ANÁLISE MORFOLÓGICA DOS PROCESSOS PATOLÓGICOS GERAIS EM RINS DE RATOS OBESOS E SUA RELAÇÃO COM O EXERCÍCIO FÍSICO

The objective of the study was to compare the morphological findings, quantify the inflammation and renal fibrosis in obese rats submitted to physical exercise. Male Wistar rats were used. Control animals were fed standard feed and tap water, and the obese were fed a hyperlipidic diet. These animals were submitted to intermittent training, where the movement of complete extension of the paw was performed, lifting a load positioned at the back of the vest. The animals' kidneys were collected to evaluate the findings. Regarding the histopathological analysis, no general pathological processes were evidenced. When the fibrosis quantification was performed, no significant results were observed between the groups. It was concluded that no general pathological processes were observed in the kidneys of obese rats and their relationship with physical exercise. In this way, it is suggested to carry out new studies with longer protocols in order to better elucidate the results.   KEYWORDS: Obesity, Kidney, Inflammation, Fibrosis, Exercise.O objetivo do estudo foi comparar os achados morfológicos, quantificar a inflamação e fibrose renal em ratos obesos submetidos ao exercício físico. Foram utilizados 56 ratos machos Wistar. Os animais controles foram alimentados com ração padrão e água de torneira, e os obesos por uma dieta hiperlipídica. Esses animaisforam submetidos ao treinamento intermitente, onde se realizou o movimento de extensão completa da pata, levantando uma carga posicionada na parte posterior do colete. Foram coletados os rins dos animais para avaliar os achados. Com relação à análise histopatológica, não foram evidenciados processos patológicos gerais. Ao ser realizada a quantificação de fibrose, não foram evidenciados resultados significativos entre os grupos. Conclui-se que não foram evidenciados processos patológicos gerais em rins de ratos obesos e sua relação com o exercício físico. Desta forma, é sugerida a realização de novos estudos com protocolos mais longos a fim de elucidar melhor os resultados. Palavras-chaves: Obesidade, rim, Inflamação, fibrose, exercício

Impact of COVID-19 In-hospital Mortality in Chagas Disease Patients

The COVID-19 virus infection caused by the new SARS-CoV-2 was first identified in Rio de Janeiro (RJ), Brazil, in March 2020. Until the end of 2021, 504,399 COVID-19 cases were confirmed in RJ, and the total death toll reached 68,347. The Evandro Chagas National Institute of Infectious Diseases from Oswaldo Cruz Foundation (INI-Fiocruz) is a referral center for treatment and research of several infectious diseases, including COVID-19 and Chagas disease (CD). The present study aimed to evaluate the impact of COVID-19 on in-hospital mortality of patients with CD during the COVID-19 pandemic period. This observational, retrospective, longitudinal study evaluated all patients with CD hospitalized at INI-Fiocruz from May 1, 2020, to November 30, 2021. One hundred ten hospitalizations from 81 patients with CD (58% women; 68 ± 11 years) were evaluated. Death was the study's main outcome, which occurred in 20 cases. The mixed-effects logistic regression was performed with the following variables to test whether patients admitted to the hospital with a COVID-19 diagnosis would be more likely to die than those admitted with other diagnoses: admission diagnosis, sex, age, COVID-19 vaccination status, CD clinical classification, and the number of comorbidities. Results from multiple logistic regression analysis showed a higher risk of in-hospital mortality in patients diagnosed with COVID-19 (OR 6.37; 95% CI 1.78–22.86) compared to other causes of admissions. In conclusion, COVID-19 infection had a significant impact on the mortality risk of INI-Fiocruz CD patients, accounting for one-third of deaths overall. COVID-19 presented the highest percentage of death significantly higher than those admitted due to other causes during the COVID-19 pandemic

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

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

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