Biology subjects

Zhao, Y. C.

Publications and source records attributed to Zhao, Y. C..

5 recordsLinked to original sources

bioRxiv2026

Patient-specific "Physical Twin" artery-on-chip platform reveals complex flow-dependent VWF mechanobiology and guides personalized antithrombotic therapy

Ischemic stroke triggered by carotid atherosclerosis remains unpredictable because the thrombosis embolization is governed by patient-specific vascular geometry and hemodynamics that cannot be recapitulated in conventional models. Here, we engineered "Physical Twin" artery-on-chip platforms that reproduce individualized carotid anatomy with humanized subendothelial matrix composition and arterial endothelial phenotype under physiological flow patterns. We then established thrombotic microenvironments following laser-induced injury. Computational fluid dynamics-guided experiments across distinct patient geometries reveal that local shear dictates a mechanobiological hierarchy: high-shear bifurcations ([~]3,000 s-{superscript 1}) produce von Willebrand factor (VWF) A1-dependent thrombi suppressible by conformationally sensitive inhibitors, whereas low-shear stenoses generate fragile aggregates where VWF-integrin IIb{beta} coupling governs embolization overgrowth. Pulsatile flow suppresses thrombotic growth independent of mean shear. Molecular dynamics simulations reveal why conformationally sensitive nanobodies (caplacizumab) outperform shear-independent aptamers (ARC1172) in high-shear bifurcation flow zones. This Physical Twin platform provides a mechanistic blueprint for geometry-informed, personalized antithrombotic therapy selection.

bioengineering↗

bioRxiv2026

Flow molecular dynamics simulations reveal mechanosensitive regulation of von Willebrand factor through glycan-modulated autoinhibitory modules

Force-induced protein conformational changes govern many essential biological processes, yet their molecular mechanisms remain difficult to resolve. Von Willebrand factor (VWF), a central regulator of haemostasis, is activated by hydrodynamic forces in blood flow, but how mechanical signals propagate across its multidomain architecture is poorly understood. Here, we use flow molecular dynamics (FMD), a simulation framework that applies fluid forces via controlled solvent flow to interrogate mechanosensitive proteins. Using VWF as a model system, we reconstructed the complete mechanomodule (D'D3-A1-A2-A3; 1,109 residues) with native glycosylation by integrating crystallographic data and AlphaFold predictions. FMD simulations capture a force-driven transition from a compact, autoinhibited "birds-nest" ensemble to an extended, activated state, revealing asymmetric autoinhibitory strengths within the N'AIM and C'AIM modules of the A1 domain. By directly linking static structures to dynamic, force-regulated behaviour, this work establishes a generalizable platform for dissecting protein mechanosensitivity and enabling the rational design of force-responsive therapeutics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=48 SRC="FIGDIR/small/716521v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@edba50org.highwire.dtl.DTLVardef@1630df4org.highwire.dtl.DTLVardef@292887org.highwire.dtl.DTLVardef@23cdb7_HPS_FORMAT_FIGEXP M_FIG C_FIG Flow molecular dynamics simulations reveal that GPIb engages the A1 domain only after the disruption of key interdomain and intermodular interactions.

bioinformatics↗

bioRxiv2026

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational "digital twins" for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging--a [~]100-fold improvement over manual methods--achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a "physical twin" experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm--combining automated fabrication with domain-specialized generative AI--establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

bioengineering↗

bioRxiv2022

3D spheroid-microvasculature-on-a-chip for tumor-endothelium mechanobiology interplay

In the final step of cancer metastasis, tumor cells become lodged in a distant capillary bed, where they can undergo extravasation and form a secondary tumor. While increasing evidence suggests blood/lymphatic flow and shear stress play a critical role in the tumor extravasation process, there is a lack of systematic and biomechanical approaches to recapitulate sophisticated 3D microtissue interactions within the controllable hydrodynamic microenvironment. Here, we report a simple-to-use 3D spheroid-microvasculature-on-a-chip (SMAC) model. Under static and controlled flow conditions, the SMAC recapitulates the biomechanical crosstalk between heterogeneous tumor spheroids and the endothelium in a high-throughput and quantitative manners. As an in vitro metastasis mechanobiology model, we discover 3D spheroid-induced endothelial compression and cell-cell junction degradation in the process of tumor migration and expansion. Lastly, we examine the shear stress effects on the endothelial orientation, polarization as well as the tumor spheroid expansion. Taken together, our SMAC model offers a miniaturized, cost-efficient and versatile platform for future investigation on metastasis mechanobiology, enhanced permeability and retention effect and even personalized therapeutic evaluation.

bioengineering↗

bioRxiv2020

Predicting Antibody Developability from Sequence using Machine Learning

Antibodies are prominent therapeutic agents but costly to develop. Existing approaches to predict developability depend on structure, which requires expensive laboratory or computational work to obtain. To address this issue, we present a machine learning pipeline to predict developability from sequence alone using physicochemical and learned embedding features. Our approach achieves high sensitivity and specificity on a dataset of 2400 antibodies. These results suggest that sequence is predictive of developability, enabling more efficient development of antibodies.

bioinformatics↗