Biology subjects

Karniadakis, G.

Publications and source records attributed to Karniadakis, G..

5 recordsLinked to original sources

bioRxiv2026

Physics-informed neural network for predicting myocardial mechanics from in vivo physiological measurements

Structural heart diseases (SHDs) are characterized by progressive myocardial remodeling, yet the tissue-level mechanical properties underlying this process cannot be measured directly in vivo. This limitation restricts the ability of conventional physiological measurements to fully characterize myocardial remodeling and disease progression. Here, we present a physics-informed neural network (PINN) framework that integrates constitutive biomechanics with organ-level physiological measurements to simultaneously infer myocardial biomechanical descriptors and predict physically consistent stress responses. By embedding a hyperelastic constitutive model within the learning process, the framework links physiological measurements with tissue-level mechanics while preserving biomechanical consistency. Using right ventricular remodeling in pulmonary hypertension (PH) as a representative model of SHDs, the proposed framework accurately reproduced nonlinear stress-stretch behavior and outperformed conventional data-driven machine learning approaches for stress prediction. Furthermore, incorporation of PINN-derived tissue-level descriptors with organ-level physiological measurements improved disease stratification and enhanced separation between control, mild, and severe PH remodeling states. These findings support physics-informed constitutive learning as an interpretable framework for integrating organ-level physiology with tissue-level biomechanics and establish a foundation for future biomechanics-informed characterization of myocardial remodeling across SHDs.

bioengineering↗

bioRxiv2024

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

biophysics↗

bioRxiv2018

Quantifying platelet margination in diabetic blood flow

Patients with type 2 diabetes mellitus (T2DM) develop thrombotic abnormalities strongly associated with cardiovascular diseases. In addition to the changes of numerous coagulation factors such as elevated levels of thrombin and fibrinogen, the abnormal rheological effects of red blood cells (RBCs) and platelets flowing in blood are crucial in platelet adhesion and thrombus formation in T2DM. An important process contributing to the latter is the platelet margination. We employ the dissipative particle dynamics method to seamlessly model cells, plasma, and vessel walls. We perform a systematic study on the RBC and platelet transport in cylindrical vessels by considering different cell shapes, sizes and RBC deformabilities in healthy and T2DM blood, as well as variable flowrates and hematocrit. In particular, we use cellular-level RBC and platelet models with parameters derived from patient-specific data and present a sensitivity study. We find T2DM RBCs, which are less deformable compared to normal RBCs, lower the transport of platelets toward the vessel walls whereas platelets with higher mean volume (often observed in T2DM) lead to enhanced margination. Furthermore, increasing the flowrate or hematocrit enhances platelet margination. We also investigated the effect of platelet shape and observed a non-monotonic variation with the highest near-wall concentration corresponding to platelets with moderate aspect ratio of 0.38. We examine the role of white blood cells (WBCs), whose count is increased notably in T2DM patients. We find that WBC rolling or WBC adhesion tend to decrease platelet margination due to hydrodynamic effects. To the best of our knowledge, such simulations of blood including all blood cells have not been performed before, and our quantitative findings can help separate the effects of hydrodynamic interactions from adhesive interactions, and potentially shed light on the associated pathological processes in T2DM such as increased inflammatory response, platelet activation and adhesion, and ultimately thrombus formation.

biophysics↗

bioRxiv2017

Data-driven Modeling of Thrombus Size and Shape in Aortic Dissections: Role of Hemodynamics

Aortic dissection is a pathology that manifests due to micro-structural defects in the aortic wall. Blood enters the damaged wall through an intimal tear, thereby creating a so-called false lumen and exposing the blood to thrombogenic intramural constituents such as collagen. The natural history of this acute vascular injury thus depends, in part, on thrombus formation, maturation, and possible healing within the false lumen. A key question is: Why do some false lumens thrombose completely while other thrombose partially or little at all? An ability to predict the location and extent of thrombus in subjects with dissection could contribute significantly to clinical decision-making, including interventional design. We develop, for the first time, a data-driven particle-continuum model for thrombus formation in a murine model of aortic dissection. In the proposed model, we simulate a final-value problem in lieu of the original initial-value problem with significantly fewer particles that may grow in size upon activation, representing the local concentration of blood-borne species. Numerical results confirm that geometry and local hemodynamics play significant roles in the acute progression of thrombus. Despite geometrical differences between murine and human dissections, mouse models can provide considerable insight and have gained in popularity owing to their reproducibility. Our results for three classes of geometrically different false lumens show that thrombus forms and extends to a greater extent in regions with lower bulk shear rates. Dense thrombi are less likely to form in high-shear zones and in the presence of strong vortices. The present data-driven study suggests that the proposed model is robust and can be employed to assess thrombus formation in human aortic dissections.

bioengineering↗

bioRxiv2017

Seamless Multiscale Modeling of Coagulation Using Dissipative Particle Dynamics

We propose a new multiscale framework that seamlessly integrates four key components of blood clotting namely, blood rheology, cell mechanics, coagulation kinetics and transport of species and platelet adhesive dynamics. We use transport dissipative particle dynamics (tDPD), which is the extended form of original DPD, as the base solver, while a coarse-grained representation of blood cells membrane accounts for its mechanics. Our results show the dominant effect of blood flow and high Peclet numbers on the reactive transport of the chemical species signifying the importance of membrane bound reactions on the surface of adhered platelets. This new multiscale particle-based methodology helps us probe synergistic mechanisms of thrombus formation, and can open new directions in addressing other biological processes from sub-cellular to macroscopic scales.

biophysics↗