RESUMO
Background: Vascular adhesion protein-1 (VAP-1), an inflammation-inducible endothelial cell molecule, was reported to be implicated in a variety of cardiovascular diseases. However, the clinical significance of circulating VAP-1 levels in patients with coronary heart disease (CHD) remains less studied. Patients and Methods: We retrospectively analyzed clinical data of 336 hospitalized patients in the Second Affiliated Hospital of Soochow University from May 2020 to September 2022, 174 of which were diagnosed with CHD. Serum VAP-1 was measured by enzyme-linked immunosorbent assay at enrollment. The primary end point of this study was the occurrence of major adverse cardiovascular events (MACE). The coronary stenosis and clinical manifestations of CHD were assessed and recorded from medical records or follow-up calls. The relevant results were obtained, and the reliability of the conclusions was verified through regression analysis, curve fitting, and survival curve. Results: After adjusting for potential confounders, higher serum VAP-1 level was associated with increased risk of MACE in patients with CHD [(HR = 5.11, 95% CI = 1.02-25.59), (HR = 5.81, 95% CI = 1.16-29.11)]. The results of curve fitting and survival analysis were consistent with those of regression analysis. However, no significant association was observed between VAP-1 and MACE in the entire study population [(HR = 5.11, 95% CI = 0.41-1.93), (HR = 1.17, 95% CI = 0.52-2.62)]. Furthermore, the level of VAP-1 did not show a significant correlation with coronary stenosis and the clinical manifestations of CHD. Conclusion: These findings suggested that CHD patients with higher serum levels of VAP-1 are at a higher risk of adverse cardiovascular outcomes.
RESUMO
How do pedestrians choose their paths within city street networks? Researchers have tried to shed light on this matter through strictly controlled experiments, but an ultimate answer based on real-world mobility data is still lacking. Here, we analyze salient features of human path planning through a statistical analysis of a massive dataset of GPS traces, which reveals that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases and (2) chosen paths are statistically different when origin and destination are swapped. We posit that direction to goal is a main driver of path planning and develop a vector-based navigation model; the resulting trajectories, which we have termed pointiest paths, are a statistically better predictor of human paths than a model based on minimizing distance with stochastic effects. Our findings generalize across two major US cities with different street networks, hinting to the fact that vector-based navigation might be a universal property of human path planning.