Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a global public health emergency. Periodontitis, the most prevalent disease that leads to tooth loss, is caused by infection by periodontopathic bacteria. Periodontitis is also a risk factor for pneumonia and the exacerbation of chronic obstructive pulmonary disease, presumably because of the aspiration of saliva contaminated with periodontopathic bacteria into the lower respiratory tract. Patients with these diseases have increased rates of COVID-19 aggravation and mortality. Because periodontopathic bacteria have been isolated from the bronchoalveolar lavage fluid of patients with COVID-19, periodontitis may be a risk factor for COVID-19 aggravation. However, the molecular links between periodontitis and COVID-19 have not been clarified. In this study, we found that the culture supernatant of the periodontopathic bacterium Fusobacterium nucleatum (CSF) upregulated the SARS-CoV-2 receptor angiotensin-converting enzyme 2 in A549 alveolar epithelial cells. In addition, CSF induced interleukin (IL)-6 and IL-8 production by both A549 and primary alveolar epithelial cells. CSF also strongly induced IL-6 and IL-8 expression by BEAS-2B bronchial epithelial cells and Detroit 562 pharyngeal epithelial cells. These results suggest that when patients with mild COVID-19 frequently aspirate periodontopathic bacteria, SARS-CoV-2 infection is promoted, and inflammation in the lower respiratory tract may become severe in the presence of viral pneumonia.

Aspiration pneumonia is a major health problem owing to its high mortality rate in elderly people. The secretion of proinflammatory cytokines such as interleukin (IL)-8 and IL-6 by respiratory epithelial cells, which is induced by infection of respiratory bacteria such as Streptococcus pneumoniae, contributes to the onset of pneumonia. These cytokines thus play a key role in orchestrating inflammatory responses in the lower respiratory tract. In contrast, chronic periodontitis, a chronic inflammatory disease caused by the infection of periodontopathic bacteria, typically Porphyromonas gingivalis, is one of the most prevalent microbial diseases affecting humans globally. Although emerging evidence has revealed an association between aspiration pneumonia and chronic periodontitis, a causal relationship between periodontopathic bacteria and the onset of aspiration pneumonia has not been established. Most periodontopathic bacteria are anaerobic and are therefore unlikely to survive in the lower respiratory organs of humans. Therefore, in this study, we examined whether simple contact by heat-inactivated P. gingivalis induced proinflammatory cytokine production by several human respiratory epithelial cell lines. We found that P. gingivalis induced strong IL-8 and IL-6 secretion by BEAS-2B bronchial epithelial cells. P. gingivalis also induced strong IL-8 secretion by Detroit 562 pharyngeal epithelial cells but not by A549 alveolar epithelial cells. Additionally, Toll-like receptor (TLR) 2 but not TLR4 was involved in the P. gingivalis-induced proinflammatory cytokine production. Furthermore, P. gingivalis induced considerably higher IL-8 and IL-6 production than heat-inactivated S. pneumoniae. Our results suggest that P. gingivalis is a powerful inflammatory stimulant for human bronchial and pharyngeal epithelial cells and can stimulate TLR2-mediated cytokine production, thereby potentially contributing to the onset of aspiration pneumonia.

Melatonin has many roles, including bone remodeling and osseointegration of dental implants. The topical application of melatonin facilitated bone regeneration in bone defects. We evaluated the effects of topical application of melatonin on vertical bone augmentation in rat calvaria secluded spaces. In total, 12 male Fischer rats were used and two plastic caps were fixed in the calvarium. One plastic cap was filled with melatonin powder and the other was left empty. Newly generated bone at bone defects and within the plastic caps was evaluated using micro-CT and histological sections. New bone regeneration within the plastic cap was increased significantly in the melatonin versus the control group. Melatonin promoted vertical bone regeneration in rat calvaria in the secluded space within the plastic cap.

Periodontitis is an inflammatory condition that causes the destruction of the supporting tissues of teeth and is a major public health problem affecting more than half of the adult population worldwide. Recently, members of the herpes virus family, such as the Epstein–Barr virus (EBV), have been suggested to be involved in the etiology of periodontitis because bacterial activity alone does not adequately explain the clinical characteristics of periodontitis. However, the role of EBV in the etiology of periodontitis is unknown. This study aimed to examine the effect of inactivated EBV on the expression of inflammatory cytokines in human gingival fibroblasts (HGFs) and the induction of osteoclast differentiation. We found that extremely high levels of interleukin (IL)-6 and IL-8 were induced by inactivated EBV in a copy-dependent manner in HGFs. The levels of IL-6 and IL-8 in HGFs were higher when the cells were treated with EBV than when treated with lipopolysaccharide and lipoteichoic acid. EBV induced IκBα degradation, NF-κB transcription, and RAW264.7 cell differentiation into osteoclast-like cells. These findings suggest that even without infecting the cells, EBV contributes to inflammatory cytokine production and osteoclast differentiation by contact with oral cells or macrophage lineage, resulting in periodontitis onset and progression.

Fibroblast growth factor-2 (FGF-2) regulates bone formation. The concept of guided bone regeneration using a resorbable collagen membrane (RCM) is generally accepted in implant dentistry. This study aimed to investigate the bone healing pattern in rat mandibular bone defects in real-time with and without RCM containing FGF-2 (RCM/FGF-2). Critical-size circular bone defects (4.0 mm diameter) were created on both sides of the rat mandibular bone. The defects were randomly divided into the following groups: control, RCM alone, RCM containing low (0.5 μg) or high (2.0 μg) concentration of FGF-2. We performed real-time in vivo micro-computerized tomography scans at the baseline and at 2, 4, and 6 weeks, and measured the volume of newly formed bone (NFB), bone mineral density (BMD) of NFB, and the closure percentage of the NFB area. At 6 weeks, the mandibular specimens were assessed histologically and histomorphometrically to evaluate the area of new bone regeneration. Real-time assessment revealed a significant increase in the volume, BMD, and closure percentage of the NFB area in the RCM/FGF-2-treated groups than that in the control and RCM groups. In the H-FGF-2 group, the volume and BMD of NFB exhibited a significant increase at 6 weeks than that at the baseline. Histological evaluation revealed the presence of osteoblasts, osteocytes, and blood vessels within the NFB. The real-time in vivo experiment demonstrated that RCM/FGF-2 effectively promoted bone regeneration within the critical-size mandibular defects in rats and verified new bone formation starting in the early postoperative phase.

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.