Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies

Most patients survive acute myocardial infarction (MI). Yet this encouraging development has certain drawbacks: heart failure (HF) prevalence is increasing and patients affected tend to have more comorbidities worsening economic strain on healthcare systems and impeding effective medical management. The heart's pathological changes in structure and/or function, termed myocardial remodelling, significantly impact on patient outcomes. Risk factors like diabetes, chronic obstructive pulmonary disease, female sex, and others distinctly shape disease progression on the 'road to HF'. Despite the availability of HF drugs that interact with general pathways involved in myocardial remodelling, targeted drugs remain absent, and patient risk stratification is poor. Hence, in this review, we highlight the pathophysiological basis, current diagnostic methods and available treatments for cardiac remodelling following MI. We further aim to provide a roadmap for developing improved risk stratification and novel medical and interventional therapies

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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