The nonsynaptic plasticity in Parkinson's disease: Insights from an animal model.

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.

Paradox between adequate sanitation and rainfall in dengue fever cases.

BACKGROUND: Dengue fever is a tropical disease and a major public health concern, and almost half of the world's population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever. METHODS: We evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method. RESULTS: Co-movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city. CONCLUSION: With climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.

The nonsynaptic plasticity in Parkinson's disease: Insights from an animal model

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.

Conservation in the Amazon rainforest and Google searches: A DCCA approach.

In this paper we analyze the descriptive statistics of the Google search volume for the terms related to the National Reserve of Copper and Associates (RENCA), a Brazilian mineral reserve in the Amazon of 4.6 million hectares, before and after the government signed the decree releasing it for exploration. First, we analyze the volume of searches for expressions related to RENCA in Google Trends using descriptive statistics; second, we assess the cross-correlation coefficient ρDCCA, which measures the cross-correlation between two nonstationary time series across different time scales. After the government announced the release of the RENCA reserve, there was an increase in the average volume of Google searches for related terms, showing people's concern about the announcement. By using the cross-correlation coefficient ρDCCA, we identify strong cross-correlations between the different expressions related to RENCA in Google Trends. Our work shows the utility of Google Trends as an indicator of the perception of environmental policies. Additionally, we show that ρDCCA can be used as a tool to measure the cross-correlation between synonyms extracted from Google Trends for various time scales.

Retrospective Cohort Study of COVID-19 in Patients of the Brazilian Public Health System with SARS-CoV-2 Omicron Variant Infection.

Several vaccines against COVID-19 are now available, based on different techniques and made by different laboratories spread around the world. With the roll out of the vaccination process in an advanced stage in many countries, the reduced risk of hospitalization due to the Omicron variant relative to the Delta variant infection, despite the higher transmission risk of Omicron, may lead to a misinterpretation of the results, as infection by Omicron is associated with a significant reduction in severe outcomes and shorter hospitalization time than the Delta variant. We compared the in-hospital mortality due to the Omicron (Jan-Mar 2022) with Gamma (Jan 2021) and Delta (Oct-Dec 2021) variants of patients in the Brazilian public health system. This study also discusses the decrease in booster vaccine effectiveness in patients hospitalized due to the Omicron variant compared with the Delta variant. Without a remodeling of vaccines for new variants, booster doses may be necessary with a shorter time interval.

Uso de Python para detecção de fake news sobre a covid-19: desafios e possibilidades / Using Python to detect fake news about covid-19: challenges and possibilities / Usar Python para detectar noticias falsas sobre covid-19: desafíos y posibilidades

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.

Relating SARS-CoV-2 variants using cellular automata imaging.

We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.

Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster.

The novel coronavirus SARS CoV-2 responsible for the COVID-19 pandemic and SARS CoV-1 responsible for the SARS epidemic of 2002-2003 share an ancestor yet evolved to have much different transmissibility and global impact 1. A previously developed thermodynamic model of protein conformations hypothesized that SARS CoV-2 is very close to a new thermodynamic critical point, which makes it highly infectious but also easily displaced by a spike-based vaccine because there is a tradeoff between transmissibility and robustness 2. The model identified a small cluster of four key mutations of SARS CoV-2 that predicts much stronger viral attachment and viral spreading compared to SARS CoV-1. Here we apply the model to the SARS-CoV-2 variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) and Delta (B.1.617.2)3 and predict, using no free parameters, how the new mutations will not diminish the effectiveness of current spike based vaccines and may even further enhance infectiousness by augmenting the binding ability of the virus.

Optimization of COVID-19 vaccination and the role of individuals with a high number of contacts: A model based approach.

We report strong evidence of the importance of contact hubs (or superspreaders) in mitigating the current COVID-19 pandemic. Contact hubs have a much larger number of contacts than the average in the population, and play a key role on the effectiveness of vaccination strategies. By using an age-structures compartmental SEIAHRV (Susceptible, Exposed, Infected symptomatic, Asymptomatic, Hospitalized, Recovered, Vaccinated) model, calibrated from available demographic and COVID-19 incidence, and considering separately those individuals with a much greater number of contacts than the average in the population, we show that carefully choosing who will compose the first group to be vaccinated can impact positively the total death toll and the demand for health services. This is even more relevant in countries with a lack of basic resources for proper vaccination and a significant reduction in social isolation. In order to demonstrate our approach we show the effect of hypothetical vaccination scenarios in two countries of very different scales and mitigation policies, Brazil and Portugal.

A Transnational and Transregional Study of the Impact and Effectiveness of Social Distancing for COVID-19 Mitigation.

We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman's rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman's correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

A spatio-temporal analysis of dengue spread in a Brazilian dry climate region.

We investigated the relation between the spread, time scale, and spatial arrangement of dengue in Bahia, a Brazilian dry climate region, for the period 2000 to 2009. The degree of cross-correlation is calculated for 15 economic regions. We propose a multiscale statistical analysis to datasets of dengue cases in order to verify the effect of infection dispersal on the economic regions from the metropolitan region of Salvador. Our empirical results support a significant and persistent cross-correlation between most regions, reinforcing the idea that economic regions or climatic conditions are non-statistically significant in the spread of dengue in the State of Bahia. Our main contribution lies in the cross-correlation results revealing multiple aspects related to the propagation of dengue in dry climate regions.