Patients suffering from Parkinson's disease must periodically undergo a series of tests, usually performed at medical facilities, to diagnose the current state of the disease. Parkinson's disease progression assessment is an important set of procedures that supports the clinical diagnosis. A common part of the diagnostic train is analysis of speech signal to identify the disease-specific communication issues. This contribution describes two types of computational models that map speech signal measurements to clinical outputs. Speech signal samples were acquired through measurements from patients suffering from Parkinson's disease. In addition to direct mapping, the developed systems must be able of generalization so that correct clinical scale values can be predicted from future, previously unseen speech signals. Computational methods considered in this paper are artificial neural networks, particularly feedforward networks with several variants of backpropagation learning algorithm, and adaptive network-based fuzzy inference system (ANFIS). In order to speed up the learning process, some of the algorithms were parallelized. Resulting diagnostic system could be implemented in an embedded form to support individual assessment of Parkinson's disease progression from patients' homes.
Parkinson's disease (PD) is currently the second most common
neurodegenerative disorder in the world. Major features of cell
pathology of the disease include the presence of cytoplasmic
inclusions called Lewy bodies, which are composed of aggregated
proteins. The presence of Lewy's body is associated with more
advanced stages of the disease when considering irreversible
changes. Precise identification of the disease stage at a cellular
level presents the critical tool in developing early diagnostics
and/or prevention of PD. The aim of our work is to introduce
sensitive microscopic analysis in living cells, focused on initial
intracellular changes and thus capable to detect earlier stages of
the disease.
Parkinson's disease (PD) is a neurodegenerative disease with
a progressive loss of mesencephalic dopaminergic neurons of the
substantia nigra (SN). To further evaluate its pathophysiology,
accurate animal models are needed. The current study aims to
verify the impact of a 6-hydroxydopamine (6-OHDA) bilateral
microinjection into the SN on gastrointestinal symptoms in rats
and confirm that the 6-OHDA rat model is an appropriate tool to
investigate the mechanisms of Parkinsonian GI disorders.
Immunohistochemistry, digital X-ray imaging, short-circuit
current, FITC-dextran permeability and ultra-performance liquid
chromatography tandem mass spectrometry were used in this
study. The results indicated that the dopaminergic neurons in SN
and fibres in the striatum were markedly reduced in 6-OHDA
rats. The 6-OHDA rats manifested reductions in occupancy in
a rotarod test and increases in daily food debris but no difference
in body mass or daily consumption. Compared with control rats,
faecal pellets and their contents were significantly decreased,
whereas gastric emptying and intestinal transport were delayed
in 6-OHDA rats. The increased in vivo FITC-dextran permeability
and decreased intestinal transepithelial resistance in the model
suggest attenuated barrier function in the digestive tract in the
PD model. Moreover, inflammatory factors in the plasma showed
that pro-inflammatory factors IL-1β and IL-8 were significantly
increased in 6-OHDA rats. Collectively, these findings indicate
that the model is an interesting experimental tool to investigate
the mechanisms involved in the progression of gastrointestinal
dysfunction in PD.
It is unknown whether the longer duration of vibration training (VT) has a beneficial effect on Parkinson's disease (PD). And also, the mechanisms underlying the reported sensorimotorimprovement in PD induced by short-duration of VT has not been determined. Here, we investigated the effects of longer duration (4 weeks) of low amplitude vibration (LAV) training on the numbers of dopaminergic neurons in the substantia nigra by immunostaining and the levels of dopamine (DA) and brainderived neurotrophic factor (BDNF) in the striatum by HPLC and ELISA in the chronic MPTP lesion mouse. We demonstrated for the first time that the longer duration of VT could significantly increase the numbers of nigrostriatal DA neurons and the contents of striatal DA and BDNF in the MPTP mice. Our findings implied that longer duration of VT could protect dopaminergic neurons from the MPTP-induced damage probably by upregulating BDNF and also provided evidence for the beneficial effect of longer duration of VT on PD at the cellular and molecular level., L. Zhao, L. X. He, S. N. Huang, L. J. Gong, L. Li, Y. Y. Lv, Z. M. Qian., and Obsahuje bibliografii