Aging of biological systems is accompanied by degeneration of mitochondrial functions. Different pathways are active to counteract the processes which lead to mitochondrial dysfunction. Mitochondrial dynamics, the fission and fusion of mitochondria, is one of these quality control pathways. Mitophagy, the controlled degradation of mitochondria, is another one. Here we show that these pathways are linked. A double deletion mutant of Saccharomyces cerevisiae in which two essential components of the fission and fusion machinery, Dnm1 and Mgm1, are simultaneously ablated, contain wild-type like filamentous mitochondria, but are characterized by impaired respiration, an increased sensitivity to different stressors, increased mitochondrial protein carbonylation, and a decrease in mitophagy and replicative lifespan. These data show that a balanced mitochondrial dynamics and not a filamentous mitochondrial morphotype per se is the key for a long lifespan and demonstrate a cross-talk between two different mitochondrial quality control pathways.
A fundamental impact of mitochondria on biological aging has been suggested decades ago. One prominent theory explains aging as the result of the age-related accumulation of random molecular damage of biomolecules resulting from the reaction of reactive oxygen species, the majority of which are generated in mitochondria. Although this concept appeared to be very attractive and strongly influenced aging research, in recent years more and more data accumulated which seem to contradict this theory. However, since these data are derived from reductionist approaches and do not integrate the various components and pathways which are affected as a result of a primary experimental intervention, they are prone to misinterpretation and have to be taken with some caution. Here, after a general introduction of mitochondrial function, we discuss the relevance of various pathways which are involved in keeping mitochondria functional over time. Moreover, we provide examples which emphasize the importance of a critical interpretation of experimental data and the necessity for a holistic analysis of the aging process. The success of such a systems biology approach is strongly dependent on the development of methods for data mining and an efficient analysis and modeling of the huge data sets that are raised.
We introduce a Computational Fluid Dynamics (CFD) based method for performing patient-specific coronary hemodynamic computations under two conditions: at rest and during drug-induced hyperemia. The proposed method is based on a novel estimation procedure for determining the boundary conditions from non-invasively acquired patient data at rest. A multi-variable feedback control framework ensures that the computed mean arterial pressure and the flow distribution matches the estimated values for an individual patient during the rest state. The boundary conditions at hyperemia are derived from the respective rest-state values via a transfer function that models the vasodilation phenomenon. Simulations are performed on a coronary tree where a 65% diameter stenosis is introduced in the left anterior descending (LAD) artery, with the boundary conditions estimated using the proposed method. The results demonstrate that the estimation of the hyperemic resistances is crucial in order to obtain accurate values for pressure and flow rates. Results from an exhaustive sensitivity analysis have been presented for analyzing the variability of trans-stenotic pressure drop and Fractional Flow Reserve (FFR) values with respect to various measurements and assumptions.
Abstract.Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen crosssection estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.
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