14 research outputs found

    Drying shrinkage behavior of metakaolin-based and bamboo fiber reinforced geopolymers

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    This Brazil-USA collaborative research uses bamboo cultivated in the Amazon region and metakaolin attained from calcined Amazonian kaolin. The durability of sustainable geopolymer materials is studied by means of the drying shrinkage aging behavior. Scanning electron microscopy and energy dispersive x-ray fluorescence were used to investigate the microstructure of the composite materials. X-ray diffraction was used to confirm the formation of geopolymer. The water treated geopolymer matrix (GP) samples dried at room conditions for the periods of 3-7-14-21-28-56-112 days showed very close and increasing weight and length changes. The GP reinforced with bamboo fiber (GPBF) treated samples weight and length changes increased from the 3-day sample up to the 21-day, then it dropped down to the 112-day. The GP water treated samples dried at room conditions for the aging periods showed increasing flexural strength (MOR) and modulus of elasticity (E). The GPBF treated samples MOR were higher and very close to each other

    Strength and elastic behavior of metakaolin-based and bamboo fiber reinforced geopolymers

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    Amazonian metakaolin-based and bamboo fiber reinforced geopolymers were studied by means of the strength and elastic aging behaviors for construction materials applications. Scanning electron microscopy and energy dispersive x-ray fluorescence were used to investigate the microstructure of the composite materials. X-ray diffraction was used to confirm the reliability of the samples as being geopolymers. The geopolymer matrix (GP) and the GP reinforced with bamboo fiber (GPBF) samples were aged-dried at room conditions for the periods of 1-7-28 days. The GP and GPBF ultimate compressive stress increased with age from 1-day to 28-day, while elastic modulus decreased with age. The GPBF samples ultimate compressive stresses and elastic moduli were lower than the GP samples values, but still can be suitable as sustainable construction materials

    Multi-state models for defining degrees of chronicity related to HIV-infected patient therapy adherence

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    Submitted by Fábio Marques ([email protected]) on 2018-11-05T14:12:44Z No. of bitstreams: 1 Multi-state models for defining degrees of chronicity related to HIV_Beatriz_Grinsztejn_INI_Lapclin-AIDS_2013.pdf: 272944 bytes, checksum: 10b54768af600f98f05d7af5d2b44bd8 (MD5)Approved for entry into archive by Regina Costa ([email protected]) on 2018-11-05T17:16:19Z (GMT) No. of bitstreams: 1 Multi-state models for defining degrees of chronicity related to HIV_Beatriz_Grinsztejn_INI_Lapclin-AIDS_2013.pdf: 272944 bytes, checksum: 10b54768af600f98f05d7af5d2b44bd8 (MD5)Made available in DSpace on 2018-11-05T17:16:19Z (GMT). No. of bitstreams: 1 Multi-state models for defining degrees of chronicity related to HIV_Beatriz_Grinsztejn_INI_Lapclin-AIDS_2013.pdf: 272944 bytes, checksum: 10b54768af600f98f05d7af5d2b44bd8 (MD5) Previous issue date: 2013Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.Universidade Federal do Paraná. Setor de Ciências Exatas. Curitiba, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em DST/AIDS. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em DST/AIDS. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em DST/AIDS. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, Brasil.Poucos estudos sobre AIDS que avaliam fatores associados à falha terapêutica consideram sua evolução lenta, com a passagem por múltiplos estados de saúde, consequência do uso de antirretrovirais. Nesse artigo foram estudados fatores associados à progressão entre estados imunes, enfocando adesão, em 722 pacientes HIV+ acompanhados por 3 anos. O desfecho foi a contagem de células CD4 classificada em s1 (CD4 ≥ 500), s2 (350 ≤ CD4 < 500) e s3 (CD4 < 350). As transições entre estados foram modeladas por modelos multiestado. A adesão à terapia antirretroviral e o tempo de doença estão associados diferentemente à mudança do estado imune vivido pelo paciente. Baixa adesão à terapia aumentou o risco de s1→s2 e adesão intermediária aumentou o de s2→s3. Por outro lado, idades elevadas e tempo de doença de 2 a 4 anos se apresentam como fatores de proteção na progressão da AIDS. A modelagem multiestado é uma abordagem poderosa no estudo de doenças crônicas, por estimar os fatores associados a cada etapa da evolução de doenças crônicas, possibilitando a adoção de intervenções mais individualizadas e eficazes.Few studies on AIDS that evaluate factors associated with treatment failure have considered the slow evolution of the disease and multiple health state transitions following the use of antiretrovirals. In this article we study factors associated with the progression between different stages of the disease, focusing on therapy adherence using a sample of 722 HIV+ patients followed up for 3 years. States were defined using the following classifications of the CD4 cell count: s1 (CD4 ≥ 500); s2 (350 ≤ CD4 < 500); and s3 (CD4 < 350). The transitions between states were modeled using multi-state models. Antiretroviral therapy adherence and disease duration were associated with transitions between immune states during follow-up. Low adherence increased the hazard ratio of a transition between s1 to s2 and intermediate adherence increased the hazard ratio of a transition between s2 to s3. On the other hand, older age and disease duration between two and four years are protective factors for AIDS progression. Multi-state modeling is a powerful approach for studying chronic diseases and estimating factors associated with transitions between each stage of progression, thus enabling the use of more individualized and effective interventions.Existen pocos estudios sobre el SIDA que evalúan factores asociados al fallo terapéutico, consideran su evolución lenta, con el pasaje por múltiples estados de salud, consecuencia del uso de antirretrovirales. En ese artículo se estudiaron factores asociados a la progresión entre estados inmunes, enfocando adhesión, en 722 pacientes VIH+ acompañados durante 3 años. El desenlace fue el cómputo de células CD4, clasificado en s1 (CD4 ≥ 500), s2 (350 ≤ CD4 < 500) y s3 (CD4 < 350). Las transiciones entre estados se modelaron por modelos multi-estado. La adhesión a la terapia antirretroviral y el tiempo de enfermedad están asociados diferentemente al cambio del estado inmune vivido por el paciente. Baja adhesión a la terapia aumentó el riesgo de s1→s2 y una adhesión intermedia aumentó de un s2→s3. Por otro lado, edades elevadas y tiempo de enfermedad de 2 a 4 años se presentan como factores de protección en la progresión del SIDA. El modelo multi-estado es un enfoque poderoso en el estudio de enfermedades crónicas, por estimar los factores asociados a cada etapa de la evolución de enfermedades crónicas, posibilitando la adopción de intervenciones más individualizadas y eficaces

