https://repositorio.ufba.br/handle/ri/9938 Sun, 17 May 2026 06:09:51 GMT 2026-05-17T06:09:51Z https://repositorio.ufba.br/handle/ri/44160 Associação entre obesidade e inflamação periodontal em adultos Carneiro, Daline Oliveira Soares, Johelle de Santana Passos The aim of this study was to investigate the relationship between obesity and periodontal inflammation. To this end, two studies were conducted: a systematic review with meta-analysis to assess whether obesity interferes with the control of clinical periodontal parameters after periodontal treatment, and a cross-sectional study to investigate the association between obesity and periodontal inflammation in users of public health services. The systematic review included 14 articles submitted to methodological quality assessment using the ROBBINS-1 tool, which presented a low to moderate risk of bias. The findings of the qualitative assessment of the studies showed that there was an improvement in clinical periodontal inflammatory parameters in all investigations for obese and non-obese individuals after non-surgical periodontal treatment. The meta-analysis results showed that there were statistically significant differences between obese and non-obese individuals for the percentages of probing depth and clinical attachment level between 4 and 6 mm after three months of periodontal therapy. For the crosssectional study, all the subjects underwent a periodontal examination, had anthropometric measurements taken and answered a questionnaire containing information on their socioeconomic status, health and lifestyle. Excess body weight was defined using the Body Mass Index (BMI) and waist circumference (WC) criteria of the International Diabetes Federation (IDF) and the National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP-III) criteria. Periodontal inflammation was quantified using Periodontal Inflamed Surface Area (PISA) values with a cut-off point of ≥191mm². The measure of association was obtained by Poisson regression analysis with robust variance, adjusted and controlled. The results indicated poor oral conditions among obese individuals and the association between obesity and periodontal inflammation was statistically significant for the obesity criterion according to NCEP-ATP-III (PR: 1.22; 95%CI: 1.04-1.41, p=0.01), after adjusting for gender, age, schooling and hypertension. The results of this investigation indicate the influence of obesity on the control of periodontal inflammation. Universidade Federal da Bahia Tese Tue, 15 Oct 2024 00:00:00 GMT https://repositorio.ufba.br/handle/ri/44160 2024-10-15T00:00:00Z https://repositorio.ufba.br/handle/ri/43261 Adenocarcinoma polimorfo de glândula salivar: caracterização histopatológica com investigação do gene PRKD1 e proteínas relacionadas à via de sinalização Hedgehog Santana, Dandara Andrade de Santos, Jean Nunes dos Polymorphous adenocarcinoma (PAC) is a rare malignant epithelial neoplasm that principally affects the minor salivary glands. It typically shows indolent growth, a generally favorable prognosis, and broad morphological heterogeneity, factors that often complicate its differential diagnosis from other salivary gland tumors. This thesis aimed to investigate the clinical, histopathological, immunohistochemical, and molecular aspects of PAC. A total of 18 cases were analyzed for clinical and histopathological evaluation, of which 15 were also subjected to molecular analysis of the PRKD1 gene and the expression of proteins in the Hedgehog (HH) signaling pathway. The lesions predominated in women (76.5%), with a mean age of 59.6 years, and the palate was the most frequently affected site (55.6%). Histologically, architectural diversity was observed with the coexistence of different growth patterns, particularly lobular, tubular, and cribriform. The cribriform subtype was associated with a higher frequency of invasion, including perineural invasion and infiltration of mucous acini. Among the 15 cases initially included in the PRKD1 gene analysis, eight met the quality requirements for PCR amplification and sequencing. The PRKD1 c.2130A>C/T (p.p.Glu710Asp) hotspot mutation was identified in four of them (50%). A novel variant at codon 704 (p.H704Y) was also detected in a case that simultaneously carried the p.Glu710Asp mutation. Immunohistochemistry consistently demonstrated expression of HH pathway proteins, suggesting their involvement in PAC tumor morphogenesis. A significant positive correlation was observed between PRKD1 expression and the HH components IHH, SMO, and GLI-1, indicating a possible interaction independent of SHH activation. Taken together, these findings