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Heart failure (HF) following myocardial infarction (MI) is associated with high incidence of cardiac arrhythmias. Development of therapeutic strategy requires detailed understanding of electrophysiological remodeling. However, changes of ionic currents in ischemic HF remain incompletely understood, especially in translational large-animal models. Here, we systematically measure the major ionic currents in ventricular myocytes from the infarct border and remote zones in a porcine model of post-MI HF. We recorded eight ionic currents during the cell's action potential (AP) under physiologically relevant conditions using AP-clamp sequential dissection. Compared with healthy controls, HF-remote zone myocytes exhibited increased late Na current, Ca-activated K current, Ca-activated Cl current, decreased rapid delayed rectifier K current, and altered Na/Ca exchange current profile. In HF-border zone myocytes, the above changes also occurred but with additional decrease of L-type Ca current, decrease of inward rectifier K current, and Ca release-dependent delayed after-depolarizations. Our data reveal that the changes in any individual current are relatively small, but the integrated impacts shift the balance between the inward and outward currents to shorten AP in the border zone but prolong AP in the remote zone. This differential remodeling in post-MI HF increases the inhomogeneity of AP repolarization, which may enhance the arrhythmogenic substrate. Our comprehensive findings provide a mechanistic framework for understanding why single-channel blockers may fail to suppress arrhythmias, and highlight the need to consider the rich tableau and integration of many ionic currents in designing therapeutic strategies for treating arrhythmias in HF.

Abstract-Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic surgical assistant, we explore a "Learning By Observation" (LBO) approach where we identify, segment, and parameterize sub-trajectories ("surgemes") and sensor conditions to build a finite state machine (FSM) for each subtask. The robot then executes the FSM repeatedly to tune parameters and if necessary update the FSM structure. We evaluate the approach on two surgical subtasks: debridement of 3D Viscoelastic Tissue Phantoms (3d-DVTP), in which small target fragments are removed from a 3D viscoelastic tissue phantom, and Pattern Cutting of 2D Orthotropic Tissue Phantoms (2d-PCOTP), a step in the standard Fundamentals of Laparoscopic Surgery training suite, in which a specified circular area must be cut from a sheet of orthotropic tissue phantom. We describe the approach and physical experiments, which yielded a success rate of 96% for 50 trials of the 3d-DVTP subtask and 70% for 20 trials of the 2d-PCOTP subtask.

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