Blood transcriptomics of drug-na\uefve sporadic Parkinson's disease patients

BACKGROUND: Parkinson's disease (PD) is a chronic progressive neurodegenerative disorder that is clinically defined in terms of motor symptoms. These are preceded by prodromal non-motor manifestations that prove the systemic nature of the disease. Identifying genes and pathways altered in living patients provide new information on the diagnosis and pathogenesis of sporadic PD. METHODS: Changes in gene expression in the blood of 40 sporadic PD patients and 20 healthy controls ("Discovery set") were analyzed by taking advantage of the Affymetrix platform. Patients were at the onset of motor symptoms and before initiating any pharmacological treatment. Data analysis was performed by applying Ranking-Principal Component Analysis, PUMA and Significance Analysis of Microarrays. Functional annotations were assigned using GO, DAVID, GSEA to unveil significant enriched biological processes in the differentially expressed genes. The expressions of selected genes were validated using RT-qPCR and samples from an independent cohort of 12 patients and controls ("Validation set"). RESULTS: Gene expression profiling of blood samples discriminates PD patients from healthy controls and identifies differentially expressed genes in blood. The majority of these are also present in dopaminergic neurons of the Substantia Nigra, the key site of neurodegeneration. Together with neuronal apoptosis, lymphocyte activation and mitochondrial dysfunction, already found in previous analysis of PD blood and post-mortem brains, we unveiled transcriptome changes enriched in biological terms related to epigenetic modifications including chromatin remodeling and methylation. Candidate transcripts as CBX5, TCF3, MAN1C1 and DOCK10 were validated by RT-qPCR. CONCLUSIONS: Our data support the use of blood transcriptomics to study neurodegenerative diseases. It identifies changes in crucial components of chromatin remodeling and methylation machineries as early events in sporadic PD suggesting epigenetics as target for therapeutic intervention

Nitrated α-Synuclein Induces the Loss of Dopaminergic Neurons in the Substantia Nigra of Rats

BACKGROUND: The pathology of Parkinson's disease (PD) is characterized by the degeneration of the nigrostriatal dopaminergic pathway, as well as the formation of intraneuronal inclusions known as Lewy bodies and Lewy neurites in the substantia nigra. Accumulations of nitrated alpha-synuclein are demonstrated in the signature inclusions of Parkinson's disease. However, whether the nitration of alpha-synuclein is relevant to the pathogenesis of PD is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In this study, effect of nitrated alpha-synuclein to dopaminergic (DA) neurons was determined by delivering nitrated recombinant TAT-alpha-synuclein intracellular. We provide evidence to show that the nitrated alpha-synuclein was toxic to cultured dopaminergic SHSY-5Y neurons and primary mesencephalic DA neurons to a much greater degree than unnitrated alpha-synuclein. Moreover, we show that administration of nitrated alpha-synuclein to the substantia nigra pars compacta of rats caused severe reductions in the number of DA neurons therein, and led to the down-regulation of D(2)R in the striatum in vivo. Furthermore, when administered to the substantia nigra of rats, nitrated alpha-synuclein caused PD-like motor dysfunctions, such as reduced locomotion and motor asymmetry, however unmodified alpha-synuclein had significantly less severe behavioral effects. CONCLUSIONS/SIGNIFICANCE: Our results provide evidence that alpha-synuclein, principally in its nitrated form, induce DA neuron death and may be a major factor in the etiology of PD

Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning

Targeting Huntington’s disease through histone deacetylases

Huntington’s disease (HD) is a debilitating neurodegenerative condition with significant burdens on both patient and healthcare costs. Despite extensive research, treatment options for patients with this condition remain limited. Aberrant post-translational modification (PTM) of proteins is emerging as an important element in the pathogenesis of HD. These PTMs include acetylation, phosphorylation, methylation, sumoylation and ubiquitination. Several families of proteins are involved with the regulation of these PTMs. In this review, I discuss the current evidence linking aberrant PTMs and/or aberrant regulation of the cellular machinery regulating these PTMs to HD pathogenesis. Finally, I discuss the evidence suggesting that pharmacologically targeting one of these protein families the histone deacetylases may be of potential therapeutic benefit in the treatment of HD

Project management between will and representation

This article challenges some deep-rooted assumptions of project management. Inspired by the work of the German philosopher, Arthur Schopenhauer, it calls for looking at projects through two complementary lenses: one that accounts for cognitive and representational aspects and one that accounts for material and volitional aspects. Understanding the many ways in which these aspects transpire and interact in projects sheds new light on project organizations, as imperfect and fragile representations that chase a shifting nexus of intractable human, social, technical, and material processes. This, in turn, can bring about a new grasp of notions such as value,\ud knowledge, complexity, and risk

Mídias Sociais e Administração Pública: Análise do sentimento social perante a atuação do governo federal brasileiro

Este estudo procurou identificar como a análise de sentimento, baseada em textos extraídos de mídias sociais, pode ser um instrumento de mensuração da opinião pública sobre a atuação do governo de forma a contribuir para a avaliação da administração pública. Trata-se de um estudo aplicado, interdisciplinar, exploratório, qualitativo e quantitativo. Foram revisadas as principais formulações teóricas e conceituais acerca do tema e realizadas demonstrações práticas, utilizando-se uma ferramenta de mineração de opinião que proporcionou precisão satisfatória no processamento de dados. Para fins de demonstração, foram selecionados temas que motivaram a realização da onda de protestos que envolveu milhões de pessoas no Brasil em junho de 2013. Foram coletadas, processadas e analisadas, aproximadamente, 130.000 mensagens postadas no Facebook e no Twitter sobre esses temas em dois períodos distintos. Por meio desta investigação, observou-se que a análise de sentimento pode revelar a opinião polarizada dos cidadãos quanto à atuação do governo

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms