Old demands, new times: analysis of Special Education in Goias and Tocantins

After decades of discussions and legislation regarding Special Education, the demands remain urgent and daily. This study, qualitative and documental analysis, interprets how Special Education has been understood in federal and state legislation, from Goias and Tocantins, stressing the challenges brought by the pandemic. After inductive analysis of the data, the results indicate that the Base Nacional Comum Curricular approaches the theme of inclusion in a fragile way and, in the state documents, an alignment of its goals with the Plano Nacional de Educagao is noted, although adaptations are still needed to the existing social demands, previous or that emerged during the pandemic.

A hierarchical Bayesian approach for modeling the evolution of the 7-day moving average of the number of deaths by COVID-19.

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.

Pharmacological Modulators of Autophagy as a Potential Strategy for the Treatment of COVID-19

The family of coronaviruses (CoVs) uses the autophagy machinery of host cells to promote their growth and replication;thus, this process stands out as a potential target to combat COVID-19. Considering the different roles of autophagy during viral infection, including SARS-CoV-2 infection, in this review, we discuss several clinically used drugs that have effects at different stages of autophagy. Among them, we mention (1) lysosomotropic agents, which can prevent CoVs infection by alkalinizing the acid pH in the endolysosomal system, such as chloroquine and hydroxychloroquine, azithromycin, artemisinins, two-pore channel modulators and imatinib;(2) protease inhibitors that can inhibit the proteolytic cleavage of the spike CoVs protein, which is necessary for viral entry into host cells, such as camostat mesylate, lopinavir, umifenovir and teicoplanin and (3) modulators of PI3K/AKT/mTOR signaling pathways, such as rapamycin, heparin, glucocorticoids, angiotensin-converting enzyme inhibitors (IECAs) and cannabidiol. Thus, this review aims to highlight and discuss autophagy-related drugs for COVID-19, from in vitro to in vivo studies. We identified specific compounds that may modulate autophagy and exhibit antiviral properties. We hope that research initiatives and efforts will identify novel or "off-label" drugs that can be used to effectively treat patients infected with SARS-CoV-2, reducing the risk of mortality.