Purpose: To evaluate the changes in mutans streptococci counts in saliva after short term probiotic intervention and its delayed effects on salivary mutans streptococci count. Methods: 40 children in the age group of 12-15 years with medium to high caries activity were randomly divided into Group I Control (plain milk group) and Group II Experimental (probiotic supplemented milk group). Duration of the study was 9 weeks; which was evenly divided into three phases: baseline, intervention and post-treatment period; each phase consisting of three weeks. After baseline period of 3 weeks, children in group I were given plain milk and in group II milk containing probiotic Lactobacillus rhamnosus hct 70 for 3 weeks; followed by a 3 weeks follow up period. After every phase saliva samples were collected to estimate salivary mutans streptococci counts. Results: The difference in the post follow up mutans streptococci count of group I and group II, was highly significant with p value < 0.001. In the control group, the difference in the mean salivary baseline, post treatment and post follow up mutans streptococci counts was not statistically significant (p = 0.001). In the experimental probiotic group, the difference in mean salivary baseline, post treatment and post follow up mutans streptococci counts was statistically highly significant ( p = 0.000, p ≤ 0.001). Conclusions: Statistically significant reduction in salivary mutans streptococci counts immediately after consumption of probiotic Lactobacillus rhamnosus hct 70 containing milk suggest a beneficial effect of probiotic Lactobacillus rhamnosus hct 70 in the prevention of dental caries.
Introduction: Parkinson's disease (PD) is a neurological disorder, which is diagnosed on the basis of clinical history and examination alone as there are no diagnostic tests available. However, the current diagnosis highly depends on the knowledge and experience of clinicians and hence subjective in nature. Thus, the focus of this study is to develop a computer-aided diagnosis (CAD) method using T1-weighted magnetic resonance imaging (MRI) to differentiate PD from controls. Method: The proposed method utilizes graph-theory-based spectral feature selection method to select a set of discriminating features from whole brain volume. A decision model is built using support vector machine as a classifier with leave-one-out cross-validation scheme. The performance measures, namely, sensitivity, specificity, and classification accuracy, are utilized to evaluate the performance of the decision model. The efficacy of the proposed method is checked on volumetric 3D T1-weighted (1 mm iso-voxel) MRI dataset of 30 PD patients and 30 age and gender matched controls acquired with 1.5T MRI scanner. Results: Experimental results demonstrate that the proposed method is able to differentiate PD from controls with an accuracy of 86.67%, which encourages the use of CAD. The performance of the proposed method outperforms the existing methods except one. In addition, it is observed that the maximum number of selected features belong to caudate region followed by cuneus region. Thus, these regions may be considered as potential biomarkers in diagnosis of PD. Conclusion: The proposed method may be utilized by the clinicians for diagnosis of PD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.