Are Panoramic Images a Good Tool to Detect Calcified Carotid Atheroma? A Systematic Review.

To investigate the reliability of panoramic dental images to detect calcified carotid atheroma, electronic databases (PubMed, IEEE/Xplore and Embase) were searched. Outcomes included cerebrovascular disease events, cardiovascular disease events, patient previous diseases, and combined endpoints. Risk of bias was evaluated using the Newcastle-Ottawa Scale. Hence, 15 studies were selected from 507 potential manuscripts. Five studies had a low risk of bias, while the remaining nine studies were found to have a moderate risk. Heterogeneous results were obtained but showed that patients with risk factors, such as obesity, diabetes mellitus, hypertension, and smoking, and with calcified carotid atheroma on panoramic images, have a higher prevalence than healthy patients. The evidence in the literature was found to be equivocal. However, the findings of this systematic review exhibit that panoramic radiographs can be used for dental diagnosis and treatment planning, as well as to detect calcified carotid artery atheroma.

Spike Protein Subunits of SARS-CoV-2 Alter Mitochondrial Metabolism in Human Pulmonary Microvascular Endothelial Cells: Involvement of Factor Xa.

Background: Mitochondria have been involved in host defense upon viral infections. Factor Xa (FXa), a coagulating factor, may also have influence on mitochondrial functionalities. The aim was to analyze if in human pulmonary microvascular endothelial cells (HPMEC), the SARS-CoV-2 (COVID-19) spike protein subunits, S1 and S2 (S1+S2), could alter mitochondrial metabolism and what is the role of FXA. Methods: HPMEC were incubated with and without recombinants S1+S2 (10 nmol/L each). Results: In control conditions, S1+S2 failed to modify FXa expression. However, in LPS (1 µg/mL)-incubated HPMEC, S1+S2 significantly increased FXa production. LPS tended to reduce mitochondrial membrane potential with respect to control, but in higher and significant degree, it was reduced when S1+S2 were present. LPS did not significantly modify cytochrome c oxidase activity as compared with control. Addition of S1+S2 spike subunits to LPS-incubated HPMEC significantly increased cytochrome c oxidase activity with respect to control. Lactate dehydrogenase activity was also increased by S1+S2 with respect to control and LPS alone. Protein expression level of uncoupled protein-2 (UCP-2) was markedly expressed when S1+S2 were added together to LPS. Rivaroxaban (50 nmol/L), a specific FXa inhibitor, significantly reduced all the above-mentioned alterations induced by S1+S2 including UCP-2 expression. Conclusions: In HPMEC undergoing to preinflammatory condition, COVID-19 S1+S2 spike subunits promoted alterations in mitochondria metabolism suggesting a shift from aerobic towards anaerobic metabolism that was accompanied of high FXa production. Rivaroxaban prevented all the mitochondrial metabolic changes mediated by the present COVID-19 S1 and S2 spike subunits suggesting the involvement of endogenous FXa.

Mitochondrial mitophagy protection combining rivaroxaban and aspirin in high glucose-exposed human coronary artery endothelial cell. An in vitro study.

PURPOSE: Combination of Rivaroxaban plus Aspirin improved cardiovascular outcome in patients with stable cardiovascular disease. The aim was to determine if Rivaroxaban and acetylsalicylic acid alone or in combination may protect mitochondrial mitophagy in human coronary artery endothelial cells (HCAEC) exposed to D-glucose. METHODS: HCAEC were incubated under different conditions: 5 mmol/L glucose D-glucose (control), 30 mmol/L D-Glucose with and without 50 nmol/L Rivaroxaban (Rivaroxaban), 0.33 mmol/L ASA (ASA) or Rivaroxaban (12.5 nmol/L)+ASA (0.33 mmol/L; (Riva+ASA). RESULTS: HCAEC incubated with D-glucose showed an increased Factor Xa expression. The mitochondrial content of Pink-1 and Parkin were significantly reduced in high glucose-incubated HCAEC compared to control. Rivaroxaban+ASA significantly increased the mitochondrial content of Pink-1 and Parkin, and the mitochondrial membrane potential compared to D-Glucose group. Both ASA alone and Riva+ASA reduced reactive oxygen species (ROS) and tissue factor production induced by high glucose exposure. CONCLUSION: Under high glucose condition combining Rivaroxaban+ASA increased the mitochondrial content of Pink-1 and Parkin, restored mitochondria membrane potential and reduced ROS and tissue factor expression in HCAEC. It suggests potential effects induced by dual use of Rivaroxaban and ASA on the coronary endothelium subjected to high glucose condition.

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images.

Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth.

Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages.

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review.

Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.