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Creating an Urban Legend: A System for Electrophysiology Data Management and Exploration

AAAI Conferences

We have created a tool to identify and store experimental metadata during the execution of an electrophysiological experiment, and a semantic architecture to enable access, manipulation and integration of this data to support a collaborative research environment. We discuss possible extensions of this work to aid data sharing and semantic research frameworks.


Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv.org Machine Learning

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions --- that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.


Smart Support: Artificial Intelligence Will Help, Not Replace, Electrophysiologists

#artificialintelligence

Artificial intelligence (AI)–assisted electrophysiology (EP) shows promise, but even its most ardent advocates aren't ready for full-fledged endorsement--yet.


Pacemakers, other devices at risk for hacking, study says

Daily Mail - Science & tech

Life-saving medical devices such as pacemakers and insulin pumps are incredibly vulnerable to hacking by fraudsters who could blackmail victims, a new study warns.


Self-Rolling Sensor Measures Electrical Activity of Heart Organoids

#artificialintelligence

Researchers from Carnegie Mellon University and Nanyang Technological University in Singapore have developed a new microfabricated sensor array that performs 3D electrophysiology of cellular organoids. Their work demonstrates that the device can be designed to wrap around small organoids and measure voltage changes across the surface of the organoids without leading to significant loss of viability of the cells. This is an exciting development that can permit more advanced scientific discovery using organoids, permit organ-on-chip bioelectronic measurements, and help quickly test new drugs for toxicity. Various methods for studying cell electrophysiology have been developed, yet have various limitations. Patch clamp electrophysiology via micropipette is a challenging technique and difficult to perform at multiple recording sites.