June 12, 2017 — Researchers have evaluated the long-term efficacy and safety of long-duration dual anti-platelet therapy ...

July 12, 2017 — SanBio Inc. recently announced it has been awarded a $20 million grant from the California Institute of ...

iRhythm Technologies Inc. announced a collaboration with the Stanford Machine Learning Group that has resulted in the development of a deep learning algorithm capable of expert-level detection of 14 cardiac output classes, including 12 arrhythmias as well as sinus rhythm and noise from artifact. The collaboration leveraged the iRhythm data science and clinical teams’ expertise in electrocardiogram (ECG) analysis, as well as iRhythm’s expansive labeled electrocardiogram (ECG) dataset to produce an arrhythmia detection algorithm. The classifications include atrial fibrillation, atrial flutter, complete heart block, second degree AV block and ventricular tachycardia, among others.

Blood clots in veins and arteries can lead to heart attack, stroke and pulmonary embolism, which are major causes of mortality. In the featured article of The Journal of Nuclear Medicine’s (JNM) July 2017 issue, German researchers show that targeting GPIIb/IIIa receptors, the key receptor involved in platelet clumping, with a fluorine-18 (18F) labeled ligand is a promising approach for diagnostic imaging. Current imaging modalities rely on structural characteristics, such as vascular flow impairment, and do not address the critical molecular components.