Growth differentiation factor 11 delivered by dairy Lactococcus lactis strains modulates inflammation and prevents mucosal damage in a mice model of intestinal mucositis

Mucositis is an inflammation of the gastrointestinal mucosa that debilitate the quality of life of patients undergoing chemotherapy treatments. In this context, antineoplastic drugs, such as 5-fluorouracil, provokes ulcerations in the intestinal mucosa that lead to the secretion of pro-inflammatory cytokines by activating the NF-κB pathway. Alternative approaches to treat the disease using probiotic strains show promising results, and thereafter, treatments that target the site of inflammation could be further explored. Recently, studies reported that the protein GDF11 has an anti-inflammatory role in several diseases, including in vitro and in vivo results in different experimental models. Hence, this study evaluated the anti-inflammatory effect of GDF11 delivered by Lactococcus lactis strains NCDO2118 and MG1363 in a murine model of intestinal mucositis induced by 5-FU. Our results showed that mice treated with the recombinant lactococci strains presented improved histopathological scores of intestinal damage and a reduction of goblet cell degeneration in the mucosa. It was also observed a significant reduction of neutrophil infiltration in the tissue in comparison to positive control group. Moreover, we observed immunomodulation of inflammatory markers Nfkb1, Nlrp3, Tnf, and upregulation of Il10 in mRNA expression levels in groups treated with recombinant strains that help to partially explain the ameliorative effect in the mucosa. Therefore, the results found in this study suggest that the use of recombinant L. lactis (pExu:gdf11) could offer a potential gene therapy for intestinal mucositis induced by 5-FU

III Diretriz Brasileira de Insuficiência Cardíaca Crônica

Universidade de São Paulo Faculdade de Medicina Hospital das ClínicasUniversidade Federal do Rio Grande do Sul Hospital de Clínicas de Porto AlegreUniversidade de Pernambuco Faculdade de Ciências Médicas de PernambucoUniversidade Federal de São Paulo (UNIFESP) Escola Paulista de MedicinaUniversidade Federal de Minas Gerais Faculdade de MedicinaFaculdade de Medicina de São José do Rio PretoFundação Universitária de Cardiologia do Rio Grande do Sul Instituto de CardiologiaRede Labs D'OrUniversidade Federal FluminenseUniversidade do Estado do Rio de Janeiro Faculdade de Ciencias MédicasInstituto Dante Pazzanese de CardiologiaSanta Casa de MisericórdiaUniversidade de Pernambuco Pronto Socorro Cardiológico de PernambucoHospital Pró CardíacoHospital de MessejanaPontifícia Universidade Católica do ParanáUniversidade Federal de Goiás Faculdade de MedicinaUniversidade de São Paulo Faculdade de Medicina de Ribeirão PretoReal e Benemerita Sociedade de Beneficência PortuguesaFaculdade de Ciências Médicas de Minas GeraisUNIFESP, EPMSciEL

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

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate