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.

Cerebellar Cognitive Affective Syndrome in Machado Joseph Disease: Core Clinical Features

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Characterization of Neural Stem/Progenitor Cells Expressing VEGF and its Receptors in the Subventricular Zone of Newborn Piglet Brain

Neural stem/progenitor cell (NSP) biology and neurogenesis in adult central nervous system (CNS) are important both towards potential future therapeutic applications for CNS repair, and for the fundamental function of the CNS. In the present study, we report the characterization of NSP population from subventricular zone (SVZ) of neonatal piglet brain using in vivo and in vitro systems. We show that the nestin and vimentin-positive neural progenitor cells are present in the SVZ of the lateral ventricles of neonatal piglet brain. In vitro, piglet NSPs proliferated as neurospheres, expressed the typical protein of neural progenitors, nestin and a range of well-established neurodevelopmental markers. Upon dissociation and subculture, piglet NSPs differentiated into neurons and glial cells. Clonal analysis demonstrates that piglet NSPs are multi-potent and retain the capacity to generate both glia and neurons. These cells expressed VEGF, VEGFR1, VEGFR2 and Neuropilin-1 and -2 mRNAs. Real time PCR revealed that SVZ NSPs from newborn piglet expressed total VEGF and all VEGF splice variants. These findings show that piglet NSPs may be helpful to more effectively design growth factor based strategies to enhance endogenous precursor cells for cell transplantation studies potentially leading to the application of this strategy in the nervous system disease and injury.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Recidiva Neural em hanseníase multibacilar após 6 anos de tratamento

This article presents a case of relapse, with isolated neural manifestation, in a multibacillary patient previously treated with multidrug therapy for multibacillary leprosy (24 doses). The patient returned to the service six years after the end of treatment, with pain in hands and legs. He was investigated, and the serological monitoring showed an important increase in anti-phenolic glycolipid serum levels. A neural recurrence was suspected, since the patient had no new skin lesions. A new biopsy in the right ulnar nerve showed a bacilloscopy of 2 +, compatible with relapse. This is a literature review of the etiological, clinical, propedeutical and diagnostic aspects of this situation so poorly understood. © 2012 by Anais Brasileiros de Dermatologia.

White matter abnormalities associated with Alzheimer's disease and mild cognitive impairment: A critical review of MRI studies

In this article, the authors aim to present a critical review of recent MRI studies addressing white matter (WM) abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI), by searching PubMed and reviewing MRI studies evaluating subjects with AD or MCI using WM volumetric methods, diffusion tensor imaging and assessment of WM hyperintensities. Studies have found that, compared with healthy controls, AD and MCI samples display WM volumetric reductions and diffusion tensor imaging findings suggestive of reduced WM integrity. These changes affect complex networks relevant to episodic memory and other cognitive processes, including fiber connections that directly link medial temporal structures and the corpus callosum. Abnormalities in cortico-cortical and cortico-subcortical WM interconnections are associated with an increased risk of progression from MCI to dementia. It can be concluded that WM abnormalities are detectable in early stages of AD and MCI. Degeneration of WM networks causes disconnection among neural cells and the degree of such changes is related to cognitive decline. © 2013 2013 Expert Reviews Ltd.

Estudo comportamental das aferências do núcleo ventral do corpo trapezóide ao primeiro núcleo do circuito neural do reflexo auditivo de sobressalto: núcleo da raiz coclear

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Reduced platelet amyloid precursor protein ratio (APP ratio) predicts conversion from mild cognitive impairment to Alzheimer's disease

Studies have shown that platelet APP ratio (representing the percentage of 120-130 kDa to 110 kDa isoforms of the amyloid precursor protein) is reduced in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). In the present study, we sought to determine if baseline APP ratio predicts the conversion from MCI to AD dementia after 4 years of longitudinal assessment. Fifty-five older adults with varying degrees of cognitive impairment (34 with MCI and 21 with AD) were assessed at baseline and after 4 years. MCI patients were re-classified according to the conversion status upon follow-up: 25 individuals retained the diagnostic status of MCI and were considered as stable cases (MCI-MCI); conversely, in nine cases the diagnosis of dementia due to AD was ascertained. The APP ratio (APPr) was determined by the Western blot method in samples of platelets collected at baseline. We found a significant reduction of APPr in MCI patients who converted to dementia upon follow-up. These individuals had baseline APPr values similar to those of demented AD patients. The overall accuracy of APPr to identify subjects with MCI who will progress to AD was 0.74 +/- A 0.10, p = 0.05. The cut-off of 1.12 yielded a sensitivity of 75 % and a specificity of 75 %. Platelet APPr may be a surrogate marker of the disease process in AD, with potential implications for the assessment of abnormalities in the APP metabolism in patients with and at risk for dementia. However, diagnostic accuracy was relatively low. Therefore, studies in larger samples are needed to determine whether APPr may warrant its use as a biomarker to support the early diagnosis of AD.

