Background: A heart failure (HF) model using coronary microembolization in large animals is indispensable for medical research. However, the heterogeneity of myocardial response to microembolization is a limitation. We hypothesized that adjusting the number of injected microspheres according to coronary blood flow could stabilize the severity of HF. This study aimed to evaluate the effect of microsphere injection based on the left coronary artery blood flow in an animal model.
Methods: Microembolization was induced by injecting different numbers of microspheres (polystyrene, diameter: 90 μm) into the left descending coronary artery of the two groups of sheep (400 and 600 times coronary blood flow [ml/min]). Hemodynamic parameters, the pressure-volume loop of the left ventricle, and echocardiography findings were examined at 0.5, 1.5, 3.5, and 6.5 h after microembolization.
Results: End-diastolic pressure and normalized heart rate increased over time, and were significantly higher in 600 × coronary blood flow group than those in 400 × coronary blood flow group (p = 0.04 and p < 0.01, respectively). The maximum rate of left-ventricular pressure rise and normalized stroke volume decreased over time, and were significantly lower in 600 × coronary blood flow group than those in 400 × coronary blood flow group (p < 0.01 and p < 0.01, respectively). The number of microspheres per coronary blood flow was significantly correlated with the decrease in stroke volume and the maximum rate of left ventricular pressure rise in 6.5 h (r = 0.74, p = 0.01 and r = 0.71, p = 0.02, respectively).
Conclusions: Adjusting the number of injected microspheres based on the coronary blood flow enabled the creation of HF models with different degrees of severity.
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http://dx.doi.org/10.1111/aor.14385 | DOI Listing |
J Cereb Blood Flow Metab
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Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
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Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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