Análisis factorial exploratorio de la Escala de Miedos al Coronavirus

The COVID-19 pandemic and the implemented measures have generated fear, stress, depression and anxiety worldwide. To measure these effects on mental health, useful scales have been developed for research and to measure the effectiveness of interventions. The Coronavirus Fears Scale [Escala de Miedos al Coronavirus, EMC] was created and validated originally in the Spanish population, showing adequate psychometric properties. The aim of the present research was to examine the factorial structure and internal consistency of the EMC in Colombia. Based on a sample of 151 participants three factors with an eigenvalue > 1 were identified which explained 64% of the variance. In conclusion, the EMC with 18 items possesses adequate psychometric properties in working adults, both in terms of its factorial structure and reliability, and can be useful to identify COVID-19-related fears in this kind of population.La pandemia de la COVID-19 y las medidas instauradas han generado miedo, estrés, depresión y ansiedad a nivel mundial. Para medir estos efectos sobre la salud mental se han desarrollado escalas útiles en investigación y para medir la eficacia de las intervenciones. La Escala de Miedos al Coronavirus (EMC) fue creada y validada originalmente en población española mostrando propiedades psicométricas adecuadas. El objetivo del presente estudio fue examinar la estructura factorial y la consistencia interna de la EMC en Colombia. Basándose en una muestra de 151 participantes se identificaron tres factores con un valor propio > 1 que explicaron un 64% de la varianza. En conclusión, la EMC con 18 ítems posee adecuadas propiedades psicométricas en adultos trabajadores, tanto en lo que concierne a su estructura factorial como a su fiabilidad, y puede ser de utilidad para identificar los temores relacionados con la COVID-19 en este tipo de población

Assessing Pandemic Preparedness, Response, and Lessons Learned From the Covid-19 Pandemic in Four South American Countries: Agenda for the Future

INTRODUCTION: The COVID-19 pandemic emerged in a context that lacked adequate prevention, preparedness, and response (PPR) activities, and global, regional, and national leadership. South American countries were among world\u27s hardest hit by the pandemic, accounting for 10.1% of total cases and 20.1% of global deaths. METHODS: This study explores how pandemic PPR were affected by political, socioeconomic, and health system contexts as well as how PPR may have shaped pandemic outcomes in Argentina, Brazil, Colombia, and Peru. We then identify lessons learned and advance an agenda for improving PPR capacity at regional and national levels. We do this through a mixed-methods sequential explanatory study in four South American countries based on structured interviews and focus groups with elite policy makers. RESULTS: The results of our study demonstrate that structural and contextual barriers limited PPR activities at political, social, and economic levels in each country, as well as through the structure of the health care system. Respondents believe that top-level government officials had insufficient political will for prioritizing pandemic PPR and post-COVID-19 recovery programs within their countries\u27 health agendas. DISCUSSION: We recommend a regional COVID-19 task force, post-pandemic recovery, social and economic protection for vulnerable groups, improved primary health care and surveillance systems, risk communication strategies, and community engagement to place pandemic PPR on Argentina, Brazil, Colombia, and Peru and other South American countries\u27 national public health agendas

Assessing pandemic preparedness, response, and lessons learned from the COVID-19 pandemic in four south American countries: agenda for the future

IntroductionThe COVID-19 pandemic emerged in a context that lacked adequate prevention, preparedness, and response (PPR) activities, and global, regional, and national leadership. South American countries were among world’s hardest hit by the pandemic, accounting for 10.1% of total cases and 20.1% of global deaths.MethodsThis study explores how pandemic PPR were affected by political, socioeconomic, and health system contexts as well as how PPR may have shaped pandemic outcomes in Argentina, Brazil, Colombia, and Peru. We then identify lessons learned and advance an agenda for improving PPR capacity at regional and national levels. We do this through a mixed-methods sequential explanatory study in four South American countries based on structured interviews and focus groups with elite policy makers.ResultsThe results of our study demonstrate that structural and contextual barriers limited PPR activities at political, social, and economic levels in each country, as well as through the structure of the health care system. Respondents believe that top-level government officials had insufficient political will for prioritizing pandemic PPR and post-COVID-19 recovery programs within their countries’ health agendas.DiscussionWe recommend a regional COVID-19 task force, post-pandemic recovery, social and economic protection for vulnerable groups, improved primary health care and surveillance systems, risk communication strategies, and community engagement to place pandemic PPR on Argentina, Brazil, Colombia, and Peru and other South American countries’ national public health agendas

Multivariate analysis in genetic mapping of quantitative traits

Em pesquisa Genômica é de grande interesse o mapeamento de genes que controlam traços ou fenótipos quantitativos. Metodologias estatsticas para identicar genes que tenham efeitos sobre um unico traço são bem conhecidas na literatura e têm sido exaustivamente aplicadas no mapeamento genético de muitas doenças. Porem, na pratica, diferentes traços são correlacionados, como é o caso de hipertensão e obesidade, possivelmente, devido a aço de genes comuns envolvidos na sua regulação. Nestes casos, por meio de tecnicas estatísticas multivariadas, que exploram a estrutura de covariância entre os traços, é possvel identificar genes não detectados por analises univariadas, ganhar precisão nas estimativas dos efeitos e conhecer a posicão desses genes, alem de testar efeitos de pleiotropia (um mesmo gene controlando varios traços) e interacções gene-ambiente (os genes que controlam a pressão antes e depois de dieta com sal). Neste trabalho diferentes alternativas de analise estatstica são consideradas para explorar a informacão de vários tracos conjuntamente: modelo de regressão intervalar multivariado (Jiang & Zeng, 1995), mapeamento multivariado via a teoria espectral (Mangin et al.,1998), via medidas resumo relevantes (como a diferenca entre respostas antes e depois de uma exposição) e via ajustes por covariaveis. Também são introduzidas algumas abordagens graficas para o estudo do efeito de pleiotropia e interação geneambiente. As metodologias supracitadas são aplicadas a dados reais fornecidos pelo Laboratorio de Cardiologia e Genética Molecular do InCor/USP, que consideram várias medidas de pressão arterial em ratos provenientes de uma população F2.In Genomic research, the mapping of genes which control quantitative traits has been of great interest. Statistical methods for detection of genes, in uencing a single trait, are well known in the literature and they have been exhaustive used in the genetic mapping of many diseases. However, in real situations, dierent kind of traits are correlated, such as hypertention and obesity, that would be due to the action of a set of commom genes involved in the regulation of these traits. In these cases, through of multivariate statistical techniques, which explore the covariance structure between the traits, it is possible to identify genes that are not detected by univariated analysis. In addition multivariate analysis are useful to obtain accurate estimates and to know the position of these genes, besides testing eects of pleiotropic (a gene controlling several traits) and geneenvironmental interations (genes that control the pressure before and after salt diet). In this work dierent alternatives from statistical analysis are considered to explore information of several traits jointly: Interval multivariate regression models (Jiang and Zeng, 1995); multivariate mapping through the espectral theory (Mangin et al. 1998), summary measures (for example, models formulated in terms of the dierence between two traits) and adjustments including covariates. Also, graphics procedures are introduced in order to study eects of pleiotropy and geneenvironmental interactions . The methodologies mentioned above are applied to real data set, supplied by the Cardiology and Molecular Genetic Laboratory of Heart institute (InCor-USP), that consider several measurements of blood pressure in rats that come from a F2 population

