Estrada, J. B. | bioRxiv Science

bioRxiv2021

Neural cell injury pathology due to high-rate mechanical loading

Successful detection and prevention of brain injuries relies on the quantitative identification of cellular injury thresholds associated with the underlying pathology. Here, by combining a recently developed inertial microcavitation rheology technique with a 3D in vitro neural tissue model, we quantify and resolve the structural pathology and critical injury strain thresholds of neural cells occurring at high loading rates such as encountered in blast, cavitation or directed energy exposures. We find that neuronal dendritic spines characterized by MAP2 displayed the lowest physical failure strain at 7.3%, whereas microtubules and filamentous actin were able to tolerate appreciably higher strains (14%) prior to injury. Interestingly, while these critical injury thresholds were similar to previous literature values reported for moderate and lower strain rates (< 100 1/s), the pathology of primary injury reported here was distinctly different by being purely physical in nature as compared to biochemical activation during apoptosis or necrosis. TeaserControlled microcavitation enables quantitative identification of injury thresholds in neural cells.

biophysics↗

bioRxiv2020

Discovery of deformation mechanisms and constitutive response of soft material surrogates of biological tissue by full-field characterization and data-driven variational system identification

We present a novel, fully three-dimensional approach to soft material characterization and constitutive modeling with relevance to soft biological tissue. Our approach leverages recent advances in experimental techniques and data-driven computation. The experimental component of this approach involves in situ mechanical loading in a magnetic field (using MRI), yielding the entire deformation tensor field throughout the specimen regardless of the possible irregularities in its three-dimensional shape. Characterization can therefore be accomplished with data at a reduced number of deformation states. We refer to this experimental technique as MR-u. Its combination with powerful approaches to inverse modelling, specifically methods of model inference, would open the door to insightful mechanical characterization for soft materials. In recent computational advances that answer this need, we have developed new, data-driven inverse techniques to infer the model that best explains the physics governing observed phenomena from a spectrum of admissible ones, while maintaining parsimony of representation. This approach is referred to as Variational System Identification (VSI). In this communication, we apply the MR-u approach to characterize soft polymers regarding them as surrogates of soft biological tissue, and using VSI, we infer the physically best-suited and parsimonious mathematical models of their mechanical response. We demonstrate the performance of our methods in the face of noisy data with physical constraints that challenge the identification of mathematical models, while attaining high accuracy in the predicted response of the inferred models.

biophysics↗