GRAU DE DEPENDÊNCIA DE CUIDADOS DE ENFERMAGEM EM PACIENTES HOSPITALIZADOS APÓS INTERNAÇÃO EM TERAPIA INTENSIVA

Introdução: a definição do grau de dependência de cuidados de enfermagem subsidia o estabelecimento de prioridades assistenciais e o dimensionamento de pessoal em uma unidade de internação hospitalar. Objetivo: identificar o grau de dependência de cuidados de enfermagem em pacientes transferidos da Unidade de Terapia Intensiva para as demais unidades de internação. Método: estudo observacional, transversal e de abordagem quantitativa realizado em um hospital de referência regional do Estado do Rio Grande do Norte. A amostra foi composta por 101 pacientes; os dados foram coletados em formulário onde constava o Sistema de Classificação de Pacientes de Fugulin. Resultados: 62,38% dos pacientes eram do sexo masculino, 61,39% possuíam idade de até 60 anos. Os principais motivos de internação foram agravos traumáticos (33,66%), agravos respiratórios (29,70%), agravos neurológicos (26,73%). Os graus de dependência identificados foram: Cuidados Mínimos (17,82%), Cuidados Intermediários (13,86%), Cuidados Semi-Intensivo (35,64%), Cuidado Intensivo (7,92%) e Cuidado de Alta Dependência (24,76%). Conclusão: houve uma prevalência dos graus de dependência de cuidados Semi-Intensivos e de Alta Dependência, principalmente para o atendimento de necessidades básicas como alimentação, higienização e locomoção. Este perfil de pacientes demanda uma assistência diferenciada dada a transição de um setor que presta cuidados intensivos para uma unidade que, de certa forma, pressupõe menores graus de dependência de cuidados. O estudo também apresenta dados que podem subsidiar o dimensionamento de pessoal de enfermagem no intuito de assegurar uma assistência compatível com as necessidades de cuidado

Effect of 3'UTR RET variants on RET mRNA secondary structure and disease presentation in medullary thyroid carcinoma

The RET S836S variant has been associated with early onset and increased risk for metastatic disease in medullary thyroid carcinoma (MTC). However, the mechanism by which this variant modulates MTC pathogenesis is still open to discuss. Of interest, strong linkage disequilibrium (LD) between RET S836S and 3'UTR variants has been reported in Hirschsprung's disease patients. Objective To evaluate the frequency of the RET 3’UTR variants (rs76759170 and rs3026785) in MTC patients and to determine whether these variants are in LD with S836S polymorphism. Methods Our sample comprised 152 patients with sporadic MTC. The RET S836S and 3’UTR (rs76759170 and rs3026785) variants were genotyped using Custom TaqMan Genotyping Assays. Haplotypes were inferred using the phase 2.1 program. RET mRNA structure was assessed by Vienna Package. Results The mean age of MTC diagnosis was 48.5±15.5 years and 57.9%were women. The minor allele frequencies of RET polymorphisms were as follows: S836S, 5.6%; rs76759170, 5.6%; rs3026785, 6.2%. We observed a strong LD among S836S and 3’UTR variants (|D’| = -1, r2 = 1 and |D’| = -1, r2 = 0,967). Patients harboring the S836S/3’UTR variants presented a higher percentage of lymph node and distant metastasis (P = 0.013 and P<0.001, respectively). Accordingly, RNA folding analyses demonstrated different RNA secondarystructure predictions for WT(TCCGT), S836S(TTCGT) or 3’UTR(GTCAC) haplotypes. The S836S/3’UTR haplotype presented a greater number of double helices sections and lower levels of minimal free energy when compared to the wild-type haplotype, suggesting that these variants provides the most thermodynamically stable mRNA structure, which may have functional consequences on the rate of mRNA degradation. Conclusion The RET S836S polymorphism is in LD with 3’UTR variants. In silico analysis indicate that the 3’UTR variants may affect the secondary structure of RET mRNA, suggesting that these variants might play a role in posttranscriptional control of the RET transcripts

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

Espectroscopia de campo-próximo em sistemas bidimensionais

Exportado OPUSMade available in DSpace on 2019-08-12T14:49:39Z (GMT). No. of bitstreams: 1 rodolfo_vieira_maximiano_master_thesis.pdf: 6266137 bytes, checksum: 866b06981cbfd7070131c3d75a052474 (MD5) Previous issue date: 3Neste trabalho, desenvolvemos um modelo teórico que descreve o aumento do sinal Raman em sistemas 2D para experimentos de Tip-Enhanced Raman Spectroscopy (TERS), ou Espectroscopia Raman de campo-próximo. A análise quantifica o valor da intensidade Raman em regime de campo-próximo como função da distância ponta-amostra, do tensor polarizabilidade Raman, configuração do laser incidente e orientação da ponta relativa ao plano. A análise leva em conta ambos os regimes espacialmente coerentes e incoerentes de espalhamento, cujas intensidades variam proporcionalmente ao inverso da 10a e 8ia potência, respectivamente. Nós analisamos os resultados para os modos vibracionais que ocorrem em sistemas bidimensionais, por exemplo, grafeno e nitreto de boro, levando em conta a polarização da luz incidente nos modos linear e radial. Nossos resultados mostram que, para cada modo vibracional, há uma competição entre o melhor ângulo para excitar o dipolo formado na ponta e o dipolo Raman no material. Determinamos os ângulos ótimos para a medida em cada um desses casos. Todos esses parâmetros formam um guia para experimentos de TERS em materiais bidimensionais, como grafeno ou gases de elétrons bidimensionais, podendo ser estendido para materiais opacos como superfícies planas.A theory describing the near-field Raman enhancement in two-dimensional (2D) systems is presented. The analysis quanties the near-field Raman intensity as a function of the tip-sample distance, Raman polarizability tensor components, incident laser beam configuration, and tip orientation relative to the sample plane. Our results show that the near-field Raman intensity is inversely proportional to the 10th and 8th power of the tip-sample distance in the incoherent and coherent scattering regimes, respectively. Optimal conditions for the tipinclination angle for different congurations are determined, and the results can be used as a guide for TERS experiments in 2D systems, such as graphene and two-dimensional electron gases, and can be extended to opaque bulk materials with at surfaces

Effect of 3'UTR RET Variants on RET mRNA Secondary Structure and Disease Presentation in Medullary Thyroid Carcinoma.

The RET S836S variant has been associated with early onset and increased risk for metastatic disease in medullary thyroid carcinoma (MTC). However, the mechanism by which this variant modulates MTC pathogenesis is still open to discuss. Of interest, strong linkage disequilibrium (LD) between RET S836S and 3'UTR variants has been reported in Hirschsprung's disease patients.info:eu-repo/semantics/publishe

The figure shows the optimal mRNA secondary structure of the RET haplotypes.

<p>A) Total mRNA secondary structure of the <i>RET</i> wild-type (WT) haplotype. B) Part of mRNA secondary structure of the <i>RET</i> WT haplotype. C) Part of mRNA secondary structure of the <i>RET</i> S836S haplotype (</p><p>T<u>T</u>CGT</p>). D) Part of mRNA secondary structure of the <i>RET</i> 3’UTR haplotype (<p><u>GT</u>C<u>AC</u></p>). Haplotypes generated by RNAfold program (Vienna Package).<p></p