Quantification and Localization of Mast Cells in Periapical Lesions

Background: Periapical lesions occur in response to chronic irritation in periapical tissue, generally resulting from an infected root canal. Specific etiological agents of induction, participating cell population and growth factors associated with maintenance and resolution of periapical lesions are incompletely understood. Among the cells found in periapical lesions, mast cells have been implicated in the inflammatory mechanism. Aim: Quantifications and the possible role played by mast cells in the periapical granuloma and radicular cyst. Hence, this study is to emphasize the presence (localization) and quantification of mast cells in periapical granuloma and radicular cyst. Materials and Methods: A total of 30 cases and out of which 15 of periapical granuloma and 15 radicular cyst, each along with the case details from the previously diagnosed cases in the department of oral pathology were selected for the study. The gender distribution showed male 8 (53.3%) and females 7 (46.7%) in periapical granuloma cases and male 10 (66.7%) and females 5 (33.3%) in radicular cyst cases. The statistical analysis used was unpaired t‑test.Results: Mean mast cell count in periapical granuloma subepithelial and deeper connective tissue, was 12.40 (0.99%) and 7.13 (0.83%), respectively. The mean mast cell counts in subepithelial and deeper connective tissue of radicular cyst were 17.64 (1.59%) and 12.06 (1.33%) respectively, which was statistically significant. No statistical significant difference was noted among males and females.Conclusion: Mast cells were more in number in radicular cyst. Based on the concept that mast cells play a critical role in the induction of inflammation, it is logical to use therapeutic agents to alter mast cell function and secretion, to thwart inflammation at its earliest phases. These findings may suggest the possible role of mast cells in the pathogenesis of periapical lesions. Keywords: Mast cells, Periapical granuloma, Radicular cyst, Toluidine blu

Building automation pipeline for diagnostic classification of sporadic odontogenic keratocysts and non-keratocysts using whole-slide images

The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy