Relationship between electrocardiographic characteristics of left bundle branch block and echocardiographic findings

Background: Complete left bundle branch block (CLBBB) is an electrocardiographic (ECG) dromotropic disorder seen in patients with various structural heart diseases and sometimes is associated with poor prognosis. Its presence confounds the application of standard ECG criteria for the diagnosis of left ventricular hypertrophy (LVH), myocardial infarction (MI) in the chronic phase, and pathologies that produce changes on ST-T segment. The aim of this investigation was to establish the relationship between CLBBB and cardiac structural abnormalities assessed by echocardiography. Methods: This observational, cross-sectional study included ECG with CLBBB from 101 patients who also had transthoracic echocardiogram (TTE) performed within 6 months. Results: The prevalence of structural heart disease on TTE was 90%. No ECG criterion was useful to diagnose LVH since no relationship was observed between 9 different ECG signs and increased left ventricular mass index. QRS duration (p = 0.16) and left axis deviation (p = 0.09) were unrelated to reduced left ventricular ejection fraction (LVEF). Eight ECG signs proposed for the diagnosis of the chronic phase of MI demonstrated similar effectiveness, with high specificity and reduced sensitivity. Conclusions: CLBBB is associated with elevated prevalence of cardiac structural disease and hinders the application of common ECG criteria for the diagnosis of LVH, reduced LVEF, or chronic phase of MI. No ECG finding distinguished patients with structural heart disease from those with normal hearts. Electrocardiographic criteria for the diagnosis of MI in the chronic phase are useful when present, but when absent cannot rule it out.

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