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Daily new information emerges regarding the COVID-19, infection of SARS-CoV-2, which is considered a global pandemic. Angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) are required to complete the viral invasion pathway and are present in the oral mucosa, gingiva and periodontal pocket. Thus, increasing the likelihood of periodontitis and gingivitis caused by COVID-19. The cytokine storm during COVID-19 similarly arises during periodontal inflammation. Studies have reported that NOD-Like Receptor family pyrin domain-containing 3 (NLRP3) inflammasome is significant in the cytokine storm. Recently, the course of the COVID-19 has been related to the melatonin levels in both COVID-19 and periodontal diseases. It is known that melatonin prevents the activation of NLRP3 inflammasome. In light of these findings, we think that melatonin treatment during COVID-19 or periodontal diseases may prevent the damage seen in periodontal tissues by preventing the activation of NLRP3 inflammasome.

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%–75.9%) and no higher than 98.19 (95% CI, 97.8%–98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%–93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

The aim of the present study was to evaluate the inhibitory effects of oxytocin on the development of periodontitis based on its properties against bone loss and resorption. Thirty-two Wistar albino rats were divided into four equal groups: control, periodontitis + saline, periodontitis + 0.5 mg/kg/day oxytocin, and periodontitis + 1 mg/kg/day oxytocin. Periodontitis groups received 4.0 silk ligatures around their cervixes of the right and left mandibular incisors in an "8" shape, kept for 14 days. Animals in oxytocin groups were injected once every day during 14 days with oxytocin. The mandibles were fixed and scanned using microcomputed tomography to quantify bone resorption and volumetric measurements. Blood samples were collected to analyze the concentrations of macrophage colony-stimulating factor (M-CSF), receptor activator of nuclear factor-κΒ ligand (RANKL), osteoprotegerin (OPG), matrix metalloproteinase-8 (MMP-8), tumor necrosis factor-alpha (TNFα), interleukin (IL)-6, glutathione peroxidase (GPx), superoxide dismutase (SOD), and malondialdehyde (MDA). Histopathological evaluations were conducted to examine the gingiva and alveolar bone. Oxytocin prevented the development of periodontitis by decreasing ligament deteriorations and leukocytes in the gingival connective tissue and promoting reintegration with the alveolar bone. Bone resorption in all regions was less in the periodontitis + 1 mg/kg/day oxytocin group than in the periodontitis + saline group. Although TNF-α, IL-6, and RANKL values were lower in the periodontitis + 1 mg/kg/ day oxytocin group, OPG was higher than that in the periodontitis + saline group. M-CSF, MMP-8, and MDA were lower in the oxytocin groups than in the periodontitis + saline group. Oxytocin may be an effective agent for periodontal diseases because it decreased bone resorption, oxidative stress, and inflammation in an experimental periodontitis.

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