Adsorption of cobalt ferrite nanoparticles within layer-by-layer films: a kinetic study carried out using quartz crystal microbalance

The paper reports on the successful use of the quartz crystal microbalance technique to assess accurate kinetics and equilibrium parameters regarding the investigation of in situ adsorption of nanosized cobalt ferrite particles (CoFe2O4-10.5 nm-diameter) onto two different surfaces. Firstly, a single layer of nanoparticles was deposited onto the surface provided by the gold-coated quartz resonator functionalized with sodium 3-mercapto propanesulfonate (3-MPS). Secondly, the layer-by-layer (LbL) technique was used to build multilayers in which the CoFe2O4 nanoparticle-based layer alternates with the sodium sulfonated polystyrene (PSS) layer. The adsorption experiments were conducted by modulating the number of adsorbed CoFe2O4/PSS bilayers (n) and/or by changing the CoFe2O4 nanoparticle concentration while suspended as a stable colloidal dispersion. Adsorption of CoFe2O4 nanoparticles onto the 3-MPS-functionalized surface follows perfectly a first order kinetic process in a wide range (two orders of magnitude) of nanoparticle concentrations. These data were used to assess the equilibrium constant and the adsorption free energy. Alternatively, the Langmuir adsorption constant was obtained while analyzing the isotherm data at the equilibrium. Adsorption of CoFe2O4 nanoparticles while growing multilayers of CoFe2O4/PSS was conducted using colloidal suspensions with CoFe2O4 concentration in the range of 10-8 to 10-6 (moles of cobalt ferrite per litre) and for different numbers of cycles n = 1, 3, 5, and 10. We found the adsorption of CoFe2O4 nanoparticles within the CoFe2O4/PSS bilayers perfectly following a first order kinetic process, with the characteristic rate constant growing with the increase of CoFe2O4 nanoparticle concentration and decreasing with the rise of the number of LbL cycles (n). Additionally, atomic force microscopy was employed for assessing the LbL film roughness and thickness. We found the film thickness increasing from about 20 to 120 nm while shifting from 3 to 10 CoFe2O4/PSS bilayers, using the 8.9 × 10-6 (moles of cobalt ferrite per litre) suspension.MCT/CNPqFINEPCAPESFUNAPEFINATE

Consenso brasileiro para o tratamento da esclerose múltipla : Academia Brasileira de Neurologia e Comitê Brasileiro de Tratamento e Pesquisa em Esclerose Múltipla

O crescent arsenal terapêutico na esclerose múltipla (EM) tem permitido tratamentos mais efetivos e personalizados, mas a escolha e o manejo das terapias modificadoras da doença (TMDs) tem se tornado cada vez mais complexos. Neste contexto, especialistas do Comitê Brasileiro de Tratamento e Pesquisa em Esclerose Múltipla e do Departamento Científico de Neuroimunologia da Academia Brasileira de Neurologia reuniram-se para estabelecer este Consenso Brasileiro para o Tratamento da EM, baseados no entendimento de que neurologistas devem ter a possibilidade de prescrever TMDs para EM de acordo com o que é melhor para cada paciente, com base em evidências e práticas atualizadas. Por meio deste documento, propomos recomendações práticas para o tratamento da EM, com foco principal na escolha e no manejo das TMDs, e revisamos os argumentos que embasam as estratégias de tratamento na EM.The expanding therapeutic arsenal in multiple sclerosis (MS) has allowed for more effective and personalized treatment, but the choice and management of disease-modifying therapies (DMTs) is becoming increasingly complex. In this context, experts from the Brazilian Committee on Treatment and Research in Multiple Sclerosis and the Neuroimmunology Scientific Department of the Brazilian Academy of Neurology have convened to establish this Brazilian Consensus for the Treatment of MS, based on their understanding that neurologists should be able to prescribe MS DMTs according to what is better for each patient, based on up-to-date evidence and practice. We herein propose practical recommendations for the treatment of MS, with the main focus on the choice and management of DMTs, as well as present a review of the scientific rationale supporting therapeutic strategies in MS

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost