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Andrew J. Buda
Researcher at Tulane University
Publications - 102
Citations - 4497
Andrew J. Buda is an academic researcher from Tulane University. The author has contributed to research in topics: Myocardial infarction & Coronary occlusion. The author has an hindex of 35, co-authored 102 publications receiving 4438 citations. Previous affiliations of Andrew J. Buda include University of Michigan & Stanford University.
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Journal ArticleDOI
Effect of Intrathoracic Pressure on Left Ventricular Performance
Andrew J. Buda,Michael R. Pinsky,Neil B. Ingels,George T. Daughters,Edward B. Stinson,Edwin L. Alderman +5 more
TL;DR: It is concluded that large intrathoracic-pressure changes, such as those that occur in acute pulmonary disease, can influence cardiac performance and particularly left ventricular transmural pressures and thus afterload.
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The physiologic basis of dobutamine as compared with dipyridamole stress interventions in the assessment of critical coronary stenosis.
TL;DR: The physiologic rationale underlying the optimal choice of pharmacologic stress for functional versus perfusion imaging is investigated and wall thickening abnormalities in all dogs while dipyridamole induced dysfunction in only 55% of the animals studied are investigated.
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Failure of superoxide dismutase and catalase to alter size of infarction in conscious dogs after 3 hours of occlusion followed by reperfusion.
TL;DR: It is concluded that myocardial protection by SOD or CAT is model dependent and in conscious dogs subjected to 3 hr of coronary occlusion followed by reperfusion, SOD and CAT failed to alter size of infarction.
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Direct measurement of inner and outer wall thickening dynamics with epicardial echocardiography.
TL;DR: The experimental findings corresponded closely to theoretical predictions, supporting the conclusion that a gradient of thickening exists across the myocardial wall, with the inner portion of the wall contributing the largest fraction to total systolic thickening.
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Detecting left ventricular endocardial and epicardial boundaries by digital two-dimensional echocardiography
TL;DR: A novel algorithm that first detects spatially significant features based on the measurement of image intensity variations and uses high-level knowledge about the heart wall to label the detected features for noise rejection and to fill in the missing points by interpolation.