Suporte interpares na doença mental

Background Peer support is a mutual aid system based on the belief that someone who faced/overcome adversity can provide support, encouragement and guidance to those who experience similar situations. Objective To conduct a systematic review that describes this concept and characterizes peer supporters, its practice and efficacy. Method Research on ISI Web of Science, EBSCO Psychology and Behavioral Sciences Collection and Medline databases (from 2001 to December 2013) was conducted using as keywords “mental illness”, “mental health”, “psychiatric disability”, “mental health services”, combined with “peer support”, “mutual support”, “self-help groups”, “consumers as providers”, “peer-run services”, “peer-run programs” and “social support”. Results We found 1,566 articles and the application of both the exclusion (studies with children, teenagers and elderly people; disease in comorbidity; peer support associated to physical illnesses or family members/caregivers) and the inclusion criteria (full text scientific papers, peer support or similar groups directed for schizophrenia, depression, bipolar or psychotic disorders) lead to 165 documents, where 22 were excluded due to repetition and 31 to incomplete text. We analyzed 112 documents, identifying as main peer support categories: characterization, peer supporter, practices and efficacy. Discussion Despite an increasing interest about this topic, there is no consensus, suggesting realizing more studies.Contexto O suporte interpares é um sistema de ajuda mútua baseado na crença de que alguém que enfrentou/superou adversidades pode oferecer apoio, encorajamento e orientação a outros que enfrentam situações similares. Objetivo Realizar uma revisão sistemática que caracterize o suporte interpares como prática, analise a sua eficácia e caracterize os pares prestadores de suporte interpares. Método Pesquisa nas bases de dados ISI Web of Science, EBSCO Psychology and Behavioral Sciences Collection e Medline (2001 a dezembro de 2013), utilizando as palavras-chave “mental illness”, “mental health”, “psychiatric disability”, “mental health services”, combinadas com “peer support”, “mutual support”, “self-help groups”, “consumers as providers”, “peer-run services”, “peer-run programs” e “social support”. Resultados Encontraram-se 1.566 artigos e foram aplicados os critérios de exclusão (artigos com crianças, adolescentes e idosos; doença mental em comorbidade; suporte interpares em doenças físicas ou familiares/cuidadores) e de inclusão (revistas científicas com texto integral disponível; suporte interpares ou grupos similares dirigidos a esquizofrenia, depressão, transtorno bipolar e outras perturbações psicóticas), resultando em 165 documentos. Excluíram-se 22 por repetição e 31 por texto incompleto, resultando em 112, os quais se identificaram como principais categorias do suporte interpares: caracterização, prestador de suporte, práticas e eficácia. Conclusão Existe interesse crescente pelo tema, embora alguns domínios não sejam consensuais, sugerindo necessidade de mais estudos

A influência da electrolipólise associada ao exercício físico na atividade metabólica

O excesso de gordura abdominal associa-se a patologias cardiometabólicas. A microcorrente poderá ser coadjuvante ao exercício aeróbio na estimulação da lipólise, prevenindo riscos de saúde. O exercício físico aeróbio constitui uma modalidade capaz de diminuir o tecido adiposo, por estimular a lipólise através da elevação do nível de catecolaminas, resultantes do aumento da actividade do sistema nervoso simpático. O tipo de exercício físico mais adequado para consumo predominante de lípidos é o exercício aeróbio prolongado e de intensidade moderada. No entanto, na prática de exercício físico as fontes lipídicas são globais o que sustenta a utilidade de procedimentos que promovam o gasto de gordura localizada na região abdominal. A electrolipólise, que pode ser realizada pela aplicação de microcorrente de baixa frequência, poderá ser usada com este intuito, uma vez que a activação do sistema nervoso simpático, juntamente com a alteração da polaridade da membrana celular dos adipócitos e o aumento do fluxo sanguíneo, parecem ser os responsáveis pela promoção da lipólise e aumento do metabolismo local. Para além destes mecanismos, a microcorrente favorece a degradação de triglicerídeos ao promover a activação das enzimas triglicerídeo lípase e lípase hormono-sensível. Após a hidrólise de triglicerídeos torna-se fulcral o exercício físico para que os ácidos gordos em circulação sejam utilizados como fonte de energia. Porém não existe evidência científica que comprove os efeitos da electrolipólise associada ao exercício físico bem como consenso nos parâmetros que devem ser utilizados para electrolipólise, neste sentido vários estudos tem sido realizados no âmbito da linha de investigação de sistema tegumentar e metabólico da ESTSP

