Background-The choice of imaging techniques in patients with suspected coronary artery disease (CAD) varies between countries, regions, and hospitals. This prospective, multicenter, comparative effectiveness study was designed to assess the relative accuracy of commonly used imaging techniques for identifying patients with significant CAD. Methods and Results-A total of 475 patients with stable chest pain and intermediate likelihood of CAD underwent coronary computed tomographic angiography and stress myocardial perfusion imaging by single photon emission computed tomography or positron emission tomography, and ventricular wall motion imaging by stress echocardiography or cardiac magnetic resonance. If ≥1 test was abnormal, patients underwent invasive coronary angiography. Significant CAD was defined by invasive coronary angiography as >50% stenosis of the left main stem, >70% stenosis in a major coronary vessel, or 30% to 70% stenosis with fractional flow reserve ≤0.8. Significant CAD was present in 29% of patients. In a patient-based analysis, coronary computed tomographic angiography had the highest diagnostic accuracy, the area under the receiver operating characteristics curve being 0.91 (95% confidence interval, 0.88-0.94), sensitivity being 91%, and specificity being 92%. Myocardial perfusion imaging had good diagnostic accuracy (area under the curve, 0.74;

In patients at intermediate risk of CAD, hybrid imaging allows non-invasive co-localization of myocardial perfusion defects and subtending coronary arteries, impacting clinical decision-making in almost one every five subjects.

Our results support a more pronounced reparative potential of HBR-pre-treated FMhMSCs in a clinically relevant animal model of infarction and highlight the necessity of using combined diagnostic imaging to avoid underestimations of stem cell therapeutic effects in the heart.

Interaction with machines is mediated by Human-Machine Interfaces (HMIs). Brainmachine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

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