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Abstract BackgroundSuboptimal maternal oral health during pregnancy is potentially associated with adverse birth outcomes and increased dental caries risks in children. This study aimed to assess the oral microbiome and immune response following an innovative clinical regimen, Prenatal Total Oral Rehabilitation (PTOR), that fully restores women’s oral health to a “disease-free status” before delivery. MethodsThis prospective cohort study assessed 15 pregnant women at baseline and 3 follow-up visits (1 week, 2 weeks, and 2 months) after receiving PTOR. The salivary and supragingival plaque microbiomes were analyzed using metagenomic sequencing. Multiplexed Luminex cytokine assays were performed to examine immune response following PTOR. The association between salivary immune markers and oral microbiome was further examined. ResultsPTOR was associated with a reduction of periodontal pathogens in plaque, for instance, a lower relative abundance ofTannerella forsythiaandTreponema denticolaat 2 weeks compared to the baseline (p < 0.05). The alpha diversity of plaque microbial community was significantly reduced at the 1-week follow-up (p < 0.05). Furthermore, we observed significant changes in theActinomyces defective-associated carbohydrate degradation pathway andStreptococcus Gordonii-associated fatty acid biosynthesis pathway. Two immune markers related to adverse birth outcomes significantly differed between baseline and follow-up. ITAC, negatively correlated with preeclampsia severity, significantly increased at 1-week follow-up; MCP-1, positively correlated with gestational age, was elevated at 1-week follow-up. Association modeling between immune markers and microbiome further revealed specific oral microorganisms that are potentially correlated with the host immune response. ConclusionsPTOR is associated with alteration of the oral microbiome and immune response among a cohort of underserved US pregnant women. Future randomized clinical trials are warranted to comprehensively assess the impact of PTOR on maternal oral flora, birth outcomes, and their offspring’s oral health. 

This study aimed to evaluate the impact of Nystatin oral rinse on salivary and supragingival microbiota in adults with oral candidiasis and identify predictive factors related to individuals’ responses to Nystatin. The trial involved twenty participants who used 600,000 International Units/application of Nystatin oral rinse for seven days, four times a day, and were followed up at one week and three months after the rinse. The salivary and plaque microbiome of the participants were assessed via 16S rDNA amplicon sequencing. Overall, salivary and plaque microbiomes remained stable. However, among the participants (53 percent) who responded to Nystatin rinse (defined as free of oral Candida albicans post treatment), Veillonella emerged as a core genus alongside Streptococcus and Actinomyces in supragingival plaque at the 3-month follow-up. Furthermore, statistical models were fit to identify predictive factors of Nystatin rinse success (elimination of C. albicans) or failure (remaining C. albicans). The results revealed that an increased level of salivary Interferon (IFN)-γ-inducible protein (IP-10), also known as C-X-C motif chemokine ligand 10 (CXCL10), was an indicator of a failure of responding to Nystatin rinse. Future clinical trials are warranted to comprehensively assess the impact of antifungal treatment on the oral flora. 

Early Childhood Caries (ECC) is the most common childhood disease worldwide and a health disparity among underserved children. ECC is preventable and reversible if detected early. However, many children from low-income families encounter barriers to dental care. An at-home caries detection technology could potentially improve access to dental care regardless of patients’ economic status and address the overwhelming prevalence of ECC. Our team has developed a smartphone application (app), AICaries, that uses artificial intelligence (AI)-powered technology to detect caries using children’s teeth photos. We used mixed methods to assess the acceptance, usability, and feasibility of the AICaries app among underserved parent-child dyads. We conducted moderated usability testing (Step 1) with ten parent-child dyads using "Think-aloud" methods to assess the flow and functionality of the app and analyze the data to refine the app and procedures. Next, we conducted unmoderated field testing (Step 2) with 32 parent-child dyads to test the app within their natural environment (home) over two weeks. We administered the System Usability Scale (SUS) and conducted semi-structured individual interviews with parents and conducted thematic analyses. AICaries app received a 78.4 SUS score from the participants, indicating an excellent acceptance. Notably, the majority (78.5%) of parent-taken photos of children’s teeth were satisfactory in quality for detection of caries using the AI app. Parents suggested using community health workers to provide training to parents needing assistance in taking high quality photos of their young child’s teeth. Perceived benefits from using the AICaries app include convenient at-home caries screening, informative on caries risk and education, and engaging family members. Data from this study support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children. 

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated ( Prevotella histicola, Streptococcus mutans , and Rothia muciloginosa ) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.