Vascular endothelial growth factors (VEGFs) are vital regulators of angiogenesis that are expressed in response to soluble mediators, such as cytokines and growth factors. Their physiologic functions include blood vessel formation, regulation of vascular permeability, stem cell and monocyte/macrophage recruitment and maintenance of bone homeostasis and repair. In addition, angiogenesis plays a pivotal role in chronic pathologic conditions, such as tumorigenesis, inflammatory immune diseases and bone loss. According to their prevalence, morbidity and mortality, inflammatory diseases affecting periodontal tissues and oral cancer are relevant non-communicable diseases. Whereas oral squamous cell carcinoma (OSCC) is considered one of the most common cancers worldwide, destructive inflammatory periodontal diseases, on the other hand, are amongst the most prevalent chronic inflammatory conditions affecting humans and also represent the main cause of tooth loss in adults. In the recent years, while knowledge regarding the role of VEGF signaling in common oral diseases is expanding, new potential translational applications emerge. In the present narrative review we aim to explore the role of VEGF signaling in oral cancer and destructive periodontal inflammatory diseases, with emphasis in its translational applications as potential biomarkers and therapeutic targets.

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models—Faster R-CNN, YOLO V2, and SSD—using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter’s classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter’s classification criterion. This criterion characterizes the third molar’s position relative to the second molar’s longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications.

Psoriasis and periodontitis are immunologically mediated chronic inflammatory diseases. Epidemiologic evidence has linked both; however, the change of markers in gingival crevicular fluid has been poorly evaluated. To evaluate the levels of IL-17A, IL-22, IL-23, S100A7, S100A8, and S100A9 in gingival crevicular fluid of psoriatic and healthy subjects with and without periodontitis and their relations to psoriasis severity. Cross-sectional study. Sample comprised the following groups: healthy controls without periodontitis or with mild periodontitis (n=21), healthy controls with moderate or severe periodontitis (n=18), individuals with psoriasis without or mild periodontitis (n=11), and individuals with psoriasis and moderate or severe periodontitis (n=32). Levels of IL-17A, IL-22, IL-23, S100A8, and S100A9 were determined by multiplex assay and S100A7 was measured by ELISA. No inter-group differences in the levels of IL-17A, IL-22, IL-23, and S100A7 were found. S100A8 levels were higher in psoriatic patients than controls (p<0.05). S100A8 was positively correlated with psoriasis severity in the group with psoriasis (p<0.05). S100A9 exceeded the detection limits. This pilot study presents a small sample size. The concentrations of S100A8 were highest in psoriatic patients regardless of periodontal health/status. S100A8 was associated with the severity of psoriasis. The concentrations of interleukins and S100A7 were similar in psoriatic patients with or without periodontitis vs. healthy controls.

Apical Lesions of Endodontic Origin (ALEO) are initiated by polymicrobial endodontic canal infection. Porphyromonas gingivalis (Pg) and Porphyromonas endodontalis (Pe) lipopolysaccharides (LPS) can induce a pro-inflammatory macrophage response through their recognition by TLR2 and TLR4. However, polarization responses induced by Pg and/or Pe LPS in macrophages are not fully understood. We aimed to characterize the polarization profiles of macrophages differentiated from THP-1 cells following Pg and/or Pe LPS stimulation from reference strain and clinical isolates. A modified LPS purification protocol was implemented and the electrophoretic LPS profiles were characterized. THP-1 human monocytes differentiated to macrophages were stimulated with Pg and Pe LPS. Polarization profiles were characterized through cell surface markers and secreted cytokines levels after 24 h of stimulation. TLR2 and TLR4 cell surfaces and transcriptional levels were determined after 24 or 2 h of LPS stimulation, respectively. LPS from Pg induced a predominant M1 profile in macrophages evidenced by changes in the expression of the surface marker CD64 and pro-inflammatory cytokine profiles, TNF-α, IL-1β, IL-6, and IL-12. Pe LPS was unable to induce a significant response. TLR2 and TLR4 expressions were neither modified by Pg or Pe LPS. Pg LPS, but not Pe LPS, induced a macrophage M1 Profile.

Atopic dermatitis (AD) is a protease-modulated chronic disorder with heterogenous clinical manifestations which may lead to an imprecise diagnosis. To date, there are no diagnostic protease tests for AD. We explored the gingival crevicular fluid (GCF) protease profile of individuals with moderate/severe AD compared to healthy controls. An exploratory case-control study was conducted. AD patients (n = 23) and controls (n = 21) were enrolled at the International Center for Clinical Studies, Santiago, Chile. Complete dermatological and periodontal evaluations (involving the collection of GCF samples) were made. The levels of 35 proteases were analyzed using a human protease antibody array in matching AD patients (n = 6) and controls (n = 6) with healthy periodontium. The GCF levels of zinc-binding ADAM8, ADAM9, MMP8, Neprilysin/CD10, aspartyl-binding Cathepsin E, serin-binding Protein convertase9, and uPA/Urokinase proteases were lower in moderate/severe AD patients compared to controls (p < 0.05). No inter-group differences in the levels of the other 28 proteases were found. MMP8, Cathepsin E, and ADAM9 were the biomarkers with the highest sensitivity and specificity regarding the detection of AD (p < 0.05). The area under receiver operating characteristic (ROC) curve for MMP8 was 0.83 and MMP8 + ADAMP9 was 0.90, with no significant differences (p = 0.132). A combined model of MMP8, Cathepsin E, and ADAM9 was not considered since it did not converge. Then, levels of MMP8 in GCF were determined using a multiplex bead immunoassay in 23 subjects with AD and 21 healthy subjects. Lower levels of MMP8 in the GCF from the AD group versus healthy group (p = 0.029) were found. This difference remained significant after adjustment by periodontitis (p = 0.042). MMP8 revealed the diagnostic potential to identify AD patients versus healthy controls, (ROC area = 0.672, p < 0.05). In conclusion, differences in the protease profile between AD and control patients were associated with MMP8, Cathepsin E, and ADAM9. Based on the multiplex assay results, MMP8 was lower in AD patients than controls, suggesting that MMP8 may be a diagnostic biomarker candidate.