibliometric analysis based on the web of science database

2026-01-28T05:58:12Z

Title: Mapping the knowledge landscape of mobile teledentistry: A bibliometric analysis based on the web of science database Authors: Lin, Yue; Choi, Yiseul; Park, Wonse Abstract: Objective: Teledentistry has rapidly grown; yet, the role of mobile devices remains under-investigated. We performed an analysis of mobile teledentistry to elucidate its historical development, collaboration networks, thematic hotspots, and future directions. Methods: We searched the Web of Science; 99 articles, published between January 2013 and April 2025, were included. Publication trends, collaboration networks, and research themes were analysed using co-occurrence, clustering, and co-citation analyses with CiteSpace and VOSviewer. Results: The COVID-19 pandemic accelerated mobile teledentistry research, primarily in high-income countries. A core group of prolific authors and institutions is not yet established; however, some nations have emerged as significant contributors. Keyword analysis revealed three primary research hotspots: diagnostic accuracy, care accessibility, and oral hygiene applications. Eleven thematic clusters revealed three principal research themes: comparisons with traditional methods, investigations of specific time periods or populations, and studies of mobile dental products. Co-citation analysis identified a foundational literature base centred on the feasibility and validity of mobile teledentistry diagnostics. Conclusions: The static panorama and dynamic evolution of mobile teledentistry were comprehensively elucidated, highlighting the urgent need for international collaboration to support implementation, especially in low-and middle-income countries. Future research should further evaluate diagnostic accuracy and explore sustainable strategies that may enhance health equity and help reduce global dental disparities. Clinical Significance: Existing studies on mobile teledentistry consistently report the potential to support diagnostic accuracy and suggest improved access to care, particularly in underserved settings. Evidence from the analysed studies reflects the use of mobile devices for remote screening, diagnosis, and monitoring. Therefore, this cumulative evidence base suggests the possibility of integrating mobile teledentistry into routine clinical practice, intending to reduce health disparities and improve patient outcomes pending further validation.

Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review.

2026-02-05T00:26:14Z

Title: Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review. Authors: Ahn, Suji; Kim, Min-Ji; Kim, Jun-Young; Park, Wonse Abstract: Background: With advancements in deep learning-based dental imaging analysis, artificial intelligence (AI) models are increasingly being employed to assist in mandibular third molar surgery. However, a comprehensive overview of the clinical utility remains limited. This scoping review aimed to identify and compare deep learning models used in the radiographic evaluation of mandibular third molar surgery, with a focus on AI model types, key performance metrics, imaging modalities, and clinical applicability. Material and Methods: Following the PRISMA-ScR guidelines, a comprehensive search was conducted in the PubMed and Scopus databases for original research articles published between 2015 and 2024. Systematic reviews, editorial articles, and studies with insufficient datasets were excluded. Studies utilising panoramic radiographs and cone-beam computed tomography (CBCT) images for AI-based mandibular third molar analyses were included. The extracted data were charted according to the AI model types, performance metrics (accuracy, sensitivity, and specificity), dataset size and distribution, validation processes, and clinical applicability. Comparative performance tables and heat maps were utilised for visualisation. Results: Of the initial 948 articles, 16 met the inclusion criteria. Various convolutional neural network (CNN)-based models have been developed, with U-Net demonstrating the highest accuracy and clinical utility. Most studies employed panoramic and CBCT images, with U-Net outperforming other models in predicting nerve injury and evaluating extraction difficulty. However, substantial variations in dataset size, validation procedures, and performance metrics were noted, highlighting inconsistencies in model generalisability. Conclusions: Deep learning shows promising potential in the radiographic evaluation of mandibular third molars. To date, most studies have relied on two-dimensional images and focused on detection and segmentation, while predictive modeling and three-dimensional CBCT-based analysis are relatively limited. To enhance clinical utility, larger standardized datasets, transparent multi-expert annotation, task-specific benchmarking, and robust external/multicenter validation are needed. These measures will enable reliable pre-extraction risk prediction and support clinical decision-making.