Biocerâmicas e polímero para a regeneração de defeitos ósseos críticos

Introdução: Um grande desafio para a medicina e para os pesquisadores da área de Bioengenharia Tecidual Óssea é desenvolver e aperfeiçoar técnicas e biomateriais que possam devolver a estrutura e funcionalidade do tecido ósseo em situações de perdas ósseas extensas. Objetivo: Avaliar a resposta tecidual após a implantação de biomateriais biocerâmicos: hidroxiapatita (HA) e compósitos, HA associado ao biovidro (Bv) ou ao alginato (alg). Metodologia: Uma amostra de 12 ratos da linhagem Wistar, machos adultos, foram utilizados e distribuídos para a composição de 4 grupos experimentais, avaliados no ponto biológico de 15 dias de pós-operatório: MHA – grupo com defeito preenchido com microesferas de HA; MHABv – grupo com defeito preenchido com microesferas de HA associada ao Bv; MHAalg – grupo com defeito preenchido com microesferas de HA associada ao alg; e Controle – defeito ósseo vazio sem implantação de biomaterial. Resultados: Os grupos MHA e MHAalg apresentaram uma neoformação óssea associada a borda óssea mais evidente que os grupos MHABv e Controle, estes últimos obtiveram uma neoformação restrita a borda do defeito. Em todos os grupos foi observada uma reação inflamatória crônica discreta, a qual nos grupos com implantação de biomaterial foi granulomatosa com a presença de células gigantes multinucleadas. Conclusão: Os biomateriais apresentaram-se como biocompatíveis e osteocondutores.

Avaliação da fase inicial do reparo ósseo após implantação de biomateriais

Introdução: Dentre os biomateriais à base de fosfato de cálcio utilizados como substitutos ósseos, a hidroxiapatita (HA) e biovidro (BV) são bastantes promissores devido à sua biocompatibilidade, união química ao o sso natural e possuir propriedades osteocondutivas. Objetivo: Avaliar a resposta tecidual após a implantação de arcabouços de HA e HA com BV em dois diferentes formatos. Metodologia: Em 15 ratos Wistar foi confeccionado um defeito crítico na região de calvária. Estes animais foram distribuídos aleatoriamente para implantação de: grânulos de HA; grânulos de HABV; disco de HA; disco de HABV; e controle (sem implantação de biomaterial), com eutanásia em 15 dias pós-operatórios. Resultado: Após análise histomorfométrica observou-se que a neoformação óssea em todos os grupos foi restrita às regiões de bordas do defeito, com maior extensão nos grupos em que foram implantados os discos. O disco de HABV favoreceu a uma maior área de matriz osteóide. Os biomateriais com biovidro demonstraram ser mais rígidos e mantiveram-se no defeito, enquanto que os que tinham apenas HA foram degradados rapidamente. Em todos os grupos, a resposta tecidual aos biomateriais foi adequada, com uma discreta reação inflamatória crônica granulomatosa distribuída de forma difusa. Conclusões: Os biomateriais foram biocompatíveis. Os biomateriais em formato de disco favoreceram uma melhor neoformação óssea do que quando em formato de grânulos, independente das composições; quando em formato de disco, a composição HABV, favoreceu uma maior área de neoformação óssea; quando em formato de grânulos, as composições HA e HABV, favoreceram uma neoformação óssea semelhante, em área e extensão

The Milky Way's circular velocity curve between 4 and 14 kpc from APOGEE data

We measure the Milky Way's rotation curve over the Galactocentric range 4 kpc <~ R <~ 14 kpc from the first year of data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We model the line-of-sight velocities of 3,365 stars in fourteen fields with b = 0 deg between 30 deg < l < 210 deg out to distances of 10 kpc using an axisymmetric kinematical model that includes a correction for the asymmetric drift of the warm tracer population (\sigma_R ~ 35 km/s). We determine the local value of the circular velocity to be V_c(R_0) = 218 +/- 6 km/s and find that the rotation curve is approximately flat with a local derivative between -3.0 km/s/kpc and 0.4 km/s/kpc. We also measure the Sun's position and velocity in the Galactocentric rest frame, finding the distance to the Galactic center to be 8 kpc < R_0 < 9 kpc, radial velocity V_{R,sun} = -10 +/- 1 km/s, and rotational velocity V_{\phi,sun} = 242^{+10}_{-3} km/s, in good agreement with local measurements of the Sun's radial velocity and with the observed proper motion of Sgr A*. We investigate various systematic uncertainties and find that these are limited to offsets at the percent level, ~2 km/s in V_c. Marginalizing over all the systematics that we consider, we find that V_c(R_0) 99% confidence. We find an offset between the Sun's rotational velocity and the local circular velocity of 26 +/- 3 km/s, which is larger than the locally-measured solar motion of 12 km/s. This larger offset reconciles our value for V_c with recent claims that V_c >~ 240 km/s. Combining our results with other data, we find that the Milky Way's dark-halo mass within the virial radius is ~8x10^{11} M_sun.Comment: submitted to Ap

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

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

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