Early Diagnosis of Invasive Aspergillosis in Neutropenic Patients. Comparison between Serum Galactomannan and Polymerase Chain Reaction

Background Invasive aspergillosis (IA) is a major cause of morbidity and mortality in profoundly neutropenic patients, so early diagnosis is mandatory. Aim Consecutive patients with hematological malignancies undergoing intensive chemotherapy were screened for IA with two different methods which were compared. Methods From October 2000 to August 2003 we tested 1311 serum samples from 172 consecutive patients with a polymerase chain reaction assay and between April 2005 and April 2008 we tested 806 serum samples from 169 consecutive patients with a Galactomannan (GM) test. Bronchoalveolar (BAL) samples were obtained whenever the patient's condition allowed and tested with either method. Results: The serum PCR assay had a sensitivity of 75.0% and a specificity of 91.9% and the serum GM assay had a sensitivity of 87.5% and a specificity of 93.1%, ( P > 0.05). The presence of two or more consecutive positive serum samples was predictive of IA for both assays. BAL GM/PCR was positive in some patients without serum positivity and in patients with 2 or more positive serum GM/PCR. Conclusions: No significant differences between the 2 serum tests were found. The GM assay has the advantage of being standardized among several laboratories and is incorporated in the criteria established by the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycosis Study Group (EORTC/MSG), however is much more expensive. BAL GM and PCR sampling aids in IA diagnosis but needs further validation studies to differentiate between colonization and true infection in cases where serum GM or PCR are negative

Estilos de vida em adolescentes : educação e promoção da saúde

info:eu-repo/semantics/publishedVersio

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Genome of the Avirulent Human-Infective Trypanosome—Trypanosoma rangeli

Background: Trypanosoma rangeli is a hemoflagellate protozoan parasite infecting humans and other wild and domestic mammals across Central and South America. It does not cause human disease, but it can be mistaken for the etiologic agent of Chagas disease, Trypanosoma cruzi. We have sequenced the T. rangeli genome to provide new tools for elucidating the distinct and intriguing biology of this species and the key pathways related to interaction with its arthropod and mammalian hosts.  Methodology/Principal Findings: The T. rangeli haploid genome is ,24 Mb in length, and is the smallest and least repetitive trypanosomatid genome sequenced thus far. This parasite genome has shorter subtelomeric sequences compared to those of T. cruzi and T. brucei; displays intraspecific karyotype variability and lacks minichromosomes. Of the predicted 7,613 protein coding sequences, functional annotations could be determined for 2,415, while 5,043 are hypothetical proteins, some with evidence of protein expression. 7,101 genes (93%) are shared with other trypanosomatids that infect humans. An ortholog of the dcl2 gene involved in the T. brucei RNAi pathway was found in T. rangeli, but the RNAi machinery is non-functional since the other genes in this pathway are pseudogenized. T. rangeli is highly susceptible to oxidative stress, a phenotype that may be explained by a smaller number of anti-oxidant defense enzymes and heatshock proteins.  Conclusions/Significance: Phylogenetic comparison of nuclear and mitochondrial genes indicates that T. rangeli and T. cruzi are equidistant from T. brucei. In addition to revealing new aspects of trypanosome co-evolution within the vertebrate and invertebrate hosts, comparative genomic analysis with pathogenic trypanosomatids provides valuable new information that can be further explored with the aim of developing better diagnostic tools and/or therapeutic targets

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