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Background In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of dental caries (DC) using DL algorithms from 2D radiographs. Materials and Methods This comprehensive umbrella review adhered to the “Reporting guideline for overviews of reviews of healthcare interventions” (PRIOR). Specific keywords were generated to assess the accuracy of AI and DL algorithms in detecting DC from radiographical images. To ensure the highest quality of research, thorough searches were performed on PubMed/Medline, Web of Science, Scopus, and Embase. Additionally, bias in the selected articles was rigorously assessed using the Joanna Briggs Institute (JBI) tool. Results In this umbrella review, seven systematic reviews (SRs) were assessed from a total of 77 studies included. Various DL algorithms were used across these studies, with conventional neural networks and other techniques being the predominant methods for detecting DC. The SRs included in the study examined 24 original articles that used 2D radiographical images for caries detection. Accuracy rates varied between 0.733 and 0.986 across datasets ranging in size from 15 to 2,500 images. Conclusion The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes.

This systematic review aimed to evaluate existing randomized controlled trials (RCT) and cohort studies on the efficacy of mouthwashes in reducing SARS-CoV-2 viral loads in human saliva.Searches with pertinent search terms were conducted in PubMed, MEDLINE, Scopus, and Web of Science databases for relevant records published up to Oct 15, 2022. Google Scholar and ProQuest were searched for grey literature. Manual searches were conducted as well for any pertinent articles. The protocol was prospectively registered at PROSPERO (CRD42022324894). Eligible studies were critically appraised for risk of bias and quality of evidence to assess the efficacy of mouthwash in reducing the SARS-CoV-2 viral load in human saliva.Eleven studies were included. The effect on viral load using various types of mouthwash was observed, including chlorhexidine (CHX), povidone-iodine (PI), cetylpyridinium chloride (CPC), hydrogen peroxide (HP), ß-cyclodextrin-citrox mouthwash (CDCM), and Hypochlorous acid (HCIO). Eight articles discussed CHX use. Five were found to be significant and three did not show any significant decrease in viral loads. Eight studies reviewed the use of PI, with five articles identifying a significant decrease in viral load, and three not showing a significant decrease in viral load. HP was reviewed in four studies, two studies identified significant viral load reductions, and two did not. CPC was reviewed in four studies, two of which identified significant viral load reductions, and two did not. CDCM was reviewed in one article which found a significant decrease in viral load reduction. Also, HCIO which was evaluated in one study indicated no significant difference in CT value.The current systematic review indicates that based on these eleven studies, mouthwashes are effective at reducing the SARS-CoV-2 viral load in human saliva. However, further studies should be performed on larger populations with different mouthwashes. The overall quality of evidence was high.

Introduction: This study aimed to compare the subcutaneous tissue responses to MTAD (mixture of a tetracycline isomer, an acid, and a detergent), 17% EDTA, and 2.6% NaOCl. Materials and Methods: Thirty-six Wistar albino rats were used for this study. Test solutions were injected subcutaneously into predetermined areas on the animal dorsum. The rats were then randomly divided into three groups of twelve each and sacrificed at 2 hours, 2 days, and 2 weeks. The severity of inflammation induced by each irrigant at different time intervals was assessed histologically. The data were analyzed using Kruskal-Wallis and Friedman tests. Results: The difference in severity of inflammatory reactions induced by tested irrigants at the different time intervals was statically significant (P0.05). Subcutaneous tissue responses to MTAD were not different from those observed in 17% EDTA specimens at 2-hour and 2-day intervals (P>0.05). Conclusion: Under the conditions of this study, MTAD has the same toxicity as 2.6% NaOCl.

A wideband stacked magnetoelectric (ME) dipole antenna with a microstrip line aperture-coupled excitation (MLACE) consisting of a bow-tie-shaped slot and a mounted high- permittivity substrate as a dielectric resonator (DR) is proposed. The bow-tie-shaped slot was etched on the ground plane to simultaneously excite the rectangular-shaped magnetic dipole and E-shaped electric dipole. The antenna’s operating impedance bandwidth and realized gain can be improved by almost 400 MHz and 1 dB with the appropriate stacking arrangement of the high-permittivity substrate on top of the electric dipole. It was found that the cooperating of the stacked high-permittivity substrate with ME dipole yielded an operating frequency band of as wide as 58% (2.35–4.27 GHz) and a peak gain of 7.8 dB. This study employs a 1.28 mm thick Rogers RO3010 substrate with a permittivity of 10.2 as a resonator. The fabricated antenna has a stable unidirectional radiation pattern suitable for 5G applications.