Association of the fibronectin type III domain–containing protein 5 rs1746661 single nucleotide polymorphism with reduced brain glucose metabolism in elderly humans

Fibronectin type III domain–containing protein 5 (FNDC5) and its derived hormone, irisin, have been associated with metabolic control in humans, with described FNDC5 single nucleotide polymorphisms being linked to obesity and metabolic syndrome. Decreased brain FNDC5/irisin has been reported in subjects with dementia due to Alzheimer’s disease. Since impaired brain glucose metabolism develops in ageing and is prominent in Alzheimer’s disease, here, we examined associations of a single nucleotide polymorphism in the FNDC5 gene (rs1746661) with brain glucose metabolism and amyloid-β deposition in a cohort of 240 cognitively unimpaired and 485 cognitively impaired elderly individuals from the Alzheimer’s Disease Neuroimaging Initiative. In cognitively unimpaired elderly individuals harbouring the FNDC5 rs1746661(T) allele, we observed a regional reduction in low glucose metabolism in memory-linked brain regions and increased brain amyloid-β PET load. No differences in cognition or levels of cerebrospinal fluid amyloid-β42, phosphorylated tau and total tau were observed between FNDC5 rs1746661(T) allele carriers and non-carriers. Our results indicate that a genetic variant of FNDC5 is associated with low brain glucose metabolism in elderly individuals and suggest that FNDC5 may participate in the regulation of brain metabolism in brain regions vulnerable to Alzheimer’s disease pathophysiology. Understanding the associations between genetic variants in metabolism-linked genes and metabolic brain signatures may contribute to elucidating genetic modulators of brain metabolism in humans

Exposure computational models with voxel phantoms coupled to EGSnrc Monte Carlo code

In computational dosimetry of ionizing radiation, the energy deposited in radiosensitive organs and tissues is evaluated when an anthropomorphic simulator (phantom) is irradiated using Exposure Computational Models (ECMs). An ECM is a virtual scene with a phantom positioned mathematically relative to a radioactive source. The initial state includes information like the type of primary particle, its energy, starting point coordinates, and direction. Subsequently, robust Monte Carlo (MC) codes are used to simulate the particle's mean free path, interaction with the medium's atoms, and energy deposition. These are common steps for simulations involving photons and/or primary electrons. The GDN (Research Group on Numerical Dosimetry and the Research Group on Computational Dosimetry and Embedded Systems) has published ECMs with voxel phantoms irradiated by photons using the MC code EGSnrc. This work has led to specific computational tools development for various numerical dosimetry stages, including input file preparation, ECM execution, and result analysis. Since 2004, the GDN developed in-house applications like FANTOMAS, CALDose_X, DIP, and MonteCarlo. Certain previously used phantoms are reintroduced to provide historical context in the ECMs' production timeline, emphasizing additive modifications inherent in systematic theme studies. The dosimetric evaluations used the binary version of the MASH (Male Adult mesh) phantom, converted to the SID (Dosimetric Information System) text file type. This format has been used by the group since 2021 to couple a voxel phantom to the EGSnrc user code. The ECM included an environmental dosimetry problem simulation. Most of these tools are accessible on the GDN page (http://dosimetrianumerica.org)

Tonometria Gastrointestinal no Perioperatório do Transplante Hepático: Uma Revisão Integrativa