Multivariate analysis in genetic mapping of quantitative traits

Em pesquisa Genômica é de grande interesse o mapeamento de genes que controlam traços ou fenótipos quantitativos. Metodologias estatsticas para identicar genes que tenham efeitos sobre um unico traço são bem conhecidas na literatura e têm sido exaustivamente aplicadas no mapeamento genético de muitas doenças. Porem, na pratica, diferentes traços são correlacionados, como é o caso de hipertensão e obesidade, possivelmente, devido a aço de genes comuns envolvidos na sua regulação. Nestes casos, por meio de tecnicas estatísticas multivariadas, que exploram a estrutura de covariância entre os traços, é possvel identificar genes não detectados por analises univariadas, ganhar precisão nas estimativas dos efeitos e conhecer a posicão desses genes, alem de testar efeitos de pleiotropia (um mesmo gene controlando varios traços) e interacções gene-ambiente (os genes que controlam a pressão antes e depois de dieta com sal). Neste trabalho diferentes alternativas de analise estatstica são consideradas para explorar a informacão de vários tracos conjuntamente: modelo de regressão intervalar multivariado (Jiang & Zeng, 1995), mapeamento multivariado via a teoria espectral (Mangin et al.,1998), via medidas resumo relevantes (como a diferenca entre respostas antes e depois de uma exposição) e via ajustes por covariaveis. Também são introduzidas algumas abordagens graficas para o estudo do efeito de pleiotropia e interação geneambiente. As metodologias supracitadas são aplicadas a dados reais fornecidos pelo Laboratorio de Cardiologia e Genética Molecular do InCor/USP, que consideram várias medidas de pressão arterial em ratos provenientes de uma população F2.In Genomic research, the mapping of genes which control quantitative traits has been of great interest. Statistical methods for detection of genes, in uencing a single trait, are well known in the literature and they have been exhaustive used in the genetic mapping of many diseases. However, in real situations, dierent kind of traits are correlated, such as hypertention and obesity, that would be due to the action of a set of commom genes involved in the regulation of these traits. In these cases, through of multivariate statistical techniques, which explore the covariance structure between the traits, it is possible to identify genes that are not detected by univariated analysis. In addition multivariate analysis are useful to obtain accurate estimates and to know the position of these genes, besides testing eects of pleiotropic (a gene controlling several traits) and geneenvironmental interations (genes that control the pressure before and after salt diet). In this work dierent alternatives from statistical analysis are considered to explore information of several traits jointly: Interval multivariate regression models (Jiang and Zeng, 1995); multivariate mapping through the espectral theory (Mangin et al. 1998), summary measures (for example, models formulated in terms of the dierence between two traits) and adjustments including covariates. Also, graphics procedures are introduced in order to study eects of pleiotropy and geneenvironmental interactions . The methodologies mentioned above are applied to real data set, supplied by the Cardiology and Molecular Genetic Laboratory of Heart institute (InCor-USP), that consider several measurements of blood pressure in rats that come from a F2 population

Genetic mapping using the theory of the Added Variable Plot in the mixed models

Atualmente, um dos problemas mais importantes da Genética é a identificação de genes associados com doenças complexas. Um delineamento adequado para esta finalidade corresponde à coleta de dados de famílias e plataformas de marcadores moleculares do tipo SNP (do inglês, Single Nucleotide Polimorphism). Estas plataformas representam pontos de referência estrategicamente dispostos ao longo do genoma dos indivíduos e são de alta dimensão. A análise destes dados traz desafios analíticos como o problema de múltiplos testes e a seleção de variáveis preditoras. Nesta tese, propõe-se um critério para discriminar as variáveis preditoras genéticas em efeitos devidos ao componente aleatório poligênico e ao componente residual, sob a estrutura de um modelo linear misto. Também, considerando que o efeito individual das variáveis preditoras é esperado ser pequeno, é sugerido um método para encontrar subconjuntos ordenados destas variáveis e estudar o seu efeito simultâneo sobre a variável resposta em estudo. Neste contexto, utiliza-se a teoria associada ao Gráfico da Variável Adicionada em modelos mistos. As propostas são validadas por meio de um estudo de simulação, o qual é baseado em estruturas de famílias envolvidas no Projeto ``Corações de Baependi\" (InCor/USP), cujo objetivo é identificar genes associados a fatores de risco cardiovascular na população brasileira. Para a implementação dos procedimentos, usa-se o programa R e na geração das variáveis preditoras genéticas adota-se o aplicativo SimPed.Recently, one of the most important problems in genetics is the identification of genes associated with complex diseases. A useful design for this proposal corresponds to collect data from extended families and molecular markers platforms SNPs (Single Nucleotide polymorphism). These platforms represent points of reference strategically placed along the genome of the individuals and are high dimensional. Analysis of these data brings analytical challenges as the problem of multiple testing and selection of predictive variables. In this thesis, we propose a criterion for discriminating predictors of genetic effects due to random polygenic component and the residual component, under the framework of a linear mixed model. Also, considering that the individual effects of predictor variables is expected to be small, it is suggested a method for finding ordered subsets of these variables and study their simultaneous effect on the response variable under study. In this context, is used the theory of the added variable plot under a mixed model framework. The proposals are validated through a simulation study, which is based on structures of families involved in the Project `` Baependi Heart Study (FAPESP Process 2007/58150-7), whose objective is to identify genes associated with cardiovascular risk factors in the Brazilian population. This proposal is implemented by using the R statistical environment and for the simulation of genetic predictors is adopted the SimPed application

