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Heart-on-a-chip Model with Integrated Extra- and Intra-cellular Bioelectronics for Monitoring Cardiac Electrophysiology under Acute Hypoxia

Published in Nano Letters, 2020

We demonstrated a bioelectronic heart-on-a-chip model for studying the effects of acute hypoxia on cardiac function. A microfluidic channel enabled rapid modulation of medium oxygenation, which mimicked the regimes induced by a temporary coronary occlusion and reversibly activated hypoxia-related transduction pathways in HL-1 cardiac model cells. Extracellular bioelectronics provided continuous readouts demonstrating that hypoxic cells experienced an initial period of tachycardia followed by a reduction in beat rate and eventually arrhythmia. Intracellular bioelectronics consisting of Pt nanopillars temporarily entered the cytosol following electroporation, yielding action potential (AP)-like readouts. We found that APs narrowed during hypoxia, consistent with proposed mechanisms by which oxygen deficits activate ATP-dependent K+ channels that promote membrane repolarization. Significantly, both extra- and intracellular devices could be multiplexed, enabling mapping capabilities unachievable by other electrophysiological tools. Our platform represents a significant advance toward understanding electrophysiological responses to hypoxia and could be applicable to disease modeling and drug development.

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From Biomimicry to Bioelectronics: Smart Materials for Cardiac Tissue Engineering

Published in Nano Research, 2020

Effective strategies in cardiac tissue engineering require matrices that recapitulate the mechanical, topographic and electrical cues present in the native extracellular matrix. In this review, we discuss recent efforts in materials science and nanotechnology to achieve functional 3D scaffolds that modulate and monitor cardiac tissue function. We consider key design considerations, including choice of biopolymer matrix, cell sources, and delivery methods for eventual therapies. We then discuss how solid-state nanomaterials may be integrated within these systems to provide unique electrical and nanotopographic cues that improve electromechanical synchrony. We describe how these approaches may be extended to complex, spatially heterogeneous constructs using 3D bioprinting techniques. Finally, we describe how scaffold materials may be augmented with bioelectronic components to achieve hybrid myocardium that monitors or controls electrophysiological activity. Collectively, these approaches provide a framework for achieving cardiac tissues with tunable, rationally-designed functionalities. We discuss future prospects and remaining challenges for clinical translation.

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An Integrated Optogenetic and Bioelectronic Platform for Regulating Cardiomyocyte Function

Published in Advanced Science, 2024

Bioelectronic medicine is emerging as a powerful approach for restoring lost endogenous functions and addressing life-altering maladies such as cardiac disorders. Systems that incorporate both modulation of cellular function and recording capabilities can enhance the utility of these approaches and their customization to the needs of each patient. Here we report an integrated optogenetic and bioelectronic platform for stable and long-term stimulation and monitoring of cardiomyocyte function in vitro. Optical inputs are achieved through the expression of a photoactivatable adenylyl cyclase, that when irradiated with blue light causes a dose-dependent and time-limited increase in the secondary messenger cyclic adenosine monophosphate with subsequent rise in autonomous cardiomyocyte beating rate. Bioelectronic readouts are obtained through a multi-electrode array that measures real-time electrophysiological responses at 32 spatially-distinct locations. Irradiation at 27 µW mm−2 results in a 14% elevation of the beating rate within 20–25 min, which remains stable for at least 2 h. The beating rate can be cycled through “on” and “off” light states, and its magnitude is a monotonic function of irradiation intensity. The integrated platform can be extended to stretchable and flexible substrates, and can open new avenues in bioelectronic medicine, including closed-loop systems for cardiac regulation and intervention, for example, in the context of arrythmias.

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Machine Learning Methods to Track Dynamic Facial Function in Facial Palsy

Published in IEEE Transactions on Biomedical Engineering, 2025

Objective: For patients with facial palsy, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, and psychological morbidity from the inability to smile or express emotions can be devastating. There are limited methods to assess ongoing facial nerve regeneration: clinicians rely on subjective descriptions, imprecise scales, and static photographs to evaluate facial functional recovery. We propose a more precise evaluation of dynamic facial function through video-based machine learning analysis to facilitate a better understanding of the sometimes subtle onset of facial nerve recovery and improve guidance for facial reanimation surgery. Methods: We present machine learning methods employing likelihood ratio tests, optimal transport theory, and Mahalanobis distances to: 1) assess the use of defined facial landmarks for binary classification of different facial palsy types; 2) identify regions of asymmetry and potential palsy during specific facial cues; and 3) quantify palsy severity and map it directly to widely used clinical scores, offering clinicians an objective way to assess facial nerve function. Results: Our results demonstrate that video analysis provides a significantly more accurate and detailed assessment of facial movements than previously reported. Conclusions: Our work allows for precise classification of facial palsy types, identification of asymmetric regions, and assessment of palsy severity. Significance: This project enables clinicians to have more accurate and timely information to make decisions for facial reanimation surgery, which will have drastic consequences on the quality of life for affected patients.

