ABSTRACT
Abstract Background The 6-OHDA nigro-striatal lesion model has already been related to disorders in the excitability and synchronicity of neural networks and variation in the expression of transmembrane proteins that control intra and extracellular ionic concentrations, such as cation-chloride cotransporters (NKCC1 and KCC2) and Na+/K+-ATPase and, also, to the glial proliferation after injury. All these non-synaptic mechanisms have already been related to neuronal injury and hyper-synchronism processes. Objective The main objective of this study is to verify whether mechanisms not directly related to synaptic neurotransmission could be involved in the modulation of nigrostriatal pathways. Methods Male Wistar rats, 3 months old, were submitted to a unilateral injection of 24 µg of 6-OHDA, in the striatum (n= 8). The animals in the Control group (n= 8) were submitted to the same protocol, with the replacement of 6-OHDA by 0.9% saline. The analysis by optical densitometry was performed to quantify the immunoreactivity intensity of GFAP, NKCC1, KCC2, Na+/K+-ATPase, TH and Cx36. Results The 6-OHDA induced lesions in the striatum, were not followed by changes in the expression cation-chloride cotransporters and Na+/K+-ATPase, but with astrocytic reactivity in the lesioned and adjacent regions of the nigrostriatal. Moreover, the dopaminergic degeneration caused by 6-OHDA is followed by changes in the expression of connexin-36. Conclusions The use of the GJ blockers directly along the nigrostriatal pathways to control PD motor symptoms is conjectured. Electrophysiology of the striatum and the substantia nigra, to verify changes in neuronal synchronism, comparing brain slices of control animals and experimental models of PD, is needed.
ABSTRACT
Este trabalho tem como objetivo relatar estratégias para coleta de um conjunto de dados em português para treinamento de modelos de Inteligência Artificial com vistas a identificar de forma automática fake news sobre covid-19 disseminadas durante a pandemia, a partir de código Python. Analisamos um método de detecção de fake news baseado em uma Rede Neural Recorrente e de aprendizagem supervisionada. Selecionamos um corpus com 7,2 mil textos coletados em websites e agências de notícias por Monteiro et al. (2018) com cada um previamente catalogado como verdadeiro ou falso como conjunto de dados de treino e validação. O modelo foi usado para detecção de fake news sobre covid-19 em um conjunto de notícias coletadas e classificadas pelos autores deste trabalho. O índice de acerto foi de 70%, ou seja, essa foi a taxa de sucesso da detecção dos itens catalogados.
This work aims to report strategies for collecting a dataset in Portuguese for training Artificial Intelligence models to automatically identify fake news about covid-19 disseminated during the pandemic, using Python code. We analyze a fake news detection method based on a Recurrent Neural Network and supervised learning. We selected a corpus with 7,200 texts collected on websites and news agencies by Monteiro et al. (2018), each one of them previously cataloged as true or false as a training and validation dataset. This model was used to detect fake news about covid-19 in a set of news collected and classified by the authors of this work. The hit rate was 70%.
Este trabajo tiene como objetivo informar estrategias para recopilar un conjunto de datos en portugués para entrenar modelos de Inteligencia Artificial para identificar automáticamente noticias falsas sobre covid-19 difundidas durante la pandemia, utilizando el código Python. Analizamos un método de detección de noticias falsas basado en una Red Neuronal Recurrente y de aprendizaje supervisado. Seleccionamos un corpus de 7.200 textos recogidos en webs y agencias de noticias por Monteiro et al. (2018) con cada uno catalogado previamente como verdadero o falso como un conjunto de datos de entrenamiento y validación. El modelo se utilizó para detectar noticias falsas sobre covid-19 en un conjunto de noticias recopiladas y clasificadas por los autores de este trabajo. La tasa de acierto fue del 70%, es decir, esta fue la tasa de éxito de detección de los artículos catalogados.
Subject(s)
Humans , Programming Languages , Artificial Intelligence , Communication , COVID-19 , Disinformation , Data Collection , News , Health Information ExchangeABSTRACT
Resumen La actina es una proteína que se polimeriza para formar citoesqueletos y cuya función es estabilizar y dirigir el movimiento de las paredes celulares. Es una de las proteínas más estables, habiendo evolucionado poco a partir de algas y levaduras, y muy poco desde los peces. Aquí analizamos la evolución de la actina usando las teorías modernas de las interacciones de conformación proteína-agua, y cómo estas han evolucionado para optimizar las funciones de la proteína. Llegamos a la conclusión de que el fracaso del análisis filogenético para identificar positivamente la evolución darwiniana de las proteínas ha sido causado por las limitaciones técnicas propias del siglo XX. Estas limitaciones pueden ser superadas mediante el escalamiento termodinámico y el promedio modular ambos llevados a niveles técnicos del siglo XXI. Los resultados para la actina son especialmente llamativos y reflejan estructuras duales estables, globulares y polimerizadas.
Abstract Actin polymerizes to form cytoskeletons which stabilize and direct motion of cellular walls. It is one of the most stable proteins, having evolved little from algae and yeast, and very little from fish. Here we analyze actin evolution using modern theories of water-protein shaping interactions, and how these have evolved to optimize protein functions. We conclude that the failure of phylogenetic analysis to identify positive Darwinian evolution has been caused by 20th century technical limitations. These are overcome using 21st century thermodynamic scaling and modular averaging. The results for actin are especially striking, and reflect dual stable structures, globular and polymerized.