reinforce the morphological complexity of PAC, confirm PRKD1 p.Glu710Asp as a relevant diagnostic marker, and demonstrate the involvement of the HH signaling pathway in tumor morphogenesis. By deepening the understanding of PAC biology, this study opens new avenues for the diagnosis and management of this rare neoplasm. UNIVERSIDADE FEDERAL DA BAHIA Tese Wed, 01 Jan 2025 00:00:00 GMT https://repositorio.ufba.br/handle/ri/43261 2025-01-01T00:00:00Z https://repositorio.ufba.br/handle/ri/42392 Da predição à precisão: aprendizado de máquina e mapeamento dosimétrico para mucosite oral em câncer de cabeça e pescoço Fontes, Elisa Kauark Ramalho, Luciana Maria Pedreira Oral mucositis (OM) is a common side effect of head and neck radiotherapy (RT), resulting from a complex interplay of multiple risk factors. Strong evidence identifies RT dose as a key contributor to OM development. A detailed dose-response analysis of individual organs at risk (OARs) may help establish dose constraints to improve patient outcomes. Additionally, machine learning (ML) offers a promising approach by integrating both dosimetric and non-dosimetric factors for a more comprehensive risk assessment. This study aimed to assess OM risk prediction using ML and investigate the impact of dose distribution on OM development, identifying potential OARs related to OM. In the first study, an ML performance was tested to predict MO risk using a cross-validation strategy based on two dataset versions: one with all features and another with feature selection. Comparative analysis showed no relevant results with the full dataset, while feature selection improved performance, with the K-Nearest Neighbors algorithm achieving 64% accuracy, 58% sensitivity, and 68% specificity. The second study involved a dosimetric analysis of 57 head and neck cancer patients. Potential OARs for OM were identified, and dose-volume histograms were generated for OM onset and the final RT session, comparing Dmean and Dmax with OM incidence and distribution. Significant dosimetric differences were observed across all OARs except the upper lip. A Dmean cutoff of 48.4 Gy for the oral tongue was identified (92% accuracy, 96% specificity, 78% sensitivity). Additionally, each incremental 1 Gy increase in dose to the OARs was associated with a 1% higher risk of OM. These findings highlight the need for standardized OAR delineation to optimize RT planning and reduce OM incidence. Universidade Federal da Bahia Tese Fri, 09 May 2025 00:00:00 GMT https://repositorio.ufba.br/handle/ri/42392 2025-05-09T00:00:00Z https://repositorio.ufba.br/handle/ri/36801 Segmentação automática da placa dentária com base em aprendizado profundo Andrade, Katia Montanha de Cury, Patrícia Ramos Dental plaque biofilm is the main etiologic factor for dental caries and periodontal diseases. However, its visualization is difficult, and the use of disclosing agents is a laborious and unpleasant process. Therefore, plaque identification through an automatic process is important. The present research aimed to apply a Convolution Neural Network (CNN) model to segment dental plaque in intraoral digital photographs without the use of disclosing agents. The dataset used to evaluate the proposed system included 480 intraoral photos including lateral and frontal views of permanent and deciduous dentition, contemplating the presence and absence of orthodontic appliances. The photographs were divided into three subsets: 360 images were used for training; 60 photos were used for validation; and 60 photos were used for testing. All images have been labeled by a specialist dentist with over 30 years of experience. The U-Net architecture was used for image segmentation. Metrics of accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model in each dental unit. The trained model obtained 91.8% accuracy, 67.2% sensitivity, 94.4% specificity and 60.6% F1 score. These metrics were chosen for their easy interpretability (accuracy), their use in health areas (sensitivity and specificity) and for weighting unbalanced classes (F1 score). A higher plaque fraction was observed in the lateral view images, as well as in the images with orthodontic appliances. These images also exhibited higher F1 scores (61.7% and 61.5%, respectively) and specificity (94.5% and 95.6%, respectively). In conclusion, a deep learning method for segmenting dental biofilm in permanent and deciduous dentitions is feasible and could be a visual aid tool to improve oral hygiene and patient control of dental plaque. UNIVERSIDADE FEDERAL DA BAHIA Tese Wed, 04 May 2022 00:00:00 GMT https://repositorio.ufba.br/handle/ri/36801 2022-05-04T00:00:00Z