A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

Cathepsin S as a biomarker of low-grade inflammation, insulin resistance, and cardiometabolic disease risk

Cathepsin S is a protease important in major histocompatibility complex (MHC) class II antigen presentation and also in degrading the extracellular matrix. Studies, most of them experimental, have shown that cathepsin S is involved in different pathological conditions such as obesity, inflammation, atherosclerosis, diabetes, and cancer.    The overall hypothesis of this report is that high levels of circulating cathepsin S, is a biomarker that reflects pathology induced by inflammation and obesity. The overall aim of this report was to investigate possible associations between circulating cathepsin S, inflammation, glucometabolic disturbance, and its associated diseases in the community. As cathepsin S appears to be a novel risk marker for several pathological conditions, we also wanted to examine the effect of dietary intervention on circulating cathepsin S concentrations.    This thesis is based on data from three community-based cohorts, the Uppsala longitudinal study of adult men (ULSAM), the prospective investigation of the vasculature in Uppsala seniors (PIVUS), and a post-hoc study from the randomized controlled NORDIET trial.    In the first study, we identified a cross-sectional positive association between serum cathepsin S and two markers of cytokine-mediated inflammation, CRP and IL-6. These associations were similar in non-obese individuals. In longitudinal analyses, higher cathepsin S at baseline was associated with higher CRP and IL-6 levels after six years of follow-up. In the second study, we identified a cross-sectional association between increased serum levels of cathepsin S and reduced insulin sensitivity. These associations were similar in non-obese individuals. No significant association was observed between cathepsin S and insulin secretion. In longitudinal analysis, higher cathepsin S levels were associated with an increased risk of developing diabetes during the six-year follow-up. In the third study, we found that higher serum levels of cathepsin S were associated with increased mortality risk. Moreover, in the ULSAM cohort, serum cathepsin S was independently associated with cause-specific mortality from cardiovascular disease and cancer. In the fourth study, we identified that adherence to an ad libitum healthy Nordic diet for 6 weeks slightly decreased the levels of plasma cathepsin S in normal or marginally overweight individuals, relative to the control group. Changes in circulating cathepsin S concentrations were correlated with changes in body weight, LDL-C, and total cholesterol.    Conclusion: This thesis shows that circulating cathepsin S is a biomarker that independently reflects inflammation, insulin resistance, the risk of developing diabetes, and mortality risk. Furthermore, a Nordic diet moderately reduced cathepsin S levels in normal-weight and overweight men and women. This effect may be partially mediated by diet-induced weight loss and possibly by reduced LDL-C concentrations.