Análise clínica e laboratorial para a identificação da Talassemia Alfa

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Time course and mechanisms of left ventricular systolic and diastolic dysfunction in monocrotaline-induced pulmonary hypertension

Although pulmonary hypertension (PH) selectively overloads the right ventricle (RV), neuroendocrine activation and intrinsic myocardial dysfunction have been described in the left ventricle (LV). In order to establish the timing of LV dysfunction development in PH and to clarify underlying molecular changes, Wistar rats were studied 4 and 6 weeks after subcutaneous injection of monocrotaline (MCT) 60 mg/kg (MCT-4, n = 11; MCT-6, n = 11) or vehicle (Ctrl-4, n = 11; Ctrl-6, n = 11). Acute single beat stepwise increases of systolic pressure were performed from baseline to isovolumetric (LVPiso). This hemodynamic stress was used to detect early changes in LV performance. Neurohumoral activation was evaluated by measuring angiotensin-converting enzyme (ACE) and endothelin-1 (ET-1) LV mRNA levels. Cardiomyocyte apoptosis was evaluated by TUNEL assay. Extracellular matrix composition was evaluated by tenascin-C mRNA levels and interstitial collagen content. Myosin heavy chain (MHC) composition of the LV was studied by protein quantification. MCT treatment increased RV pressures and RV/LV weight ratio, without changing LV end-diastolic pressures or dimensions. Baseline LV dysfunction were present only in MCT-6 rats. Afterload elevations prolonged tau and upward-shifted end-diastolic pressure dimension relations in MCT-4 and even more in MCT-6. MHC-isoform switch, ACE upregulation and cardiomyocyte apoptosis were present in both MCT groups. Rats with severe PH develop LV dysfunction associated with ET-1 and tenascin-C overexpression. Diastolic dysfunction, however, could be elicited at earlier stages in response to hemodynamic stress, when only LV molecular changes, such as MHC isoform switch, ACE upregulation, and myocardial apoptosis were present.Supported by Portuguese grants from FCT (POCI/SAU-FCF/60803/2004 and POCI/SAU-MMO/61547/2004) through Cardiovascular R&D Unit (FCT No. 51/94)

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