★ First author

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Dynamic stomach-brain electrical coupling in human sleep

Published in bioRxiv, 2025

Sleep involves continuous communication between the brain and body, yet the dynamics of peripheral signals during human sleep remain poorly understood. Here we tested whether gastric electrophysiology exhibits structured dynamics that align with cortical oscillations across sleep. Simultaneous high-density electroencephalography (EEG) and electrogastrography (EGG) were recorded in sixty healthy adults across multiple nights. Gastric activity persisted throughout sleep, increased during NREM, and showed infraslow amplitude fluctuations that were selectively amplified during NREM. Gastric rhythms synchronized with cortical slow oscillations and sleep spindles, with gastric power tracking spindle occurrence and infraslow fluctuations in cortical sigma power. Stomach-brain coupling predicted next-day memory recall beyond cortical measures alone, and gastric infraslow dynamics predicted subjective sleep quality beyond standard polysomnographic and cardiac measures. Together these findings position the human stomach as a peripheral oscillator whose dynamics track thalamocortical activity during sleep, reframing sleep as a coordinated multi-organ state.

★ First author

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Dynamic Facial Analysis for Predicting Facial Palsy Outcomes: Comparing Landmark Detection Models and Integrating Ordinal Regression

Published in 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025

This study aims to enhance the prediction and video-based assessment of facial nerve (FN) recovery in facial palsy patients through incorporating modern landmark detection models and regression techniques. Our goal is to determine if these methods offer significant improvements to our previously reported predictive framework over conventional approaches. Methods: We extend our previous methodology by comparing state-of-the-art facial landmark detection models, such as ones that use deep learning, with Dlib. These models are evaluated based on their accuracy, computational cost, and impact on clinical score predictions. Additionally, we replace our previous least-squares linear regression model with ordinal regression to predict House-Brackmann (HB) scores, leveraging Wasserstein and Mahalanobis distances to better capture the ordered nature of the HB grading system. Results: Dlib offered the best balance of computational efficiency and clinical accuracy, while other higher-resolution models did not improve performance in predicting clinical scores. Ordinal regression significantly outperformed naive linear regression, demonstrating better interpretability, improved accuracy, and reduced mean absolute error by properly accounting for the ordinal structure of the HB scale. Significance: This study extends our previous work by incorporating modern landmark detection techniques and a more clinically appropriate predictive model for FN assessment. By bridging the gap between computational models and real-world clinical applications, this framework enhances the precision of facial palsy monitoring, offering a more robust tool for surgical decision-making and longitudinal patient assessment.

★ First author

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Building a community of educators: A peer-led pedagogical course for Bioengineering graduate students as TAs and research mentors

Published in 2026 ASEE Annual Conference & Exposition, 2026

A conference paper on BioE 296: Promoting Effective & Equitable Teaching in Bioengineering — the peer-led Stanford graduate course in which Bioengineering PhD students train for their roles as teaching assistants and research mentors. I was a teaching assistant for the course in 2024 and its instructor in 2025.

A nonlinear dynamics approach to assess amplitude-frequency coupling in non-invasive human electrogastrography recordings

Published in arXiv, 2026

Conventional electrogastrography (EGG) analyses summarize gastric slow-wave activity using dominant frequency, power, or phase-based metrics, without explicitly modeling amplitude-dependent frequency dynamics. We developed a statistical nonlinear-dynamics framework that represents the gastric-band analytic EGG signal as a stochastic Stuart-Landau oscillator and estimate local effective phase-shear, a normalized measure of amplitude-frequency coupling. In overnight recordings from 60 healthy participants, effective phase-shear was consistently negative, generalized to held-out halves of each recording, and was abolished by amplitude surrogates that disrupted amplitude-phase alignment while preserving amplitude-series structure. We then applied the measure to fasted and fed recordings from 16 healthy participants and 24 participants with gastric dysfunction. Phase-shear magnitude differed between groups and classified dysfunction comparable to the strongest multielectrode phase-based traveling-wave measures. These findings establish amplitude-frequency coupling as a dynamical feature of gastric-band EGG that complements conventional spectral and spatial phase metrics, while motivating future multichannel observation models to determine its relationship to slow-wave propagation and gastric function.

★ First author

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