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DENİZ, ŞULE TUĞBA

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ŞULE TUĞBA
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End date: 2019 × Start date: 2019 × Type: Article ×

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    Antimicrobial Activity of Ozone against Pathogenic Oral Microorganisms on Different Denture Base Resins
    (2019-06-07T00:00:00Z) Hayran, Yeliz; DENİZ, ŞULE TUĞBA; Aydin, Ali; DENİZ, ŞULE TUĞBA
    The present study aimed to evaluate the antimicrobial effect of gaseous ozone against specific oral pathogens on denture base resins. 1080 round samples were prepared (10mm-diameter, 2mm-thickness). Candida albicans, Streptococcus mutans, Streptococcus gordonii, and Aggregatibacter actinomycetemcomitans, polyamide-Deflex, heat-cured polymethyl-methacrylate (PMMA)-QC-20, and cold-cured-PMMA-Meliodent. The doses and durations: 25, 50 and 100 mu g/ml, 5, 10, 20, 30 minutes. For Cell viability (CV) MTT was used. 100 mu g/ml was most effective dose for C. albicans, S .gordonii, and A. actinomycetemcomitans were in heat-cured-PMMA and polyamide for S.mutans. For polyamide, lowest CV was 43% in S.mutans and A.actinomycetemcomitans. CV of heat-cure and cold-cure PMMA were 31% and 32% in S.gordonii, respectively. CV was similar for all resins and durations in S.mutans and A.actinomycetemcomitans and for polyamide for C.albicans and for heat-cure PMMA for S.gordonii. 30-min ozone application killed 80% of all microorganisms in all resins except for C.albicans in polyamide (65% cell death) and cold-cure PMMA (57% cell death). Optimal dose/duration combination was 100 mu g/ml-10 min. Gaseous ozone can be considered as an effective cleansing agent for denture base resins.
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    The accuracy of the prediction models for surface roughness and micro hardness of denture teeth
    (2019-12-01T00:00:00Z) DENİZ, ŞULE TUĞBA; Ozkan, Pelin; Ozkan, Gulay; DENİZ, ŞULE TUĞBA
    The paper aimed to compare the performance of artificial neural network (ANN) model with the results of in vitro experiments. For these experiments, maxillary molars of four different denture teeth were subjected to tea, coffee, cola, cherry juice, distilled water. Vickers microhardness and surface roughness values were measured. Subsequently, ANN model for the prediction of microhardness and surface roughness of different denture teeth were examined. A back-propagation ANN has been used to develop a model relating to the amount of microhardness and surface roughness. The independent variables of the model are distilled water, tea, filtered coffee, cola, cherry juice, time and denture teeth. Microhardness and surface roughness were chosen as the dependent variables. According to the results, a neural network architecture having one input layer with ten neurons, two hidden layers with six neurons, one output layer with two neurons and an epoch size of 48 gives better prediction. Prediction models for dental materials could also be supportive for in vitro studies.