The spectrum of indications for the use of membranes and scaffolds in the field of oral and maxillofacial surgery includes, amongst others, guided bone regeneration (GBR). Currently available membrane systems face certain disadvantages such as difficult clinical handling, inconsistent degradation, undirected cell growth and a lack of stability that often complicate their application. Therefore, new membranes which can overcome these issues are of great interest in this field. In this pilot study, we investigated polycaprolactone (PCL) scaffolds intended to enhance oral wound healing by means of melt electrospinning writing (MEW), which allowed for three-dimensional (3D) printing of micron scale fibers and very exact fiber placement. A singular set of box-shaped scaffolds of different sizes consisting of medical-grade PCL was examined and the scaffolds’ morphology was evaluated via scanning electron microscopy (SEM). Each prototype sample with box sizes of 225 μm, 300 μm, 375 μm, 450 μm and 500 μm was assessed for cytotoxicity and cell growth by seeding each scaffold with human osteoblast-like cell line MG63. All scaffolds demonstrated good cytocompatibility according to cell viability, protein concentration, and cell number. SEM analysis revealed an exact fiber placement of the MEW scaffolds and the growth of viable MG63 cells on them. For the examined box-shaped scaffolds with pore sizes between 225 μm and 500 μm, a preferred box size for initial osteoblast attachment could not be found. These well-defined 3D scaffolds consisting of medical-grade materials optimized for cell attachment and cell growth hold the key to a promising new approach in GBR in oral and maxillofacial surgery.

A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.

Head and neck squamous cell carcinoma (HNSCC) is known to overexpress a variety of receptor tyrosine kinases, such as the HGF receptor Met. Like other malignancies, HNSCC involves a mutual interaction between the tumor cells and surrounding tissues and cells. We hypothesized that activation of HGF/Met signaling in HNSCC influences glucose metabolism and therefore substantially changes the tumor microenvironment. To determine the effect of HGF, we submitted three established HNSCC cell lines to mRNA sequencing. Dynamic changes in glucose metabolism were measured in real time by an extracellular flux analyzer. As expected, the cell lines exhibited different levels of Met and responded differently to HGF stimulation. As confirmed by mRNA sequencing, the level of Met expression was associated with the number of upregulated HGF-dependent genes. Overall, Met stimulation by HGF leads to increased glycolysis, presumably mediated by higher expression of three key enzymes of glycolysis. These effects appear to be stronger in Met^high-expressing HNSCC cells. Collectively, our data support the hypothesized role of HGF/Met signaling in metabolic reprogramming of HNSCC.

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease characterized by a tumor microenvironment (TME) that overexpresses vascular endothelial growth factor receptor (VEGFR) and fibroblast growth factor receptor (FGFR), which can lead to neovascularization, tumor growth and metastasis. Therapeutic strategies inhibiting these signaling pathways might lead to innovative HNSCC treatments. Five HNSCC cell lines were characterized based on VEGFR1-3 and FGFR1-4 expression by sqRT-PCR and treated with three different tyrosine kinase inhibitors (TKIs) (nintedanib, dovitinib and pazopanib), all of which are effective against VEGFR and FGFR family members. Crystal violet assays were performed to analyze the effect of the treatments on cell growth (viability). Additionally, VEGFR1-3 and FGFR1-4 expression data were retrieved from The Cancer Genome Atlas (TCGA), and statistical analyses were performed to investigate the receptor expression level in the different cell lines and the efficacy of the single-agent treatments. A correlation analysis was performed to quantify the degree of relationship between receptor expression and drug efficacy. With the exception of VEGFR2, the targeted receptors were expressed at different levels in all of the cell lines. The cell lines exhibited concentration-dependent responses with cell line-specific differences toward two of the three TKIs (nintedanib and dovitinib). Notably, all of the cell lines were resistant to pazopanib. TKIs have potential as therapeutic agents for HNSCC. Cell line-specific differences were observed in our in vitro experiments. The observed pazopanib resistance could be explained by receptor expression. Further investigation is required to determine TKI efficacy in HNSCC.

The enzyme pyruvate kinase M2 (PKM2) plays a major role in the switch of tumor cells from oxidative phosphorylation to aerobic glycolysis, one of the hallmarks of cancer. Different allosteric inhibitors or activators and several posttranslational modifications regulate its activity. Head and neck squamous cell carcinoma (HNSCC) is a common disease with a high rate of recurrence. To find out more about PKM2 and its modulation in HNSCC, we examined a panel of HNSCC cells using real-time cell metabolic analysis and Western blotting with an emphasis on phosphorylation variant Tyr105 and two reagents known to impair PKM2 activity. Our results show that in HNSCC, PKM2 is commonly phosphorylated at Tyrosine 105. Its levels depended on tyrosine kinase activity, emphasizing the importance of growth factors such as EGF (epidermal growth factor) on HNSCC metabolism. Furthermore, its correlation with the expression of CD44 indicates a role in cancer stemness. Cells generally reacted with higher glycolysis to PKM2 activator DASA-58 and lower glycolysis to PKM2 inhibitor Compound 3k, but some were more susceptible to activation and others to inhibition. Our findings emphasize the need to further investigate the role of PKM2 in HNSCC, as it could aid understanding and treatment of the disease.

Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen’s kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100% for InceptionV3) but low sensitivity (highest sensitivity 42.86% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1–4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74% to 95.02%, whereas sensitivity ranged from 14.14% to 59.60%. Cohen’s kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen’s kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14% to 95.24%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.