Abstract In early diagnosis of lung cancer, a polarization microscopy is a powerful tool to obtain the optical information of biological tissues. In this paper, a new microfluidic polarization imaging and analysis method was proposed for the detection and classification of cancer‐associated fibroblasts and the two kinds of non‐small cell lung cancer cells, A549 and H322. A polarizing microscopy system was constructed based on a commercial microscope to obtain 3*3 Mueller matrix of cells. Based on the Muller matrix decomposition algorithm and analysis in spatial domain and frequency domain, appropriate classification parameters were selected for the characterization of different polarization characteristics of cells. Finally, the logistic regression models based on machine learning were applied to determine optimal feature parameters and classify cells. This method integrated the morphological information of the cells, and the polarization characteristics of the cells in different polarization states. It is for the first time that the polarization microscopic image analysis method has been applied to the detection and classification of non‐small cell lung cancer cells. The results show that the presented microfluidic polarization microscopic image analysis method could classify cells effectively. Compared with the Muller matrix measurement and calculation methods, the method proposed in this paper was greatly simplified in both the acquisition of polarized images and the analysis and processing of polarized images.
Ship ballast water contains high concentration of plankton, bacteria, and other microorganisms. If the huge amount of ballast water is discharged without being inactivated, it will definitely spell disaster to the marine environment. Microalgae is the most common species exiting in ballast water, so the detection of the concentration and viability of microalgae is a very important issue. The traditional methods of detecting microalgae in ballast water were costly and need the help of bulky equipment. Herein, a novel method based on microalgae cell intracellular chlorophyll fluorescence (CF) imaging combines with cell bright field (BF) microscopy was proposed. The geometric features of microalgae cells were obtained by BF image, and the cell viability was obtained by CF image. The two images were fused through the classic image registration algorithm to achieve simultaneous detection of the viability and concentration of microalgae cells. Furthermore, a low-cost, miniaturized CF/BF microscopy imaging prototype system based on the above principles was designed. In order to verify the effectiveness of the proposed method, four typical microalgae in ballast water (
One of the functions of route guidance system is estimating the drive time on the rode,namely dynamic shortest path.In the traditional shortest path forecast method,often cannot manifest the dynamic characteristic.A model of road network is built,use one kind of improved Dijkstra algorithm to realize dynamic route guidance algorithm.
With the exponential growth in mobile data volume, cellular networks are under severe capacity pressure. To address this issue, Unmanned Aerial Vehicles (UAVs) are being used as mobile Base Stations (BSs) for traffic offloading. However, coordinating and scheduling traffic across multiple UAVs and BSs remains a challenge in complex environments. This paper proposes a solution that optimizes UAV deployment locations and user resource allocation, the goal is to maximize traffic offloading and minimize UAV energy consumption simultaneously. We introduce a hierarchical intelligent traffic offloading network optimization framework based on Deep Federated Learning (DFL). Through federated learning, the UAV swarm is organized hierarchically. Additionally, we developed the CPRAFT algorithm, which uses capacity values as criterion to select the Leader UAV (L-UAV). The L-UAV then becomes the top-level central server for model aggregation in the federated learning environment. Furthermore, we formalize the traffic offloading problem as a Markov Decision Process (MDP). Based on MDP, this paper proposes FL-SNTD3 algorithm to optimize dynamic decision-making, which adapts to the ever-changing network environment and fluctuating traffic demands. Simulation experiments demonstrate that the proposed framework and algorithm exhibit outstanding performance in various aspects, providing robust support for future research in intelligent traffic offloading networks.
There are a huge number, and abundant types, of microalgae in the ocean; and most of them have various values in many fields, such as food, medicine, energy, feed, etc. Therefore, how to identify and separation of microalgae cells quickly and effectively is a prerequisite for the microalgae research and utilization. Herein, we propose a microfluidic system that comprised microalgae cell separation, treatment and viability characterization. Specifically, the microfluidic separation function is based on the principle of deterministic lateral displacement (DLD), which can separate various microalgae species rapidly by their different sizes. Moreover, a concentration gradient generator is designed in this system to automatically produce gradient concentrations of chemical reagents to optimize the chemical treatment of samples. Finally, a single photon counter was used to evaluate the viability of treated microalgae based on laser-induced fluorescence from the intracellular chlorophyll of microalgae. To the best of our knowledge, this is the first laboratory prototype system combining DLD separation, concentration gradient generator and chlorophyll fluorescence detection technology for fast analysis and treatment of microalgae using marine samples. This study may inspire other novel applications of micro-analytical devices for utilization of microalgae resources, marine ecological environment protection and ship ballast water management.
Circulating tumor cells (CTCs) are cancer cells that fall off from cancer lesions and enter the blood of human extracorporeal circulation. They are the early basis of judging cancer metastasis. However, CTCs are extremely rare, there is a great challenge to create an effective platform to isolate these cells. Herein, a two-stage CTCs separation method that combines deterministic lateral displacement (DLD) and dielectrophoresis (DEP) technology based on microfluidic chips was proposed. In order to verify the effectiveness of the proposed method, the human non-small cell lung cancer cells (H322) were spiked into diluted human blood as the samples. Firstly, a designed DLD chip was used, and the overwhelming majority of RBCs and the platelets were removed based on size. However, due to the size overlapping, the separation of WBCs and H322 cells cannot be achieved by DLD technology. In the second stage, a novel DEP chip was used to separate the mixed cells (H322 cells, WBCs and few residual RBCs) according to their difference dielectric properties. The experimental results show that after the two-stage separation, H322 cells with high purity can be obtained. The separation efficiency of H322 cells was 91%, and the purity was 80.7%, and maintains its activity. In addition, the DEP response of H322 cells under various frequencies was investigated. Those findings will provide a useful reference to the separation and enrichment of CTCs in peripheral blood.
Precise trap and manipulation of individual cells is a prerequisite for single-cell analysis, which has a wide range of applications in biology, chemistry, medicine, and materials. Herein, a microfluidic trapping system with a 3D electrode based on AC dielectrophoresis (DEP) technology is proposed, which can achieve the precise trapping and release of specific microparticles. The 3D electrode consists of four rectangular stereoscopic electrodes with an acute angle near the trapping chamber. It is made of Ag-PDMS material, and is the same height as the channel, which ensures the uniform DEP force will be received in the whole channel space, ensuring a better trapping effect can be achieved. The numerical simulation was conducted in terms of electrode height, angle, and channel width. Based on the simulation results, an optimal chip structure was obtained. Then, the polystyrene particles with different diameters were used as the samples to verify the effectiveness of the designed trapping system. The findings of this research will contribute to the application of cell trapping and manipulation, as well as single-cell analysis.
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