Low-level laser therapy (LLLT) is a non-invasive modality to promote osteoblastic activity and tissue healing. The aim of this study was to evaluate the efficacy of LLLT for improvement of dental implant stability. This randomized controlled clinical trial was performed on 80 dental implants placed in 19 patients. Implants were randomly divided into two groups (n = 40). Seven sessions of LLLT (940 nm diode laser) were scheduled for the test group implants during 2 weeks. Laser was irradiated to the buccal and palatal sides. The same procedure was performed for the control group implants with laser hand piece in "off" mode. Implant stability was measured by Osstell Mentor device in implant stability quotient (ISQ) value immediately after surgery and 10 days and 3, 6, and 12 weeks later. Repeated measures ANOVA was used to compare the mean ISQ values (implant stability) in the test and control groups. Statistical test revealed no significant difference in the mean values of implant stability between the test and control groups over time (P = 0.557). Although the mean values of implant stability changed significantly in both groups over time (P < 0.05). Although the trend of reduction in stability was slower in the laser group in the first weeks and increased from the 6th to 12th week, LLLT had no significant effect on dental implant stability.
PurposeRheumatoid arthritis (RA) is a chronic multi-systemic disease that causes damage to the bone and connective tissues. This study was conducted in order to accurately measure the correlation between RA and periodontitis, and to obtain an unbiased estimate of the effect of RA on periodontal indices.MethodsIn this historical cohort study, which was conducted from February to May 2011 in Hamadan city, Iran, 53 exposed people (with RA) were compared with 53 unexposed people (without RA) in terms of clinical periodontal indices (the outcomes of interest) including 1) plaque index (PI), 2) bleeding on probing (BOP), and 3) clinical attachment loss (CAL).ResultsA sample of 106 volunteers were evaluated, 53 rheumatoid versus 53 non-rheumatoid subjects. There was a statistically significant correlation between RA and BOP (P<0.001) and between RA and CAL (P<0.001). However, there was no statistically significant correlation between RA and any of the periodontal indices. No correlation was seen between gender and any of the indices either. There was a strong positive correlation between age and all three periodontal indices (P<0.001).ConclusionsThe present study indicated a potential effect of RA on periodontal indices. However, much more evidence based on a prospective cohort study is needed to support the cause and effect relationship between RA and periodontal indices.
Objective: Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using machine learning algorithms have attracted a lot of attention. Therefore, this study aimed to design a support vector machine (SVM) based decision-making support system to diagnosis various periodontal diseases. Data were collected from 300 patients referring to Periodontics department of Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque index and so on used as input and output variable show the individual's status as a periodontal disease. Results: Using different kernel functions in the design of the SVM classification model showed that the radial kernel function with an overall correct classification accuracy of 88.7% and the overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance. The results of the present study show that the designed classification model has an acceptable performance in predicting periodontitis.
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