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Objective: The objective of our study was to re-evaluate periapical lesions, including radicular cysts (RCs) and periapical granulomas (PGs) for locations, histopathological features, and degree of fibrosis in relation to the inflammatory response. In addition, we examined the presence of Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) since both are widely recognized pathogens in periodontal infections.
Methods: We re-evaluated samples of RCs and PGs (n = 728) and collected data for analyses by IBM's SPSS Statistics. Among these samples, we stained 93 samples to determine the immunoexpression of Pg and Fn. For immunostaining, we used Gingipain R1 antibody for Pg and Rabbit anti-Fn antibody for Fn.
Results: Fibrosis is associated with mild inflammation. We found a significant positive correlation between Pg and Fn. Thus, these pathogens are likely to occur together in periapical inflammatory lesions. We additionally noted that these periodontopathic pathogens are more likely to be present in RCs than in PGs.
Conclusions: Asymptomatic radiologically diagnosed periapical lesions may not necessarily need root canal retreatment in healthy patients since these lesions may represent scar tissue rather than active apical periodontitis. Clinical and radiological follow-up is still needed. Yet, periapical lesions, especially cysts, may contain dystopic periodontopathic pathogens, and Pg and Fn often occur together in periapical lesions.
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http://dx.doi.org/10.1002/cre2.70098 | DOI Listing |
J Endod
September 2025
University of Texas Health at San Antonio, Department of Endodontics; San Antonio, Texas, USA. Electronic address:
Introduction: Although several elegant studies have reported apical periodontitis prevalence in different populations, far less is known about its associated bone loss and its correlation to sex and age. Thus, this study investigated the impact of sex and age differences on the severity of apical periodontitis (AP) using cone-beam computed tomography (CBCT) and a volumetric periapical index (CBCT-PAI).
Material And Methods: CBCT scans of 401 patients (1,027 teeth) were analyzed by calibrated examiners in CBCTPAI.
Zhonghua Kou Qiang Yi Xue Za Zhi
September 2025
State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
Cureus
July 2025
Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, IND.
Introduction: The present study evaluated the clinical and patient-centered outcomes of nonsurgical retreatment of endodontically treated teeth. The primary goal was to assess the success rate, defined by clinical and radiographic criteria, while the secondary objectives focused on patient-reported quality-of-life outcomes using a validated oral health-related quality-of-life (OHRQoL) questionnaire.
Materials And Methods: This prospective cohort study was conducted from January 2024 to April 2025 at the Department of Conservative Dentistry and Endodontics and included 100 systemically healthy adults (aged 18-60 years) requiring retreatment of endodontically treated teeth.
Diagnostics (Basel)
August 2025
Computer Engineering Department, Engineering and Architecture Faculty, Eskisehir Osmangazi University, Eskisehir 26040, Türkiye.
: Clinicians routinely rely on periapical radiographs to identify root-end disease, but interpretation errors and inconsistent readings compromise diagnostic accuracy. We, therefore, developed an explainable, multimodal AI framework that (i) fuses two data modalities, deep CNN embeddings and radiomic texture descriptors that are extracted only from lesion-relevant pixels selected by Grad-CAM, and (ii) makes every prediction transparent through dual-layer explainability (pixel-level Grad-CAM heatmaps + feature-level SHAP values). : A dataset of 2285 periapical radiographs was processed using six CNN architectures (EfficientNet-B1/B4/V2M/V2S, ResNet-50, Xception).
View Article and Find Full Text PDFDent Med Probl
August 2025
Department of Pediatric Dentistry, Tekirdağ Namık Kemal University, Turkey.
Background: Artificial intelligence (AI) systems have the potential to revolutionize the fields of medicine and dentistry by identifying solutions for managing multiple clinical problems. This greatly facilitates the tasks of physicians. Bibliometric studies not only provide insight into the history of a particular topic, but also help to determine how the work evolves over time, and to identify interesting new research.
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