Adenosine is a potent vasodilator used extensively to study the coronary circulation of animals. Its use in humans, however, has been hampered by lack of knowledge about its effects on the human coronary circulation and by concern about its safety. We investigated in humans the effects of adenosine, administered by intracoronary bolus (2-16 jig), intracoronary infusion (10-240 ,ug/min), or intravenous infusion (35-140 pg/kg/min) on coronary and systemic hemodynamics and the electrocardiogram. Coronary blood flow velocity (CBFV) was measured with a 3F coronary Doppler catheter. The maximal CBFV was determined with intracoronary papaverine (4.5±0.2. resting CBFV). In normal left coronary arteries (n=20), 16-pg boluses of adenosine caused coronary hyperemia similar to that caused by papaverine (4.6±0.7 * resting CBFV). In the right coronary artery (n=5), 12-,ug boluses caused maximal hyperemia (4.4± 1.0 * resting CBFV). Intracoronary boluses caused a small, brief decrease in arterial pressure (similar to that caused by papaverine) and no changes in heart rate or in the electrocardiogram. The duration of hyperemia was much shorter after adenosine than after papaverine administration. Intracoronary infusions of 80 ,g/min or more into the left coronary artery (n=6) also caused maximal hyperemia (4.4±0.1. resting CBFV), and doses up to 240 pg/min caused a minimal decrease in arterial pressure (-6±2 mm Hg) and no significant change in heart rate or in electrocardiographic variables. Intravenous infusions in normal patients (n=25) at 140 ,ug/kg/min caused coronary vasodilation similar to that caused by papaverine in 84% of patients (4.4±0.9 -resting CBFV). At submaximal infusion rates, however, CBFV often fluctuated widely. During the 140-,ug/kg/min infusion, arterial pressure decreased 6±7 mm Hg, and heart rate increased 24± 14 beats/min. One patient developed 1 cycle of 2:1 atrioventricular block, but otherwise, the electrocardiogram did not change. In eight patients with microvascular vasodilator dysfunction (ACBFV, <3.5 peak/resting velocity after a maximally vasodilating dose of intracoronary papaverine), the dose-response characteristics to intracoronary boluses and intravenous infusions of adenosine were similar to those found in normal patients. These studies suggest that maximal coronary vasodilation can be achieved safely with intracoronary adenosine administration and that intravenous infusions at a rate of 140 ,ug/kg/min cause near-maximal coronary hyperemia in most patients. (Circulation 1990;82:1595-1606 M aany studies of the coronary circulation require the use of drugs that can safely and reliably produce maximal coronary hyperemia of brief duration (for example, for measurement of coronary flow reserve, thallium-201 scintigraphy, and echocardiographic imaging).1-6 An ideal agent for these studies would cause maximal

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Self-assembly of DNA with the four core histones but in the absence of Hi generates nucleosome core particles which are spaced randomly over large

Highlights d A new deep-learning method (CIGER) for chemical-induced gene expression ranking d CIGER can predict gene expression for de novo chemicals from chemical structures d We discovered drugs for the treatment of drug-resistant pancreatic cancer

Complex biological networks are strikingly robust to deletion or interruption of their components. This robustness may be mediated by network features that we can now target specifically through network analysis. Network components (‘nodes’) differ greatly in importance depending upon the extent to which a node is connected to other nodes (‘degree’), the extent to which a node connects otherwise unconnected clusters (‘betweenness’) and the extent to which there are alternative pathways through a network (‘redundancy’). While networks are highly robust to random deletions of nodes, they are much more susceptible to targeted deletions prioritised by network properties such as degree and betweenness. A small number of combinations of, for example, five proteins can have a very great impact on network integrity in most biological networks. The strategy of network pharmacology-based approaches such as combinatorial network impact analysis is to find these very few high impact protein combinations that should be addressed simultaneously in order to yield high therapeutic efficacy.