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May 16, 2024Results in Engineering15 citationsOpen Access

Healthy and diseased tensile mechanics of mouse lung parenchyma

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TNT. M. NelsonKQK. A. M. QuirosEDEdward C. Dominguez

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Abstract

The mechanics of the lung, consisting of high deformation, non-linear, rapid and cyclic loading, remain a critical unknown, hindering advancements in the field despite the pressing urgency of incurable and often fatal pulmonary diseases; such multi-scale ailments impair organ mechanical function by inducing tissue-level alterations. Here, the tensile mechanics of healthy and diseased murine lung parenchyma with induced variable fibrosis and emphysema are compared—exploring multi-rate and directional tissue elasticity and energetics. Remarkably, parenchymal tissue is found to be notably elastic and non-hysteresis prone. Additionally, chronic fibrosis-induced tissues reveal significantly reduced compliance compared to age-matched controls, indicating characteristic stiffening; moreover, maximum stress tends to be greater in exposed than controls. Emphysematous tissues tend to show lower maximum stress than controls, reflective of hallmark tissue destruction and softening. Furthermore, faster loading rate is found to generally yield elevated stiffness and maximum stress, significantly for fibrosis group exposed tissue—this interdependence between loading rate and tissue disease is notable considering the dynamics of breathing. Generally, control tissues are found to be isotropic, as are emphysematous tissues, but fibrosis may lead to increased anisotropy. Additionally, fibrotic and mature-healthy lungs are found to show age effect trends, such as increased strain energy. Along with these findings, this study additionally facilitates future analyses of other diseases by establishing lung biaxial testing protocols for small-scale murine specimens, which can serve as practical and ethical models for exploring human diseases. Overall, this study offers fundamental material characterizations which will enable future formulations of experimentally informed computational models.

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Nelson et al. (2024) studied this question.

synapsesocial.com/papers/68e69b09b6db643587620f46https://doi.org/10.1016/j.rineng.2024.102169
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