Publications by authors named "S L Abreu"

Introduction: Eliminating racial inequities in access to kidney transplantation requires multilevel interventions that target both patients and health systems.

Research Question: The aim of this study was to determine whether adding culturally sensitive, web-based patient education to a transplant center-level intervention was associated with increased knowledge, motivation to pursue living donor kidney transplant, and confidence in the behavioral skills to discuss living donation among Black/African American patients with end-stage kidney disease.

Design: A total of 411 transplant candidates were randomized to intervention (N = 222) or control groups (N = 189) and completed measures at baseline and immediate follow-up during the transplant evaluation visit.

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Background/objectives: Approximately 25% of the world's population and more than 60% of patients with type 2 diabetes (T2D) have metabolic-dysfunction-associated steatotic liver disease (MASLD). The association between these pathologies is an important cause of morbidity and mortality in Brazil and worldwide due to the high frequency of advanced fibrosis and cirrhosis. The objective of this study was to determine the epidemiologic and clinical-laboratory profile of patients with T2D and MASLD treated at an endocrinology reference service in a state in northeastern Brazil, and to investigate the association of liver fibrosis with anthropometric and laboratory measurements.

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In Streptococcus pyogenes, the type II fatty acid (FA) synthesis pathway FASII is feedback-controlled by the FabT repressor bound to an acyl-Acyl carrier protein. Although FabT defects confer reduced virulence in animal models, spontaneous fabT mutants arise in vivo. We resolved this paradox by characterizing the conditions and mechanisms requiring FabT activity, and those promoting fabT mutant emergence.

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Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR).

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