Transthyretin: No association between serum levels or gene variants and schizophrenia

It has been proposed that schizophrenia results from an environmental insult in genetically predisposed individuals. Environmental factors capable of modulating transcriptional activity and their carriers could link the genetic and environmental components of schizophrenia. Among these is transthyretin (TTR), a major carrier of thyroid hormones and retinol-binding protein (RBP). Retinoids and thyroid hormones regulate the expression of several genes, both during development and in the adult brain. Decreased TTR levels have been reported in the cerebrospinal fluid of patients with depression and Alzheimer's disease, and the absence of TTR influences behavior in mice. DNA variants capable of altering TTR ability to carry its ligands, either due to reduced transcription of the gene or to structural modifications of the protein, may influence development of the central nervous system and behavior. In the present study we searched for variants in the regulatory and coding regions of the TTR gene, and measured circulating levels of TTR and RBP. We found a novel single nucleotide polymorphism (SNP), ss46566417, 18 bp upstream of exon 4. Neither this SNP nor the previously described rs1800458 were found associated with schizophrenia. In addition, serum TTR and RBP levels did not differ between mentally healthy and schizophrenic individuals. In conclusion, our data does not support an involvement of the TTR gene in the pathophysiology of schizophrenia.http://www.sciencedirect.com/science/article/B6T8T-4K12CM6-2/1/78223a224d1392e250f7562405e6796

A avaliação do risco de parto prematuro através da medida do colo uterino por ultrassonografia transvaginal

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Avaliação ultra-sonográfica do colo uterino entre 22 e 24 semanas de gestação : correlações com características demográficas e com a história obstétrica

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Avaliação das artérias uterinas com ecografia Doppler para o rastreamento da pré-eclâmpsia, retardo do crescimento intra-uterino e mecônio

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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

The complete genome sequence of Chromobacterium violaceum reveals remarkable and exploitable bacterial adaptability

Chromobacterium violaceum is one of millions of species of free-living microorganisms that populate the soil and water in the extant areas of tropical biodiversity around the world. Its complete genome sequence reveals (i) extensive alternative pathways for energy generation, (ii) ≈500 ORFs for transport-related proteins, (iii) complex and extensive systems for stress adaptation and motility, and (iv) wide-spread utilization of quorum sensing for control of inducible systems, all of which underpin the versatility and adaptability of the organism. The genome also contains extensive but incomplete arrays of ORFs coding for proteins associated with mammalian pathogenicity, possibly involved in the occasional but often fatal cases of human C. violaceum infection. There is, in addition, a series of previously unknown but important enzymes and secondary metabolites including paraquat-inducible proteins, drug and heavy-metal-resistance proteins, multiple chitinases, and proteins for the detoxification of xenobiotics that may have biotechnological applications

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