Nonsurgical therapy reduces the probing depth, clinical attachment level, and clinical inflammation findings. This healing tendency was observed in the AZT group despite the baseline plaque scores. Therefore, AZT might be active against the bacteria in dental biofilms.
Background: Helicobacter pylori (H. Pylori) is a Gram (-), microaerophilic bacteria and the etiological factor of chronic active gastritis and peptic ulcer. Some studies indicated that this bacterium found at oral cavity which is a potential reservoir for stomach. Several studies showed that H. pylori may found in saliva and subgingival plaque of chronic periodontitis patients. However, there is no data related to aggressive periodontitis patients. In this study, we aimed to determine the prevalence of H. pylori in subgingival plaque samples of chronic, aggressive periodontitis and gingivitis patients and to increase the awareness of the patients for gastric problems.
Materials and Methods:This study included 155 patients (61 with gingivitis, 60 with chronic periodontitis, and 34 with aggressive periodontitis) who did not have gastric disease symptom and did not use antibiotics in the last 3 months. The subgingival plaque samples were taken using sterile paper points. The existence of H. pylori, A. actinomycetemcomitans, and P. gingivalis was detected by RT-PCR.Results: H. pylori was not detected in any groups at the end of microbiological analysis. However, a high occurrence of A. actinomycetemcomitans (97.1%) and P. gingivalis (100%) was observed in the aggressive periodontitis group. However, A. actinomycetemcomitans and P. gingivalis were found in 30% and 21.7% of patients, respectively, with chronic periodontitis. A. actinomycetemcomitans and P. gingivalis were found in 24.6% of patients in the gingivitis group.Conclusions: H. pylori were not detected in samples, indicating that subgingival plaque may not be a primary reservoir for this bacterium.
The pathophysiology of multisystem inflammatory syndrome in children (MIS-C) and associated oral symptoms have not been clarified yet. The aim of the present study was to compare the oral health status of children with MIS-C-associated Coronavirus disease 2019 (COVID-19) and COVID-19. A total of 54 children with SARS-CoV-2 infection, 23 with MIS-C-associated COVID-19 and 31 with asymptomatic, mild, and moderate COVID-19 were recruited for the present cross-sectional study. Sociodemographic variables, medical examinations, oral hygiene habits, and extraoral and intraoral findings (DMFT/dmft index, OHI scores, and oral mucosal changes) were recorded. The t-test for independent samples and the Mann-Whitney U test were used (p < 0.05). MIS-C was found to be associated with chapped lips (all patients) and oral mucosal changes, including erythema, white lesion, strawberry tongue, and swelling of the gingiva as compared to the COVID-19 group (frequency of more than one mucosal change: 100% vs. 35%) (p < 0.001). Children with MIS-C presented higher DMFT/dmft scores (DMFT/dmft 5.52 ± 3.16 for the MIS-C group vs. 2.26 ± 1.80 for the COVID-19 group) (p < 0.01). Elevated OHI scores were also associated with MIS-C (mean ± SD: 3.06 ± 1.02 (MIS-C) vs. 2.41 ± 0.97 (COVID-19) (p < 0.05). Oral manifestations, mainly strawberry and erythematous tongue, were characteristic features of MIS-C. Prevalence of oral/dental symptoms was elevated in children with MIS-C when compared to COVID-19. Therefore, dental professionals should be aware of the oral manifestations associated with MIS-C, which may have high mortality and morbidity rates.
The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.
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