In Vivo Regulation of Active Matrix Metalloproteinase-8 (aMMP-8) in Periodontitis: From Transcriptomics to Real-Time Online Diagnostics and Treatment Monitoring.

BACKGROUND: This study investigated in vivo regulation and levels of active matrix metalloproteinase-8 (aMMP-8), a major collagenolytic protease, in periodontitis. METHODS: Twenty-seven adults with chronic periodontitis (CP) and 30 periodontally healthy controls (HC) were enrolled in immunohistochemistry and transcriptomics analytics in order to assess Treponema denticola (Td) dentilisin and MMP-8 immunoexpression, mRNA expression of MMP-8 and its regulators (IL-1ß, MMP-2, MMP-7, TIMP-1). Furthermore, the periodontal anti-infective treatment effect was monitored by four different MMP-8 assays (aMMP-8-IFMA, aMMP-8-Oralyzer, MMP-8-activity [RFU/minute], and total MMP-8 by ELISA) among 12 CP (compared to 25 HC). RESULTS: Immunohistochemistry revealed significantly more Td-dentilisin and MMP-8 immunoreactivities in CP vs. HC. Transcriptomics revealed significantly elevated IL-1ß and MMP-7 RNA expressions, and MMP-2 RNA was slightly reduced. No significant differences were recorded in the relatively low or barely detectable levels of MMP-8 mRNAs. Periodontal treatment significantly decreased all MMP-8 assay levels accompanied by the assessed clinical indices (periodontal probing depths, bleeding-on-probing, and visual plaque levels). However, active but not total MMP-8 levels persisted higher in CP than in periodontally healthy controls. CONCLUSION: In periodontal health, there are low aMMP-8 levels. The presence of Td-dentilisin in CP gingivae is associated with elevated aMMP-8 levels, potentially contributing to a higher risk of active periodontal tissue collagenolysis and progression of periodontitis. This can be detected by aMMP-8-specific assays and online/real-time aMMP-8 chair-side testing.

Active Matrix Metalloproteinase-8 (aMMP-8) Versus Total MMP-8 in Periodontal and Peri-Implant Disease Point-of-Care Diagnostics.

Active matrix metalloproteinase-8 (aMMP-8) is a promising biomarker candidate for the modern periodontal and peri-implant disease diagnostics utilizing the chairside/point-of-care oral fluid technologies. These rapid biomarker analysis technologies utilize gingival crevicular fluid (GCF), peri-implant sulcular fluid (PISF), or mouth rinse as the oral fluid matrices that can be collected patient-friendly and non-invasively without causing bacteremia. aMMP-8, but not total or latent proMMP-8, has been shown to be a relevant biomarker to be implemented to the latest 2017 classification system of periodontitis and peri-implantitis. Thus, aMMP-8 point-of-care-testing (POCT)-but not total or latent proMMP-8-can be conveniently used as an adjunctive and preventive diagnostic tool to identify and screen the developing and ongoing periodontal and peri-implant breakdown and disease as well as predict its episodic progression. Similarly, aMMP-8 POCT provides an important tool to monitor the treatment effect of these diseases, but also other diseases such as head and neck cancer, where it can identify and predict the rapid tissue destructive oral side-effects during and after the radiotherapy. Additionally, recent studies support aMMP-8 POCT benefitting the identification of periodontitis and diabetes as the escalating risk diseases for COVID-19 infection. Overall, aMMP-8 POCT has launched a new clinical field in oral medicine and dentistry, i.e., oral clinical chemistry.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis.

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.