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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.

The development of biomaterials capable of regulating cellular processes and guiding cell fate decisions has broad implications in tissue engineering, regenerative medicine, and cell-based assays for drug development and disease modeling. Recent studies have shown that three-dimensional (3D) nanoscale physical cues such as nanotopography can modulate various cellular processes like adhesion and endocytosis by inducing nanoscale curvature on the plasma and nuclear membranes. Two-dimensional (2D) biochemical cues such as protein micropatterns can also regulate cell function and fate by controlling cellular geometries. Development of biomaterials with precise control over nanoscale physical and biochemical cues can significantly influence programming cell function and fate. In this study, we utilized a laser-assisted micropatterning technique to manipulate the 2D architectures of cells on 3D nanopillar platforms. We performed a comprehensive analysis of cellular and nuclear morphology and deformation on both nanopillar and flat substrates. Our findings demonstrate the precise engineering of single cell architectures through 2D micropatterning on nanopillar platforms. We show that the coupling between the nuclear and cell shape is disrupted on nanopillar surfaces compared to flat surfaces. Furthermore, our results suggest that cell elongation on nanopillars enhances nanopillar-induced endocytosis. We believe our platform serves as a versatile tool for further explorations into programming cell function and fate through combined physical cues that create nanoscale curvature on cell membranes and biochemical cues that control the geometry of the cell.

Materials with engineered nano‐scale surface topographies, such as nanopillars, nanoneedles, and nanowires, mimic natural structures like viral spike proteins, enabling them to bypass biological barriers like the plasma membrane. These properties have led to applications in nanoelectronics for intracellular sensing and drug delivery platforms, some of which are already in clinical trials. Here, evidence is present that nanotopographic materials can induce transient openings in the nuclear membranes of various cell types without penetrating the cells, breaching the nucleo‐cytoplasmic barrier, and allowing uncontrolled molecular exchange across the nuclear membrane. These openings, induced by nanoscale curvature, are temporary and repaired through the Endosomal Sorting Complexes Required for Transport (ESCRT)‐mediated mechanisms. The findings suggest a potential for nano\topographic materials to temporarily breach the nuclear membrane with potential applications in direct nuclear sensing and delivery. [ABSTRACT FROM AUTHOR]

Objectives: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning., Methods: We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net convolutional neural network machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks, including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL., Results: Over 98% of predicted GM, CEJ, and ABC distances are within 200 µm of the manual annotation. Bland-Altman analysis revealed biases (bias of machine learning vs ground truth) of -0.1 µm, -37.6 µm, and -40.9 µm, with 95% limits of agreement of [-281.3, 281.0] µm, [-203.1, 127.9] µm, and [-297.6, 215.8] µm for iGR, iGH, and iABL, respectively, when compared to manual annotations. On the test dataset, the biases were 167.5 µm, 40.1 µm, and 78.7 µm with 95% CIs of [-1175 to 1510] µm, [-910.3 to 990.4] µm, and [-1954 to 1796] µm for iGR, iGH, and iABL, respectively., Conclusions: The proposed machine learning model demonstrates robust prediction performance, with the potential to enhance the efficiency of clinical periodontal diagnosis by automating landmark identification and clinical metrics measurements., (© The Author(s) 2025. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)