Search

Cell mechanics are pivotal in regulating cellular activities, diseases progression, and cancer development. However, the understanding of how cellular viscoelastic properties vary in physiological and pathological stimuli remains scarce. Here, we develop a hybrid self-similar hierarchical theory-microrheology approach to accurately and efficiently characterize cellular viscoelasticity. Focusing on two key cell types associated with livers fibrosis—the capillarized liver sinusoidal endothelial cells and activated hepatic stellate cells—we uncover a universal two-stage power-law rheology characterized by two distinct exponents, αshortand αlong. The mechanical profiles derived from both exponents exhibit significant potential for discriminating among diverse cells. This finding suggests a potential common dynamic creep characteristic across biological systems, extending our earlier observations in soft tissues. Using a tailored hierarchical model for cellular mechanical structures, we discern significant variations in the viscoelastic properties and their distribution profiles across different cell types and states from the cytoplasm (elastic stiffness E1and viscosity η), to a single cytoskeleton fiber (elastic stiffness E2), and then to the cell level (transverse expansion stiffness E3). Importantly, we construct a logistic-regression-based machine-learning model using the dynamic parameters that outperforms conventional cell-stiffness-based classifiers in assessing cell states, achieving an area under the curve of 97% vs. 78%. Our findings not only advance a robust framework for monitoring intricate cell dynamics but also highlight the crucial role of cellular viscoelasticity in discerning cell states across a spectrum of liver diseases and prognosis, offering new avenues for developing diagnostic and therapeutic strategies based on cellular viscoelasticity.

Understanding liver tissue mechanics, particularly in the context of liver pathologies like fibrosis, cirrhosis, and carcinoma, holds pivotal significance for assessing disease severity and prognosis. Although the static mechanical properties of livers have been gradually studied, the intricacies of their dynamic mechanics remain enigmatic. Here, we characterize the dynamic creep responses of healthy, fibrotic, and mesenchymal stem cells (MSCs)-treated fibrotic lives. Strikingly, we unearth a ubiquitous two-stage power-law rheology of livers across different time scales with the exponents and their distribution profiles highly correlated to liver status. Moreover, our self-similar hierarchical theory effectively captures the delicate changes in the dynamical mechanics of livers. Notably, the viscoelastic multiscale mechanical indexes (i.e., power-law exponents and elastic stiffnesses of different hierarchies) and their distribution characteristics prominently vary with liver fibrosis and MSCs therapy. This study unveils the viscoelastic characteristics of livers and underscores the potential of proposed mechanical criteria for assessing disease evolution and prognosis.

The mechanical properties of soft tissues can often be strongly correlated with the progression of various diseases, such as myocardial infarction (MI). However, the dynamic mechanical properties of cardiac tissues during MI progression remain poorly understood. Herein, we investigate the rheological responses of cardiac tissues at different stages of MI (i.e., early-stage, mid-stage, and late-stage) with atomic force microscopy-based microrheology. Surprisingly, we discover that all cardiac tissues exhibit a universal two-stage power-law rheological behavior at different time scales. The experimentally found power-law exponents can capture an inconspicuous initial rheological change, making them particularly suitable as markers for early-stage MI diagnosis. We further develop a self-similar hierarchical model to characterize the progressive mechanical changes from subcellular to tissue scales. The theoretically calculated mechanical indexes are found to markedly vary among different stages of MI. These new mechanical markers are applicable for tracking the subtle changes of cardiac tissues during MI progression.

Spinal cord injury (SCI), one of the most serious injuries of the central nervous system, causes physical functional dysfunction and even paralysis in millions of patients. As a matter of necessity, redressing the neuroleptic pathologic microenvironment to a neurotrophic microenvironment is essential in order to alleviate this dilemma and facilitate the recovery of the spinal cord. Herein, based on cell-sheet technology, two functional cell types─uninduced and neural-induced stem cells from human exfoliated deciduous teeth─were formed into a composite membrane that subsequently self-assembled to form a bioactive scaffold with a spinal-cord-like structure, called a spinal cord assembly (SCA). In a stable extracellular matrix microenvironment, SCA continuously released SCA-derived exosomes containing various neurotrophic factors, which effectively promoted neuronal regeneration, axonal extension, and angiogenesis and inhibited glial scar generation in a rat model of SCI. Neurotrophic exosomes significantly improved the pathological microenvironment and promoted in situcentralis neuroplasticity, ultimately eliciting a strong repair effect in this model. SCA therapy is a promising strategy for the effective treatment of SCI based on neurotrophic exosome delivery.

As a natural interaction approach, handwriting acts as an essential role in human–computer interaction. In order to achieve ubiquitous and reliable applications, a handwriting recognition system should be easy to use without any intrusion and robust to environment changes, which cannot be satisfied in most of existing approaches. In this article, we present RFPad, a device-free handwriting recognition system with a tag square consisting of four low-cost passive radio frequency identification (RFID) tags. With such an ingenious tag square, we transform the flat surface into a handwriting platform, and build a geometry-based theoretical model between the finger positions and the tags' phase variations so that the finger trajectory can be accurately tracked and handwriting can be recognized with the phases segmented from continuous signals. We implement a prototype of RFPad using commercial off-the-shelf RFID devices and conduct extensive experiments to evaluate its performance. Experiment results show that our RFPad can track finger movement with an average error of 1 cm and achieve average recognition accuracy of 94.08$\%$ for all 26 handwriting capital letters.

The performance of solar-thermal conversion systems can be improved by incorporation of nanocarbon-stabilized microencapsulated phase change materials (MPCMs). The geometry of MPCMs in the microcapsules plays an important role for improving their heating efficiency and reliability. Yet few efforts have been made to critically examine the formation mechanism of different geometries and their effect on MPCMs-shell interaction. Herein, through changing the cooling rate of original emulsions, we acquire MPCMs within the nanocarbon microcapsules with a hollow structure of MPCMs (h-MPCMs) or solid PCM core particles (s-MPCMs). X-ray photoelectron spectroscopy and atomic force microscopy reveals that the capsule shell of the h-MPCMs is enriched with nanocarbons and has a greater MPCMs-shell interaction compared to s-MPCMs. This results in the h-MPCMs being more stable and having greater heat diffusivity within and above the phase transition range than the s-MPCMs do. The geometry-dependent heating efficiency and system stability may have important and general implications for the fundamental understanding of microencapsulation and wider breadth of heating generating systems.