Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Maltsev AV

PROVIDER: S-EPMC5500000 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Maltsev Alexander V AV Parsons Sean P SP Kim Mary S MS Tsutsui Kenta K Stern Michael D MD Lakatta Edward G EG Maltsev Victor A VA Monfredi Oliver O

PloS one 20170706 7

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 i ...[more]

PMID: 28683095

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Project description:Recent data suggest that cardiac pacemaker cell function is determined by numerous time-, voltage-, and Ca-dependent interactions of cell membrane electrogenic proteins (M-clock) and intracellular Ca cycling proteins (Ca-clock), forming a coupled-clock system. Many aspects of the coupled-clock system, however, remain underexplored. The key players of the system are Ca release channels (ryanodine receptors), generating local Ca releases (LCRs) from sarcoplasmic reticulum, electrogenic Na/Ca exchanger (NCX) current, and L-type Ca current (ICaL). We combined numerical model simulations with experimental simultaneous recordings of action potentials (APs) and Ca to gain further insight into the complex interactions within the system. Our simulations revealed a positive feedback mechanism, dubbed AP ignition, which accelerates the diastolic depolarization (DD) to reach AP threshold. The ignition phase begins when LCRs begin to occur and the magnitude of inward NCX current begins to increase. The NCX current, together with funny current and T-type Ca current accelerates DD, bringing the membrane potential to ICaL activation threshold. During the ignition phase, ICaL-mediated Ca influx generates more LCRs via Ca-induced Ca release that further activates inward NCX current, creating a positive feedback. Simultaneous recordings of membrane potential and confocal Ca images support the model prediction of the positive feedback among LCRs and ICaL, as diastolic LCRs begin to occur below and continue within the voltage range of ICaL activation. The ignition phase onset (identified within the fine DD structure) begins when DD starts to notably accelerate (?0.15 V/s) above the recording noise. Moreover, the timing of the ignition onset closely predicted the duration of each AP cycle in the basal state, in the presence of autonomic receptor stimulation, and in response to specific inhibition of either the M-clock or Ca-clock, thus indicating general importance of the new coupling mechanism for regulation of the pacemaker cell cycle duration, and ultimately the heart rate.

Project description:ObjectiveTo develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR).MethodsWe conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years.ResultsThirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients' disease improved.ConclusionsThis study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.

Dataset Information

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

Publications

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

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