Introdução: A tonometria gástrica é uma ferramenta útil para examinar a perfusão esplâncnica regional por permitir o fornecimento de uma avaliação indireta do estado do enxerto hepático. Isso ocorre devido à hipoperfusão esplâncnica ser um parâmetro crítico no contexto do transplante de fígado, estando associada ao desenvolvimento de problemas como insuficiência hepática aguda e falência de múltiplos órgãos. Objetivo: Analisar os efeitos da tonometria gastrointestinal no perioperatório dos pacientes submetidos ao transplante hepático. Metodologia: Trata-se de uma Revisão Integrativa realizada nas bases de dados PubMed e BVS. Foram utilizados os descritores: “Tonometry”, “Splanchnic circulation” e “Liver transplantation”, incluindo o operador booleano “AND”, e selecionados artigos de relevância para o tema. Foram selecionados inicialmente 24 artigos, todos publicados nos últimos 20 anos, em português e/ou inglês. Após análise, seis artigos corresponderam ao objetivo proposto. Resultado: Observou-se que a diferença entre a PraCO2 no final da cirurgia e na fase anepática foi maior em pacientes sem disfunção do enxerto hepático. Foi identificada uma correlação positiva entre ΔpraCO2 e o pico de ALT após o transplante de fígado. Em outro estudo, verificou-se que a presença de enzimas hepáticas elevadas e a piora da função hepática sintética, coagulopatia e encefalopatia estava relacionada à má função do enxerto. Também foi comprovado que o pH gástrico intramucoso pode predizer a funcionalidade precoce do enxerto. Em um grupo com disfunção hepática, os pacientes apresentaram pH gástrico intramucoso abaixo de 7,3 no período perioperatório, mantendo-se baixo até a 24ª hora pós-operatória, enquanto o grupo sem disfunção apresentou pH gástrico intramucoso acima de 7,3, exceto na fase anepática, quando ficou abaixo desse valor. Conclusão: Descreve-se a utilidade da tonometria gastrointestinal para monitorar a circulação esplâncnica e a função do enxerto hepático durante o transplante hepático. Embora alguns estudos ofereçam suporte a essa afirmação, esta revisão apresenta limitações devido à quantidade restrita de artigos disponíveis, o que a impede de abranger uma ampla gama de evidências científicas

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

Altered plasma protein profiles in genetic FTD – a GENFI study

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Plasma biomarkers reflecting the pathology of frontotemporal dementia would add significant value to clinical practice, to the design and implementation of treatment trials as well as our understanding of disease mechanisms. The aim of this study was to explore the levels of multiple plasma proteins in individuals from families with genetic frontotemporal dementia. Methods: Blood samples from 693 participants in the GENetic Frontotemporal Dementia Initiative study were analysed using a multiplexed antibody array targeting 158 proteins. Results: We found 13 elevated proteins in symptomatic mutation carriers, when comparing plasma levels from people diagnosed with genetic FTD to healthy non-mutation controls and 10 proteins that were elevated compared to presymptomatic mutation carriers. Conclusion: We identified plasma proteins with altered levels in symptomatic mutation carriers compared to non-carrier controls as well as to presymptomatic mutation carriers. Further investigations are needed to elucidate their potential as fluid biomarkers of the disease process.Open access funding provided by Karolinska Institute. C.G. received funding from EU Joint Programme—Neurodegenerative Disease Research -Prefrontals Vetenskapsrådet Dnr 529–2014-7504, Vetenskapsrådet 2015–02926, Vetenskapsrådet 2018–02754, the Swedish FTD Inititative-Schörling Foundation, Alzheimer Foundation, Brain Foundation, Dementia Foundation and Region Stockholm ALF-project. PN received funding from KTH Center for Applied Precision Medicine (KCAP) funded by the Erling-Persson Family Foundation, the Swedish FTD Inititative-Schörling Foundation and Åhlén foundation. D.G. received support from the EU Joint Programme—Neurodegenerative Disease Research and the Italian Ministry of Health (PreFrontALS) grant 733051042. E.F. has received funding from a Canadian Institute of Health Research grant #327387. F.M. received funding from the Tau Consortium and the Center for Networked Biomedical Research on Neurodegenerative Disease. J.B.R. has received funding from the Welcome Trust (103838) and is supported by the Cambridge University Centre for Frontotemporal Dementia, the Medical Research Council (SUAG/051 G101400) and the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215–20014). J.C.V.S. was supported by the Dioraphte Foundation grant 09–02-03–00, Association for Frontotemporal Dementias Research Grant 2009, Netherlands Organization for Scientific Research grant HCMI 056–13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland and the Bluefield Project. J.D.R. is supported by the Bluefield Project and the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and a Miriam Marks Brain Research UK Senior Fellowship. M.M. has received funding from a Canadian Institute of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. M.O. has received funding from Germany’s Federal Ministry of Education and Research (BMBF). R.S-V. is supported by Alzheimer’s Research UK Clinical Research Training Fellowship (ARUK-CRF2017B-2) and has received funding from Fundació Marató de TV3, Spain (grant no. 20143810). R.V. has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. This work was also supported by the EU Joint Programme—Neurodegenerative Disease Research GENFI-PROX grant [2019–02248; to J.D.R., M.O., B.B., C.G., J.C.V.S. and M.S.info: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