Epstein-Barr Virus Promotes the Production of Inflammatory Cytokines in Gingival Fibroblasts and RANKL-Induced Osteoclast Differentiation in RAW264.7 Cells.

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.

Resorbable bilayer membrane made of L-lactide-ε-caprolactone in guided bone regeneration: an in vivo experimental study.

PURPOSE: Guided bone regeneration (GBR) is an accepted method in dental practice that can successfully increase the bone volume of the host at sites chosen for implant placement; however, existing GBR membranes exhibit rapid absorption and lack of adequate space maintenance capabilities. We aimed to compare the effectiveness of a newly developed resorbable bilayer membrane composed of poly (L-lactic acid) and poly (-caprolactone) (PLACL) with that of a collagen membrane in a rat GBR model. METHODS: The rat calvaria was used as an experimental model, in which a plastic cylinder was placed. We operated on 40 male Fisher rats and subsequently performed micro-computed tomography and histomorphometric analyses to assess bone regeneration. RESULTS: Significant bone regeneration was observed, which was and similar across all the experimental groups. However, after 24 weeks, the PLACL membrane demonstrated significant resilience, and sporadic partial degradation. This extended preservation of the barrier effect has great potential to facilitate optimal bone regeneration. CONCLUSIONS: The PLACL membrane is a promising alternative to GBR. By providing a durable barrier and supporting bone regeneration over an extended period, this resorbable bilayer membrane could address the limitations of the current membranes. Nevertheless, further studies and clinical trials are warranted to validate the efficacy and safety of The PLACL membrane in humans.

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment.

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.