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Ludovica Maga

Publications and source records attributed to Ludovica Maga.

2 recordsLinked to original sources

Collagen and myocyte interplay in cardiac volume overload: a multi-constituent growth and remodeling framework

Hearts subjected to volume overload (VO) are prone to detrimental anatomical and functional changes in response to elevated mechanical loading, ultimately leading to heart failure. Experimental findings now emphasize that organ-scale changes following VO cannot be explained by myocyte growth alone, as traditionally proposed in the literature. Collagen degradation, in particular, has been associated with VO and assumed to play a central role in both its acute and chronic stages. This hypothesis, however, remains to be substantiated by comprehensive mechanistic evidence, and each constituent contribution to myocardial growth and remodeling (G&R) processes is yet to be quantified. In this work, we present a multi-constituent G&R framework that integrates a mixture-based constitutive model within the kinematic growth formulation. This framework enables us to mechanistically assess the relative contributions of collagen and myocyte changes to alterations in tissue properties, ventricular dimensions, and growth phenotype. Our numerical results confirm that collagen remodeling affects the passive mechanical response of the myocardium, whereas myocytes predominantly influence the extent and phenotype of VO-induced growth. Importantly, collagen degradation exacerbates myocyte hypertrophy, revealing a synergistic interplay that accelerates the left ventricular eccentric growth and thereby promotes systolic dysfunction. This work constitutes an important step towards an integrated characterization of the early compensatory stages of VO-induced cardiac G&R.

physics.med-ph↗

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

q-bio.TO↗