    Multi-state models for defining degrees of chronicity related to HIV-infected patient therapy adherence Modelos multi-estado para la determinación de los grados de cronicidad, de acuerdo con la adhesión del paciente infectado por el VIH Modelos multiestado para determinação dos graus de cronicidade de acordo com a adesão de paciente infectado pelo HIV

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    Few studies on AIDS that evaluate factors associated with treatment failure have considered the slow evolution of the disease and multiple health state transitions following the use of antiretrovirals. In this article we study factors associated with the progression between different stages of the disease, focusing on therapy adherence using a sample of 722 HIV+ patients followed up for 3 years. States were defined using the following classifications of the CD4 cell count: s1 (CD4 &#8805; 500); s2 (350 &#8804; CD4 Existen pocos estudios sobre el SIDA que evalúan factores asociados al fallo terapéutico, consideran su evolución lenta, con el pasaje por múltiples estados de salud, consecuencia del uso de antirretrovirales. En ese artículo se estudiaron factores asociados a la progresión entre estados inmunes, enfocando adhesión, en 722 pacientes VIH+ acompañados durante 3 años. El desenlace fue el cómputo de células CD4, clasificado en s1 (CD4 &#8805; 500), s2 (350 &#8804; CD4 Poucos estudos sobre AIDS que avaliam fatores associados à falha terapêutica consideram sua evolução lenta, com a passagem por múltiplos estados de saúde, consequência do uso de antirretrovirais. Nesse artigo foram estudados fatores associados à progressão entre estados imunes, enfocando adesão, em 722 pacientes HIV+ acompanhados por 3 anos. O desfecho foi a contagem de células CD4 classificada em s1 (CD4 &#8805; 500), s2 (350 &#8804; CD4 < 500) e s3 (CD4 < 350). As transições entre estados foram modeladas por modelos multiestado. A adesão à terapia antirretroviral e o tempo de doença estão associados diferentemente à mudança do estado imune vivido pelo paciente. Baixa adesão à terapia aumentou o risco de s1&#8594;s2 e adesão intermediária aumentou o de s2&#8594;s3. Por outro lado, idades elevadas e tempo de doença de 2 a 4 anos se apresentam como fatores de proteção na progressão da AIDS. A modelagem multiestado é uma abordagem poderosa no estudo de doenças crônicas, por estimar os fatores associados a cada etapa da evolução de doenças crônicas, possibilitando a adoção de intervenções mais individualizadas e eficazes

    Expression in , purification, refolding and antifungal activity of an osmotin from -1

