In this paper, the method using lensless microscopic imaging techniques, to reconstruct the three-dimensional morphology of tumor cells is given. We demonstrate lensless microscopic platform which only use a CMOS image sensor and a controllable color LED array. Based on the principle of lensless computational imaging, we use the phase recovery algorithm under multi-wavelength and the diffraction tomography algorithm under multi-angle illumination, to reconstruct the three-dimensional morphology of tumor cells. Experiments have verified that this method can achieve large field of view imaging without labeling and reconstruct the three-dimensional morphology of tumor cells, which provides reliable morphological parameters for clinical research and is of great significance to the development of portable medical care.
The deformability of red blood cells is the main factor affecting blood flow and viscosity, and the study of red blood cells (RBCs) deformability is an important subject of hemorheology. At present, most of the measurement methods for red blood cell deformability are achieved by physical contact with cells or static measurement, and non-destructive high-throughput detection methods still need to be studied. A high throughput measurement method of human red blood cells deformability combined with optical tweezers technology and the microfluidic chip was proposed to accurately characterize the deformability of RBCs statistically. Firstly, the effective stretching deformation of RBCs was realized by the interaction of photo-trapping force and fluid viscous resistance. Secondly, the characteristic parameters before and after the deformation of the single cell were extracted through the image processing method to obtain the deformation index of area and circumference. Finally, statistical analysis was performed, and the average deformation index parameters were used to characterize the deformability of RBCs. A high-throughput detection system was built, and the optimal experimental conditions were obtained through a large number of experiments. Several groups of samples with different deformability were used for statistical verification. The results show that high-throughput detection and characterization methods can effectively distinguish different deformed RBCs statistically, which provides a solution for high-throughput deformation analysis of other types of samples.
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