The systemic autoimmune disease rheumatoid arthritis (RA) is characterized by increased cardiovascular mortality and morbidity and is an independent cardiovascular risk factor. Cardiovascular diseases (CVDs) result from accelerated atherogenesis, which is a consequence of endothelial dysfunction in the early stages of the disease. Endothelial dysfunction is a functional and reversible alteration of endothelial cells and leads to a shift in the properties of the endothelium towards reduced vasodilation, a pro-inflammatory state, and proliferative and prothrombotic properties. In RA, endothelial dysfunction can occur in the large vessels (such as the conduit arteries) and in the small vessels of the microvasculature, which supply oxygen and nutrients to the tissue and control inflammation, repair and fluid exchange with the surrounding tissues. Growing evidence suggests that microvascular endothelial dysfunction contributes to CVD development, as it precedes and predicts the development of conduit artery atherosclerosis and associated risk factors. As such, numerous studies have investigated microvascular endothelial dysfunction in RA, including its link with disease activity, disease duration and inflammation, the effect of treatments on endothelial function, and possible circulating biomarkers of microvascular endothelial dysfunction. Such findings could have important implications in the cardiovascular risk management of patients with RA.
Introduction and objective: Nowadays, investigations of heart physiology and pathophysiology rely more and more upon image analysis, whether for the detection and characterization of events in single cells or for the mapping of events and their characteristics across an entire tissue. These investigations require extensive skills in image analysis and/or expensive software, and their reproducibility may be a concern. Our objective was to build a robust, reliable and open-source software tool to quantify excitation–contraction related experimental data at multiple scales, from single isolated cells to the whole heart. Methods and results: A free and open-source ImageJ plugin, Spiky, was developed to detect and analyze peaks in experimental data streams. It allows rapid and easy analysis of action potentials, intracellular calcium transient and contraction data from cardiac research experiments. As shown in the provided examples, both classical bi-dimensional data (XT signals) and video data obtained from confocal microscopy and optical mapping experiments (XYT signals) can be analyzed. Spiky was written in ImageJ Macro Language and JAVA, and works under Windows, Mac and Linux operating systems. Conclusion: Spiky provides a complete working interface to process and analyze cardiac physiology research data.
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