This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson’s disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.
The tissue kallikrein-kinin system (KKS) is an endogenous multiprotein metabolic cascade which is implicated in the homeostasis of the cardiovascular, renal and central nervous system. Human tissue kallikrein (KLK1) is a serine protease, component of the KKS that has been demonstrated to exert pleiotropic beneficial effects in protection from tissue injury through its anti-inflammatory, anti-apoptotic, anti-fibrotic and anti-oxidative actions. Mesenchymal stem cells (MSCs) or endothelial progenitor cells (EPCs) constitute populations of well-characterized, readily obtainable multipotent cells with special immunomodulatory, migratory and paracrine properties rendering them appealing potential therapeutics in experimental animal models of various diseases. Genetic modification enhances their inherent properties. MSCs or EPCs are competent cellular vehicles for drug and/or gene delivery in the targeted treatment of diseases. KLK1 gene delivery using adenoviral vectors or KLK1 protein infusion into injured tissues of animal models has provided particularly encouraging results in attenuating or reversing myocardial, renal and cerebrovascular ischemic phenotype and tissue damage, thus paving the way for the administration of genetically modified MSCs or EPCs with the human tissue KLK1 gene. Engraftment of KLK1-modified MSCs and/or KLK1-modified EPCs resulted in advanced beneficial outcome regarding heart and kidney protection and recovery from ischemic insults. Collectively, findings from pre-clinical studies raise the possibility that tissue KLK1 may be a novel future therapeutic target in the treatment of a wide range of cardiovascular, cerebrovascular and renal disorders.
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