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

A New Approach for the Development of Multiple Cardiovascular Risk Factors in Two Rat Models of Hypertension

Cardiovascular disease (CVD) is the leading cause of death among non-communicable diseases. There is a lack of valid animal models that mimic associations among multiple cardiovascular risk factors in humans. The present study developed an animal model that uses multiple cardiovascular risk factors—namely, hypertension, hypothyroidism, and a high-fat diet (HFD). Two models of hypertension were used: renovascular hypertension (two-kidney, one clip [2K1C]) and spontaneously hypertensive rats (SHRs). The naive group was composed of normotensive rats. Twelve weeks after surgery to induce renovascular hypertension, rats in the 2K1C and SHR groups underwent thyroidectomy. The HFD was then implemented for 6 weeks. Renal function, serum redox status, biochemical CVD markers, electrocardiographic profile, blood pressure, mesenteric vascular bed reactivity, histopathology, and morphometry were investigated. Both experimental models induced dyslipidemia, renal function impairment, and hepatic steatosis, accompanied by higher levels of different inflammatory markers and serum oxidative stress. These alterations contributed to end-organ damage in all hypertensive rats. Our findings corroborate a viable alternative model that involves multiple cardiovascular risk factors and resembles conditions that are seen in humans. Both models mimicked CVD, but our data show that SHRs exhibit more significant pathophysiological changes

Development of a Predictive Model to Induce Atherogenesis and Hepato-Renal Impairment in Female Rats

Therapeutic approaches for the treatment of dyslipidemia and atherosclerosis have radically changed in recent decades. Part of this advance undeniably stems from basic biomedical research that has provided a better understanding and identification of new therapeutic targets. The aim of this work was to develop a model to induce atherogenesis and hepato-renal impairment in female Wistar rats. The following groups received the respective treatments for 60 days: control animals, non-ovariectomized rats that received an atherogenic diet (NEAD), ovariectomized rats that received an atherogenic diet (NOAD), non-ovariectomized rats that received an atherogenic diet and oral Nω-nitro-l-arginine methyl ester hydrochloride (l-NAME; LEAD), and ovariectomized rats that received an atherogenic diet and oral l-NAME (LOAD). Animals in the NEAD, NOAD, LEAD, and LOAD groups also received methimazole and cholecalciferol daily. Urinary, biochemical, hemodynamic, and electrocardiographic parameters and renal function were assessed. Samples of the liver, heart, kidney, and arteries were collected to investigate redox status and perform histopathological analyses. All of the groups developed dyslipidemia and hepatic steatosis. Only the NEAD group developed arterial lesions that were compatible with fatty streaks. Renal function was significantly impaired in the LEAD and NOAD groups. These results indicate a viable alternative to induce atherogenesis and hepato-renal impairment in female rats