Temporal Change of Extracellular Matrix during Vein Arterialization Remodeling in Rats

The global expression profile of the arterialized rat jugular vein was established to identify candidate genes and cellular pathways underlying the remodeling process. The arterialized jugular vein was analyzed on days 3 and 28 post-surgery and compared with the normal jugular vein and carotid artery. A gene array platform detected 9846 genes in all samples. A heatmap analysis uncovered patterns of gene expression showing that the arterialized vein underwent a partial transition from vein to artery from day 3 to 28 post-surgery. The same pattern was verified for 1845 key differentially expressed genes by performing a pairwise comparison of the jugular vein with the other groups. Interestingly, hierarchical clustering of 60 genes with altered expression on day 3 and day 28 displayed an expression pattern similar to that of the carotid artery. Enrichment analysis results and the network relationship among genes modulated during vein arterialization showed that collagen might play a role in the early remodeling process. Indeed, the total collagen content was increased, with the augmented expression of collagen I, collagen IV, and collagen V in arterialized veins. Additionally, there was an increase in the expression of versican and Thy-1 and a decrease in the expression of biglycan and β1-integrin. Overall, we provide evidence that vein arterialization remodeling is accompanied by consistent patterns of gene expression and that collagen may be an essential element underlying extracellular matrix changes that support the increased vascular wall stress of the new hemodynamic environment

PBMCs express a transcriptome signature predictor of oxygen uptake responsiveness to endurance exercise training in men

Peripheral blood cells are an accessible environment in which to visualize exercise-induced alterations in global gene expression patterns. We aimed to identify a peripheral blood mononuclear cell (PBMC) signature represented by alterations in gene expression, in response to a standardized endurance exercise training protocol. In addition, we searched for molecular classifiers of the variability in oxygen uptake ((V) over dotO(2)). Healthy untrained policemen recruits (n = 13, 25 +/- 3 yr) were selected. Peak (V) over dotO(2) (measured by cardiopulmonary exercise testing) and total RNA from PBMCs were obtained before and after 18 wk of running endurance training (3 times/wk, 60 min). Total RNA was used for whole genome expression analysis using Affymetrix GeneChip Human Gene 1.0 ST. Data were normalized by the robust multiarray average algorithm. Principal component analysis was used to perform correlations between baseline gene expression and (V) over dotO(2peak). A set of 211 transcripts was differentially expressed (ANOVA, P 1.3). Functional enrichment analysis revealed that transcripts were mainly related to immune function, cell cycle processes, development, and growth. Baseline expression of 98 and 53 transcripts was associated with the absolute and relative (V) over dotO(2)peak response, respectively, with a strong correlation (r > 0.75, P < 0.01), and this panel was able to classify the 13 individuals according to their potential to improve oxygen uptake. A subset of 10 transcripts represented these signatures to a similar extent. PBMCs reveal a transcriptional signature responsive to endurance training. Additionally, a baseline transcriptional signature was associated with changes in (V) over dotO(2peak). Results might illustrate the possibility of obtaining molecular classifiers of endurance capacity changes through a minimally invasive blood sampling procedure4721323CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP482863/2011-0; 483509/2012-42005/59740-7Zerbini Foundationvvvvvvvvvvvvvvvvvvv