Kinin-B2 Receptor Activity Determines the Differentiation Fate of Neural Stem Cells

Bradykinin is not only important for inflammation and blood pressure regulation, but also involved in neuromodulation and neuroprotection. Here we describe novel functions for bradykinin and the kinin-B2 receptor (B2BkR) in differentiation of neural stem cells. In the presence of the B2BkR antagonist HOE-140 during rat neurosphere differentiation, neuron-specific beta 3-tubulin and enolase expression was reduced together with an increase in glial protein expression, indicating that bradykinin- induced receptor activity contributes to neurogenesis. In agreement, HOE-140 affected in the same way expression levels of neural markers during neural differentiation of murine P19 and human iPS cells. Kinin-B1 receptor agonists and antagonists did not affect expression levels of neural markers, suggesting that bradykinin-mediated effects are exclusively mediated via B2BkR. Neurogenesis was augmented by bradykinin in the middle and late stages of the differentiation process. Chronic treatment with HOE-140 diminished eNOS and nNOS as well as M1-M4 muscarinic receptor expression and also affected purinergic receptor expression and activity. Neurogenesis, gliogenesis, and neural migration were altered during differentiation of neurospheres isolated from B2BkR knock-out mice. Whole mount in situ hybridization revealed the presence of B2BkR mRNA throughout the nervous system in mouse embryos, and less beta 3-tubulin and more glial proteins were expressed in developing and adult B2BkR knock-out mice brains. As a underlying transcriptional mechanism for neural fate determination, HOE-140 induced up-regulation of Notch1 and Stat3 gene expression. Because pharmacological treatments did not affect cell viability and proliferation, we conclude that bradykinin-induced signaling provides a switch for neural fate determination and specification of neurotransmitter receptor expression.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

Motor control system in Parkinson’s disease: a modeling approach

Parkinson’s disease is a neurodegenerative disorder due to the death of the dopaminergic neurons of the substantia nigra of the basal ganglia. The process that leads to these neural alterations is still unknown. Parkinson’s disease affects most of all the motor sphere, with a wide array of impairment such as bradykinesia, akinesia, tremor, postural instability and singular phenomena such as freezing of gait. Moreover, in the last few years the fact that the degeneration in the basal ganglia circuitry induces not only motor but also cognitive alterations, not necessarily implicating dementia, and that dopamine loss induces also further implications due to dopamine-driven synaptic plasticity got more attention. At the present moment, no neuroprotective treatment is available, and even if dopamine-replacement therapies as well as electrical deep brain stimulation are able to improve the life conditions of the patients, they often present side effects on the long term, and cannot recover the neural loss, which instead continues to advance. In the present thesis both motor and cognitive aspects of Parkinson’s disease and basal ganglia circuitry were investigated, at first focusing on Parkinson’s disease sensory and balance issues by means of a new instrumented method based on inertial sensor to provide further information about postural control and postural strategies used to attain balance, then applying this newly developed approach to assess balance control in mild and severe patients, both ON and OFF levodopa replacement. Given the inability of levodopa to recover balance issues and the new physiological findings than underline the importance in Parkinson’s disease of non-dopaminergic neurotransmitters, it was therefore developed an original computational model focusing on acetylcholine, the most promising neurotransmitter according to physiology, and its role in synaptic plasticity. The rationale of this thesis is that a multidisciplinary approach could gain insight into Parkinson’s disease features still unresolved.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Implicit Memory for Music in Alzheimer's Disease

Short, unfamiliar melodies were presented to young and older adults and to Alzheimer's disease (AD) patients in an implicit and an explicit memory task. The explicit task was yes–no recognition, and the implicit task was pleasantness ratings, in which memory was shown by higher ratings for old versus new melodies (the mere exposure effect). Young adults showed retention of the melodies in both tasks. Older adults showed little explicit memory but did show the mere exposure effect. The AD patients showed neither. The authors considered and rejected several artifactual reasons for this null effect in the context of the many studies that have shown implicit memory among AD patients. As the previous studies have almost always used the visual modality for presentation, they speculate that auditory presentation, especially of nonverbal material, may be compromised in AD because of neural degeneration in auditory areas in the temporal lobes.