Objective To investigate the use of peri‐implant crevicular fluid (PICF) interleukin‐1β (IL‐1β), IL‐6, tumor necrosis factor‐α (TNF‐α), and matrix metalloproteinase‐8 (MMP‐8) biomarkers in distinguishing between healthy implants (H), peri‐implant mucositis (MU), and peri‐implantitis (PI). Material and Methods Electronic using three databases (Pubmed, EMBASE, and Cochrane) and manual searches were conducted for articles published up to March 2018 by two independent calibrated reviewers. Meta‐analyses using a random‐effects model were conducted for each of the cytokines; IL‐1β, IL‐6, and TNF‐α, to analyze standardized mean difference (SMD) between H and MU, MU and PI, H and PI with their associated 95% confidence intervals (CI). Qualitative assessment of MMP‐8 was provided consequent to the lack of studies that provide valid data for a meta‐analysis. Results Nineteen articles were included in this review. IL‐1β, IL‐6, and TNF‐α, levels were significantly higher in MU than H groups (SMD: 1.94; 95% CI: 0.87, 3.35; P < .001, SMD: 1.17; 95% CI: 0.16, 3.19; P = .031 and SMD: 3.91; 95% CI: 1.13, 6.70; P = .006, respectively). Similar results were obtained with PI compared to H sites (SMD: 2.21, 95% CI: 1.32, 3.11; P < .001, SMD: 1.72; 95% CI: 0.56, 2.87; P = .004 and SMD: 3.78; 95% CI: 1.67, 5.89; P < .001, respectively). IL‐6 was statistically higher in PI than MU sites (SMD = 1.46; 95% CI: 0.36, 2.55; P = .009); while IL‐1ß increase was not significant. Despite absence of meta‐analysis, MMP‐8 show to be a promising biomarker in detection of PI in literature. Conclusion Within the limitations of this study, pro‐inflammatory cytokines in PICF, such as IL‐1ß and IL‐6, can be used as adjunct tools to clinical parameters to differentiate H from MU and PI.

Despite a number of reports in the literature on the role of epigenetic mechanisms in periodontal disease, a thorough assessment of the published studies is warranted to better comprehend the evidence on the relationship between epigenetic changes and periodontal disease and its treatment. Therefore, the aim of this systematic review is to identify and synthesize the evidence for an association between DNA methylation/histone modification and periodontal disease and its treatment in human adults. A systematic search was independently conducted to identify articles meeting the inclusion criteria. DNA methylation and histone modifications associated with periodontal diseases, gene expression, epigenetic changes after periodontal therapy, and the association between epigenetics and clinical parameters were evaluated. Sixteen studies were identified. All included studies examined DNA modifications in relation to periodontitis, and none of the studies examined histone modifications. Substantial variation regarding the reporting of sample sizes and patient characteristics, statistical analyses, and methodology, was found. There was some evidence, albeit inconsistent, for an association between DNA methylation and periodontal disease. IL6, IL6R, IFNG, PTGS2, SOCS1, and TNF were identified as candidate genes that have been assessed for DNA methylation in periodontitis. While several included studies found associations between methylation levels and periodontal disease risk, there is insufficient evidence to support or refute an association between DNA methylation and periodontal disease/therapy in human adults. Further research must be conducted to identify reproducible epigenetic markers and determine the extent to which DNA methylation can be applied as a clinical biomarker.

Background Unsupervised clustering is a method used to identify heterogeneity among groups and homogeneity within a group of patients. Without a prespecified outcome entry, the resulting model deciphers patterns that may not be disclosed using traditional methods. This is the first time such clustering analysis is applied in identifying unique subgroups at high risk for periodontitis in National Health and Nutrition Examination Surveys (NHANES 2009 to 2014 data sets using >500 variables. Methods Questionnaire, examination, and laboratory data (33 tables) for >1,000 variables were merged from 14,072 respondents who underwent clinical periodontal examination. Participants with ≥6 teeth and available data for all selected categories were included (N = 1,222). Data wrangling produced 519 variables. k‐means/modes clustering (k = 2:14) was deployed. The optimal k‐value was determined through the elbow method, formula = ∑ (xi2) – ((∑ xi)2 /n). The 5‐cluster model showing the highest variability (63.08%) was selected. The 2012 Centers for Disease Control and Prevention/American Academy of Periodontology (AAP) and 2018 European Federation of Periodontology/AAP periodontitis case definitions were applied. Results Cluster 1 (n = 249) showed the highest prevalence of severe periodontitis (43%); 39% self‐reported “fair” general health; 55% had household income <$35,000/year; and 48% were current smokers. Cluster 2 (n = 154) had one participant with periodontitis. Cluster 3 (n = 242) represented the greatest prevalence of moderate periodontitis (53%). In Cluster 4 (n = 35) only one participant had no periodontitis. Cluster 5 (n = 542) was the systemically healthiest with 77% having no/mild periodontitis. Conclusion Clustering of NHANES demographic, systemic health, and socioeconomic data effectively identifies characteristics that are statistically significantly related to periodontitis status and hence detects subpopulations at high risk for periodontitis without costly clinical examinations.

Structured Abstract The reconstruction of alveolar bone defects associated with teeth and dental implants remains a clinical challenge in the treatment of patients affected by disease or injury of the alveolus. The aim of this review was to provide an overview on advances made in the use of personalized scaffolding technologies coupled with biologics, cells and gene therapies that offer future clinical applications for the treatment of patients requiring periodontal and alveolar bone regeneration. Over the past decade, advancements in three‐dimensional (3D) imaging acquisition technologies such as cone‐beam computed tomography (CBCT) and precise scaffold fabrication methods such as 3D bioprinting have resulted in personalized scaffolding constructs based on individual patient‐specific anatomical data. Furthermore, ‘fiber‐guiding’ scaffold designs utilize topographical cues to guide ligamentous fibers to form in orientation towards the root surface to improve tooth support. Therefore, a topic‐focused literature search was conducted looking into fiber‐guiding and image‐based scaffolds and their associated clinical applications.

Introduction and motivation Prevention is one of the most important pillars of public health. The Institute of Medicine (IOM) estimates that missed prevention opportunities cost the US $55 billion every year, and an estimate of ~ 30 cents on every healthcare dollar. In total, the healthcare system squanders $750 billion a year [1]. Accordingly, amongst all the countries of the OECD (the Organization for Economic Cooperation and Development), the US has the Abstract Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don't tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures-a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer's V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.