Impact of cooling on shaping ability of thermally treated files in canal models with double curvature.

Background: This study compared the ability of thermally treated files in shaping simulated canals with double curvature. Fifty-six canals were enlarged to a final size of 25 with ProTaper Next (PTN) or ZenFlex (ZF). Materials: Half of the samples were shaped with cooled files (n = 14 each). The amount of removed resin was measured and canal deviation was determined at eight levels. Shaping time and maximum shaping torque values were also recorded. Data were statistically analyzed using analysis of variance and LSD, Kruskal-Wallis, and chi-square tests at a 0.05 significance level. Results: Compared to PTN and cooled PTN, ZF and cooled ZF required lesser time to shape the canals. The maximum torques were found comparable between the groups. All the groups generated negligible deviations at every canal level evaluated and maintained the canal geometry. Although not significant, the cooled PTN and ZF files exhibited lesser canal deviations than their counterparts. Conclusion: All groups demonstrated similar shaping ability whilst maintaining the original curvature of the canal in simulated canals with double curvature. However, ZF groups were able to shape the canals faster than PTN groups. There was a trend that cooled files made lesser canal deviations compared to their counterparts.

TLR9 Signaling Is Required for the Porphyromonas gingivalis-Induced Activation of IL-10-Expressing B Cells.

Immune cell pattern-recognition receptors such as Toll-like receptors (TLRs) play important roles in the regulation of host responses to periodontal pathogens. Our previous studies have demonstrated that immune regulatory B cells were activated by TLRs and alleviated periodontitis inflammation and bone loss. The purpose of this study is to determine the role of TLR9 signaling in the activation and IL-10 production of the primed-immune B cells in vitro. Wild-type (WT) and TLR9 knockout (TLR9KO) mice (C57BL/6 background, n = 5) were pre-immunized intraperitoneally with 1 × 108 formalin-fixed P. gingivalis and boosted once with 1 × 107 formalin-fixed P. gingivalis. Isolated splenocytes and purified B cells from each mouse were cultured with 1 × 108 formalin-fixed P. gingivalis for 48 h. Immunocytochemistry was performed to detect CD45+ IL-10+ cells. Levels of IL-10 expression and secretion in splenocytes and B cells were detected using qRT-PCR and ELISA, respectively. After stimulation with fixed P. gingivalis, the percentage of CD45+ IL-10+ B cells and the level of IL-10 expression were significantly increased (p < 0.01) in splenocytes and purified B cells isolated from WT mice. However, these changes were not observed in splenocytes and purified B cells from TLR9KO mice when the cells were treated with fixed P. gingivalis. The percentage of CD45+ IL-10+ B cells was significantly reduced in splenocytes and purified B cells from TLR9KO mice compared to those from WT mice when challenged with P. gingivalis. IL-10 expression in B cells from TLR9KO mice was significantly decreased compared to those from WT mice at both the mRNA and protein levels. Additionally, P. gingivalis-induced up-regulation of TNF-α mRNA expressions were consistently observed in B cells from both WT and TLR9KO mice. P. gingivalis-induced B10 activation and IL-10 production during adaptive responses by primed B cells requires TLR9 signaling and can be achieved independent of T-cell help.

Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review.

Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

INTRODUCTION: Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. METHODS: Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. RESULTS: Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm3, there was 80% probability of a cyst. If volume was <247 mm3 and root displacement was present, cyst probability was 60% (78% accuracy). CONCLUSIONS: The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings.