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Active curling of epithelial tissues, as an indispensable component of developmental morphogenesis, occurs frequently both in vivo and in vitro microenvironments. Deciphering the mechanisms underlying the active curling of epithelial monolayers is crucial for understanding many physiological and pathological processes. Here, a multiscale structure-based cell monolayer model and an active constitutive relation are established to characterize this spontaneous curling of the epithelial tissue. It is shown that the asymmetric distribution of Myosin II along the apicobasal axis generates an active moment that drives spontaneous curling of the suspended epithelial tissue. The time-dependent deflection and rotation angle of the active curling are analytically solved, proving that the curling is driven by the active bending moment directly associated with the apicobasal asymmetric contractile force. Moreover, we demonstrate that the rotation angle is proportional to the apicobasal force ratio and inversely proportional to the thickness of epithelial tissues. In addition, we derive an approximate analytical relation between the out-of-plane curling behavior and in-plane stress, in good agreement with the experimental data and our simulation results. This study provides a pathway to elucidate the mechanical mechanism underlying complex morphological development as well as the physiological and pathological phenomena of epithelial tissues.

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues., Competing Interests: J-TH, ZC, and G-KX were employed by the SVL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Hang, Chang, Yu, Yang and Xu.)