SUMMARY: Periodontal diseases (gingivitis and periodontitis), result from a disruption of the host–oral microbiome homoeostasis. Whereas the pathological role of some specific bacterial strains during periodontal diseases is well documented, the impact of parasites in periodontium pathophysiology is still under debate. This review aims to collect data about the prevalence and the potential role ofTrichomonas tenaxduring periodontal diseases. Data from 47 studies revealed thatT. tenaxprevalence in diseased periodontium ranged from 0 to 94·1%. The prevalence of oral protozoan infections was found to be largely greater in patients with periodontal diseases than with healthy periodontium. The parasite detection was mainly performed by direct microscopy.Trichomonas tenaxpresence was clearly correlated with periodontal disease. The high heterogeneity of its periodontal prevalence may be correlated with the diversity of the population screened (age, sex, systemic diseases), and the methods used for diagnosis. This protozoan seems to have the capacity to be involved in the inflammatory process of gum disease. Animal experimentation, using relevant physiopathological models of periodontitis, needs to be performed to investigate the ability ofT. tenaxto cause and/or worsen the disease. Further investigations using standardized experimental designs of epidemiologic studies are also needed.

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.

Necrotizing ulcerative gingivitis, sometimes observed in young children, may lead to necrotizing stomatitis and noma. Therefore, its interception is a necessity and a challenge for the paediatric practitioners. First, this article aims to propose a systematic review of recent literature on the use of local antiseptic and antibiotic prescription in this particular periodontal condition. Then, a protocol is proposed to have a simple, costless and reproducible treatment on children.