WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Tooth Decay Promotes Senescence in Dental Pulp Stem Cells, Modifying Their Biological and Proteomic Profiles(Wiley, 2026) Durukan, Sebahat Melike; Tez, Banu Cicek; Ozcan, Servet; Simsek, Ahmet; Al-Sammarrie, Sura Hilal Ahmed; Gunaydin, Zeynep; Acar, Mustafa BurakDental caries is a prevalent oral health problem that significantly reduces an individual's quality of life; although, it can be effectively managed through restorative treatments. Even in cases where the caries does not reach the pulp, released microbial products from the lesion can still penetrate the pulp chamber, potentially inducing stress on pulp cells. In this study, we conducted a comparative analysis of the biological and proteomic profiles of dental pulp stem cells (DPSCs) isolated from clinically asymptomatic teeth with dentinal caries that had not reached the pulp and isolated from healthy teeth. Following biological evaluations, we examined proteomes of these DPSCs by conducting a shotgun proteomics approach. Our findings show that DPSCs from decayed teeth exhibit a significantly higher proportion of senescent cells. Proteomic profiling revealed upregulation of inflammatory signaling, extracellular matrix remodeling, and senescence-associated secretory phenotype (SASP) related proteins. Additionally, we observed an upregulation in the expression of proteins associated with extracellular matrix (ECM) remodeling and components of the SASP, which are hallmarks of the senescence process. The study reveals that DPSCs can be affected by stress from carious lesions, even when the pulp appears clinically intact. Senescence and inflammatory response in these affected cells may have deleterious effects on other tissues within the organism. Consequently, restorative treatments should consider targeting not only the decayed tissue but also the senescent cells within the pulp that may have been affected by the stress induced by caries.Article Citation - WoS: 2Citation - Scopus: 4Deep-Learning AI-Model for Predicting Dental Plaque in the Young Permanent Teeth of Children Aged 8-13 Years(MDPI, 2025-04-07) Tez, Banu Cicek; Guzel, Yasin; Eliacik, Bahar Basak Kiziltan; Aydin, Zafer; Kızıltan Eliaçık, Bahar BaşakBackground/Objectives: Dental plaque is a significant contributor to various prevalent oral health conditions, including caries, gingivitis, and periodontitis. Consequently, its detection and management are of paramount importance for maintaining oral health. Manual plaque assessment is time-consuming, error-prone, and particularly challenging in uncooperative pediatric patients. These limitations have encouraged researchers to seek faster, more reliable methods. Accordingly, this study aims to develop a deep learning model for detecting and segmenting plaque in young permanent teeth and to evaluate its diagnostic precision. Methods: The dataset comprises 506 dental images from 31 patients aged between 8 and 13 years. Six state-of-the-art models were trained and evaluated using this dataset. The U-Net Transformer model, which yielded the best performance, was further compared against three experienced pediatric dentists for clinical feasibility using 35 randomly selected images from the test set. The clinical trial was registered on under the ID NCT06603233 (1 June 2023). Results: The Intersection over Union (IoU) score of the U-Net Transformer on the test set was measured as 0.7845, and the p-values obtained from the three t-tests conducted for comparison with dentists were found to be below 0.05. Compared with three experienced pediatric dentists, the deep learning model exhibited clinically superior performance in the detection and segmentation of dental plaque in young permanent teeth. Conclusions: This finding highlights the potential of AI-driven technologies in enhancing the accuracy and reliability of dental plaque detection and segmentation in pediatric dentistry.