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    Ed at 37°C for 3 h and induced by 0.4 mM IPTG (when indicated). . Molecular mass marker (LMW, Amersham Pharmacia Biotech); . Total protein fraction from non-induced culture; . Total protein fraction from IPTG-induced culture; . Soluble protein fraction from IPTG-induced culture; . Insoluble protein fraction from IPTG-induced culture. . Western blot analysis of expressed His-tagged mature SnOLP probed with His-monoclonal antibody. . Soluble protein fraction from IPTG-induced culture; . Insoluble protein fraction from IPTG-induced culture. . SDS-PAGE analysis of insoluble His-tagged mature SnOLP which was urea solubilized and subsequently purified by immobilized-metal (Ni) affinity chromatography (IMAC). and . Eluates from IMAC. In . and . the gels were stained with Coomassie brilliant blue. His-tagged mature SnOLP protein is indicated by arrow heads.<p><b>Copyright information:</b></p><p>Taken from "Expression in , purification, refolding and antifungal activity of an osmotin from "</p><p>http://www.microbialcellfactories.com/content/7/1/7</p><p>Microbial Cell Factories 2008;7():7-7.</p><p>Published online 11 Mar 2008</p><p>PMCID:PMC2362109.</p><p></p

    Expression in , purification, refolding and antifungal activity of an osmotin from -2

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    proteins were placed on plates 3 days after inoculation with fungal mycelia (plugs). Petri dishes were incubated at 29°C during the entire bioassay. Inhibitory effects of purified and renatured His-tagged mature SnOLP upon the fungi are observed as areas lacking mycelial growth. . var. ; 1 day after adding protein (a.a.p.); . , 5 days a.a.p. . Corresponds to the doses of 1 μg, 2 μg and 3 μg of purified and renatured His-tagged mature SnOLP, or to the concentrations of 0,1 μg/μL, 0,2 μg/μL and 0,3 μg/μL, respectively; . Corresponds to the dose of 10 μg of Bovine Serum Albumin (BSA), or to the concentration of 1 μg/μL; . Corresponds to the dose of 2000 U Nistatin, or to the concentration of 200 U/μL. . Average area (mm) of mycelial growth inhibition caused by renatured His-tagged mature SnOLP, as measured (software UTHSCSA Image Tool, Version 3.00 [57]) in three replicates, similar to the bioassay Petri dishes shown in A and B, for each fungus/oomycete and for each dose/concentration of SnOLP separately. Standard deviation bars are shown for each average column. The averages were statistically compared by using ANOVA and Tukey Test at the probability level of 1% (software Genes [58]). Different letters above the average columns (i.e. a, b and c), indicate that the average values were considered to be statistically different among each other, whereas statistically identical average values are indicated by the same letter. . var. ; 1 day after adding protein (a.a.p.); . , 5 days a.a.p. ; . , 12 days a.a.p.; . var. , 5 days a.a.p.; . f. sp. , 5 days a.a.p.<p><b>Copyright information:</b></p><p>Taken from "Expression in , purification, refolding and antifungal activity of an osmotin from "</p><p>http://www.microbialcellfactories.com/content/7/1/7</p><p>Microbial Cell Factories 2008;7():7-7.</p><p>Published online 11 Mar 2008</p><p>PMCID:PMC2362109.</p><p></p

    Expression in , purification, refolding and antifungal activity of an osmotin from -4

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    Ed at 37°C for 3 h and induced by 0.4 mM IPTG (when indicated). . Molecular mass marker (LMW, Amersham Pharmacia Biotech); . Total protein fraction from non-induced culture; . Total protein fraction from IPTG-induced culture; . Soluble protein fraction from IPTG-induced culture; . Insoluble protein fraction from IPTG-induced culture. . Western blot analysis of expressed His-tagged mature SnOLP probed with His-monoclonal antibody. . Soluble protein fraction from IPTG-induced culture; . Insoluble protein fraction from IPTG-induced culture. . SDS-PAGE analysis of insoluble His-tagged mature SnOLP which was urea solubilized and subsequently purified by immobilized-metal (Ni) affinity chromatography (IMAC). and . Eluates from IMAC. In . and . the gels were stained with Coomassie brilliant blue. His-tagged mature SnOLP protein is indicated by arrow heads.<p><b>Copyright information:</b></p><p>Taken from "Expression in , purification, refolding and antifungal activity of an osmotin from "</p><p>http://www.microbialcellfactories.com/content/7/1/7</p><p>Microbial Cell Factories 2008;7():7-7.</p><p>Published online 11 Mar 2008</p><p>PMCID:PMC2362109.</p><p></p
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