Participation of 5-HT and AT 1 Receptors within the Rostral Ventrolateral Medulla in the Maintenance of Hypertension in the Goldblatt 1 Kidney-1 Clip Model

The hypothesis that changes in neurotransmission within the rostral ventrolateral medulla (RVLM) are important to maintain the high blood pressure (BP) was tested in Goldblatt one kidney-one clip hypertension model (1K-1C). Male Wistar rats were anesthetized (urethane 1.2 g/kg, i.v.), and the effects of bilateral microinjections into the RVLM of the following drugs were measured in 1K-1C or control groups: glutamate (0.1 mol/L, 100 nL) and its antagonist kynurenic acid (0.02 mol/L, 100 nL), the angiotensin AT 1 receptor antagonist candesartan (0.01 mol/L, 100 nL), and the nonselective 5-HT receptor antagonist methiothepin (0.06 mol/L, 100 nL). Experiments in 1K-1C rats were performed 6 weeks after surgery. In anesthetized rats glutamate response was larger in hypertensive than in normotensive rats (H: Δ67 ± 6.5; N: Δ43 ± 3.54 mmHg). In contrast, kynurenic acid microinjection into the RVLM did not cause any change in BP in either group. The blockade of either AT 1 or 5-HT receptors within the RVLM decreased BP only in 1K-1C rats. A largest depressor response was caused by 5-HT receptor blockade. The data suggest that 5-HT and AT 1 receptors act tonically to drive RVLM in 1K-1C rats, and these actions within RVLM contribute to the pathogenesis of this model of hypertension

A coleção fotográfica V-8

This article is about the historical photos of Campinas city and its characters, shot\ud by photographer Aristides Pedro da Silva, known as V-8. The study relates the paths taken by\ud the photographer, the collections sources and composition, the circulation of the images in\ud Campinas and the collections purchase by Unicamp/Centro de Memória (Memory Centre),\ud in addition to its organisation and preservation

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