Avanços recentes na compreensão da patogênese da Artrite Reumatoide: insights e implicações terapêuticas

A artrite reumatoide (AR) é uma doença autoimune sistêmica que afeta cerca de 1% da população mundial. Caracterizada por inflamação sinovial crônica e erosões ósseas progressivas, a AR provoca deformidade articular e incapacidade funcional, prejudicando seriamente a qualidade de vida dos pacientes. A patogênese da AR é complexa e envolve uma interação dinâmica entre fatores genéticos, imunológicos e ambientais. Nesta revisão, analisamos aprofundadamente o papel da imunidade inata e adaptativa na patogênese da AR e exploramos os avanços recentes na terapêutica da doença, com destaque para os medicamentos modificadores da doença (DMARDs), tanto biológicos quanto sintéticos. Discutimos também as perspectivas futuras para a pesquisa em AR e o desenvolvimento de terapias, com o objetivo de identificar as áreas de pesquisa mais promissoras e as lacunas ainda presentes no nosso entendimento

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

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

Tuning light-driven water oxidation efficiency of molybdenum-doped BiVO4BiVO_{4} by means of multicomposite catalysts containing nickel, iron, and chromium oxides

Mo-doped $BiVO_{4}$ has emerged as a promising material for photoelectrodes for photoelectrochemical water splitting, however, still shows a limited efficiency for light-driven water oxidation. We present the influence of an oxygen-evolution catalyst composed of Ni, Fe, and Cr oxides on the activity of Mo:$BiVO_{4}$ photoanodes. The photoanodes are prepared by spray-coating, enabling compositional and thickness gradients of the incorporated catalyst. Two different configurations are evaluated, namely with the catalyst embedded into the Mo:$BiVO_{4}$ film or deposited on top of it. Both configurations provide a significantly different impact on the photoelectrocatalytic efficiency. Structural characterisation of the materials by means of SEM, TEM and XRD as well as the photoelectrocatalytic activity investigated by means of an optical scanning droplet cell and $\textit {in situ}$ detection of oxygen using scanning photoelectrochemical microscopy are presented

Importance of catalyst–photoabsorber interface design configuration on the performance of Mo-doped BiVO4BiVO_{4} water splitting photoanodes

Photoelectrochemical water splitting is mostly impeded by the slow kinetics of the oxygen evolution reaction. The construction of photoanodes that appreciably enhance the efficiency of this process is of vital technological importance towards solar fuel synthesis. In this work, Mo-modified $BiVO_{4}$ (Mo:$BiVO_{4}$), a promising water splitting photoanode, was modified with various oxygen evolution catalysts in two distinct configurations, with the catalysts either deposited on the surface of Mo:$BiVO_{4}$ or embedded inside a Mo:$BiVO_{4}$ film. The investigated catalysts included monometallic, bimetallic, and trimetallic oxides with spinel and layered structures, and nickel boride ($Ni_{x}B$). In order to follow the influence of the incorporated catalysts and their respective properties, as well as the photoanode architecture on photoelectrochemical water oxidation, the fabricated photoanodes were characterised for their optical, morphological, and structural properties, photoelectrocatalytic activity with respect to evolved oxygen, and recombination rates of the photogenerated charge carriers. The architecture of the catalyst-modified Mo:$BiVO_{4}$ photoanode was found to play a more decisive role than the nature of the catalyst on the performance of the photoanode in photoelectrocatalytic water oxidation. Differences in the photoelectrocatalytic activity of the various catalyst-modified Mo:$BiVO_{4}$ photoanodes are attributed to the electronic structure of the materials revealed through differences in the Fermi energy levels. This work thus expands on the current knowledge towards the design of future practical photoanodes for photoelectrocatalytic water oxidation

Splicing the active phases of copper/cobalt-based catalysts achieves high-rate tandem electroreduction of nitrate to ammonia

Electrocatalytic recycling of waste nitrate ($NO_{3}$$^-$) to valuable ammonia ($NH_3$) at ambient conditions is a green and appealing alternative to the Haber−Bosch process. However, the reaction requires multi-step electron and proton transfer, making it a grand challenge to drive high-rate $NH_3$ synthesis in an energy-efficient way. Herein, we present a design concept of tandem catalysts, which involves coupling intermediate phases of different transition metals, existing at low applied overpotentials, as cooperative active sites that enable cascade $NO_{3}$$^-$-to-$NH_3$ conversion, in turn avoiding the generally encountered scaling relations. We implement the concept by electrochemical transformation of Cu−Co binary sulfides into potential-dependent core−shell $Cu/CuO_x$ and Co/CoO phases. Electrochemical evaluation, kinetic studies, and in−situ Raman spectra reveal that the inner $Cu/CuO_x$ phases preferentially catalyze $NO_{3}$$^-$ reduction to $NO_{2}$$^-$, which is rapidly reduced to $NH_3$ at the nearby Co/CoO shell. This unique tandem catalyst system leads to a $NO_{3}$$^-$-to-$NH_3$ Faradaic efficiency of 93.3 $\pm$ 2.1% in a wide range of $NO_{3}$$^-$ concentrations at pH 13, a high $NH_3$ yield rate of 1.17 mmol $cm^{−2} h^{−1}$ in 0.1 M $NO_{3}$$^-$ at −0.175 V vs. RHE, and a half-cell energy efficiency of ~36%, surpassing most previous reports

In situ carbon corrosion and Cu leaching as a strategy for boosting oxygen evolution reaction in multimetal electrocatalysts

The number of active sites and their intrinsic activity are key factors in designing high-performance catalysts for the oxygen evolution reaction (OER). The synthesis, properties, and in-depth characterization of a homogeneous CoNiFeCu catalyst are reported, demonstrating that multimetal synergistic effects improve the OER kinetics and the intrinsic activity. In situ carbon corrosion and Cu leaching during the OER lead to an enhanced electrochemically active surface area, providing favorable conditions for improved electronic interaction between the constituent metals. After activation, the catalyst exhibits excellent activity with a low overpotential of 291.5 (\pm\) 0.5 mV at 10 mA $cm^{−2}$ and a Tafel slope of 43.9 mV $dec^{−1}$. It shows superior stability compared to $RuO_{2}$ in 1 $\tiny M$ KOH, which is even preserved for 120 h at 500 mA $cm^{−2}$ in 7 $\tiny M$ KOH at 50 °C. Single particles of this CoNiFeCu after their placement on nanoelectrodes combined with identical location transmission electron microscopy before and after applying cyclic voltammetry are investigated. The improved catalytic performance is due to surface carbon corrosion and Cu leaching. The proposed catalyst design strategy combined with the unique single-nanoparticle technique contributes to the development and characterization of high-performance catalysts for electrochemical energy conversion