A novel method for the remote measurement of trismus in clinical trials using mobile phone cameras.

OBJECTIVE: To evaluate the reliability and validity of a novel method for remotely measuring trismus. MATERIALS AND METHODS: We recruited 60 volunteers who took three types of photographs at a fixed restricted jaw position mimicking limited mouth opening, including one selfie and one portrait with or without a reference frame. Additionally, the interincisal distance and the width of the upper central incisors were measured with a ruler, as per common practice. Measurements of trismus were made using image analysis software comparing different types of photos and calibration methods. Intraclass correlation coefficient (ICC) and 95% limits of agreement (LoA) with 95% confidence interval were calculated to evaluate reliability and validity. RESULTS: The proposed method demonstrated high reliability (ICC 0.998; 95% CI 0.997, 0.999). Calibration of photographs using at least a baseline photograph with an external reference frame yielded unbiased measurements and minimised variability. The use of selfies compared to portrait photos also increased variability. CONCLUSION: The measurement of trismus can be performed using images taken remotely by patients using their mobile phone cameras. The proposed method is highly accurate, with best results obtained by using a reference frame for calibration of portrait photographs. CLINICAL RELEVANCE: We propose an easy, cheap, and accurate method that allows for remote and frequent monitoring of trismus in clinical studies using patients' mobile phones.

Under pressure-mechanisms and risk factors for orthodontically induced inflammatory root resorption: a systematic review.

BACKGROUND: The application of orthodontic forces causes root resorption of variable severity with potentially severe clinical ramifications. OBJECTIVE: To systematically review reports on the pathophysiological mechanisms of orthodontically induced inflammatory root resorption (OIIRR) and the associated risk factors based on in vitro, experimental, and in vivo studies. SEARCH METHODS: We undertook an electronic search of four databases and a separate hand-search. SELECTION CRITERIA: Studies reporting on the effect of orthodontic forces with/without the addition of potential risk factors on OIIRR, including (1) gene expression in in-vitro studies, the incidence root resorption in (2) animal studies, and (3) human studies. DATA COLLECTION AND ANALYSIS: Potential hits underwent a two-step selection, data extraction, quality assessment, and systematic appraisal performed by duplicate examiners. RESULTS: One hundred and eighteen articles met the eligibility criteria. Studies varied considerably in methodology, reporting of results, and variable risk of bias judgements.In summary, the variable evidence identified supports the notion that the application of orthodontic forces leads to (1) characteristic alterations of molecular expression profiles in vitro, (2) an increased rate of OIIRR in animal models, as well as (3) in human studies. Importantly, the additional presence of risk factors such as malocclusion, previous trauma, and medications like corticosteroids increased the severity of OIIRR, whilst other factors decreased its severity, including oral contraceptives, baicalin, and high caffeine. CONCLUSIONS: Based on the systematically reviewed evidence, OIIRR seems to be an inevitable consequence of the application of orthodontic forces-with different risk factors modifying its severity. Our review has identified several molecular mechanisms that can help explain this link between orthodontic forces and OIIRR. Nevertheless, it must be noted that the available eligible literature was in part significantly confounded by bias and was characterized by substantial methodological heterogeneity, suggesting that the results of this systematic review should be interpreted with caution. REGISTRATION: PROSPERO (CRD42021243431).

Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues.

-Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, microbiome, miRNome, and methylome of gingival and peri-implant tissues from human subjects and further studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens.Our data offer new insight on the pathophysiology underlying periodontal and peri-implant diseases, a possible route to a better and earlier diagnosis of these highly prevalent chronic inflammatory diseases and thus, to a personalized and efficient treatment approach.Herein, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools.

Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences. Furthermore, machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Herein, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data using open-source tools. We outline a differential expression analysis pipeline that can be used for data from both arrays and sequencing experiments, and offers the possibility to account for random or fixed effects. Furthermore, we present an overview of the possibilities for a functional analysis of the obtained data including subsequent machine learning approaches in form of (i) supervised classification algorithms in class validation and (ii) unsupervised clustering in class discovery.