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Introduction: Tobacco use is one of the main causes of periodontitis. E-cigarette are gaining in popularity, and studies are needed to better understand the impact of e-cigarettes on oral health. Objective: To perform a longitudinal study to evaluate the adverse effects of e-cigarettes on periodontal health. Methods: Naïve E-cigarette users, cigarette smokers, and non-smokers were recruited using newspaper and social media. Age, gender, and ethnicity, were recorded. Participants were scheduled for two visits 6 months apart. At each visit, we collected data on the frequency and magnitude of e-cigarette and cigarette use, and alcohol consumption. Carbon monoxide (CO) levels, cotinine levels, salivary flow rate, periodontal probing depth (PD), bleeding on probing (BoP), and clinical attachment loss (CAL) were also determined at both baseline and follow-up visits and compared between groups with two-way repeated measures ANOVA. Periodontal diagnosis and other categorical variables were compared between groups with the chi-square statistic and logistic regression. Results: We screened 159 subjects and recruited 119 subjects. One-hundred-one subjects (31 cigarette smokers, 32 e-cigarette smokers, and 38 non-smokers) completed every assessment in both visits. The retention and compliance rate of subjects was 84.9%. The use of social media and craigslist was significant in recruiting e-cigarette subjects. Ethnicity and race differed between groups, as did average age in the male subjects. Carbon monoxide and salivary cotinine levels were highest among cigarette smokers. Bleeding on probing and average PDs similarly increased over time in all three groups, but CAL uniquely increased in e-cigarette smokers. Rates of severe periodontal disease were higher in cigarette smokers and e-cigarette users than non-smokers, but interpretation is confounded by the older age of the cigarette smokers. Conclusion: Among the recruited participants, CAL after 6 months was significantly worse only in the e-cigarette smokers. This study design and protocol will assist in future larger studies on e-cigarette and oral health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Xu, Aboseria, Janal, Pushalkar, Bederoff, Vasconcelos, Sapru, Paul, Queiroz, Makwana, Solarewicz, Guo, Aguallo, Gomez, Shelly, Aphinyanaphongs, Gordon, Corby, Kamer, Li and Saxena.)

Objectives: Silver diamine fluoride (SDF) is a nonsurgical therapy for the arrest and prevention of dental caries with demonstrated clinical efficacy. Approximately 20% of children receiving SDF fail to respond to treatment. The objective of this study was to develop a predictive model of treatment non-response using machine learning. Methods: An observational pilot study ( N = 20) consisting of children with and without active decay and who did and did not respond to silver diamine fluoride provided salivary samples and plaque from infected and contralateral sites. 16S rRNA genes from samples were amplified and sequenced on an Illumina Miseq and analyzed using QIIME. The association between operational taxonomic units and treatment non-response was assessed using lasso regression and artificial neural networks. Results: Bivariate group comparisons of bacterial abundance indicate a number of genera were significantly different between non-responders and those who responded to SDF therapy. No differences were found between non-responders and caries-active subjects. Prevotella pallens and Veillonella denticariosi were retained in full lasso models and combined with clinical variables in a six-input multilayer perceptron. Discussion: The acidogenic and acid-tolerant nature of retained bacterial species may overcome the antimicrobial effects of SDF. Further research to validate the model in larger external samples is needed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ruff, Paul, Sierra, Xu, Li, Crystal and Saxena.)