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In this study, we obtain experimental understanding of the mode characteristics of a droplet placed on a flat surface under periodic forced vibrations. The detachment conditions for the droplet on the surface were also studied. In order to estimate the resonance frequency of a droplet placed on a hydrophobic surface, theoretical modelling was combined with experimental approaches. Two high speed cameras were used to observe droplet characteristics, including mode shape, detachment, occurrence of secondary droplet breakup, and horizontal torsional motion. Two cameras were installed to the right above the droplet and at the side of the droplet. There was no more than an 18% discrepancy between the theoretical and experimental resonance frequencies. This discrepancy was likely caused by several factors such as contact line friction, nonlinear wall adhesion, and experimental uncertainty. When applying a relatively low voltage to a speaker, the contact line of a droplet was pinned and shape oscillations of the droplet appeared in a bilaterally symmetric way. In contrast, at higher voltages, the contact line depinned and the shape oscillations became more active. For excitation frequencies identical to the mode frequency, the lobe size of the droplet was relatively larger than that at neighbouring frequencies. The experimental results also indicate that the generation and complete detachment of small-scale droplets occur only at the 2nd mode.
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Rationale: Osteosarcoma (OS), a common malignant bone tumor, calls for the investigation of novel treatment strategies. Low-intensity vibration (LIV) presents itself as a promising option, given its potential to enhance bone health and decrease cancer susceptibility. This research delves into the effects of LIV on OS cells and mesenchymal stem cells (MSCs), with a primary focus on generating induced tumor-suppressing cells (iTSCs) and tumor-suppressive conditioned medium (CM). Methods: To ascertain the influence of vibration frequency, we employed numerical simulations and conducted experiments to determine the most effective LIV conditions. Subsequently, we generated iTSCs and CM through LIV exposure and assessed the impact of CM on OS cells. We also explored the underlying mechanisms of the tumor-suppressive effects of LIV-treated MSC CM, with a specific focus on vinculin (VCL). We employed cytokine array, RNA sequencing, and Western blot techniques to investigate alterations in cytokine profiles, transcriptomes, and tumor suppressor proteins. Results: Numerical simulations validated LIV frequencies within the 10-100 Hz range. LIV induced notable morphological changes in OS cells and MSCs, confirming its dual role in inhibiting OS cell progression and promoting MSC conversion into iTSCs. Upregulated VCL expression enhanced MSC responsiveness to LIV, significantly bolstering CM's efficacy. Notably, we identified tumor suppressor proteins in LIV-treated CM, including procollagen C endopeptidase enhancer (PCOLCE), histone H4 (H4), peptidylprolyl isomerase B (PPIB), and aldolase A (ALDOA). Consistently, cytokine levels decreased significantly in LIV-treated mouse femurs, and oncogenic transcript levels were downregulated in LIV-treated OS cells. Moreover, our study demonstrated that combining LIV-treated MSC CM with chemotherapy drugs yielded additive anti-tumor effects. Conclusions: LIV effectively impeded the progression of OS cells and facilitated the transformation of MSCs into iTSCs. Notably, iTSC-derived CM demonstrated robust anti-tumor properties and the augmentation of MSC responsiveness to LIV via VCL. Furthermore, the enrichment of tumor suppressor proteins within LIV-treated MSC CM and the reduction of cytokines within LIV-treated isolated bone underscore the pivotal tumor-suppressive role of LIV within the bone tumor microenvironment.
Assuntos
Neoplasias Ósseas , Células-Tronco Mesenquimais , Osteossarcoma , Animais , Camundongos , Vibração/uso terapêutico , Células-Tronco Mesenquimais/metabolismo , Osteossarcoma/patologia , Citocinas/metabolismo , Neoplasias Ósseas/patologia , Proteínas Supressoras de Tumor/metabolismo , Microambiente TumoralRESUMO
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved.
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The objective of this study is to understand the mode pattern of the internal flow in a water droplet placed on a hydrophobic surface that periodically and vertically vibrates. As a result, a water droplet on a vibrating hydrophobic surface has a typical shape that depends on each resonance mode, and, additionally, we observed a diversified lobe size and internal flows in the water droplet. The size of each lobe at the resonance frequency was relatively greater than that at the neighboring frequencies, and the internal flow of the nth order mode was also observed in the flow visualization. In general, large symmetrical flow streams were generated along the vertical axis in each mode, with a large circulating movement from the bottom to the top, and then to the triple contact line along the droplet surface. In contrast, modes 2 and 4 generated a Y-shaped flow pattern, in which the flow moved to the node point in the lower part of the droplet, but modes 6 and 8 had similar patterns, with only a little difference. In addition, as a result of the PIV measurement, while the flow velocity of mode 4 was faster than that of model 2, those of modes 6 and 8 were almost similar.