2026

Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors

The development of effective antibody therapeutics has been hampered by a lack of methods to measure drug delivery and activity within tumors at single-cell resolution. Here we introduce single-cell spatial pharmacobiology (SSP), an experimental and analytical framework that integrates in situ imaging of a systemically infused, fluorescently labeled therapeutic antibody with high-plex spatial proteomics to…

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Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the…

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No Guide, No Cheat: Detecting Smart Glasses via AR Optical Signatures

Smart glasses are gaining increasing attention as they are expected to complement smartphones as next-generation mobile platforms, providing an Augmented Reality (AR) interface that bridges human experience and Al perception. With the goal of making smart glasses suitable for daily use, a new security risk has emerged: smart glasses can be used as cheating tools…

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Control the ARmada: LMM Coordination for Multi-Robot AR-HRC

Augmented reality (AR) has shown strong potential to enable more flexible interaction in human-robot collaboration (HRC) by conveying a robot’s state and intent and enabling intuitive control. However, while many systems exist that demonstrate AR’s ability to enhance robot control, they often incorporate control for only a singular robotic collaborator, with interaction techniques that require…

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Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling

Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)—the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this…

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Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach

Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover…

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Investigating the Influence of Red Blood Cell Heterogeneity on Cell Transport and Blood Flow Hemodynamics

Red blood cell distribution width (RDW), a routinely measured biomarker of red blood cell (RBC) size heterogeneity, is strongly associated with cardiovascular events, cancer progression, and mortality. However, its mechanistic role remains poorly understood due to limited experimental control and the computational difficulty in modeling heterogeneous cell populations. Therefore, we develop and verify a high-resolution,…

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Physiological Assessment of Coronary Artery Disease

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Instrumenting Lightweight, Modular Machine Learning Training and Inference

Recent advances in exascale computing have increased the resolution and fidelity of large-scale simulations, while rapid progress in deep learning has accelerated efforts to couple machine learning with physics-based solvers. We present a lightweight, modular in situ coupling methodology that embeds machine learning training and infer-ence directly into simulation workflows using the ParaView and Cat-alyst…

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Multiscale Modeling of Shear-Dependent Tumor Cell Adhesion

Circulating tumor cells (CTCs) interact with the vascular endothelium under hemodynamic forces that influence where metastatic seeding occurs. Wall shear stress (WSS) has been shown to regulate adhesive signaling, yet its mechanistic role in shaping spatial patterns of CTC arrest within complex vascular networks remains poorly understood. In this work, we present a three dimensional…

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