ABSTRACT
OBJECTIVE: This study aims to evaluate the discoloration rate of two types of composites, microhybrid and nanohybrid, after exposure to natural and commercially-produced juices. MATERIALS AND METHODS: In this experimental study, 90 disc-shaped specimens with a thickness of 2 mm and a diameter of 10 mm were taken from two composites, microhybrid P4 (Kerr-ITALY) and nanohybrid Filtek Z250XT (3M-ESPE-USA) (two groups of 45). Then, the samples of each group were divided into five subgroups of nine and were immersed for 10 days for 4 h in five solutions of commercially-produced orange juice, natural orange juice, commercially-produced pomegranate juice, natural pomegranate juice, and distilled water (control group). The colour of the samples was measured by a reflective spectrophotometer using the CLEl*a*b colour space at baseline and after discoloration. RESULT: The independent t-test showed that the mean discoloration rate (∆E) of nanohybrid composite exposed to commercially-produced orange juice, natural pomegranate juice, and commercially-produced pomegranate juice was significantly higher than microhybrid composite (P < 0.01). In addition, the test found that the highest discoloration rate of the nanohybrid composite was related to the effect of commercially-produced orange juice (∆E = 13.03) and the highest discoloration rate of microhybrid composite was related to the effect of natural pomegranate juice (∆E = 4.79). CONCLUSION: According to the results, it seems that microhybrid composites are more resistant to discoloration than nanohybrid composites. According to the results, consumption of dyed drinks, particularly natural pomegranate juice, commercially-produced orange juice, and commercially-produced pomegranate juice, is not recommended in the first few days after composite restoration.
ABSTRACT
BACKGROUND: Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses. MATERIALS AND METHODS: Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes. RESULTS: Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree. CONCLUSION: BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources.
