Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells' electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput. However, accessing intracellular potentials with NEAs remains challenging. This study presents an AI-supported technique that leverages thousands of synchronous eAP and iAP pairs from stem-cell-derived cardiomyocytes on NEAs. Our analysis revealed strong correlations between specific eAP and iAP features, such as amplitude and spiking velocity, indicating that extracellular signals could be reliable indicators of intracellular activity. We developed a physics-informed deep learning model to reconstruct iAP waveforms from extracellular recordings recorded from NEAs and Microelectrode arrays (MEAs), demonstrating its potential for non-invasive, long-term, high-throughput drug cardiotoxicity assessments. This AI-based model paves the way for future electrophysiology research across various cell types and drug interactions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1038/s41467-024-55571-6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.
Nat Commun
January 2025
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA.
Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells' electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput.
View Article and Find Full Text PDFPhysics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination.
Sci Rep
January 2025
Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan.
Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement.
View Article and Find Full Text PDFPhysics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.
Sensors (Basel)
December 2024
Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, Taiwan.
The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.
View Article and Find Full Text PDFNeural network emulator for atmospheric chemical ODE.
Neural Netw
January 2025
School of Engineering Sciences, Lappeenranta-Lahti University of Technology LUT, Lahti, 15110, Finland; Atmospheric Modelling Centre Lahti, Lahti University Campus, Lahti, 15140, Finland; Institute for Atmospheric and Earth System Research (INAR), The University of Helsinki, Helsinki, 00014, Finland.
Article Synopsis
- Modelling atmospheric chemistry is complicated and involves intensive computations; however, the proposed ChemNNE uses Deep Neural Networks to quickly simulate chemical concentrations by treating them as time-dependent equations.
- This emulator employs an attention-based mechanism, sinusoidal time embedding for capturing periodic patterns, and a Fourier neural operator to improve efficiency and handle complex behaviors in the chemical processes.
- The model is trained with three physics-informed loss functions to adhere to conservation laws and reaction rates, and it is validated using a large-scale dataset that sets a benchmark for accuracy and speed in future research.
Active Physics-Informed Deep Learning: Surrogate Modeling for Nonplanar Wavefront Excitation of Topological Nanophotonic Devices.
Nano Lett
January 2025
Institute of Experimental and Applied Physics, Kiel University, Leibnizstr. 11-19, Kiel 24098, Germany.
Topological plasmonics combines principles of topology and plasmonics to provide new methods for controlling light, analogous to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high-dimensional design space. We present a novel method that uses supervised, physics-informed deep learning and surrogate modeling to design topological devices for desired wavelengths.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!