Displacement and strain mapping for osteocytes under fluid shear stress using digital holographic microscopy and digital image correlation

Numerical Simulation and Comparison of Flow Field in Different Dynamic Co-Culture Conditions

Bioreactor technology facilitates the gradual automation of cell expansion and the development of biofunctional synthetic alternatives. However, it is difficult to fully understand the flow field and force field environments formed in it by experimental means. Computational fluid dynamics (CFD) offers a robust framework for analyzing and understanding the impacts of fluid flow, material diffusion, and fluid shear stress (FSS) on in vitro cell and tissue regeneration dynamics. In this study, the FLUENT software is used to simulate and calculate the flow field environment of the rotary cell culture system (RCCS) and spinner flask (SF), including dynamic pressure, shear stress, and velocity distribution. Particles of two diameters for three-dimensional cell culture were randomly arranged in different radial/axial positions, and the FSS on the particles in RCCS and SF at different rotational speeds was also analyzed. It is expected to visualize the flow field distribution of the bioreactor and local hydrodynamic changes near the particles, and provide positive assistance for the dynamic culture/co-culture of different cells-microcarriers complex. The distribution of FSS on randomly arranged L and S particles was analyzed in detail to evaluate and screen the suitable operating conditions of these two bioreactors. Visually understanding the flow field distribution and local hydrodynamic changes within the bioreactor is expected to provide positive assistance for dynamic culture. The particles may periodically contact the fresh oxygenated medium during rotation with the fluid. Two fluid circulations in SF were generated in the upper/lower area of the blade, and a relatively static fluid circulation area was formed at the bottom with low velocity and pressure in the center, which was not conducive to material exchange. Rotary bioreactors may be more suitable than spinner flasks as a dynamic culture tool for some types of cells or other constructs.

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods

The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

Panoramic quantitative phase imaging of adherent live cells in a microfluidic environment

Understanding how cells respond to external stimuli is crucial. However, there are a lack of inspection systems capable of simultaneously stimulating and imaging cells, especially in their natural states. This study presents a novel microfluidic stimulation and observation system equipped with flat-fielding quantitative phase contrast microscopy (FF-QPCM). This system allowed us to track the behavior of organelles in live cells experiencing controlled microfluidic stimulation. Using this innovative imaging platform, we successfully quantified the cellular response to shear stress including directional cellular shrinkage and mitochondrial distribution change in a label-free manner. Additionally, we detected and characterized the cellular response, particularly mitochondrial behavior, under varying fluidic conditions such as temperature and drug induction time. The proposed imaging platform is highly suitable for various microfluidic applications at the organelle level. We advocate that this platform will significantly facilitate life science research in microfluidic environments.

Digital holographic microscopy implementation for capillary filling measurements in nanoporous materials

We report the implementation of lensless off-axis digital holographic microscopy as a non-destructive optical analyzer for nano-scale structures. The measurement capacity of the system was validated by analyzing the topography of a metallic grid with ≈150nm thick opaque features. In addition, an experimental configuration of self-reference was included to study the dynamics of the capillary filling phenomena in nanostructured porous silicon. The fluid front position as a function of time was extracted from the holograms, and the typical square root of time kinematics was recovered. The results shown are in agreement with previous works on capillary imbibition in similar structures and confirm a first step towards unifying holographic methods with fluid dynamics theory to develop a spatially resolved capillary tomography system for nanoporous materials characterization.