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Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells

机译:自动检测和分析自发搏动的心脏起搏器细胞中局部Ca2 +释放的计算机算法

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摘要

Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle cells and Ca2+ puffs and syntillas in neurons.
机译:Ca 2 + 的局部释放(LCR)是涉及心脏起搏器细胞功能的关键事件。但是,尚未在自发跳动的起搏器活细胞中开发出用于自动LCR检测和分析的特定算法。在本研究中,我们使用高速2D相机在从兔子和豚鼠中分离出的自发收缩窦房结(SA)结节细胞中测量了LCR,并开发了一种能够二维检测空间和分析LCR的新算法。时间。我们的算法沿收缩单元的中线跟踪点。它使用这些点作为仿射变换的坐标系,从而生成单元不收缩的变换图像序列。随后通过应用一系列空间滤波器,将动作电位诱导的Ca 2 + 瞬变和LCR从记录噪声中分离出来。通过应用到每个像素(单元位置)的差分(逐帧)灵敏度算法检测LCR的出生和死亡事件。当LCR的信号在足够大的区域内变化足够快时,便检测到LCR。当LCR的幅度大幅衰减或合并到上升的整个细胞Ca 2 + 瞬态中时,它被认为已经死亡。最终,我们的算法提供了主要的LCR参数,例如周期,信号质量,持续时间和传播路径面积。当LCR在活细胞中传播时,该算法识别分裂和合并行为,表明局部传播Ca 2 + -诱导的Ca 2 + -释放对于命运的重要性。 LCRs并产生强大的集成Ca 2 + 信号。因此,我们的新计算机算法消除了运动伪影,并从记录噪声和全局信号中检测了2D局部时空事件。虽然开发了算法来检测窦房结细胞中的LCR,但它们有潜力在生物物理学和细胞生理学中的其他应用中使用,例如,检测Ca 2 + 小波(流产波),肌肉细胞中产生火花和余烬,神经元中出现Ca 2 + 抽吸和突触。

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