Induction of activating transcription factor 3 limits survival following infarct-induced heart failure in mice.

Numerous fibrotic and inflammatory changes occur in the failing heart. Recent evidence indicates that certain transcription factors, such as activating transcription factor 3 (ATF3), are activated during heart failure. Because ATF3 may be upregulated in the failing heart and affect inflammation, we focused on the potential role of ATF3 on postinfarct heart failure.

Endoplasmic reticulum stress-dependent activation of ATF3 mediates the late phase of ischemic preconditioning.

Ischemic preconditioning (PC) is an adaptive response to transient myocardial ischemia that protects the heart from subsequent ischemia/reperfusion (I/R) injury. However, the mechanisms underlying its cardioprotective effects remain unclear. Myocardium of adult male C57/BL6 mice, preconditioned by 6 cycles of 4 minute coronary occlusion and reperfusion, showed nuclear translocation of ATF3 and ATF6 and PERK phosphorylation 30 min after PC.

Metabolomic analysis of pressure-overloaded and infarcted mouse hearts.

Background: Cardiac hypertrophy and heart failure are associated with metabolic dysregulation and a state of chronic energy deficiency. Although several disparate changes in individual metabolic pathways have been described, there has been no global assessment of metabolomic changes in hypertrophic and failing hearts in vivo. Hence, we investigated the impact of pressure overload and infarction on myocardial metabolism.

Structural similarity quality metrics in a coding context: exploring the space of realistic distortions.

Perceptual image quality metrics have explicitly accounted for human visual system (HVS) sensitivity to subband noise by estimating just noticeable distortion (JND) thresholds. A recently proposed class of quality metrics, known as structural similarity metrics (SSIM), models perception implicitly by taking into account the fact that the HVS is adapted for extracting structural information from images. We evaluate SSIM metrics and compare their performance to traditional approaches in the context of realistic distortions that arise from compression and error concealment in video compression/transmission applications.